From d21b6a5d4f7e41000686e0d0041e507b1eb2a56e Mon Sep 17 00:00:00 2001 From: Julian Risch Date: Wed, 23 Feb 2022 11:36:04 +0100 Subject: [PATCH] update docs for 1.2.0 relase (#2234) --- docs/v1.2.0/Makefile | 26 + docs/v1.2.0/_src/api/Makefile | 20 + .../_src/api/_static/floating_sidebar.css | 29 + docs/v1.2.0/_src/api/_templates/xxlayout.html | 46 + docs/v1.2.0/_src/api/api/crawler.md | 80 + .../_src/api/api/document_classifier.md | 101 + docs/v1.2.0/_src/api/api/document_store.md | 3384 +++++++++++++++++ docs/v1.2.0/_src/api/api/evaluation.md | 104 + docs/v1.2.0/_src/api/api/extractor.md | 58 + docs/v1.2.0/_src/api/api/file_classifier.md | 28 + docs/v1.2.0/_src/api/api/file_converter.md | 345 ++ docs/v1.2.0/_src/api/api/generator.md | 201 + docs/v1.2.0/_src/api/api/other.md | 36 + docs/v1.2.0/_src/api/api/pipelines.md | 1076 ++++++ docs/v1.2.0/_src/api/api/preprocessor.md | 67 + docs/v1.2.0/_src/api/api/primitives.md | 244 ++ docs/v1.2.0/_src/api/api/query_classifier.md | 120 + 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docs/v1.2.0/_src/usage/usage/terms.md create mode 100644 docs/v1.2.0/_src/usage/usage/translator.md create mode 100644 docs/v1.2.0/_src/usage/usage/use_cases.md create mode 100644 docs/v1.2.0/make.bat create mode 100644 docs/v1.2.0/static/rtd_theme.css create mode 100644 docs/v1.2.0/templates/layout.html create mode 100644 docs/v1.2.0/templates/pages/index.html diff --git a/docs/v1.2.0/Makefile b/docs/v1.2.0/Makefile new file mode 100644 index 0000000000..8ee2a0520b --- /dev/null +++ b/docs/v1.2.0/Makefile @@ -0,0 +1,26 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. + +SPHINXBUILD := sphinx-build +MAKEINFO := makeinfo + +BUILDDIR := build +SOURCE := _src/ +# SPHINXFLAGS := -a -W -n -A local=1 -d $(BUILDDIR)/doctree +SPHINXFLAGS := -A local=1 -d $(BUILDDIR)/doctree +SPHINXOPTS := $(SPHINXFLAGS) $(SOURCE) + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + $(SPHINXBUILD) -M $@ $(SPHINXOPTS) $(BUILDDIR)/$@ + diff --git a/docs/v1.2.0/_src/api/Makefile b/docs/v1.2.0/_src/api/Makefile new file mode 100644 index 0000000000..d4bb2cbb9e --- /dev/null +++ b/docs/v1.2.0/_src/api/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/v1.2.0/_src/api/_static/floating_sidebar.css b/docs/v1.2.0/_src/api/_static/floating_sidebar.css new file mode 100644 index 0000000000..e59adc6722 --- /dev/null +++ b/docs/v1.2.0/_src/api/_static/floating_sidebar.css @@ -0,0 +1,29 @@ +div.sphinxsidebarwrapper { + position: relative; + top: 0px; + padding: 0; +} + +div.sphinxsidebar { + margin: 0; + padding: 0 15px 0 15px; + width: 210px; + float: left; + font-size: 1em; + text-align: left; +} + +div.sphinxsidebar .logo { + font-size: 1.8em; + color: #0A507A; + font-weight: 300; + text-align: center; +} + +div.sphinxsidebar .logo img { + vertical-align: middle; +} + +div.sphinxsidebar .download a img { + vertical-align: middle; +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/api/_templates/xxlayout.html b/docs/v1.2.0/_src/api/_templates/xxlayout.html new file mode 100644 index 0000000000..de71588332 --- /dev/null +++ b/docs/v1.2.0/_src/api/_templates/xxlayout.html @@ -0,0 +1,46 @@ +{# put the sidebar before the body #} +{% block sidebar1 %}{{ sidebar() }}{% endblock %} +{% block sidebar2 %}{% endblock %} + +{% block extrahead %} + +{{ super() }} +{#- if not embedded #} + + +{#- endif #} +{% endblock %} \ No newline at end of file diff --git a/docs/v1.2.0/_src/api/api/crawler.md b/docs/v1.2.0/_src/api/api/crawler.md new file mode 100644 index 0000000000..2080d58bed --- /dev/null +++ b/docs/v1.2.0/_src/api/api/crawler.md @@ -0,0 +1,80 @@ + + +# Module crawler + + + +## Crawler + +```python +class Crawler(BaseComponent) +``` + +Crawl texts from a website so that we can use them later in Haystack as a corpus for search / question answering etc. + +**Example:** +```python +| from haystack.nodes.connector import Crawler +| +| crawler = Crawler(output_dir="crawled_files") +| # crawl Haystack docs, i.e. all pages that include haystack.deepset.ai/overview/ +| docs = crawler.crawl(urls=["https://haystack.deepset.ai/overview/get-started"], +| filter_urls= ["haystack\.deepset\.ai\/overview\/"]) +``` + + + +#### crawl + +```python +def crawl(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None) -> List[Path] +``` + +Craw URL(s), extract the text from the HTML, create a Haystack Document object out of it and save it (one JSON + +file per URL, including text and basic meta data). +You can optionally specify via `filter_urls` to only crawl URLs that match a certain pattern. +All parameters are optional here and only meant to overwrite instance attributes at runtime. +If no parameters are provided to this method, the instance attributes that were passed during __init__ will be used. + +**Arguments**: + +- `output_dir`: Path for the directory to store files +- `urls`: List of http addresses or single http address +- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options: +0: Only initial list of urls +1: Follow links found on the initial URLs (but no further) +- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with. +All URLs not matching at least one of the regular expressions will be dropped. +- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content + +**Returns**: + +List of paths where the crawled webpages got stored + + + +#### run + +```python +def run(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, return_documents: Optional[bool] = False) -> Tuple[Dict, str] +``` + +Method to be executed when the Crawler is used as a Node within a Haystack pipeline. + +**Arguments**: + +- `output_dir`: Path for the directory to store files +- `urls`: List of http addresses or single http address +- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options: +0: Only initial list of urls +1: Follow links found on the initial URLs (but no further) +- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with. +All URLs not matching at least one of the regular expressions will be dropped. +- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content +- `return_documents`: Return json files content + +**Returns**: + +Tuple({"paths": List of filepaths, ...}, Name of output edge) + diff --git a/docs/v1.2.0/_src/api/api/document_classifier.md b/docs/v1.2.0/_src/api/api/document_classifier.md new file mode 100644 index 0000000000..814ef46ca7 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/document_classifier.md @@ -0,0 +1,101 @@ + + +# Module base + + + +## BaseDocumentClassifier + +```python +class BaseDocumentClassifier(BaseComponent) +``` + + + +#### timing + +```python +def timing(fn, attr_name) +``` + +Wrapper method used to time functions. + + + +# Module transformers + + + +## TransformersDocumentClassifier + +```python +class TransformersDocumentClassifier(BaseDocumentClassifier) +``` + +Transformer based model for document classification using the HuggingFace's transformers framework +(https://github.com/huggingface/transformers). +While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same. +This node classifies documents and adds the output from the classification step to the document's meta data. +The meta field of the document is a dictionary with the following format: +``'meta': {'name': '450_Baelor.txt', 'classification': {'label': 'neutral', 'probability' = 0.9997646, ...} }`` + +Classification is run on document's content field by default. If you want it to run on another field, +set the `classification_field` to one of document's meta fields. + +With this document_classifier, you can directly get predictions via predict() + + **Usage example at query time:** + ```python +| ... +| retriever = ElasticsearchRetriever(document_store=document_store) +| document_classifier = TransformersDocumentClassifier(model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion") +| p = Pipeline() +| p.add_node(component=retriever, name="Retriever", inputs=["Query"]) +| p.add_node(component=document_classifier, name="Classifier", inputs=["Retriever"]) +| res = p.run( +| query="Who is the father of Arya Stark?", +| params={"Retriever": {"top_k": 10}} +| ) +| +| # print the classification results +| print_documents(res, max_text_len=100, print_meta=True) +| # or access the predicted class label directly +| res["documents"][0].to_dict()["meta"]["classification"]["label"] + ``` + +**Usage example at index time:** + ```python +| ... +| converter = TextConverter() +| preprocessor = Preprocessor() +| document_store = ElasticsearchDocumentStore() +| document_classifier = TransformersDocumentClassifier(model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion", +| batch_size=16) +| p = Pipeline() +| p.add_node(component=converter, name="TextConverter", inputs=["File"]) +| p.add_node(component=preprocessor, name="Preprocessor", inputs=["TextConverter"]) +| p.add_node(component=document_classifier, name="DocumentClassifier", inputs=["Preprocessor"]) +| p.add_node(component=document_store, name="DocumentStore", inputs=["DocumentClassifier"]) +| p.run(file_paths=file_paths) + ``` + + + +#### predict + +```python +def predict(documents: List[Document]) -> List[Document] +``` + +Returns documents containing classification result in meta field. + +Documents are updated in place. + +**Arguments**: + +- `documents`: List of Document to classify + +**Returns**: + +List of Document enriched with meta information + diff --git a/docs/v1.2.0/_src/api/api/document_store.md b/docs/v1.2.0/_src/api/api/document_store.md new file mode 100644 index 0000000000..e69305447a --- /dev/null +++ b/docs/v1.2.0/_src/api/api/document_store.md @@ -0,0 +1,3384 @@ + + +# Module base + + + +## BaseKnowledgeGraph + +```python +class BaseKnowledgeGraph(BaseComponent) +``` + +Base class for implementing Knowledge Graphs. + + + +## BaseDocumentStore + +```python +class BaseDocumentStore(BaseComponent) +``` + +Base class for implementing Document Stores. + + + +#### write\_documents + +```python +@abstractmethod +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Indexes documents for later queries. + +**Arguments**: + +- `documents`: a list of Python dictionaries or a list of Haystack Document objects. +For documents as dictionaries, the format is {"text": ""}. +Optionally: Include meta data via {"text": "", +"meta":{"name": ", "author": "somebody", ...}} +It can be used for filtering and is accessible in the responses of the Finder. +- `index`: Optional name of index where the documents shall be written to. +If None, the DocumentStore's default index (self.index) will be used. +- `batch_size`: Number of documents that are passed to bulk function at a time. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + +**Returns**: + +None + + + +#### get\_all\_documents + +```python +@abstractmethod +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get documents from the document store. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: Number of documents that are passed to bulk function at a time. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### get\_all\_documents\_generator + +```python +@abstractmethod +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + +__Example__: +```python +filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } +} +``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### get\_all\_labels\_aggregated + +```python +def get_all_labels_aggregated(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, open_domain: bool = True, drop_negative_labels: bool = False, drop_no_answers: bool = False, aggregate_by_meta: Optional[Union[str, list]] = None, headers: Optional[Dict[str, str]] = None) -> List[MultiLabel] +``` + +Return all labels in the DocumentStore, aggregated into MultiLabel objects. + +This aggregation step helps, for example, if you collected multiple possible answers for one question and you +want now all answers bundled together in one place for evaluation. +How they are aggregated is defined by the open_domain and aggregate_by_meta parameters. +If the questions are being asked to a single document (i.e. SQuAD style), you should set open_domain=False to aggregate by question and document. +If the questions are being asked to your full collection of documents, you should set open_domain=True to aggregate just by question. +If the questions are being asked to a subslice of your document set (e.g. product review use cases), +you should set open_domain=True and populate aggregate_by_meta with the names of Label meta fields to aggregate by question and your custom meta fields. +For example, in a product review use case, you might set aggregate_by_meta=["product_id"] so that Labels +with the same question but different answers from different documents are aggregated into the one MultiLabel +object, provided that they have the same product_id (to be found in Label.meta["product_id"]) + +**Arguments**: + +- `index`: Name of the index to get the labels from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `open_domain`: When True, labels are aggregated purely based on the question text alone. +When False, labels are aggregated in a closed domain fashion based on the question text +and also the id of the document that the label is tied to. In this setting, this function +might return multiple MultiLabel objects with the same question string. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) +- `aggregate_by_meta`: The names of the Label meta fields by which to aggregate. For example: ["product_id"] +TODO drop params + + + +#### normalize\_embedding + +```python +@staticmethod +@njit +def normalize_embedding(emb: np.ndarray) -> None +``` + +Performs L2 normalization of embeddings vector inplace. Input can be a single vector (1D array) or a matrix +(2D array). + + + +#### add\_eval\_data + +```python +def add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label", batch_size: Optional[int] = None, preprocessor: Optional[PreProcessor] = None, max_docs: Union[int, bool] = None, open_domain: bool = False, headers: Optional[Dict[str, str]] = None) +``` + +Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it. + +If a jsonl file and a batch_size is passed to the function, documents are loaded batchwise +from disk and also indexed batchwise to the DocumentStore in order to prevent out of memory errors. + +**Arguments**: + +- `filename`: Name of the file containing evaluation data (json or jsonl) +- `doc_index`: Elasticsearch index where evaluation documents should be stored +- `label_index`: Elasticsearch index where labeled questions should be stored +- `batch_size`: Optional number of documents that are loaded and processed at a time. +When set to None (default) all documents are processed at once. +- `preprocessor`: Optional PreProcessor to preprocess evaluation documents. +It can be used for splitting documents into passages (and assigning labels to corresponding passages). +Currently the PreProcessor does not support split_by sentence, cleaning nor split_overlap != 0. +When set to None (default) preprocessing is disabled. +- `max_docs`: Optional number of documents that will be loaded. +When set to None (default) all available eval documents are used. +- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the +same question might be found in different contexts. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### run + +```python +def run(documents: List[dict], index: Optional[str] = None, headers: Optional[Dict[str, str]] = None, id_hash_keys: Optional[List[str]] = None) +``` + +Run requests of document stores + +Comment: We will gradually introduce the primitives. The doument stores also accept dicts and parse them to documents. +In the future, however, only documents themselves will be accepted. Parsing the dictionaries in the run function +is therefore only an interim solution until the run function also accepts documents. + +**Arguments**: + +- `documents`: A list of dicts that are documents. +- `headers`: A list of headers. +- `index`: Optional name of index where the documents shall be written to. +If None, the DocumentStore's default index (self.index) will be used. +- `id_hash_keys`: List of the fields that the hashes of the ids are generated from. + + + +## KeywordDocumentStore + +```python +class KeywordDocumentStore(BaseDocumentStore) +``` + +Base class for implementing Document Stores that support keyword searches. + + + +#### query + +```python +@abstractmethod +def query(query: Optional[str], filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query as defined by keyword matching algorithms like BM25. + +**Arguments**: + +- `query`: The query +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return per query. +- `custom_query`: Custom query to be executed. +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### get\_batches\_from\_generator + +```python +def get_batches_from_generator(iterable, n) +``` + +Batch elements of an iterable into fixed-length chunks or blocks. + + + +# Module elasticsearch + + + +## ElasticsearchDocumentStore + +```python +class ElasticsearchDocumentStore(KeywordDocumentStore) +``` + + + +#### get\_document\_by\_id + +```python +def get_document_by_id(id: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> Optional[Document] +``` + +Fetch a document by specifying its text id string + + + +#### get\_documents\_by\_id + +```python +def get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Fetch documents by specifying a list of text id strings. Be aware that passing a large number of ids might lead +to performance issues. Note that Elasticsearch limits the number of results to 10,000 documents by default. + + + +#### get\_metadata\_values\_by\_key + +```python +def get_metadata_values_by_key(key: str, query: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> List[dict] +``` + +Get values associated with a metadata key. The output is in the format: + +[{"value": "my-value-1", "count": 23}, {"value": "my-value-2", "count": 12}, ... ] + +**Arguments**: + +- `key`: the meta key name to get the values for. +- `query`: narrow down the scope to documents matching the query string. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `index`: Elasticsearch index where the meta values should be searched. If not supplied, +self.index will be used. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Indexes documents for later queries in Elasticsearch. + +Behaviour if a document with the same ID already exists in ElasticSearch: +a) (Default) Throw Elastic's standard error message for duplicate IDs. +b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents. +(This is only relevant if you pass your own ID when initializing a `Document`. +If don't set custom IDs for your Documents or just pass a list of dictionaries here, +they will automatically get UUIDs assigned. See the `Document` class for details) + +**Arguments**: + +- `documents`: a list of Python dictionaries or a list of Haystack Document objects. +For documents as dictionaries, the format is {"content": ""}. +Optionally: Include meta data via {"content": "", +"meta":{"name": ", "author": "somebody", ...}} +It can be used for filtering and is accessible in the responses of the Finder. +Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary +should be changed to what you have set for self.content_field and self.name_field. +- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used. +- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + +**Raises**: + +- `DuplicateDocumentError`: Exception trigger on duplicate document + +**Returns**: + +None + + + +#### write\_labels + +```python +def write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None, headers: Optional[Dict[str, str]] = None, batch_size: int = 10_000) +``` + +Write annotation labels into document store. + +**Arguments**: + +- `labels`: A list of Python dictionaries or a list of Haystack Label objects. +- `index`: Elasticsearch index where the labels should be stored. If not supplied, self.label_index will be used. +- `batch_size`: Number of labels that are passed to Elasticsearch's bulk function at a time. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### update\_document\_meta + +```python +def update_document_meta(id: str, meta: Dict[str, str], headers: Optional[Dict[str, str]] = None, index: str = None) +``` + +Update the metadata dictionary of a document by specifying its string id + + + +#### get\_document\_count + +```python +def get_document_count(filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of documents in the document store. + + + +#### get\_label\_count + +```python +def get_label_count(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of labels in the document store + + + +#### get\_embedding\_count + +```python +def get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the count of embeddings in the document store. + + + +#### get\_all\_documents + +```python +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get documents from the document store. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### get\_all\_labels + +```python +def get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None, batch_size: int = 10_000) -> List[Label] +``` + +Return all labels in the document store + + + +#### query + +```python +def query(query: Optional[str], filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query as defined by the BM25 algorithm. + +**Arguments**: + +- `query`: The query +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return per query. +- `custom_query`: query string as per Elasticsearch DSL with a mandatory query placeholder(query). +Optionally, ES `filter` clause can be added where the values of `terms` are placeholders +that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..) +names must match with the filters dict supplied in self.retrieve(). +:: + + **An example custom_query:** + ```python + | { + | "size": 10, + | "query": { + | "bool": { + | "should": [{"multi_match": { + | "query": ${query}, // mandatory query placeholder + | "type": "most_fields", + | "fields": ["content", "title"]}}], + | "filter": [ // optional custom filters + | {"terms": {"year": ${years}}}, + | {"terms": {"quarter": ${quarters}}}, + | {"range": {"date": {"gte": ${date}}}} + | ], + | } + | }, + | } + ``` + + **For this custom_query, a sample retrieve() could be:** + ```python + | self.retrieve(query="Why did the revenue increase?", + | filters={"years": ["2019"], "quarters": ["Q1", "Q2"]}) + ``` + +Optionally, highlighting can be defined by specifying Elasticsearch's highlight settings. +See https://www.elastic.co/guide/en/elasticsearch/reference/current/highlighting.html. +You will find the highlighted output in the returned Document's meta field by key "highlighted". +:: + + **Example custom_query with highlighting:** + ```python + | { + | "size": 10, + | "query": { + | "bool": { + | "should": [{"multi_match": { + | "query": ${query}, // mandatory query placeholder + | "type": "most_fields", + | "fields": ["content", "title"]}}], + | } + | }, + | "highlight": { // enable highlighting + | "fields": { // for fields content and title + | "content": {}, + | "title": {} + | } + | }, + | } + ``` + + **For this custom_query, highlighting info can be accessed by:** + ```python + | docs = self.retrieve(query="Why did the revenue increase?") + | highlighted_content = docs[0].meta["highlighted"]["content"] + | highlighted_title = docs[0].meta["highlighted"]["title"] + ``` +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return +- `index`: Index name for storing the docs and metadata +- `return_embedding`: To return document embedding +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +#### describe\_documents + +```python +def describe_documents(index=None) +``` + +Return a summary of the documents in the document store + + + +#### update\_embeddings + +```python +def update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) +``` + +Updates the embeddings in the the document store using the encoding model specified in the retriever. + +This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config). + +**Arguments**: + +- `retriever`: Retriever to use to update the embeddings. +- `index`: Index name to update +- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False, +only documents without embeddings are processed. This mode can be used for +incremental updating of embeddings, wherein, only newly indexed documents +get processed. +- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + +**Returns**: + +None + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. +- `filters`: Optional filters to narrow down the documents to be deleted. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + +**Returns**: + +None + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the documents from. If None, the +DocumentStore's default index (self.index) will be used +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Optional filters to narrow down the documents to be deleted. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + + If filters are provided along with a list of IDs, this method deletes the + intersection of the two query results (documents that match the filters and + have their ID in the list). +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + +**Returns**: + +None + + + +#### delete\_labels + +```python +def delete_labels(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete labels in an index. All labels are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the labels from. If None, the +DocumentStore's default label index (self.label_index) will be used +- `ids`: Optional list of IDs to narrow down the labels to be deleted. +- `filters`: Optional filters to narrow down the labels to be deleted. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + +**Returns**: + +None + + + +#### delete\_index + +```python +def delete_index(index: str) +``` + +Delete an existing elasticsearch index. The index including all data will be removed. + +**Arguments**: + +- `index`: The name of the index to delete. + +**Returns**: + +None + + + +## OpenSearchDocumentStore + +```python +class OpenSearchDocumentStore(ElasticsearchDocumentStore) +``` + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return +- `index`: Index name for storing the docs and metadata +- `return_embedding`: To return document embedding +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +## OpenDistroElasticsearchDocumentStore + +```python +class OpenDistroElasticsearchDocumentStore(OpenSearchDocumentStore) +``` + +A DocumentStore which has an Open Distro for Elasticsearch service behind it. + + + +# Module memory + + + +## InMemoryDocumentStore + +```python +class InMemoryDocumentStore(BaseDocumentStore) +``` + +In-memory document store + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Indexes documents for later queries. + +**Arguments**: + +- `documents`: a list of Python dictionaries or a list of Haystack Document objects. +For documents as dictionaries, the format is {"text": ""}. + Optionally: Include meta data via {"text": "", + "meta": {"name": ", "author": "somebody", ...}} + It can be used for filtering and is accessible in the responses of the Finder. +:param index: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a + separate index than the documents for search. +:param duplicate_documents: Handle duplicates document based on parameter options. + Parameter options : ( 'skip','overwrite','fail') + skip: Ignore the duplicates documents + overwrite: Update any existing documents with the same ID when adding documents. + fail: an error is raised if the document ID of the document being added already + exists. +:raises DuplicateDocumentError: Exception trigger on duplicate document +:return: None + + + +#### write\_labels + +```python +def write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Write annotation labels into document store. + + + +#### get\_document\_by\_id + +```python +def get_document_by_id(id: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> Optional[Document] +``` + +Fetch a document by specifying its text id string. + + + +#### get\_documents\_by\_id + +```python +def get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document] +``` + +Fetch documents by specifying a list of text id strings. + + + +#### get\_scores\_torch + +```python +def get_scores_torch(query_emb: np.ndarray, document_to_search: List[Document]) -> List[float] +``` + +Calculate similarity scores between query embedding and a list of documents using torch. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `document_to_search`: List of documents to compare `query_emb` against. + + + +#### get\_scores\_numpy + +```python +def get_scores_numpy(query_emb: np.ndarray, document_to_search: List[Document]) -> List[float] +``` + +Calculate similarity scores between query embedding and a list of documents using numpy. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `document_to_search`: List of documents to compare `query_emb` against. + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Any]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` +To use the same logical operator multiple times on the same level, logical operators take +optionally a list of dictionaries as value. +Example: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return +- `index`: Index name for storing the docs and metadata +- `return_embedding`: To return document embedding + + + +#### update\_embeddings + +```python +def update_embeddings(retriever: "BaseRetriever", index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000) +``` + +Updates the embeddings in the the document store using the encoding model specified in the retriever. + +This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config). + +**Arguments**: + +- `retriever`: Retriever to use to get embeddings for text +- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used. +- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False, +only documents without embeddings are processed. This mode can be used for +incremental updating of embeddings, wherein, only newly indexed documents +get processed. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + +**Returns**: + +None + + + +#### get\_document\_count + +```python +def get_document_count(filters: Optional[Dict[str, Any]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of documents in the document store. + + + +#### get\_embedding\_count + +```python +def get_embedding_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int +``` + +Return the count of embeddings in the document store. + + + +#### get\_label\_count + +```python +def get_label_count(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of labels in the document store. + + + +#### get\_all\_documents + +```python +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get all documents from the document store as a list. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get all documents from the document store. The methods returns a Python Generator that yields individual + +documents. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. + + + +#### get\_all\_labels + +```python +def get_all_labels(index: str = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) -> List[Label] +``` + +Return all labels in the document store. + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + +**Returns**: + +None + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + +**Returns**: + +None + + + +#### delete\_labels + +```python +def delete_labels(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete labels in an index. All labels are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the labels from. If None, the +DocumentStore's default label index (self.label_index) will be used. +- `ids`: Optional list of IDs to narrow down the labels to be deleted. +- `filters`: Narrow down the scope to documents that match the given filters. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. +Example: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + +**Returns**: + +None + + + +# Module sql + + + +## SQLDocumentStore + +```python +class SQLDocumentStore(BaseDocumentStore) +``` + + + +#### get\_document\_by\_id + +```python +def get_document_by_id(id: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> Optional[Document] +``` + +Fetch a document by specifying its text id string + + + +#### get\_documents\_by\_id + +```python +def get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Fetch documents by specifying a list of text id strings + + + +#### get\_documents\_by\_vector\_ids + +```python +def get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) +``` + +Fetch documents by specifying a list of text vector id strings + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### get\_all\_labels + +```python +def get_all_labels(index=None, filters: Optional[dict] = None, headers: Optional[Dict[str, str]] = None) +``` + +Return all labels in the document store + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> None +``` + +Indexes documents for later queries. + +**Arguments**: + +- `documents`: a list of Python dictionaries or a list of Haystack Document objects. +For documents as dictionaries, the format is {"text": ""}. +Optionally: Include meta data via {"text": "", +"meta":{"name": ", "author": "somebody", ...}} +It can be used for filtering and is accessible in the responses of the Finder. +- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance, +documents for evaluation can be indexed in a separate index than the documents for search. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. + +**Returns**: + +None + + + +#### write\_labels + +```python +def write_labels(labels, index=None, headers: Optional[Dict[str, str]] = None) +``` + +Write annotation labels into document store. + + + +#### update\_vector\_ids + +```python +def update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None, batch_size: int = 10_000) +``` + +Update vector_ids for given document_ids. + +**Arguments**: + +- `vector_id_map`: dict containing mapping of document_id -> vector_id. +- `index`: filter documents by the optional index attribute for documents in database. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### reset\_vector\_ids + +```python +def reset_vector_ids(index: Optional[str] = None) +``` + +Set vector IDs for all documents as None + + + +#### update\_document\_meta + +```python +def update_document_meta(id: str, meta: Dict[str, str], index: str = None) +``` + +Update the metadata dictionary of a document by specifying its string id + + + +#### get\_document\_count + +```python +def get_document_count(filters: Optional[Dict[str, Any]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of documents in the document store. + + + +#### get\_label\_count + +```python +def get_label_count(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of labels in the document store + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. +- `filters`: Optional filters to narrow down the documents to be deleted. + +**Returns**: + +None + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. If None, the +DocumentStore's default index (self.index) will be used. +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Optional filters to narrow down the documents to be deleted. +Example filters: {"name": ["some", "more"], "category": ["only_one"]}. +If filters are provided along with a list of IDs, this method deletes the +intersection of the two query results (documents that match the filters and +have their ID in the list). + +**Returns**: + +None + + + +#### delete\_labels + +```python +def delete_labels(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete labels from the document store. All labels are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the labels from. If None, the +DocumentStore's default label index (self.label_index) will be used. +- `ids`: Optional list of IDs to narrow down the labels to be deleted. +- `filters`: Optional filters to narrow down the labels to be deleted. +Example filters: {"id": ["9a196e41-f7b5-45b4-bd19-5feb7501c159", "9a196e41-f7b5-45b4-bd19-5feb7501c159"]} or {"query": ["question2"]} + +**Returns**: + +None + + + +# Module faiss + + + +## FAISSDocumentStore + +```python +class FAISSDocumentStore(SQLDocumentStore) +``` + +Document store for very large scale embedding based dense retrievers like the DPR. + +It implements the FAISS library(https://github.com/facebookresearch/faiss) +to perform similarity search on vectors. + +The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while +the vector embeddings are indexed in a FAISS Index. + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> None +``` + +Add new documents to the DocumentStore. + +**Arguments**: + +- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index +them right away in FAISS. If not, you can later call update_embeddings() to create & index them. +- `index`: (SQL) index name for storing the docs and metadata +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. + +**Raises**: + +- `DuplicateDocumentError`: Exception trigger on duplicate document + +**Returns**: + +None + + + +#### update\_embeddings + +```python +def update_embeddings(retriever: "BaseRetriever", index: Optional[str] = None, update_existing_embeddings: bool = True, filters: Optional[Dict[str, Any]] = None, batch_size: int = 10_000) +``` + +Updates the embeddings in the the document store using the encoding model specified in the retriever. + +This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config). + +**Arguments**: + +- `retriever`: Retriever to use to get embeddings for text +- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used. +- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False, +only documents without embeddings are processed. This mode can be used for +incremental updating of embeddings, wherein, only newly indexed documents +get processed. +- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + +**Returns**: + +None + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get all documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `return_embedding`: Whether to return the document embeddings. Unlike other document stores, FAISS will return normalized embeddings +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### get\_embedding\_count + +```python +def get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None) -> int +``` + +Return the count of embeddings in the document store. + + + +#### train\_index + +```python +def train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.ndarray] = None, index: Optional[str] = None) +``` + +Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors. + +The train vectors should come from the same distribution as your final ones. +You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on. + +**Arguments**: + +- `documents`: Documents (incl. the embeddings) +- `embeddings`: Plain embeddings +- `index`: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used. + +**Returns**: + +None + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete all documents from the document store. + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents from the document store. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Optional filters to narrow down the documents to be deleted. +Example filters: {"name": ["some", "more"], "category": ["only_one"]}. +If filters are provided along with a list of IDs, this method deletes the +intersection of the two query results (documents that match the filters and +have their ID in the list). + +**Returns**: + +None + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Any]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `top_k`: How many documents to return +- `index`: Index name to query the document from. +- `return_embedding`: To return document embedding. Unlike other document stores, FAISS will return normalized embeddings + + + +#### save + +```python +def save(index_path: Union[str, Path], config_path: Optional[Union[str, Path]] = None) +``` + +Save FAISS Index to the specified file. + +**Arguments**: + +- `index_path`: Path to save the FAISS index to. +- `config_path`: Path to save the initial configuration parameters to. +Defaults to the same as the file path, save the extension (.json). +This file contains all the parameters passed to FAISSDocumentStore() +at creation time (for example the SQL path, embedding_dim, etc), and will be +used by the `load` method to restore the index with the appropriate configuration. + +**Returns**: + +None + + + +#### load + +```python +@classmethod +def load(cls, index_path: Union[str, Path], config_path: Optional[Union[str, Path]] = None) +``` + +Load a saved FAISS index from a file and connect to the SQL database. + +Note: In order to have a correct mapping from FAISS to SQL, + make sure to use the same SQL DB that you used when calling `save()`. + +**Arguments**: + +- `index_path`: Stored FAISS index file. Can be created via calling `save()` +- `config_path`: Stored FAISS initial configuration parameters. +Can be created via calling `save()` + + + +# Module milvus + + + +## MilvusDocumentStore + +```python +class MilvusDocumentStore(SQLDocumentStore) +``` + +Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors. +Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR). +In contrast to FAISS, Milvus ... + - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment + - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index) + - encapsulates multiple ANN libraries (FAISS, ANNOY ...) + +This class uses Milvus for all vector related storage, processing and querying. +The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus +does not allow these data types (yet). + +Usage: +1. Start a Milvus server (see https://milvus.io/docs/v1.0.0/install_milvus.md) +2. Init a MilvusDocumentStore in Haystack + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None, index_param: Optional[Dict[str, Any]] = None) +``` + +Add new documents to the DocumentStore. + +**Arguments**: + +- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index +them right away in Milvus. If not, you can later call update_embeddings() to create & index them. +- `index`: (SQL) index name for storing the docs and metadata +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. + +**Raises**: + +- `DuplicateDocumentError`: Exception trigger on duplicate document + +**Returns**: + +None + + + +#### update\_embeddings + +```python +def update_embeddings(retriever: "BaseRetriever", index: Optional[str] = None, batch_size: int = 10_000, update_existing_embeddings: bool = True, filters: Optional[Dict[str, Any]] = None) +``` + +Updates the embeddings in the the document store using the encoding model specified in the retriever. + +This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config). + +**Arguments**: + +- `retriever`: Retriever to use to get embeddings for text +- `index`: (SQL) index name for storing the docs and metadata +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False, +only documents without embeddings are processed. This mode can be used for +incremental updating of embeddings, wherein, only newly indexed documents +get processed. +- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated. +Example: {"name": ["some", "more"], "category": ["only_one"]} + +**Returns**: + +None + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Any]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `top_k`: How many documents to return +- `index`: (SQL) index name for storing the docs and metadata +- `return_embedding`: To return document embedding + +**Returns**: + +list of Documents that are the most similar to `query_emb` + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete all documents (from SQL AND Milvus). + +**Arguments**: + +- `index`: (SQL) index name for storing the docs and metadata +- `filters`: Optional filters to narrow down the search space. +Example: {"name": ["some", "more"], "category": ["only_one"]} + +**Returns**: + +None + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. If None, the +DocumentStore's default index (self.index) will be used. +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Optional filters to narrow down the documents to be deleted. +Example filters: {"name": ["some", "more"], "category": ["only_one"]}. +If filters are provided along with a list of IDs, this method deletes the +intersection of the two query results (documents that match the filters and +have their ID in the list). + +**Returns**: + +None + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get all documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### get\_all\_documents + +```python +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get documents from the document store (optionally using filter criteria). + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the documents to return. +Example: {"name": ["some", "more"], "category": ["only_one"]} +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### get\_document\_by\_id + +```python +def get_document_by_id(id: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> Optional[Document] +``` + +Fetch a document by specifying its text id string + +**Arguments**: + +- `id`: ID of the document +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. + + + +#### get\_documents\_by\_id + +```python +def get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Fetch multiple documents by specifying their IDs (strings) + +**Arguments**: + +- `ids`: List of IDs of the documents +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `batch_size`: is currently not used + + + +#### get\_all\_vectors + +```python +def get_all_vectors(index: Optional[str] = None) -> List[np.ndarray] +``` + +Helper function to dump all vectors stored in Milvus server. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. + +**Returns**: + +List[np.array]: List of vectors. + + + +#### get\_embedding\_count + +```python +def get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None) -> int +``` + +Return the count of embeddings in the document store. + + + +# Module weaviate + + + +## WeaviateDocumentStore + +```python +class WeaviateDocumentStore(BaseDocumentStore) +``` + +Weaviate is a cloud-native, modular, real-time vector search engine built to scale your machine learning models. +(See https://www.semi.technology/developers/weaviate/current/index.html#what-is-weaviate) + +Some of the key differences in contrast to FAISS & Milvus: +1. Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up +2. Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset +3. Has less variety of ANN algorithms, as of now only HNSW. +4. Requires document ids to be in uuid-format. If wrongly formatted ids are provided at indexing time they will be replaced with uuids automatically. +5. Only support cosine similarity. + +Weaviate python client is used to connect to the server, more details are here +https://weaviate-python-client.readthedocs.io/en/docs/weaviate.html + +Usage: +1. Start a Weaviate server (see https://www.semi.technology/developers/weaviate/current/getting-started/installation.html) +2. Init a WeaviateDocumentStore in Haystack + +Limitations: +The current implementation is not supporting the storage of labels, so you cannot run any evaluation workflows. + + + +#### get\_document\_by\_id + +```python +def get_document_by_id(id: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> Optional[Document] +``` + +Fetch a document by specifying its uuid string + + + +#### get\_documents\_by\_id + +```python +def get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Fetch documents by specifying a list of uuid strings. + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Add new documents to the DocumentStore. + +**Arguments**: + +- `documents`: List of `Dicts` or List of `Documents`. A dummy embedding vector for each document is automatically generated if it is not provided. The document id needs to be in uuid format. Otherwise a correctly formatted uuid will be automatically generated based on the provided id. +- `index`: index name for storing the docs and metadata +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. + +**Raises**: + +- `DuplicateDocumentError`: Exception trigger on duplicate document + +**Returns**: + +None + + + +#### update\_document\_meta + +```python +def update_document_meta(id: str, meta: Dict[str, Union[List, str, int, float, bool]], index: str = None) +``` + +Update the metadata dictionary of a document by specifying its string id. +Overwrites only the specified fields, the unspecified ones remain unchanged. + + + +#### get\_embedding\_count + +```python +def get_embedding_count(filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, index: Optional[str] = None) -> int +``` + +Return the number of embeddings in the document store, which is the same as the number of documents since +every document has a default embedding. + + + +#### get\_document\_count + +```python +def get_document_count(filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False, headers: Optional[Dict[str, str]] = None) -> int +``` + +Return the number of documents in the document store. + + + +#### get\_all\_documents + +```python +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get documents from the document store. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + + + +#### query + +```python +def query(query: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query as defined by Weaviate semantic search. + +**Arguments**: + +- `query`: The query +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return per query. +- `custom_query`: Custom query that will executed using query.raw method, for more details refer +https://www.semi.technology/developers/weaviate/current/graphql-references/filters.html +- `index`: The name of the index in the DocumentStore from which to retrieve documents + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return +- `index`: index name for storing the docs and metadata +- `return_embedding`: To return document embedding + + + +#### update\_embeddings + +```python +def update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000) +``` + +Updates the embeddings in the the document store using the encoding model specified in the retriever. + +This can be useful if want to change the embeddings for your documents (e.g. after changing the retriever config). + +**Arguments**: + +- `retriever`: Retriever to use to update the embeddings. +- `index`: Index name to update +- `update_existing_embeddings`: Weaviate mandates an embedding while creating the document itself. +This option must be always true for weaviate and it will update the embeddings for all the documents. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. + +**Returns**: + +None + + + +#### delete\_all\_documents + +```python +def delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + +**Returns**: + +None + + + +#### delete\_documents + +```python +def delete_documents(index: Optional[str] = None, ids: Optional[List[str]] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, headers: Optional[Dict[str, str]] = None) +``` + +Delete documents in an index. All documents are deleted if no filters are passed. + +**Arguments**: + +- `index`: Index name to delete the document from. If None, the +DocumentStore's default index (self.index) will be used. +- `ids`: Optional list of IDs to narrow down the documents to be deleted. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` + If filters are provided along with a list of IDs, this method deletes the + intersection of the two query results (documents that match the filters and + have their ID in the list). + +**Returns**: + +None + + + +# Module graphdb + + + +## GraphDBKnowledgeGraph + +```python +class GraphDBKnowledgeGraph(BaseKnowledgeGraph) +``` + +Knowledge graph store that runs on a GraphDB instance. + + + +#### create\_index + +```python +def create_index(config_path: Path, headers: Optional[Dict[str, str]] = None) +``` + +Create a new index (also called repository) stored in the GraphDB instance + +**Arguments**: + +- `config_path`: path to a .ttl file with configuration settings, details: +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +https://graphdb.ontotext.com/documentation/free/configuring-a-repository.html#configure-a-repository-programmatically + + + +#### delete\_index + +```python +def delete_index(headers: Optional[Dict[str, str]] = None) +``` + +Delete the index that GraphDBKnowledgeGraph is connected to. This method deletes all data stored in the index. + +**Arguments**: + +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + + + +#### import\_from\_ttl\_file + +```python +def import_from_ttl_file(index: str, path: Path, headers: Optional[Dict[str, str]] = None) +``` + +Load an existing knowledge graph represented in the form of triples of subject, predicate, and object from a .ttl file into an index of GraphDB + +**Arguments**: + +- `index`: name of the index (also called repository) in the GraphDB instance where the imported triples shall be stored +- `path`: path to a .ttl containing a knowledge graph +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + + + +#### get\_all\_triples + +```python +def get_all_triples(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Query the given index in the GraphDB instance for all its stored triples. Duplicates are not filtered. + +**Arguments**: + +- `index`: name of the index (also called repository) in the GraphDB instance +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + +**Returns**: + +all triples stored in the index + + + +#### get\_all\_subjects + +```python +def get_all_subjects(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Query the given index in the GraphDB instance for all its stored subjects. Duplicates are not filtered. + +**Arguments**: + +- `index`: name of the index (also called repository) in the GraphDB instance +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + +**Returns**: + +all subjects stored in the index + + + +#### get\_all\_predicates + +```python +def get_all_predicates(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Query the given index in the GraphDB instance for all its stored predicates. Duplicates are not filtered. + +**Arguments**: + +- `index`: name of the index (also called repository) in the GraphDB instance +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + +**Returns**: + +all predicates stored in the index + + + +#### get\_all\_objects + +```python +def get_all_objects(index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Query the given index in the GraphDB instance for all its stored objects. Duplicates are not filtered. + +**Arguments**: + +- `index`: name of the index (also called repository) in the GraphDB instance +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + +**Returns**: + +all objects stored in the index + + + +#### query + +```python +def query(sparql_query: str, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Execute a SPARQL query on the given index in the GraphDB instance + +**Arguments**: + +- `sparql_query`: SPARQL query that shall be executed +- `index`: name of the index (also called repository) in the GraphDB instance +- `headers`: Custom HTTP headers to pass to http client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) + +**Returns**: + +query result + + + +# Module deepsetcloud + + + +## DeepsetCloudDocumentStore + +```python +class DeepsetCloudDocumentStore(KeywordDocumentStore) +``` + + + +#### get\_all\_documents + +```python +def get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Get documents from the document store. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: Number of documents that are passed to bulk function at a time. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### get\_all\_documents\_generator + +```python +def get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000, headers: Optional[Dict[str, str]] = None) -> Generator[Document, None, None] +``` + +Get documents from the document store. Under-the-hood, documents are fetched in batches from the + +document store and yielded as individual documents. This method can be used to iteratively process +a large number of documents without having to load all documents in memory. + +**Arguments**: + +- `index`: Name of the index to get the documents from. If None, the +DocumentStore's default index (self.index) will be used. +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + ``` +- `return_embedding`: Whether to return the document embeddings. +- `batch_size`: When working with large number of documents, batching can help reduce memory footprint. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### query\_by\_embedding + +```python +def query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Find the document that is most similar to the provided `query_emb` by using a vector similarity metric. + +**Arguments**: + +- `query_emb`: Embedding of the query (e.g. gathered from DPR) +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return +- `index`: Index name for storing the docs and metadata +- `return_embedding`: To return document embedding +- `headers`: Custom HTTP headers to pass to requests + + + +#### query + +```python +def query(query: Optional[str], filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query as defined by the BM25 algorithm. + +**Arguments**: + +- `query`: The query +- `filters`: Optional filters to narrow down the search space to documents whose metadata fulfill certain +conditions. +Filters are defined as nested dictionaries. The keys of the dictionaries can be a logical +operator (`"$and"`, `"$or"`, `"$not"`), a comparison operator (`"$eq"`, `"$in"`, `"$gt"`, +`"$gte"`, `"$lt"`, `"$lte"`) or a metadata field name. +Logical operator keys take a dictionary of metadata field names and/or logical operators as +value. Metadata field names take a dictionary of comparison operators as value. Comparison +operator keys take a single value or (in case of `"$in"`) a list of values as value. +If no logical operator is provided, `"$and"` is used as default operation. If no comparison +operator is provided, `"$eq"` (or `"$in"` if the comparison value is a list) is used as default +operation. + + __Example__: + ```python + filters = { + "$and": { + "type": {"$eq": "article"}, + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": {"$in": ["economy", "politics"]}, + "publisher": {"$eq": "nytimes"} + } + } + } + # or simpler using default operators + filters = { + "type": "article", + "date": {"$gte": "2015-01-01", "$lt": "2021-01-01"}, + "rating": {"$gte": 3}, + "$or": { + "genre": ["economy", "politics"], + "publisher": "nytimes" + } + } + ``` + + To use the same logical operator multiple times on the same level, logical operators take + optionally a list of dictionaries as value. + + __Example__: + ```python + filters = { + "$or": [ + { + "$and": { + "Type": "News Paper", + "Date": { + "$lt": "2019-01-01" + } + } + }, + { + "$and": { + "Type": "Blog Post", + "Date": { + "$gte": "2019-01-01" + } + } + } + ] + } + ``` +- `top_k`: How many documents to return per query. +- `custom_query`: Custom query to be executed. +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to requests + + + +#### write\_documents + +```python +def write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, headers: Optional[Dict[str, str]] = None) +``` + +Indexes documents for later queries. + +**Arguments**: + +- `documents`: a list of Python dictionaries or a list of Haystack Document objects. +For documents as dictionaries, the format is {"text": ""}. +Optionally: Include meta data via {"text": "", +"meta":{"name": ", "author": "somebody", ...}} +It can be used for filtering and is accessible in the responses of the Finder. +- `index`: Optional name of index where the documents shall be written to. +If None, the DocumentStore's default index (self.index) will be used. +- `batch_size`: Number of documents that are passed to bulk function at a time. +- `duplicate_documents`: Handle duplicates document based on parameter options. +Parameter options : ( 'skip','overwrite','fail') +skip: Ignore the duplicates documents +overwrite: Update any existing documents with the same ID when adding documents. +fail: an error is raised if the document ID of the document being added already +exists. +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + +**Returns**: + +None + diff --git a/docs/v1.2.0/_src/api/api/evaluation.md b/docs/v1.2.0/_src/api/api/evaluation.md new file mode 100644 index 0000000000..42360af78d --- /dev/null +++ b/docs/v1.2.0/_src/api/api/evaluation.md @@ -0,0 +1,104 @@ + + +# Module evaluator + + + +## EvalDocuments + +```python +class EvalDocuments(BaseComponent) +``` + +This is a pipeline node that should be placed after a node that returns a List of Document, e.g., Retriever or +Ranker, in order to assess its performance. Performance metrics are stored in this class and updated as each +sample passes through it. To view the results of the evaluation, call EvalDocuments.print(). Note that results +from this Node may differ from that when calling Retriever.eval() since that is a closed domain evaluation. Have +a look at our evaluation tutorial for more info about open vs closed domain eval ( +https://haystack.deepset.ai/tutorials/evaluation). + +EvalDocuments node is deprecated and will be removed in a future version. +Please use pipeline.eval() instead. + + + +#### run + +```python +def run(documents: List[Document], labels: List[Label], top_k: Optional[int] = None) +``` + +Run this node on one sample and its labels + + + +#### print + +```python +def print() +``` + +Print the evaluation results + + + +## EvalAnswers + +```python +class EvalAnswers(BaseComponent) +``` + +This is a pipeline node that should be placed after a Reader in order to assess the performance of the Reader +individually or to assess the extractive QA performance of the whole pipeline. Performance metrics are stored in +this class and updated as each sample passes through it. To view the results of the evaluation, call EvalAnswers.print(). +Note that results from this Node may differ from that when calling Reader.eval() +since that is a closed domain evaluation. Have a look at our evaluation tutorial for more info about +open vs closed domain eval (https://haystack.deepset.ai/tutorials/evaluation). + +EvalAnswers node is deprecated and will be removed in a future version. +Please use pipeline.eval() instead. + + + +#### run + +```python +def run(labels: List[Label], answers: List[Answer], correct_retrieval: bool) +``` + +Run this node on one sample and its labels + + + +#### print + +```python +def print(mode) +``` + +Print the evaluation results + + + +#### semantic\_answer\_similarity + +```python +def semantic_answer_similarity(predictions: List[List[str]], gold_labels: List[List[str]], sas_model_name_or_path: str = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2") -> Tuple[List[float], List[float]] +``` + +Computes Transformer-based similarity of predicted answer to gold labels to derive a more meaningful metric than EM or F1. + +Returns per QA pair a) the similarity of the most likely prediction (top 1) to all available gold labels + b) the highest similarity of all predictions to gold labels + +**Arguments**: + +- `predictions`: Predicted answers as list of multiple preds per question +- `gold_labels`: Labels as list of multiple possible answers per question +- `sas_model_name_or_path`: SentenceTransformers semantic textual similarity model, should be path or string +pointing to downloadable models. + +**Returns**: + +top_1_sas, top_k_sas + diff --git a/docs/v1.2.0/_src/api/api/extractor.md b/docs/v1.2.0/_src/api/api/extractor.md new file mode 100644 index 0000000000..26262ef12b --- /dev/null +++ b/docs/v1.2.0/_src/api/api/extractor.md @@ -0,0 +1,58 @@ + + +# Module entity + + + +## EntityExtractor + +```python +class EntityExtractor(BaseComponent) +``` + +This node is used to extract entities out of documents. +The most common use case for this would be as a named entity extractor. +The default model used is dslim/bert-base-NER. +This node can be placed in a querying pipeline to perform entity extraction on retrieved documents only, +or it can be placed in an indexing pipeline so that all documents in the document store have extracted entities. +The entities extracted by this Node will populate Document.entities + + + +#### run + +```python +def run(documents: Optional[Union[List[Document], List[dict]]] = None) -> Tuple[Dict, str] +``` + +This is the method called when this node is used in a pipeline + + + +#### extract + +```python +def extract(text) +``` + +This function can be called to perform entity extraction when using the node in isolation. + + + +#### simplify\_ner\_for\_qa + +```python +def simplify_ner_for_qa(output) +``` + +Returns a simplified version of the output dictionary +with the following structure: +[ + { + answer: { ... } + entities: [ { ... }, {} ] + } +] +The entities included are only the ones that overlap with +the answer itself. + diff --git a/docs/v1.2.0/_src/api/api/file_classifier.md b/docs/v1.2.0/_src/api/api/file_classifier.md new file mode 100644 index 0000000000..14add2239d --- /dev/null +++ b/docs/v1.2.0/_src/api/api/file_classifier.md @@ -0,0 +1,28 @@ + + +# Module file\_type + + + +## FileTypeClassifier + +```python +class FileTypeClassifier(BaseComponent) +``` + +Route files in an Indexing Pipeline to corresponding file converters. + + + +#### run + +```python +def run(file_paths: Union[Path, List[Path], str, List[str], List[Union[Path, str]]]) +``` + +Sends out files on a different output edge depending on their extension. + +**Arguments**: + +- `file_paths`: paths to route on different edges. + diff --git a/docs/v1.2.0/_src/api/api/file_converter.md b/docs/v1.2.0/_src/api/api/file_converter.md new file mode 100644 index 0000000000..0367234ae2 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/file_converter.md @@ -0,0 +1,345 @@ + + +# Module base + + + +## BaseConverter + +```python +class BaseConverter(BaseComponent) +``` + +Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore. + + + +#### convert + +```python +@abstractmethod +def convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]] +``` + +Convert a file to a dictionary containing the text and any associated meta data. + +File converters may extract file meta like name or size. In addition to it, user +supplied meta data like author, url, external IDs can be supplied as a dictionary. + +**Arguments**: + +- `file_path`: path of the file to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Select the file encoding (default is `utf-8`) + + + +#### validate\_language + +```python +def validate_language(text: str, valid_languages: Optional[List[str]] = None) -> bool +``` + +Validate if the language of the text is one of valid languages. + + + +# Module docx + + + +## DocxToTextConverter + +```python +class DocxToTextConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> List[Dict[str, Any]] +``` + +Extract text from a .docx file. + +Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here. +For compliance with other converters we nevertheless opted for keeping the methods name. + +**Arguments**: + +- `file_path`: Path to the .docx file you want to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Not applicable + + + +# Module image + + + +## ImageToTextConverter + +```python +class ImageToTextConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Union[Path, str], meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]] +``` + +Extract text from image file using the pytesseract library (https://github.com/madmaze/pytesseract) + +**Arguments**: + +- `file_path`: path to image file +- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents. +Can be any custom keys and values. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages supported by tessarect +(https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html). +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. + + + +# Module markdown + + + +## MarkdownConverter + +```python +class MarkdownConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]] +``` + +Reads text from a txt file and executes optional preprocessing steps. + +**Arguments**: + +- `file_path`: path of the file to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `encoding`: Select the file encoding (default is `utf-8`) +- `remove_numeric_tables`: Not applicable +- `valid_languages`: Not applicable + +**Returns**: + +Dict of format {"text": "The text from file", "meta": meta}} + + + +#### markdown\_to\_text + +```python +@staticmethod +def markdown_to_text(markdown_string: str) -> str +``` + +Converts a markdown string to plaintext + +**Arguments**: + +- `markdown_string`: String in markdown format + + + +# Module pdf + + + +## PDFToTextConverter + +```python +class PDFToTextConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> List[Dict[str, Any]] +``` + +Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html) + +**Arguments**: + +- `file_path`: Path to the .pdf file you want to convert +- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents. +Can be any custom keys and values. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding +of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or +others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...). +Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as +"xef\xac\x81c" (see test cases). That's why we keep "Latin 1" as default here. +(See list of available encodings by running `pdftotext -listenc` in the terminal) + + + +## PDFToTextOCRConverter + +```python +class PDFToTextOCRConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]] +``` + +Convert a file to a dictionary containing the text and any associated meta data. + +File converters may extract file meta like name or size. In addition to it, user +supplied meta data like author, url, external IDs can be supplied as a dictionary. + +**Arguments**: + +- `file_path`: path of the file to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Select the file encoding (default is `utf-8`) + + + +# Module tika + + + +## TikaConverter + +```python +class TikaConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> List[Dict[str, Any]] +``` + +**Arguments**: + +- `file_path`: path of the file to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Not applicable + +**Returns**: + +a list of pages and the extracted meta data of the file. + + + +# Module txt + + + +## TextConverter + +```python +class TextConverter(BaseConverter) +``` + + + +#### convert + +```python +def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]] +``` + +Reads text from a txt file and executes optional preprocessing steps. + +**Arguments**: + +- `file_path`: path of the file to convert +- `meta`: dictionary of meta data key-value pairs to append in the returned document. +- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables. +The tabular structures in documents might be noise for the reader model if it +does not have table parsing capability for finding answers. However, tables +may also have long strings that could possible candidate for searching answers. +The rows containing strings are thus retained in this option. +- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1 +(https://en.wikipedia.org/wiki/ISO_639-1) format. +This option can be used to add test for encoding errors. If the extracted text is +not one of the valid languages, then it might likely be encoding error resulting +in garbled text. +- `encoding`: Select the file encoding (default is `utf-8`) + +**Returns**: + +Dict of format {"text": "The text from file", "meta": meta}} + diff --git a/docs/v1.2.0/_src/api/api/generator.md b/docs/v1.2.0/_src/api/api/generator.md new file mode 100644 index 0000000000..65f64c9954 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/generator.md @@ -0,0 +1,201 @@ + + +# Module base + + + +## BaseGenerator + +```python +class BaseGenerator(BaseComponent) +``` + +Abstract class for Generators + + + +#### predict + +```python +@abstractmethod +def predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict +``` + +Abstract method to generate answers. + +**Arguments**: + +- `query`: Query +- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on. +- `top_k`: Number of returned answers + +**Returns**: + +Generated answers plus additional infos in a dict + + + +# Module transformers + + + +## RAGenerator + +```python +class RAGenerator(BaseGenerator) +``` + +Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on +HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html). + +Instead of "finding" the answer within a document, these models **generate** the answer. +In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages +for real-world applications: +a) it has a manageable model size +b) the answer generation is conditioned on retrieved documents, +i.e. the model can easily adjust to domain documents even after training has finished +(in contrast: GPT-3 relies on the web data seen during training) + +**Example** + +```python +| query = "who got the first nobel prize in physics?" +| +| # Retrieve related documents from retriever +| retrieved_docs = retriever.retrieve(query=query) +| +| # Now generate answer from query and retrieved documents +| generator.predict( +| query=query, +| documents=retrieved_docs, +| top_k=1 +| ) +| +| # Answer +| +| {'query': 'who got the first nobel prize in physics', +| 'answers': +| [{'query': 'who got the first nobel prize in physics', +| 'answer': ' albert einstein', +| 'meta': { 'doc_ids': [...], +| 'doc_scores': [80.42758 ...], +| 'doc_probabilities': [40.71379089355469, ... +| 'content': ['Albert Einstein was a ...] +| 'titles': ['"Albert Einstein"', ...] +| }}]} +``` + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict +``` + +Generate the answer to the input query. The generation will be conditioned on the supplied documents. + +These document can for example be retrieved via the Retriever. + +**Arguments**: + +- `query`: Query +- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on. +- `top_k`: Number of returned answers + +**Returns**: + +Generated answers plus additional infos in a dict like this: +```python +| {'query': 'who got the first nobel prize in physics', +| 'answers': +| [{'query': 'who got the first nobel prize in physics', +| 'answer': ' albert einstein', +| 'meta': { 'doc_ids': [...], +| 'doc_scores': [80.42758 ...], +| 'doc_probabilities': [40.71379089355469, ... +| 'content': ['Albert Einstein was a ...] +| 'titles': ['"Albert Einstein"', ...] +| }}]} +``` + + + +## Seq2SeqGenerator + +```python +class Seq2SeqGenerator(BaseGenerator) +``` + +A generic sequence-to-sequence generator based on HuggingFace's transformers. + +Text generation is supported by so called auto-regressive language models like GPT2, +XLNet, XLM, Bart, T5 and others. In fact, any HuggingFace language model that extends +GenerationMixin can be used by Seq2SeqGenerator. + +Moreover, as language models prepare model input in their specific encoding, each model +specified with model_name_or_path parameter in this Seq2SeqGenerator should have an +accompanying model input converter that takes care of prefixes, separator tokens etc. +By default, we provide model input converters for a few well-known seq2seq language models (e.g. ELI5). +It is the responsibility of Seq2SeqGenerator user to ensure an appropriate model input converter +is either already registered or specified on a per-model basis in the Seq2SeqGenerator constructor. + +For mode details on custom model input converters refer to _BartEli5Converter + + +See https://huggingface.co/transformers/main_classes/model.html?transformers.generation_utils.GenerationMixin#transformers.generation_utils.GenerationMixin +as well as https://huggingface.co/blog/how-to-generate + +For a list of all text-generation models see https://huggingface.co/models?pipeline_tag=text-generation + +**Example** + +```python +| query = "Why is Dothraki language important?" +| +| # Retrieve related documents from retriever +| retrieved_docs = retriever.retrieve(query=query) +| +| # Now generate answer from query and retrieved documents +| generator.predict( +| query=query, +| documents=retrieved_docs, +| top_k=1 +| ) +| +| # Answer +| +| {'query': 'who got the first nobel prize in physics', +| 'answers': +| [{'query': 'who got the first nobel prize in physics', +| 'answer': ' albert einstein', +| 'meta': { 'doc_ids': [...], +| 'doc_scores': [80.42758 ...], +| 'doc_probabilities': [40.71379089355469, ... +| 'content': ['Albert Einstein was a ...] +| 'titles': ['"Albert Einstein"', ...] +| }}]} +``` + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict +``` + +Generate the answer to the input query. The generation will be conditioned on the supplied documents. + +These document can be retrieved via the Retriever or supplied directly via predict method. + +**Arguments**: + +- `query`: Query +- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on. +- `top_k`: Number of returned answers + +**Returns**: + +Generated answers + diff --git a/docs/v1.2.0/_src/api/api/other.md b/docs/v1.2.0/_src/api/api/other.md new file mode 100644 index 0000000000..e45bd95276 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/other.md @@ -0,0 +1,36 @@ + + +# Module docs2answers + + + +## Docs2Answers + +```python +class Docs2Answers(BaseComponent) +``` + +This Node is used to convert retrieved documents into predicted answers format. +It is useful for situations where you are calling a Retriever only pipeline via REST API. +This ensures that your output is in a compatible format. + + + +# Module join\_docs + + + +## JoinDocuments + +```python +class JoinDocuments(BaseComponent) +``` + +A node to join documents outputted by multiple retriever nodes. + +The node allows multiple join modes: +* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded. +* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different + `weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents. +* reciprocal_rank_fusion: combines the documents based on their rank in multiple nodes. + diff --git a/docs/v1.2.0/_src/api/api/pipelines.md b/docs/v1.2.0/_src/api/api/pipelines.md new file mode 100644 index 0000000000..0c1c0eab2b --- /dev/null +++ b/docs/v1.2.0/_src/api/api/pipelines.md @@ -0,0 +1,1076 @@ + + +# Module base + + + +## RootNode + +```python +class RootNode(BaseComponent) +``` + +RootNode feeds inputs together with corresponding params to a Pipeline. + + + +## BasePipeline + +```python +class BasePipeline() +``` + +Base class for pipelines, providing the most basic methods to load and save them in different ways. +See also the `Pipeline` class for the actual pipeline logic. + + + +#### get\_config + +```python +def get_config(return_defaults: bool = False) -> dict +``` + +Returns a configuration for the Pipeline that can be used with `BasePipeline.load_from_config()`. + +**Arguments**: + +- `return_defaults`: whether to output parameters that have the default values. + + + +#### to\_code + +```python +def to_code(pipeline_variable_name: str = "pipeline", generate_imports: bool = True, add_comment: bool = False) -> str +``` + +Returns the code to create this pipeline as string. + +**Arguments**: + +- `pipeline_variable_name`: The variable name of the generated pipeline. +Default value is 'pipeline'. +- `generate_imports`: Whether to include the required import statements into the code. +Default value is True. +- `add_comment`: Whether to add a preceding comment that this code has been generated. +Default value is False. + + + +#### to\_notebook\_cell + +```python +def to_notebook_cell(pipeline_variable_name: str = "pipeline", generate_imports: bool = True, add_comment: bool = True) +``` + +Creates a new notebook cell with the code to create this pipeline. + +**Arguments**: + +- `pipeline_variable_name`: The variable name of the generated pipeline. +Default value is 'pipeline'. +- `generate_imports`: Whether to include the required import statements into the code. +Default value is True. +- `add_comment`: Whether to add a preceding comment that this code has been generated. +Default value is True. + + + +#### load\_from\_config + +```python +@classmethod +def load_from_config(cls, pipeline_config: Dict, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True) +``` + +Load Pipeline from a config dict defining the individual components and how they're tied together to form + +a Pipeline. A single config can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +Here's a sample configuration: + + ```python + | { + | "version": "1.0", + | "components": [ + | { # define all the building-blocks for Pipeline + | "name": "MyReader", # custom-name for the component; helpful for visualization & debugging + | "type": "FARMReader", # Haystack Class name for the component + | "params": {"no_ans_boost": -10, "model_name_or_path": "deepset/roberta-base-squad2"}, + | }, + | { + | "name": "MyESRetriever", + | "type": "ElasticsearchRetriever", + | "params": { + | "document_store": "MyDocumentStore", # params can reference other components defined in the YAML + | "custom_query": None, + | }, + | }, + | {"name": "MyDocumentStore", "type": "ElasticsearchDocumentStore", "params": {"index": "haystack_test"}}, + | ], + | "pipelines": [ + | { # multiple Pipelines can be defined using the components from above + | "name": "my_query_pipeline", # a simple extractive-qa Pipeline + | "nodes": [ + | {"name": "MyESRetriever", "inputs": ["Query"]}, + | {"name": "MyReader", "inputs": ["MyESRetriever"]}, + | ], + | } + | ], + | } + ``` + +**Arguments**: + +- `pipeline_config`: the pipeline config as dict +- `pipeline_name`: if the config contains multiple pipelines, the pipeline_name to load must be set. +- `overwrite_with_env_variables`: Overwrite the configuration with environment variables. For example, +to change index name param for an ElasticsearchDocumentStore, an env +variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. + + + +#### load\_from\_yaml + +```python +@classmethod +def load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True) +``` + +Load Pipeline from a YAML file defining the individual components and how they're tied together to form + +a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +Here's a sample configuration: + + ```yaml + | version: '1.0' + | + | components: # define all the building-blocks for Pipeline + | - name: MyReader # custom-name for the component; helpful for visualization & debugging + | type: FARMReader # Haystack Class name for the component + | params: + | no_ans_boost: -10 + | model_name_or_path: deepset/roberta-base-squad2 + | - name: MyESRetriever + | type: ElasticsearchRetriever + | params: + | document_store: MyDocumentStore # params can reference other components defined in the YAML + | custom_query: null + | - name: MyDocumentStore + | type: ElasticsearchDocumentStore + | params: + | index: haystack_test + | + | pipelines: # multiple Pipelines can be defined using the components from above + | - name: my_query_pipeline # a simple extractive-qa Pipeline + | nodes: + | - name: MyESRetriever + | inputs: [Query] + | - name: MyReader + | inputs: [MyESRetriever] + ``` + +Note that, in case of a mismatch in version between Haystack and the YAML, a warning will be printed. +If the pipeline loads correctly regardless, save again the pipeline using `Pipeline.save_to_yaml()` to remove the warning. + +**Arguments**: + +- `path`: path of the YAML file. +- `pipeline_name`: if the YAML contains multiple pipelines, the pipeline_name to load must be set. +- `overwrite_with_env_variables`: Overwrite the YAML configuration with environment variables. For example, +to change index name param for an ElasticsearchDocumentStore, an env +variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. + + + +#### load\_from\_deepset\_cloud + +```python +@classmethod +def load_from_deepset_cloud(cls, pipeline_config_name: str, pipeline_name: str = "query", workspace: str = "default", api_key: Optional[str] = None, api_endpoint: Optional[str] = None, overwrite_with_env_variables: bool = False) +``` + +Load Pipeline from Deepset Cloud defining the individual components and how they're tied together to form + +a Pipeline. A single config can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +In order to get a list of all available pipeline_config_names, call `list_pipelines_on_deepset_cloud()`. +Use the returned `name` as `pipeline_config_name`. + +**Arguments**: + +- `pipeline_config_name`: name of the config file inside the Deepset Cloud workspace. +To get a list of all available pipeline_config_names, call `list_pipelines_on_deepset_cloud()`. +- `pipeline_name`: specifies which pipeline to load from config. +Deepset Cloud typically provides a 'query' and a 'index' pipeline per config. +- `workspace`: workspace in Deepset Cloud +- `api_key`: Secret value of the API key. +If not specified, will be read from DEEPSET_CLOUD_API_KEY environment variable. +- `api_endpoint`: The URL of the Deepset Cloud API. +If not specified, will be read from DEEPSET_CLOUD_API_ENDPOINT environment variable. +- `overwrite_with_env_variables`: Overwrite the config with environment variables. For example, +to change return_no_answer param for a FARMReader, an env +variable 'READER_PARAMS_RETURN_NO_ANSWER=False' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. + + + +#### list\_pipelines\_on\_deepset\_cloud + +```python +@classmethod +def list_pipelines_on_deepset_cloud(cls, workspace: str = "default", api_key: Optional[str] = None, api_endpoint: Optional[str] = None) -> List[dict] +``` + +Lists all pipeline configs available on Deepset Cloud. + +**Arguments**: + +- `workspace`: workspace in Deepset Cloud +- `api_key`: Secret value of the API key. +If not specified, will be read from DEEPSET_CLOUD_API_KEY environment variable. +- `api_endpoint`: The URL of the Deepset Cloud API. +If not specified, will be read from DEEPSET_CLOUD_API_ENDPOINT environment variable. + +Returns: + list of dictionaries: List[dict] + each dictionary: { + "name": str -> `pipeline_config_name` to be used in `load_from_deepset_cloud()`, + "..." -> additional pipeline meta information + } + example: + [{'name': 'my_super_nice_pipeline_config', + 'pipeline_id': '2184e0c1-c6ec-40a1-9b28-5d2768e5efa2', + 'status': 'DEPLOYED', + 'created_at': '2022-02-01T09:57:03.803991+00:00', + 'deleted': False, + 'is_default': False, + 'indexing': {'status': 'IN_PROGRESS', + 'pending_file_count': 3, + 'total_file_count': 31}}] + + + +#### save\_to\_deepset\_cloud + +```python +@classmethod +def save_to_deepset_cloud(cls, query_pipeline: BasePipeline, index_pipeline: BasePipeline, pipeline_config_name: str, workspace: str = "default", api_key: Optional[str] = None, api_endpoint: Optional[str] = None, overwrite: bool = False) +``` + +Saves a Pipeline config to Deepset Cloud defining the individual components and how they're tied together to form + +a Pipeline. A single config must declare a query pipeline and a index pipeline. + +**Arguments**: + +- `query_pipeline`: the query pipeline to save. +- `index_pipeline`: the index pipeline to save. +- `pipeline_config_name`: name of the config file inside the Deepset Cloud workspace. +- `workspace`: workspace in Deepset Cloud +- `api_key`: Secret value of the API key. +If not specified, will be read from DEEPSET_CLOUD_API_KEY environment variable. +- `api_endpoint`: The URL of the Deepset Cloud API. +If not specified, will be read from DEEPSET_CLOUD_API_ENDPOINT environment variable. +- `overwrite`: Whether to overwrite the config if it already exists. Otherwise an error is being raised. + + + +## Pipeline + +```python +class Pipeline(BasePipeline) +``` + +Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components. + +Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query +flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a +Reader from multiple Retrievers, or re-ranking of candidate documents. + + + +#### add\_node + +```python +def add_node(component, name: str, inputs: List[str]) +``` + +Add a new node to the pipeline. + +**Arguments**: + +- `component`: The object to be called when the data is passed to the node. It can be a Haystack component +(like Retriever, Reader, or Generator) or a user-defined object that implements a run() +method to process incoming data from predecessor node. +- `name`: The name for the node. It must not contain any dots. +- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name +of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single +edge with a list of documents. It can be represented as ["ElasticsearchRetriever"]. + +In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output +must be specified explicitly as "QueryClassifier.output_2". + + + +#### get\_node + +```python +def get_node(name: str) -> Optional[BaseComponent] +``` + +Get a node from the Pipeline. + +**Arguments**: + +- `name`: The name of the node. + + + +#### set\_node + +```python +def set_node(name: str, component) +``` + +Set the component for a node in the Pipeline. + +**Arguments**: + +- `name`: The name of the node. +- `component`: The component object to be set at the node. + + + +#### run + +```python +def run(query: Optional[str] = None, file_paths: Optional[List[str]] = None, labels: Optional[MultiLabel] = None, documents: Optional[List[Document]] = None, meta: Optional[dict] = None, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +Runs the pipeline, one node at a time. + +**Arguments**: + +- `query`: The search query (for query pipelines only) +- `file_paths`: The files to index (for indexing pipelines only) +- `labels`: +- `documents`: +- `meta`: +- `params`: Dictionary of parameters to be dispatched to the nodes. +If you want to pass a param to all nodes, you can just use: {"top_k":10} +If you want to pass it to targeted nodes, you can do: +{"Retriever": {"top_k": 10}, "Reader": {"top_k": 3, "debug": True}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. All debug information can +then be found in the dict returned by this method under the key "_debug" + + + +#### eval + +```python +def eval(labels: List[MultiLabel], documents: Optional[List[List[Document]]] = None, params: Optional[dict] = None, sas_model_name_or_path: str = None, add_isolated_node_eval: bool = False) -> EvaluationResult +``` + +Evaluates the pipeline by running the pipeline once per query in debug mode + +and putting together all data that is needed for evaluation, e.g. calculating metrics. + +**Arguments**: + +- `labels`: The labels to evaluate on +- `documents`: List of List of Document that the first node in the pipeline should get as input per multilabel. Can be used to evaluate a pipeline that consists of a reader without a retriever. +- `params`: Dictionary of parameters to be dispatched to the nodes. +If you want to pass a param to all nodes, you can just use: {"top_k":10} +If you want to pass it to targeted nodes, you can do: +{"Retriever": {"top_k": 10}, "Reader": {"top_k": 3, "debug": True}} +- `sas_model_name_or_path`: Name or path of "Semantic Answer Similarity (SAS) model". When set, the model will be used to calculate similarity between predictions and labels and generate the SAS metric. +The SAS metric correlates better with human judgement of correct answers as it does not rely on string overlaps. +Example: Prediction = "30%", Label = "thirty percent", EM and F1 would be overly pessimistic with both being 0, while SAS paints a more realistic picture. +More info in the paper: https://arxiv.org/abs/2108.06130 +Models: +- You can use Bi Encoders (sentence transformers) or cross encoders trained on Semantic Textual Similarity (STS) data. +Not all cross encoders can be used because of different return types. +If you use custom cross encoders please make sure they work with sentence_transformers.CrossEncoder class +- Good default for multiple languages: "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" +- Large, powerful, but slow model for English only: "cross-encoder/stsb-roberta-large" +- Large model for German only: "deepset/gbert-large-sts" +- `add_isolated_node_eval`: If set to True, in addition to the integrated evaluation of the pipeline, each node is evaluated in isolated evaluation mode. +This mode helps to understand the bottlenecks of a pipeline in terms of output quality of each individual node. +If a node performs much better in the isolated evaluation than in the integrated evaluation, the previous node needs to be optimized to improve the pipeline's performance. +If a node's performance is similar in both modes, this node itself needs to be optimized to improve the pipeline's performance. +The isolated evaluation calculates the upper bound of each node's evaluation metrics under the assumption that it received perfect inputs from the previous node. +To this end, labels are used as input to the node instead of the output of the previous node in the pipeline. +The generated dataframes in the EvaluationResult then contain additional rows, which can be distinguished from the integrated evaluation results based on the +values "integrated" or "isolated" in the column "eval_mode" and the evaluation report then additionally lists the upper bound of each node's evaluation metrics. + + + +#### get\_nodes\_by\_class + +```python +def get_nodes_by_class(class_type) -> List[Any] +``` + +Gets all nodes in the pipeline that are an instance of a certain class (incl. subclasses). + +This is for example helpful if you loaded a pipeline and then want to interact directly with the document store. +Example: +| from haystack.document_stores.base import BaseDocumentStore +| INDEXING_PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=INDEXING_PIPELINE_NAME) +| res = INDEXING_PIPELINE.get_nodes_by_class(class_type=BaseDocumentStore) + +**Returns**: + +List of components that are an instance the requested class + + + +#### get\_document\_store + +```python +def get_document_store() -> Optional[BaseDocumentStore] +``` + +Return the document store object used in the current pipeline. + +**Returns**: + +Instance of DocumentStore or None + + + +#### draw + +```python +def draw(path: Path = Path("pipeline.png")) +``` + +Create a Graphviz visualization of the pipeline. + +**Arguments**: + +- `path`: the path to save the image. + + + +#### load\_from\_config + +```python +@classmethod +def load_from_config(cls, pipeline_config: Dict, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True) +``` + +Load Pipeline from a config dict defining the individual components and how they're tied together to form + +a Pipeline. A single config can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +Here's a sample configuration: + + ```python + | { + | "version": "0.9", + | "components": [ + | { # define all the building-blocks for Pipeline + | "name": "MyReader", # custom-name for the component; helpful for visualization & debugging + | "type": "FARMReader", # Haystack Class name for the component + | "params": {"no_ans_boost": -10, "model_name_or_path": "deepset/roberta-base-squad2"}, + | }, + | { + | "name": "MyESRetriever", + | "type": "ElasticsearchRetriever", + | "params": { + | "document_store": "MyDocumentStore", # params can reference other components defined in the YAML + | "custom_query": None, + | }, + | }, + | {"name": "MyDocumentStore", "type": "ElasticsearchDocumentStore", "params": {"index": "haystack_test"}}, + | ], + | "pipelines": [ + | { # multiple Pipelines can be defined using the components from above + | "name": "my_query_pipeline", # a simple extractive-qa Pipeline + | "nodes": [ + | {"name": "MyESRetriever", "inputs": ["Query"]}, + | {"name": "MyReader", "inputs": ["MyESRetriever"]}, + | ], + | } + | ], + | } + ``` + +**Arguments**: + +- `pipeline_config`: the pipeline config as dict +- `pipeline_name`: if the config contains multiple pipelines, the pipeline_name to load must be set. +- `overwrite_with_env_variables`: Overwrite the configuration with environment variables. For example, +to change index name param for an ElasticsearchDocumentStore, an env +variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. + + + +#### save\_to\_yaml + +```python +def save_to_yaml(path: Path, return_defaults: bool = False) +``` + +Save a YAML configuration for the Pipeline that can be used with `Pipeline.load_from_yaml()`. + +**Arguments**: + +- `path`: path of the output YAML file. +- `return_defaults`: whether to output parameters that have the default values. + + + +#### get\_config + +```python +def get_config(return_defaults: bool = False) -> dict +``` + +Returns a configuration for the Pipeline that can be used with `Pipeline.load_from_config()`. + +**Arguments**: + +- `return_defaults`: whether to output parameters that have the default values. + + + +#### print\_eval\_report + +```python +def print_eval_report(eval_result: EvaluationResult, n_wrong_examples: int = 3, metrics_filter: Optional[Dict[str, List[str]]] = None) +``` + +Prints evaluation report containing a metrics funnel and worst queries for further analysis. + +**Arguments**: + +- `eval_result`: The evaluation result, can be obtained by running eval(). +- `n_wrong_examples`: The number of worst queries to show. +- `metrics_filter`: The metrics to show per node. If None all metrics will be shown. + + + +## RayPipeline + +```python +class RayPipeline(Pipeline) +``` + +Ray (https://ray.io) is a framework for distributed computing. + +Ray allows distributing a Pipeline's components across a cluster of machines. The individual components of a +Pipeline can be independently scaled. For instance, an extractive QA Pipeline deployment can have three replicas +of the Reader and a single replica for the Retriever. It enables efficient resource utilization by horizontally +scaling Components. + +To set the number of replicas, add `replicas` in the YAML config for the node in a pipeline: + + ```yaml + | components: + | ... + | + | pipelines: + | - name: ray_query_pipeline + | type: RayPipeline + | nodes: + | - name: ESRetriever + | replicas: 2 # number of replicas to create on the Ray cluster + | inputs: [ Query ] + ``` + +A RayPipeline can only be created with a YAML Pipeline config. +>>> from haystack.pipeline import RayPipeline +>>> pipeline = RayPipeline.load_from_yaml(path="my_pipelines.yaml", pipeline_name="my_query_pipeline") +>>> pipeline.run(query="What is the capital of Germany?") + +By default, RayPipelines creates an instance of RayServe locally. To connect to an existing Ray instance, +set the `address` parameter when creating the RayPipeline instance. + + + +#### load\_from\_yaml + +```python +@classmethod +def load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True, address: Optional[str] = None, **kwargs, ,) +``` + +Load Pipeline from a YAML file defining the individual components and how they're tied together to form + +a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +Here's a sample configuration: + + ```yaml + | version: '0.9' + | + | components: # define all the building-blocks for Pipeline + | - name: MyReader # custom-name for the component; helpful for visualization & debugging + | type: FARMReader # Haystack Class name for the component + | params: + | no_ans_boost: -10 + | model_name_or_path: deepset/roberta-base-squad2 + | - name: MyESRetriever + | type: ElasticsearchRetriever + | params: + | document_store: MyDocumentStore # params can reference other components defined in the YAML + | custom_query: null + | - name: MyDocumentStore + | type: ElasticsearchDocumentStore + | params: + | index: haystack_test + | + | pipelines: # multiple Pipelines can be defined using the components from above + | - name: my_query_pipeline # a simple extractive-qa Pipeline + | type: RayPipeline + | nodes: + | - name: MyESRetriever + | inputs: [Query] + | replicas: 2 # number of replicas to create on the Ray cluster + | - name: MyReader + | inputs: [MyESRetriever] + ``` + + +Note that, in case of a mismatch in version between Haystack and the YAML, a warning will be printed. +If the pipeline loads correctly regardless, save again the pipeline using `RayPipeline.save_to_yaml()` to remove the warning. + +**Arguments**: + +- `path`: path of the YAML file. +- `pipeline_name`: if the YAML contains multiple pipelines, the pipeline_name to load must be set. +- `overwrite_with_env_variables`: Overwrite the YAML configuration with environment variables. For example, +to change index name param for an ElasticsearchDocumentStore, an env +variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. +- `address`: The IP address for the Ray cluster. If set to None, a local Ray instance is started. + + + +# Module standard\_pipelines + + + +## BaseStandardPipeline + +```python +class BaseStandardPipeline(ABC) +``` + +Base class for pre-made standard Haystack pipelines. +This class does not inherit from Pipeline. + + + +#### add\_node + +```python +def add_node(component, name: str, inputs: List[str]) +``` + +Add a new node to the pipeline. + +**Arguments**: + +- `component`: The object to be called when the data is passed to the node. It can be a Haystack component +(like Retriever, Reader, or Generator) or a user-defined object that implements a run() +method to process incoming data from predecessor node. +- `name`: The name for the node. It must not contain any dots. +- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name +of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single +edge with a list of documents. It can be represented as ["ElasticsearchRetriever"]. + +In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output +must be specified explicitly as "QueryClassifier.output_2". + + + +#### get\_node + +```python +def get_node(name: str) +``` + +Get a node from the Pipeline. + +**Arguments**: + +- `name`: The name of the node. + + + +#### set\_node + +```python +def set_node(name: str, component) +``` + +Set the component for a node in the Pipeline. + +**Arguments**: + +- `name`: The name of the node. +- `component`: The component object to be set at the node. + + + +#### draw + +```python +def draw(path: Path = Path("pipeline.png")) +``` + +Create a Graphviz visualization of the pipeline. + +**Arguments**: + +- `path`: the path to save the image. + + + +#### save\_to\_yaml + +```python +def save_to_yaml(path: Path, return_defaults: bool = False) +``` + +Save a YAML configuration for the Pipeline that can be used with `Pipeline.load_from_yaml()`. + +**Arguments**: + +- `path`: path of the output YAML file. +- `return_defaults`: whether to output parameters that have the default values. + + + +#### load\_from\_yaml + +```python +@classmethod +def load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True) +``` + +Load Pipeline from a YAML file defining the individual components and how they're tied together to form + +a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit `pipeline_name` must +be passed. + +Here's a sample configuration: + + ```yaml + | version: '0.8' + | + | components: # define all the building-blocks for Pipeline + | - name: MyReader # custom-name for the component; helpful for visualization & debugging + | type: FARMReader # Haystack Class name for the component + | params: + | no_ans_boost: -10 + | model_name_or_path: deepset/roberta-base-squad2 + | - name: MyESRetriever + | type: ElasticsearchRetriever + | params: + | document_store: MyDocumentStore # params can reference other components defined in the YAML + | custom_query: null + | - name: MyDocumentStore + | type: ElasticsearchDocumentStore + | params: + | index: haystack_test + | + | pipelines: # multiple Pipelines can be defined using the components from above + | - name: my_query_pipeline # a simple extractive-qa Pipeline + | nodes: + | - name: MyESRetriever + | inputs: [Query] + | - name: MyReader + | inputs: [MyESRetriever] + ``` + +**Arguments**: + +- `path`: path of the YAML file. +- `pipeline_name`: if the YAML contains multiple pipelines, the pipeline_name to load must be set. +- `overwrite_with_env_variables`: Overwrite the YAML configuration with environment variables. For example, +to change index name param for an ElasticsearchDocumentStore, an env +variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an +`_` sign must be used to specify nested hierarchical properties. + + + +#### get\_nodes\_by\_class + +```python +def get_nodes_by_class(class_type) -> List[Any] +``` + +Gets all nodes in the pipeline that are an instance of a certain class (incl. subclasses). + +This is for example helpful if you loaded a pipeline and then want to interact directly with the document store. +Example: +```python +| from haystack.document_stores.base import BaseDocumentStore +| INDEXING_PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=INDEXING_PIPELINE_NAME) +| res = INDEXING_PIPELINE.get_nodes_by_class(class_type=BaseDocumentStore) +``` + +**Returns**: + +List of components that are an instance of the requested class + + + +#### get\_document\_store + +```python +def get_document_store() -> Optional[BaseDocumentStore] +``` + +Return the document store object used in the current pipeline. + +**Returns**: + +Instance of DocumentStore or None + + + +#### eval + +```python +def eval(labels: List[MultiLabel], params: Optional[dict] = None, sas_model_name_or_path: Optional[str] = None, add_isolated_node_eval: bool = False) -> EvaluationResult +``` + +Evaluates the pipeline by running the pipeline once per query in debug mode + +and putting together all data that is needed for evaluation, e.g. calculating metrics. + +**Arguments**: + +- `labels`: The labels to evaluate on +- `params`: Params for the `retriever` and `reader`. For instance, +params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} +- `sas_model_name_or_path`: SentenceTransformers semantic textual similarity model to be used for sas value calculation, +should be path or string pointing to downloadable models. +- `add_isolated_node_eval`: Whether to additionally evaluate the reader based on labels as input instead of output of previous node in pipeline + + + +## ExtractiveQAPipeline + +```python +class ExtractiveQAPipeline(BaseStandardPipeline) +``` + +Pipeline for Extractive Question Answering. + + + +#### run + +```python +def run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +**Arguments**: + +- `query`: The search query string. +- `params`: Params for the `retriever` and `reader`. For instance, +params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. +All debug information can then be found in the dict returned +by this method under the key "_debug" + + + +## DocumentSearchPipeline + +```python +class DocumentSearchPipeline(BaseStandardPipeline) +``` + +Pipeline for semantic document search. + + + +#### run + +```python +def run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +**Arguments**: + +- `query`: the query string. +- `params`: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. +All debug information can then be found in the dict returned +by this method under the key "_debug" + + + +## GenerativeQAPipeline + +```python +class GenerativeQAPipeline(BaseStandardPipeline) +``` + +Pipeline for Generative Question Answering. + + + +#### run + +```python +def run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +**Arguments**: + +- `query`: the query string. +- `params`: params for the `retriever` and `generator`. For instance, +params={"Retriever": {"top_k": 10}, "Generator": {"top_k": 5}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. +All debug information can then be found in the dict returned +by this method under the key "_debug" + + + +## SearchSummarizationPipeline + +```python +class SearchSummarizationPipeline(BaseStandardPipeline) +``` + +Pipeline that retrieves documents for a query and then summarizes those documents. + + + +#### run + +```python +def run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +**Arguments**: + +- `query`: the query string. +- `params`: params for the `retriever` and `summarizer`. For instance, +params={"Retriever": {"top_k": 10}, "Summarizer": {"generate_single_summary": True}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. +All debug information can then be found in the dict returned +by this method under the key "_debug" + + + +## FAQPipeline + +```python +class FAQPipeline(BaseStandardPipeline) +``` + +Pipeline for finding similar FAQs using semantic document search. + + + +#### run + +```python +def run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None) +``` + +**Arguments**: + +- `query`: the query string. +- `params`: params for the `retriever`. For instance, params={"Retriever": {"top_k": 10}} +- `debug`: Whether the pipeline should instruct nodes to collect debug information +about their execution. By default these include the input parameters +they received and the output they generated. +All debug information can then be found in the dict returned +by this method under the key "_debug" + + + +## TranslationWrapperPipeline + +```python +class TranslationWrapperPipeline(BaseStandardPipeline) +``` + +Takes an existing search pipeline and adds one "input translation node" after the Query and one +"output translation" node just before returning the results + + + +## QuestionGenerationPipeline + +```python +class QuestionGenerationPipeline(BaseStandardPipeline) +``` + +A simple pipeline that takes documents as input and generates +questions that it thinks can be answered by the documents. + + + +## RetrieverQuestionGenerationPipeline + +```python +class RetrieverQuestionGenerationPipeline(BaseStandardPipeline) +``` + +A simple pipeline that takes a query as input, performs retrieval, and then generates +questions that it thinks can be answered by the retrieved documents. + + + +## QuestionAnswerGenerationPipeline + +```python +class QuestionAnswerGenerationPipeline(BaseStandardPipeline) +``` + +This is a pipeline which takes a document as input, generates questions that the model thinks can be answered by +this document, and then performs question answering of this questions using that single document. + + + +## MostSimilarDocumentsPipeline + +```python +class MostSimilarDocumentsPipeline(BaseStandardPipeline) +``` + + + +#### run + +```python +def run(document_ids: List[str], top_k: int = 5) +``` + +**Arguments**: + +- `document_ids`: document ids +- `top_k`: How many documents id to return against single document + diff --git a/docs/v1.2.0/_src/api/api/preprocessor.md b/docs/v1.2.0/_src/api/api/preprocessor.md new file mode 100644 index 0000000000..c92599f6a4 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/preprocessor.md @@ -0,0 +1,67 @@ + + +# Module base + + + +## BasePreProcessor + +```python +class BasePreProcessor(BaseComponent) +``` + + + +#### process + +```python +def process(documents: Union[dict, List[dict]], clean_whitespace: Optional[bool] = True, clean_header_footer: Optional[bool] = False, clean_empty_lines: Optional[bool] = True, split_by: Optional[str] = "word", split_length: Optional[int] = 1000, split_overlap: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = True) -> List[dict] +``` + +Perform document cleaning and splitting. Takes a single document as input and returns a list of documents. + + + +# Module preprocessor + + + +## PreProcessor + +```python +class PreProcessor(BasePreProcessor) +``` + + + +#### process + +```python +def process(documents: Union[dict, List[dict]], clean_whitespace: Optional[bool] = None, clean_header_footer: Optional[bool] = None, clean_empty_lines: Optional[bool] = None, split_by: Optional[str] = None, split_length: Optional[int] = None, split_overlap: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = None) -> List[dict] +``` + +Perform document cleaning and splitting. Can take a single document or a list of documents as input and returns a list of documents. + + + +#### clean + +```python +def clean(document: dict, clean_whitespace: bool, clean_header_footer: bool, clean_empty_lines: bool) -> dict +``` + +Perform document cleaning on a single document and return a single document. This method will deal with whitespaces, headers, footers +and empty lines. Its exact functionality is defined by the parameters passed into PreProcessor.__init__(). + + + +#### split + +```python +def split(document: dict, split_by: str, split_length: int, split_overlap: int, split_respect_sentence_boundary: bool) -> List[dict] +``` + +Perform document splitting on a single document. This method can split on different units, at different lengths, +with different strides. It can also respect sentence boundaries. Its exact functionality is defined by +the parameters passed into PreProcessor.__init__(). Takes a single document as input and returns a list of documents. + diff --git a/docs/v1.2.0/_src/api/api/primitives.md b/docs/v1.2.0/_src/api/api/primitives.md new file mode 100644 index 0000000000..0b2031bfd3 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/primitives.md @@ -0,0 +1,244 @@ + + +# Module schema + + + +## Document + +```python +@dataclass +class Document() +``` + + + +#### to\_dict + +```python +def to_dict(field_map={}) -> Dict +``` + +Convert Document to dict. An optional field_map can be supplied to change the names of the keys in the + +resulting dict. This way you can work with standardized Document objects in Haystack, but adjust the format that +they are serialized / stored in other places (e.g. elasticsearch) +Example: +| doc = Document(content="some text", content_type="text") +| doc.to_dict(field_map={"custom_content_field": "content"}) +| >>> {"custom_content_field": "some text", content_type": "text"} + +**Arguments**: + +- `field_map`: Dict with keys being the custom target keys and values being the standard Document attributes + +**Returns**: + +dict with content of the Document + + + +#### from\_dict + +```python +@classmethod +def from_dict(cls, dict, field_map={}, id_hash_keys=None) +``` + +Create Document from dict. An optional field_map can be supplied to adjust for custom names of the keys in the + +input dict. This way you can work with standardized Document objects in Haystack, but adjust the format that +they are serialized / stored in other places (e.g. elasticsearch) +Example: +| my_dict = {"custom_content_field": "some text", content_type": "text"} +| Document.from_dict(my_dict, field_map={"custom_content_field": "content"}) + +**Arguments**: + +- `field_map`: Dict with keys being the custom target keys and values being the standard Document attributes + +**Returns**: + +dict with content of the Document + + + +## Span + +```python +@dataclass +class Span() +``` + + + +#### end + +Defining a sequence of characters (Text span) or cells (Table span) via start and end index. + +For extractive QA: Character where answer starts/ends +For TableQA: Cell where the answer starts/ends (counted from top left to bottom right of table) + +**Arguments**: + +- `start`: Position where the span starts +- `end`: Position where the spand ends + + + +## Answer + +```python +@dataclass +class Answer() +``` + + + +#### meta + +The fundamental object in Haystack to represent any type of Answers (e.g. extractive QA, generative QA or TableQA). + +For example, it's used within some Nodes like the Reader, but also in the REST API. + +**Arguments**: + +- `answer`: The answer string. If there's no possible answer (aka "no_answer" or "is_impossible) this will be an empty string. +- `type`: One of ("generative", "extractive", "other"): Whether this answer comes from an extractive model +(i.e. we can locate an exact answer string in one of the documents) or from a generative model +(i.e. no pointer to a specific document, no offsets ...). +- `score`: The relevance score of the Answer determined by a model (e.g. Reader or Generator). +In the range of [0,1], where 1 means extremely relevant. +- `context`: The related content that was used to create the answer (i.e. a text passage, part of a table, image ...) +- `offsets_in_document`: List of `Span` objects with start and end positions of the answer **in the +document** (as stored in the document store). +For extractive QA: Character where answer starts => `Answer.offsets_in_document[0].start +For TableQA: Cell where the answer starts (counted from top left to bottom right of table) => `Answer.offsets_in_document[0].start +(Note that in TableQA there can be multiple cell ranges that are relevant for the answer, thus there can be multiple `Spans` here) +- `offsets_in_context`: List of `Span` objects with start and end positions of the answer **in the +context** (i.e. the surrounding text/table of a certain window size). +For extractive QA: Character where answer starts => `Answer.offsets_in_document[0].start +For TableQA: Cell where the answer starts (counted from top left to bottom right of table) => `Answer.offsets_in_document[0].start +(Note that in TableQA there can be multiple cell ranges that are relevant for the answer, thus there can be multiple `Spans` here) +- `document_id`: ID of the document that the answer was located it (if any) +- `meta`: Dict that can be used to associate any kind of custom meta data with the answer. +In extractive QA, this will carry the meta data of the document where the answer was found. + + + +## EvaluationResult + +```python +class EvaluationResult() +``` + + + +#### calculate\_metrics + +```python +def calculate_metrics(simulated_top_k_reader: int = -1, simulated_top_k_retriever: int = -1, doc_relevance_col: str = "gold_id_match", eval_mode: str = "integrated") -> Dict[str, Dict[str, float]] +``` + +Calculates proper metrics for each node. + +For document returning nodes default metrics are: +- mrr (Mean Reciprocal Rank: see https://en.wikipedia.org/wiki/Mean_reciprocal_rank) +- map (Mean Average Precision: see https://en.wikipedia.org/wiki/Evaluation_measures_%28information_retrieval%29#Mean_average_precision) +- ndcg (Normalized Discounted Cumulative Gain: see https://en.wikipedia.org/wiki/Discounted_cumulative_gain) +- precision (Precision: How many of the returned documents were relevant?) +- recall_multi_hit (Recall according to Information Retrieval definition: How many of the relevant documents were retrieved per query?) +- recall_single_hit (Recall for Question Answering: How many of the queries returned at least one relevant document?) + +For answer returning nodes default metrics are: +- exact_match (How many of the queries returned the exact answer?) +- f1 (How well do the returned results overlap with any gold answer on token basis?) +- sas if a SAS model has bin provided during during pipeline.eval() (How semantically similar is the prediction to the gold answers?) + +Lower top_k values for reader and retriever than the actual values during the eval run can be simulated. +E.g. top_1_f1 for reader nodes can be calculated by setting simulated_top_k_reader=1. + +Results for reader nodes with applied simulated_top_k_retriever should be considered with caution +as there are situations the result can heavily differ from an actual eval run with corresponding top_k_retriever. + +**Arguments**: + +- `simulated_top_k_reader`: simulates top_k param of reader +- `simulated_top_k_retriever`: simulates top_k param of retriever. +remarks: there might be a discrepancy between simulated reader metrics and an actual pipeline run with retriever top_k +- `doc_relevance_col`: column in the underlying eval table that contains the relevance criteria for documents. +values can be: 'gold_id_match', 'answer_match', 'gold_id_or_answer_match' +- `eval_mode`: the input on which the node was evaluated on. +Usually nodes get evaluated on the prediction provided by its predecessor nodes in the pipeline (value='integrated'). +However, as the quality of the node itself can heavily depend on the node's input and thus the predecessor's quality, +you might want to simulate a perfect predecessor in order to get an independent upper bound of the quality of your node. +For example when evaluating the reader use value='isolated' to simulate a perfect retriever in an ExtractiveQAPipeline. +Values can be 'integrated', 'isolated'. +Default value is 'integrated'. + + + +#### wrong\_examples + +```python +def wrong_examples(node: str, n: int = 3, simulated_top_k_reader: int = -1, simulated_top_k_retriever: int = -1, doc_relevance_col: str = "gold_id_match", document_metric: str = "recall_single_hit", answer_metric: str = "f1", eval_mode: str = "integrated") -> List[Dict] +``` + +Returns the worst performing queries. + +Worst performing queries are calculated based on the metric +that is either a document metric or an answer metric according to the node type. + +Lower top_k values for reader and retriever than the actual values during the eval run can be simulated. +See calculate_metrics() for more information. + +**Arguments**: + +- `simulated_top_k_reader`: simulates top_k param of reader +- `simulated_top_k_retriever`: simulates top_k param of retriever. +remarks: there might be a discrepancy between simulated reader metrics and an actual pipeline run with retriever top_k +- `doc_relevance_col`: column that contains the relevance criteria for documents. +values can be: 'gold_id_match', 'answer_match', 'gold_id_or_answer_match' +- `document_metric`: the document metric worst queries are calculated with. +values can be: 'recall_single_hit', 'recall_multi_hit', 'mrr', 'map', 'precision' +- `document_metric`: the answer metric worst queries are calculated with. +values can be: 'f1', 'exact_match' and 'sas' if the evaluation was made using a SAS model. +- `eval_mode`: the input on which the node was evaluated on. +Usually nodes get evaluated on the prediction provided by its predecessor nodes in the pipeline (value='integrated'). +However, as the quality of the node itself can heavily depend on the node's input and thus the predecessor's quality, +you might want to simulate a perfect predecessor in order to get an independent upper bound of the quality of your node. +For example when evaluating the reader use value='isolated' to simulate a perfect retriever in an ExtractiveQAPipeline. +Values can be 'integrated', 'isolated'. +Default value is 'integrated'. + + + +#### save + +```python +def save(out_dir: Union[str, Path]) +``` + +Saves the evaluation result. + +The result of each node is saved in a separate csv with file name {node_name}.csv to the out_dir folder. + +**Arguments**: + +- `out_dir`: Path to the target folder the csvs will be saved. + + + +#### load + +```python +@classmethod +def load(cls, load_dir: Union[str, Path]) +``` + +Loads the evaluation result from disk. Expects one csv file per node. See save() for further information. + +**Arguments**: + +- `load_dir`: The directory containing the csv files. + diff --git a/docs/v1.2.0/_src/api/api/query_classifier.md b/docs/v1.2.0/_src/api/api/query_classifier.md new file mode 100644 index 0000000000..f8674bfa97 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/query_classifier.md @@ -0,0 +1,120 @@ + + +# Module base + + + +## BaseQueryClassifier + +```python +class BaseQueryClassifier(BaseComponent) +``` + +Abstract class for Query Classifiers + + + +# Module sklearn + + + +## SklearnQueryClassifier + +```python +class SklearnQueryClassifier(BaseQueryClassifier) +``` + +A node to classify an incoming query into one of two categories using a lightweight sklearn model. Depending on the result, the query flows to a different branch in your pipeline +and the further processing can be customized. You can define this by connecting the further pipeline to either `output_1` or `output_2` from this node. + +**Example**: + + ```python + |{ + |pipe = Pipeline() + |pipe.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"]) + |pipe.add_node(component=elastic_retriever, name="ElasticRetriever", inputs=["QueryClassifier.output_2"]) + |pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]) + + |# Keyword queries will use the ElasticRetriever + |pipe.run("kubernetes aws") + + |# Semantic queries (questions, statements, sentences ...) will leverage the DPR retriever + |pipe.run("How to manage kubernetes on aws") + + ``` + + Models: + + Pass your own `Sklearn` binary classification model or use one of the following pretrained ones: + 1) Keywords vs. Questions/Statements (Default) + query_classifier can be found [here](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/model.pickle) + query_vectorizer can be found [here](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/vectorizer.pickle) + output_1 => question/statement + output_2 => keyword query + [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/readme.txt) + + + 2) Questions vs. Statements + query_classifier can be found [here](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/model.pickle) + query_vectorizer can be found [here](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/vectorizer.pickle) + output_1 => question + output_2 => statement + [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/readme.txt) + + See also the [tutorial](https://haystack.deepset.ai/tutorials/pipelines) on pipelines. + + + +# Module transformers + + + +## TransformersQueryClassifier + +```python +class TransformersQueryClassifier(BaseQueryClassifier) +``` + +A node to classify an incoming query into one of two categories using a (small) BERT transformer model. +Depending on the result, the query flows to a different branch in your pipeline and the further processing +can be customized. You can define this by connecting the further pipeline to either `output_1` or `output_2` +from this node. + +**Example**: + + ```python + |{ + |pipe = Pipeline() + |pipe.add_node(component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"]) + |pipe.add_node(component=elastic_retriever, name="ElasticRetriever", inputs=["QueryClassifier.output_2"]) + |pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]) + + |# Keyword queries will use the ElasticRetriever + |pipe.run("kubernetes aws") + + |# Semantic queries (questions, statements, sentences ...) will leverage the DPR retriever + |pipe.run("How to manage kubernetes on aws") + + ``` + + Models: + + Pass your own `Transformer` binary classification model from file/huggingface or use one of the following + pretrained ones hosted on Huggingface: + 1) Keywords vs. Questions/Statements (Default) + model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection" + output_1 => question/statement + output_2 => keyword query + [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/readme.txt) + + + 2) Questions vs. Statements + `model_name_or_path`="shahrukhx01/question-vs-statement-classifier" + output_1 => question + output_2 => statement + [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/readme.txt) + + + See also the [tutorial](https://haystack.deepset.ai/tutorials/pipelines) on pipelines. + diff --git a/docs/v1.2.0/_src/api/api/question_generator.md b/docs/v1.2.0/_src/api/api/question_generator.md new file mode 100644 index 0000000000..e5cd9d7df0 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/question_generator.md @@ -0,0 +1,20 @@ + + +# Module question\_generator + + + +## QuestionGenerator + +```python +class QuestionGenerator(BaseComponent) +``` + +The Question Generator takes only a document as input and outputs questions that it thinks can be +answered by this document. In our current implementation, input texts are split into chunks of 50 words +with a 10 word overlap. This is because the default model `valhalla/t5-base-e2e-qg` seems to generate only +about 3 questions per passage regardless of length. Our approach prioritizes the creation of more questions +over processing efficiency (T5 is able to digest much more than 50 words at once). The returned questions +generally come in an order dictated by the order of their answers i.e. early questions in the list generally +come from earlier in the document. + diff --git a/docs/v1.2.0/_src/api/api/ranker.md b/docs/v1.2.0/_src/api/api/ranker.md new file mode 100644 index 0000000000..dcc57f7c50 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/ranker.md @@ -0,0 +1,133 @@ + + +# Module base + + + +## BaseRanker + +```python +class BaseRanker(BaseComponent) +``` + + + +#### timing + +```python +def timing(fn, attr_name) +``` + +Wrapper method used to time functions. + + + +#### eval + +```python +def eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict +``` + +Performs evaluation of the Ranker. + +Ranker is evaluated in the same way as a Retriever based on whether it finds the correct document given the query string and at which +position in the ranking of documents the correct document is. + +| Returns a dict containing the following metrics: + + - "recall": Proportion of questions for which correct document is among retrieved documents + - "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank. + Only considers the highest ranked relevant document. + - "map": Mean of average precision for each question. Rewards retrievers that give relevant + documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``, + average precision is normalized by the number of retrieved relevant documents per query. + If ``open_domain=False``, average precision is normalized by the number of all relevant documents + per query. + +**Arguments**: + +- `label_index`: Index/Table in DocumentStore where labeled questions are stored +- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored +- `top_k`: How many documents to return per query +- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is +contained in the retrieved docs (common approach in open-domain QA). +If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids +are within ids explicitly stated in the labels. +- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary +contains the keys "predictions" and "metrics". + + + +# Module sentence\_transformers + + + +## SentenceTransformersRanker + +```python +class SentenceTransformersRanker(BaseRanker) +``` + +Sentence Transformer based pre-trained Cross-Encoder model for Document Re-ranking (https://huggingface.co/cross-encoder). +Re-Ranking can be used on top of a retriever to boost the performance for document search. This is particularly useful if the retriever has a high recall but is bad in sorting the documents by relevance. + +SentenceTransformerRanker handles Cross-Encoder models + - use a single logit as similarity score e.g. cross-encoder/ms-marco-MiniLM-L-12-v2 + - use two output logits (no_answer, has_answer) e.g. deepset/gbert-base-germandpr-reranking +https://www.sbert.net/docs/pretrained-models/ce-msmarco.html#usage-with-transformers + +| With a SentenceTransformersRanker, you can: + - directly get predictions via predict() + +Usage example: +... +retriever = ElasticsearchRetriever(document_store=document_store) +ranker = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-12-v2") +p = Pipeline() +p.add_node(component=retriever, name="ESRetriever", inputs=["Query"]) +p.add_node(component=ranker, name="Ranker", inputs=["ESRetriever"]) + + + +#### predict\_batch + +```python +def predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None) +``` + +Use loaded Ranker model to, for a list of queries, rank each query's supplied list of Document. + +Returns list of dictionary of query and list of document sorted by (desc.) similarity with query + +**Arguments**: + +- `query_doc_list`: List of dictionaries containing queries with their retrieved documents +- `top_k`: The maximum number of answers to return for each query +- `batch_size`: Number of samples the model receives in one batch for inference + +**Returns**: + +List of dictionaries containing query and ranked list of Document + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> List[Document] +``` + +Use loaded ranker model to re-rank the supplied list of Document. + +Returns list of Document sorted by (desc.) similarity with the query. + +**Arguments**: + +- `query`: Query string +- `documents`: List of Document to be re-ranked +- `top_k`: The maximum number of documents to return + +**Returns**: + +List of Document + diff --git a/docs/v1.2.0/_src/api/api/reader.md b/docs/v1.2.0/_src/api/api/reader.md new file mode 100644 index 0000000000..e747c95c37 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/reader.md @@ -0,0 +1,637 @@ + + +# Module base + + + +## BaseReader + +```python +class BaseReader(BaseComponent) +``` + + + +#### run\_batch + +```python +def run_batch(query_doc_list: List[Dict], top_k: Optional[int] = None) +``` + +A unoptimized implementation of running Reader queries in batch + + + +#### timing + +```python +def timing(fn, attr_name) +``` + +Wrapper method used to time functions. + + + +# Module farm + + + +## FARMReader + +```python +class FARMReader(BaseReader) +``` + +Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM). +While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same. + +| With a FARMReader, you can: + + - directly get predictions via predict() + - fine-tune the model on QA data via train() + + + +#### train + +```python +def train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo")) +``` + +Fine-tune a model on a QA dataset. Options: + +- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data) +- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool) + +Checkpoints can be stored via setting `checkpoint_every` to a custom number of steps. +If any checkpoints are stored, a subsequent run of train() will resume training from the latest available checkpoint. + +**Arguments**: + +- `data_dir`: Path to directory containing your training data in SQuAD style +- `train_filename`: Filename of training data +- `dev_filename`: Filename of dev / eval data +- `test_filename`: Filename of test data +- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here +that gets split off from training data for eval. +- `use_gpu`: Whether to use GPU (if available) +- `batch_size`: Number of samples the model receives in one batch for training +- `n_epochs`: Number of iterations on the whole training data set +- `learning_rate`: Learning rate of the optimizer +- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down. +- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached. +Until that point LR is increasing linearly. After that it's decreasing again linearly. +Options for different schedules are available in FARM. +- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset +- `save_dir`: Path to store the final model +- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing. +Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set. +Set to None to use all CPU cores minus one. +- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model. +Available options: +None (Don't use AMP) +"O0" (Normal FP32 training) +"O1" (Mixed Precision => Recommended) +"O2" (Almost FP16) +"O3" (Pure FP16). +See details on: https://nvidia.github.io/apex/amp.html +- `checkpoint_root_dir`: the Path of directory where all train checkpoints are saved. For each individual +checkpoint, a subdirectory with the name epoch_{epoch_num}_step_{step_num} is created. +- `checkpoint_every`: save a train checkpoint after this many steps of training. +- `checkpoints_to_keep`: maximum number of train checkpoints to save. +- `caching`: whether or not to use caching for preprocessed dataset +- `cache_path`: Path to cache the preprocessed dataset +- `processor`: The processor to use for preprocessing. If None, the default SquadProcessor is used. + +**Returns**: + +None + + + +#### distil\_prediction\_layer\_from + +```python +def distil_prediction_layer_from(teacher_model: "FARMReader", data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, student_batch_size: int = 10, teacher_batch_size: Optional[int] = None, n_epochs: int = 2, learning_rate: float = 3e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo"), distillation_loss_weight: float = 0.5, distillation_loss: Union[str, Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = "kl_div", temperature: float = 1.0) +``` + +Fine-tune a model on a QA dataset using logit-based distillation. You need to provide a teacher model that is already finetuned on the dataset + +and a student model that will be trained using the teacher's logits. The idea of this is to increase the accuracy of a lightweight student model. +using a more complex teacher. +Originally proposed in: https://arxiv.org/pdf/1503.02531.pdf +This can also be considered as the second stage of distillation finetuning as described in the TinyBERT paper: +https://arxiv.org/pdf/1909.10351.pdf +**Example** +```python +student = FARMReader(model_name_or_path="prajjwal1/bert-medium") +teacher = FARMReader(model_name_or_path="deepset/bert-large-uncased-whole-word-masking-squad2") +student.distil_prediction_layer_from(teacher, data_dir="squad2", train_filename="train.json", test_filename="dev.json", + learning_rate=3e-5, distillation_loss_weight=1.0, temperature=5) +``` + +Checkpoints can be stored via setting `checkpoint_every` to a custom number of steps. +If any checkpoints are stored, a subsequent run of train() will resume training from the latest available checkpoint. + +**Arguments**: + +- `teacher_model`: Model whose logits will be used to improve accuracy +- `data_dir`: Path to directory containing your training data in SQuAD style +- `train_filename`: Filename of training data +- `dev_filename`: Filename of dev / eval data +- `test_filename`: Filename of test data +- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here +that gets split off from training data for eval. +- `use_gpu`: Whether to use GPU (if available) +- `student_batch_size`: Number of samples the student model receives in one batch for training +- `student_batch_size`: Number of samples the teacher model receives in one batch for distillation +- `n_epochs`: Number of iterations on the whole training data set +- `learning_rate`: Learning rate of the optimizer +- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down. +- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached. +Until that point LR is increasing linearly. After that it's decreasing again linearly. +Options for different schedules are available in FARM. +- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset +- `save_dir`: Path to store the final model +- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing. +Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set. +Set to None to use all CPU cores minus one. +- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model. +Available options: +None (Don't use AMP) +"O0" (Normal FP32 training) +"O1" (Mixed Precision => Recommended) +"O2" (Almost FP16) +"O3" (Pure FP16). +See details on: https://nvidia.github.io/apex/amp.html +- `checkpoint_root_dir`: the Path of directory where all train checkpoints are saved. For each individual +checkpoint, a subdirectory with the name epoch_{epoch_num}_step_{step_num} is created. +- `checkpoint_every`: save a train checkpoint after this many steps of training. +- `checkpoints_to_keep`: maximum number of train checkpoints to save. +- `caching`: whether or not to use caching for preprocessed dataset and teacher logits +- `cache_path`: Path to cache the preprocessed dataset and teacher logits +- `distillation_loss_weight`: The weight of the distillation loss. A higher weight means the teacher outputs are more important. +- `distillation_loss`: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named parameters student_logits and teacher_logits) +- `temperature`: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model. +- `tinybert_loss`: Whether to use the TinyBERT loss function for distillation. This requires the student to be a TinyBERT model and the teacher to be a finetuned version of bert-base-uncased. +- `tinybert_epochs`: Number of epochs to train the student model with the TinyBERT loss function. After this many epochs, the student model is trained with the regular distillation loss function. +- `tinybert_learning_rate`: Learning rate to use when training the student model with the TinyBERT loss function. +- `tinybert_train_filename`: Filename of training data to use when training the student model with the TinyBERT loss function. To best follow the original paper, this should be an augmented version of the training data created using the augment_squad.py script. If not specified, the training data from the original training is used. +- `processor`: The processor to use for preprocessing. If None, the default SquadProcessor is used. + +**Returns**: + +None + + + +#### distil\_intermediate\_layers\_from + +```python +def distil_intermediate_layers_from(teacher_model: "FARMReader", data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 5, learning_rate: float = 5e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo"), distillation_loss: Union[str, Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = "mse", temperature: float = 1.0, processor: Optional[Processor] = None) +``` + +The first stage of distillation finetuning as described in the TinyBERT paper: + +https://arxiv.org/pdf/1909.10351.pdf +**Example** +```python +student = FARMReader(model_name_or_path="prajjwal1/bert-medium") +teacher = FARMReader(model_name_or_path="huawei-noah/TinyBERT_General_6L_768D") +student.distil_intermediate_layers_from(teacher, data_dir="squad2", train_filename="train.json", test_filename="dev.json", + learning_rate=3e-5, distillation_loss_weight=1.0, temperature=5) +``` + +Checkpoints can be stored via setting `checkpoint_every` to a custom number of steps. +If any checkpoints are stored, a subsequent run of train() will resume training from the latest available checkpoint. + +**Arguments**: + +- `teacher_model`: Model whose logits will be used to improve accuracy +- `data_dir`: Path to directory containing your training data in SQuAD style +- `train_filename`: Filename of training data. To best follow the original paper, this should be an augmented version of the training data created using the augment_squad.py script +- `dev_filename`: Filename of dev / eval data +- `test_filename`: Filename of test data +- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here +that gets split off from training data for eval. +- `use_gpu`: Whether to use GPU (if available) +- `student_batch_size`: Number of samples the student model receives in one batch for training +- `student_batch_size`: Number of samples the teacher model receives in one batch for distillation +- `n_epochs`: Number of iterations on the whole training data set +- `learning_rate`: Learning rate of the optimizer +- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down. +- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached. +Until that point LR is increasing linearly. After that it's decreasing again linearly. +Options for different schedules are available in FARM. +- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset +- `save_dir`: Path to store the final model +- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing. +Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set. +Set to None to use all CPU cores minus one. +- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model. +Available options: +None (Don't use AMP) +"O0" (Normal FP32 training) +"O1" (Mixed Precision => Recommended) +"O2" (Almost FP16) +"O3" (Pure FP16). +See details on: https://nvidia.github.io/apex/amp.html +- `checkpoint_root_dir`: the Path of directory where all train checkpoints are saved. For each individual +checkpoint, a subdirectory with the name epoch_{epoch_num}_step_{step_num} is created. +- `checkpoint_every`: save a train checkpoint after this many steps of training. +- `checkpoints_to_keep`: maximum number of train checkpoints to save. +- `caching`: whether or not to use caching for preprocessed dataset and teacher logits +- `cache_path`: Path to cache the preprocessed dataset and teacher logits +- `distillation_loss_weight`: The weight of the distillation loss. A higher weight means the teacher outputs are more important. +- `distillation_loss`: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named parameters student_logits and teacher_logits) +- `temperature`: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model. +- `processor`: The processor to use for preprocessing. If None, the default SquadProcessor is used. + +**Returns**: + +None + + + +#### update\_parameters + +```python +def update_parameters(context_window_size: Optional[int] = None, no_ans_boost: Optional[float] = None, return_no_answer: Optional[bool] = None, max_seq_len: Optional[int] = None, doc_stride: Optional[int] = None) +``` + +Hot update parameters of a loaded Reader. It may not to be safe when processing concurrent requests. + + + +#### save + +```python +def save(directory: Path) +``` + +Saves the Reader model so that it can be reused at a later point in time. + +**Arguments**: + +- `directory`: Directory where the Reader model should be saved + + + +#### predict\_batch + +```python +def predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None) +``` + +Use loaded QA model to find answers for a list of queries in each query's supplied list of Document. + +Returns list of dictionaries containing answers sorted by (desc.) score + +**Arguments**: + +- `query_doc_list`: List of dictionaries containing queries with their retrieved documents +- `top_k`: The maximum number of answers to return for each query +- `batch_size`: Number of samples the model receives in one batch for inference + +**Returns**: + +List of dictionaries containing query and answers + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) +``` + +Use loaded QA model to find answers for a query in the supplied list of Document. + +Returns dictionaries containing answers sorted by (desc.) score. +Example: + ```python + |{ + | 'query': 'Who is the father of Arya Stark?', + | 'answers':[Answer( + | 'answer': 'Eddard,', + | 'context': "She travels with her father, Eddard, to King's Landing when he is", + | 'score': 0.9787139466668613, + | 'offsets_in_context': [Span(start=29, end=35], + | 'offsets_in_context': [Span(start=347, end=353], + | 'document_id': '88d1ed769d003939d3a0d28034464ab2' + | ),... + | ] + |} + ``` + +**Arguments**: + +- `query`: Query string +- `documents`: List of Document in which to search for the answer +- `top_k`: The maximum number of answers to return + +**Returns**: + +Dict containing query and answers + + + +#### eval\_on\_file + +```python +def eval_on_file(data_dir: str, test_filename: str, device: Optional[str] = None) +``` + +Performs evaluation on a SQuAD-formatted file. + +Returns a dict containing the following metrics: + - "EM": exact match score + - "f1": F1-Score + - "top_n_accuracy": Proportion of predicted answers that overlap with correct answer + +**Arguments**: + +- `data_dir` (`Path or str`): The directory in which the test set can be found +- `test_filename` (`str`): The name of the file containing the test data in SQuAD format. +- `device` (`str`): The device on which the tensors should be processed. Choose from "cpu" and "cuda" or use the Reader's device by default. + + + +#### eval + +```python +def eval(document_store: BaseDocumentStore, device: Optional[str] = None, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold-label", calibrate_conf_scores: bool = False) +``` + +Performs evaluation on evaluation documents in the DocumentStore. + +Returns a dict containing the following metrics: + - "EM": Proportion of exact matches of predicted answers with their corresponding correct answers + - "f1": Average overlap between predicted answers and their corresponding correct answers + - "top_n_accuracy": Proportion of predicted answers that overlap with correct answer + +**Arguments**: + +- `document_store`: DocumentStore containing the evaluation documents +- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda" or use the Reader's device by default. +- `label_index`: Index/Table name where labeled questions are stored +- `doc_index`: Index/Table name where documents that are used for evaluation are stored +- `label_origin`: Field name where the gold labels are stored +- `calibrate_conf_scores`: Whether to calibrate the temperature for temperature scaling of the confidence scores + + + +#### calibrate\_confidence\_scores + +```python +def calibrate_confidence_scores(document_store: BaseDocumentStore, device: Optional[str] = None, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label") +``` + +Calibrates confidence scores on evaluation documents in the DocumentStore. + +**Arguments**: + +- `document_store`: DocumentStore containing the evaluation documents +- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda" or use the Reader's device by default. +- `label_index`: Index/Table name where labeled questions are stored +- `doc_index`: Index/Table name where documents that are used for evaluation are stored +- `label_origin`: Field name where the gold labels are stored + + + +#### predict\_on\_texts + +```python +def predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None) +``` + +Use loaded QA model to find answers for a question in the supplied list of Document. + +Returns dictionaries containing answers sorted by (desc.) score. +Example: + ```python + |{ + | 'question': 'Who is the father of Arya Stark?', + | 'answers':[ + | {'answer': 'Eddard,', + | 'context': " She travels with her father, Eddard, to King's Landing when he is ", + | 'offset_answer_start': 147, + | 'offset_answer_end': 154, + | 'score': 0.9787139466668613, + | 'document_id': '1337' + | },... + | ] + |} + ``` + +**Arguments**: + +- `question`: Question string +- `documents`: List of documents as string type +- `top_k`: The maximum number of answers to return + +**Returns**: + +Dict containing question and answers + + + +#### convert\_to\_onnx + +```python +@classmethod +def convert_to_onnx(cls, model_name: str, output_path: Path, convert_to_float16: bool = False, quantize: bool = False, task_type: str = "question_answering", opset_version: int = 11) +``` + +Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model + +can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param. + +Usage: + + `from haystack.reader.farm import FARMReader + from pathlib import Path + onnx_model_path = Path("roberta-onnx-model") + FARMReader.convert_to_onnx(model_name="deepset/bert-base-cased-squad2", output_path=onnx_model_path) + reader = FARMReader(onnx_model_path)` + +**Arguments**: + +- `model_name`: transformers model name +- `output_path`: Path to output the converted model +- `convert_to_float16`: Many models use float32 precision by default. With the half precision of float16, +inference is faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs, +float32 could still be be more performant. +- `quantize`: convert floating point number to integers +- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings". +- `opset_version`: ONNX opset version + + + +# Module transformers + + + +## TransformersReader + +```python +class TransformersReader(BaseReader) +``` + +Transformer based model for extractive Question Answering using the HuggingFace's transformers framework +(https://github.com/huggingface/transformers). +While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same. +With this reader, you can directly get predictions via predict() + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) +``` + +Use loaded QA model to find answers for a query in the supplied list of Document. + +Returns dictionaries containing answers sorted by (desc.) score. +Example: + + ```python + |{ + | 'query': 'Who is the father of Arya Stark?', + | 'answers':[ + | {'answer': 'Eddard,', + | 'context': " She travels with her father, Eddard, to King's Landing when he is ", + | 'offset_answer_start': 147, + | 'offset_answer_end': 154, + | 'score': 0.9787139466668613, + | 'document_id': '1337' + | },... + | ] + |} + ``` + +**Arguments**: + +- `query`: Query string +- `documents`: List of Document in which to search for the answer +- `top_k`: The maximum number of answers to return + +**Returns**: + +Dict containing query and answers + + + +# Module table + + + +## TableReader + +```python +class TableReader(BaseReader) +``` + +Transformer-based model for extractive Question Answering on Tables with TaPas +using the HuggingFace's transformers framework (https://github.com/huggingface/transformers). +With this reader, you can directly get predictions via predict() + +**Example**: + +```python +from haystack import Document +from haystack.reader import TableReader +import pandas as pd + +table_reader = TableReader(model_name_or_path="google/tapas-base-finetuned-wtq") +data = { + "actors": ["brad pitt", "leonardo di caprio", "george clooney"], + "age": ["57", "46", "60"], + "number of movies": ["87", "53", "69"], + "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"], +} +table = pd.DataFrame(data) +document = Document(content=table, content_type="table") +query = "When was DiCaprio born?" +prediction = table_reader.predict(query=query, documents=[document]) +answer = prediction["answers"][0].answer # "10 june 1996" +``` + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict +``` + +Use loaded TableQA model to find answers for a query in the supplied list of Documents + +of content_type ``'table'``. + +Returns dictionary containing query and list of Answer objects sorted by (desc.) score. +WARNING: The answer scores are not reliable, as they are always extremely high, even if + a question cannot be answered by a given table. + +**Arguments**: + +- `query`: Query string +- `documents`: List of Document in which to search for the answer. Documents should be +of content_type ``'table'``. +- `top_k`: The maximum number of answers to return + +**Returns**: + +Dict containing query and answers + + + +## RCIReader + +```python +class RCIReader(BaseReader) +``` + +Table Reader model based on Glass et al. (2021)'s Row-Column-Intersection model. +See the original paper for more details: +Glass, Michael, et al. (2021): "Capturing Row and Column Semantics in Transformer Based Question Answering over Tables" +(https://aclanthology.org/2021.naacl-main.96/) + +Each row and each column is given a score with regard to the query by two separate models. The score of each cell +is then calculated as the sum of the corresponding row score and column score. Accordingly, the predicted answer is +the cell with the highest score. + +Pros and Cons of RCIReader compared to TableReader: ++ Provides meaningful confidence scores ++ Allows larger tables as input +- Does not support aggregation over table cells +- Slower + + + +#### predict + +```python +def predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict +``` + +Use loaded RCI models to find answers for a query in the supplied list of Documents + +of content_type ``'table'``. + +Returns dictionary containing query and list of Answer objects sorted by (desc.) score. +The existing RCI models on the HF model hub don"t allow aggregation, therefore, the answer will always be +composed of a single cell. + +**Arguments**: + +- `query`: Query string +- `documents`: List of Document in which to search for the answer. Documents should be +of content_type ``'table'``. +- `top_k`: The maximum number of answers to return + +**Returns**: + +Dict containing query and answers + diff --git a/docs/v1.2.0/_src/api/api/retriever.md b/docs/v1.2.0/_src/api/api/retriever.md new file mode 100644 index 0000000000..4677ea8f74 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/retriever.md @@ -0,0 +1,593 @@ + + +# Module base + + + +## BaseGraphRetriever + +```python +class BaseGraphRetriever(BaseComponent) +``` + +Base classfor knowledge graph retrievers. + + + +## BaseRetriever + +```python +class BaseRetriever(BaseComponent) +``` + +Base class for regular retrievers. + + + +#### retrieve + +```python +@abstractmethod +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +#### timing + +```python +def timing(fn, attr_name) +``` + +Wrapper method used to time functions. + + + +#### eval + +```python +def eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold-label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False, headers: Optional[Dict[str, str]] = None) -> dict +``` + +Performs evaluation on the Retriever. + +Retriever is evaluated based on whether it finds the correct document given the query string and at which +position in the ranking of documents the correct document is. + +| Returns a dict containing the following metrics: + + - "recall": Proportion of questions for which correct document is among retrieved documents + - "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank. + Only considers the highest ranked relevant document. + - "map": Mean of average precision for each question. Rewards retrievers that give relevant + documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``, + average precision is normalized by the number of retrieved relevant documents per query. + If ``open_domain=False``, average precision is normalized by the number of all relevant documents + per query. + +**Arguments**: + +- `label_index`: Index/Table in DocumentStore where labeled questions are stored +- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored +- `top_k`: How many documents to return per query +- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is +contained in the retrieved docs (common approach in open-domain QA). +If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids +are within ids explicitly stated in the labels. +- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary +contains the keys "predictions" and "metrics". +- `headers`: Custom HTTP headers to pass to document store client if supported (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='} for basic authentication) + + + +# Module sparse + + + +## ElasticsearchRetriever + +```python +class ElasticsearchRetriever(BaseRetriever) +``` + + + +#### retrieve + +```python +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +## ElasticsearchFilterOnlyRetriever + +```python +class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever) +``` + +Naive "Retriever" that returns all documents that match the given filters. No impact of query at all. +Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever. + + + +#### retrieve + +```python +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents +- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='}) +Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information. + + + +## TfidfRetriever + +```python +class TfidfRetriever(BaseRetriever) +``` + +Read all documents from a SQL backend. + +Split documents into smaller units (eg, paragraphs or pages) to reduce the +computations when text is passed on to a Reader for QA. + +It uses sklearn's TfidfVectorizer to compute a tf-idf matrix. + + + +#### retrieve + +```python +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents + + + +#### fit + +```python +def fit() +``` + +Performing training on this class according to the TF-IDF algorithm. + + + +# Module dense + + + +## DensePassageRetriever + +```python +class DensePassageRetriever(BaseRetriever) +``` + +Retriever that uses a bi-encoder (one transformer for query, one transformer for passage). +See the original paper for more details: +Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering." +(https://arxiv.org/abs/2004.04906). + + + +#### retrieve + +```python +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents + + + +#### embed\_queries + +```python +def embed_queries(texts: List[str]) -> List[np.ndarray] +``` + +Create embeddings for a list of queries using the query encoder + +**Arguments**: + +- `texts`: Queries to embed + +**Returns**: + +Embeddings, one per input queries + + + +#### embed\_documents + +```python +def embed_documents(docs: List[Document]) -> List[np.ndarray] +``` + +Create embeddings for a list of documents using the passage encoder + +**Arguments**: + +- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack. + +**Returns**: + +Embeddings of documents / passages shape (batch_size, embedding_dim) + + + +#### train + +```python +def train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, max_samples: int = None, max_processes: int = 128, multiprocessing_strategy: Optional[str] = None, dev_split: float = 0, batch_size: int = 2, embed_title: bool = True, num_hard_negatives: int = 1, num_positives: int = 1, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, use_amp: str = None, optimizer_name: str = "AdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/dpr", query_encoder_save_dir: str = "query_encoder", passage_encoder_save_dir: str = "passage_encoder") +``` + +train a DensePassageRetrieval model + +**Arguments**: + +- `data_dir`: Directory where training file, dev file and test file are present +- `train_filename`: training filename +- `dev_filename`: development set filename, file to be used by model in eval step of training +- `test_filename`: test set filename, file to be used by model in test step after training +- `max_samples`: maximum number of input samples to convert. Can be used for debugging a smaller dataset. +- `max_processes`: the maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo. +It can be set to 1 to disable the use of multiprocessing or make debugging easier. +- `multiprocessing_strategy`: Set the multiprocessing sharing strategy, this can be one of file_descriptor/file_system depending on your OS. +If your system has low limits for the number of open file descriptors, and you can’t raise them, +you should use the file_system strategy. +- `dev_split`: The proportion of the train set that will sliced. Only works if dev_filename is set to None +- `batch_size`: total number of samples in 1 batch of data +- `embed_title`: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage +- `num_hard_negatives`: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer +- `num_positives`: number of positive passages +- `n_epochs`: number of epochs to train the model on +- `evaluate_every`: number of training steps after evaluation is run +- `n_gpu`: number of gpus to train on +- `learning_rate`: learning rate of optimizer +- `epsilon`: epsilon parameter of optimizer +- `weight_decay`: weight decay parameter of optimizer +- `grad_acc_steps`: number of steps to accumulate gradient over before back-propagation is done +- `use_amp`: Whether to use automatic mixed precision (AMP) or not. The options are: +"O0" (FP32) +"O1" (Mixed Precision) +"O2" (Almost FP16) +"O3" (Pure FP16). +For more information, refer to: https://nvidia.github.io/apex/amp.html +- `optimizer_name`: what optimizer to use (default: AdamW) +- `num_warmup_steps`: number of warmup steps +- `optimizer_correct_bias`: Whether to correct bias in optimizer +- `save_dir`: directory where models are saved +- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved +- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved + + + +#### save + +```python +def save(save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder") +``` + +Save DensePassageRetriever to the specified directory. + +**Arguments**: + +- `save_dir`: Directory to save to. +- `query_encoder_dir`: Directory in save_dir that contains query encoder model. +- `passage_encoder_dir`: Directory in save_dir that contains passage encoder model. + +**Returns**: + +None + + + +#### load + +```python +@classmethod +def load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product", query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder", infer_tokenizer_classes: bool = False) +``` + +Load DensePassageRetriever from the specified directory. + + + +## TableTextRetriever + +```python +class TableTextRetriever(BaseRetriever) +``` + +Retriever that uses a tri-encoder to jointly retrieve among a database consisting of text passages and tables +(one transformer for query, one transformer for text passages, one transformer for tables). +See the original paper for more details: +Kostić, Bogdan, et al. (2021): "Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models" +(https://arxiv.org/abs/2108.04049), + + + +#### embed\_queries + +```python +def embed_queries(texts: List[str]) -> List[np.ndarray] +``` + +Create embeddings for a list of queries using the query encoder + +**Arguments**: + +- `texts`: Queries to embed + +**Returns**: + +Embeddings, one per input queries + + + +#### embed\_documents + +```python +def embed_documents(docs: List[Document]) -> List[np.ndarray] +``` + +Create embeddings for a list of text documents and / or tables using the text passage encoder and + +the table encoder. + +**Arguments**: + +- `docs`: List of Document objects used to represent documents / passages in +a standardized way within Haystack. + +**Returns**: + +Embeddings of documents / passages. Shape: (batch_size, embedding_dim) + + + +#### train + +```python +def train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, max_samples: int = None, max_processes: int = 128, dev_split: float = 0, batch_size: int = 2, embed_meta_fields: List[str] = ["page_title", "section_title", "caption"], num_hard_negatives: int = 1, num_positives: int = 1, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, use_amp: str = None, optimizer_name: str = "AdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/mm_retrieval", query_encoder_save_dir: str = "query_encoder", passage_encoder_save_dir: str = "passage_encoder", table_encoder_save_dir: str = "table_encoder") +``` + +Train a TableTextRetrieval model. + +**Arguments**: + +- `data_dir`: Directory where training file, dev file and test file are present. +- `train_filename`: Training filename. +- `dev_filename`: Development set filename, file to be used by model in eval step of training. +- `test_filename`: Test set filename, file to be used by model in test step after training. +- `max_samples`: Maximum number of input samples to convert. Can be used for debugging a smaller dataset. +- `max_processes`: The maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo. +It can be set to 1 to disable the use of multiprocessing or make debugging easier. +- `dev_split`: The proportion of the train set that will sliced. Only works if dev_filename is set to None. +- `batch_size`: Total number of samples in 1 batch of data. +- `embed_meta_fields`: Concatenate meta fields with each passage and table. +The default setting in official MMRetrieval embeds page title, +section title and caption with the corresponding table and title with +corresponding text passage. +- `num_hard_negatives`: Number of hard negative passages (passages which are +very similar (high score by BM25) to query but do not contain the answer)- +- `num_positives`: Number of positive passages. +- `n_epochs`: Number of epochs to train the model on. +- `evaluate_every`: Number of training steps after evaluation is run. +- `n_gpu`: Number of gpus to train on. +- `learning_rate`: Learning rate of optimizer. +- `epsilon`: Epsilon parameter of optimizer. +- `weight_decay`: Weight decay parameter of optimizer. +- `grad_acc_steps`: Number of steps to accumulate gradient over before back-propagation is done. +- `use_amp`: Whether to use automatic mixed precision (AMP) or not. The options are: +"O0" (FP32) +"O1" (Mixed Precision) +"O2" (Almost FP16) +"O3" (Pure FP16). +For more information, refer to: https://nvidia.github.io/apex/amp.html +- `optimizer_name`: What optimizer to use (default: TransformersAdamW). +- `num_warmup_steps`: Number of warmup steps. +- `optimizer_correct_bias`: Whether to correct bias in optimizer. +- `save_dir`: Directory where models are saved. +- `query_encoder_save_dir`: Directory inside save_dir where query_encoder model files are saved. +- `passage_encoder_save_dir`: Directory inside save_dir where passage_encoder model files are saved. +- `table_encoder_save_dir`: Directory inside save_dir where table_encoder model files are saved. + + + +#### save + +```python +def save(save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder", table_encoder_dir: str = "table_encoder") +``` + +Save TableTextRetriever to the specified directory. + +**Arguments**: + +- `save_dir`: Directory to save to. +- `query_encoder_dir`: Directory in save_dir that contains query encoder model. +- `passage_encoder_dir`: Directory in save_dir that contains passage encoder model. +- `table_encoder_dir`: Directory in save_dir that contains table encoder model. + +**Returns**: + +None + + + +#### load + +```python +@classmethod +def load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, max_seq_len_table: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_meta_fields: List[str] = ["name", "section_title", "caption"], use_fast_tokenizers: bool = True, similarity_function: str = "dot_product", query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder", table_encoder_dir: str = "table_encoder", infer_tokenizer_classes: bool = False) +``` + +Load TableTextRetriever from the specified directory. + + + +## EmbeddingRetriever + +```python +class EmbeddingRetriever(BaseRetriever) +``` + + + +#### retrieve + +```python +def retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None, headers: Optional[Dict[str, str]] = None) -> List[Document] +``` + +Scan through documents in DocumentStore and return a small number documents + +that are most relevant to the query. + +**Arguments**: + +- `query`: The query +- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field +- `top_k`: How many documents to return per query. +- `index`: The name of the index in the DocumentStore from which to retrieve documents + + + +#### embed\_queries + +```python +def embed_queries(texts: List[str]) -> List[np.ndarray] +``` + +Create embeddings for a list of queries. + +**Arguments**: + +- `texts`: Queries to embed + +**Returns**: + +Embeddings, one per input queries + + + +#### embed\_documents + +```python +def embed_documents(docs: List[Document]) -> List[np.ndarray] +``` + +Create embeddings for a list of documents. + +**Arguments**: + +- `docs`: List of documents to embed + +**Returns**: + +Embeddings, one per input document + + + +# Module text2sparql + + + +## Text2SparqlRetriever + +```python +class Text2SparqlRetriever(BaseGraphRetriever) +``` + +Graph retriever that uses a pre-trained Bart model to translate natural language questions +given in text form to queries in SPARQL format. +The generated SPARQL query is executed on a knowledge graph. + + + +#### retrieve + +```python +def retrieve(query: str, top_k: Optional[int] = None) +``` + +Translate a text query to SPARQL and execute it on the knowledge graph to retrieve a list of answers + +**Arguments**: + +- `query`: Text query that shall be translated to SPARQL and then executed on the knowledge graph +- `top_k`: How many SPARQL queries to generate per text query. + + + +#### format\_result + +```python +def format_result(result) +``` + +Generate formatted dictionary output with text answer and additional info + +**Arguments**: + +- `result`: The result of a SPARQL query as retrieved from the knowledge graph + diff --git a/docs/v1.2.0/_src/api/api/summarizer.md b/docs/v1.2.0/_src/api/api/summarizer.md new file mode 100644 index 0000000000..9821271d83 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/summarizer.md @@ -0,0 +1,110 @@ + + +# Module base + + + +## BaseSummarizer + +```python +class BaseSummarizer(BaseComponent) +``` + +Abstract class for Summarizer + + + +#### predict + +```python +@abstractmethod +def predict(documents: List[Document], generate_single_summary: Optional[bool] = None) -> List[Document] +``` + +Abstract method for creating a summary. + +**Arguments**: + +- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on. +- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document. +If set to "True", all docs will be joined to a single string that will then +be summarized. +Important: The summary will depend on the order of the supplied documents! + +**Returns**: + +List of Documents, where Document.text contains the summarization and Document.meta["context"] +the original, not summarized text + + + +# Module transformers + + + +## TransformersSummarizer + +```python +class TransformersSummarizer(BaseSummarizer) +``` + +Transformer based model to summarize the documents using the HuggingFace's transformers framework + +You can use any model that has been fine-tuned on a summarization task. For example: +'`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. +See the up-to-date list of available models on +`huggingface.co/models `__ + +**Example** + +```python +| docs = [Document(text="PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions." +| "The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by" +| "the shutoffs which were expected to last through at least midday tomorrow.")] +| +| # Summarize +| summary = summarizer.predict( +| documents=docs, +| generate_single_summary=True +| ) +| +| # Show results (List of Documents, containing summary and original text) +| print(summary) +| +| [ +| { +| "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.", +| ... +| "meta": { +| "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. ..." +| }, +| ... +| }, +``` + + + +#### predict + +```python +def predict(documents: List[Document], generate_single_summary: Optional[bool] = None, truncation: bool = True) -> List[Document] +``` + +Produce the summarization from the supplied documents. + +These document can for example be retrieved via the Retriever. + +**Arguments**: + +- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on. +- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document. +If set to "True", all docs will be joined to a single string that will then +be summarized. +Important: The summary will depend on the order of the supplied documents! +- `truncation`: Truncate to a maximum length accepted by the model + +**Returns**: + +List of Documents, where Document.text contains the summarization and Document.meta["context"] +the original, not summarized text + diff --git a/docs/v1.2.0/_src/api/api/translator.md b/docs/v1.2.0/_src/api/api/translator.md new file mode 100644 index 0000000000..20c0342527 --- /dev/null +++ b/docs/v1.2.0/_src/api/api/translator.md @@ -0,0 +1,82 @@ + + +# Module base + + + +## BaseTranslator + +```python +class BaseTranslator(BaseComponent) +``` + +Abstract class for a Translator component that translates either a query or a doc from language A to language B. + + + +#### translate + +```python +@abstractmethod +def translate(results: List[Dict[str, Any]] = None, query: Optional[str] = None, documents: Optional[Union[List[Document], List[Answer], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None) -> Union[str, List[Document], List[Answer], List[str], List[Dict[str, Any]]] +``` + +Translate the passed query or a list of documents from language A to B. + + + +#### run + +```python +def run(results: List[Dict[str, Any]] = None, query: Optional[str] = None, documents: Optional[Union[List[Document], List[Answer], List[str], List[Dict[str, Any]]]] = None, answers: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None) +``` + +Method that gets executed when this class is used as a Node in a Haystack Pipeline + + + +# Module transformers + + + +## TransformersTranslator + +```python +class TransformersTranslator(BaseTranslator) +``` + +Translator component based on Seq2Seq models from Huggingface's transformers library. +Exemplary use cases: +- Translate a query from Language A to B (e.g. if you only have good models + documents in language B) +- Translate a document from Language A to B (e.g. if you want to return results in the native language of the user) + +We currently recommend using OPUS models (see __init__() for details) + +**Example:** + +```python +| DOCS = [ +| Document(text="Heinz von Foerster was an Austrian American scientist combining physics and philosophy, +| and widely attributed as the originator of Second-order cybernetics.") +| ] +| translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-de") +| res = translator.translate(documents=DOCS, query=None) +``` + + + +#### translate + +```python +def translate(results: List[Dict[str, Any]] = None, query: Optional[str] = None, documents: Optional[Union[List[Document], List[Answer], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None) -> Union[str, List[Document], List[Answer], List[str], List[Dict[str, Any]]] +``` + +Run the actual translation. You can supply a query or a list of documents. Whatever is supplied will be translated. + +**Arguments**: + +- `results`: Generated QA pairs to translate +- `query`: The query string to translate +- `documents`: The documents to translate +- `dict_key`: If you pass a dictionary in `documents`, you can specify here the field which shall be translated. + diff --git a/docs/v1.2.0/_src/api/conf.py b/docs/v1.2.0/_src/api/conf.py new file mode 100644 index 0000000000..46046eccc0 --- /dev/null +++ b/docs/v1.2.0/_src/api/conf.py @@ -0,0 +1,52 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import sphinx_rtd_theme +import os +import sys + +sys.path.append("/Users/deepset/deepset/haystack") + + +# -- Project information ----------------------------------------------------- + +project = "Haystack" +copyright = "2020, deepset" +author = "deepset" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ["sphinx.ext.autodoc", "sphinx_rtd_theme"] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. 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zwVp_K6;~e6)lK&_r3xd*5$h-j>NH#$p+A#Q004~-dFfA8P?v53XW=K4W9UNto%3*Ei2R&wyQ|2KCa>G8PLjcZ3`?A;2yMC{~@_M{=$_F1+I73oFhFe5Ixd>!Ns$F*jOA;6tNtPMQz$ zOiT;Z6TJG5XoNZ6*N0Km$c@Ig4LtJFTI-AwF%JQZaAkH9Rkyo{){CyZefO+NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/v1.2.0/_src/api/openapi/generate_openapi_specs.py b/docs/v1.2.0/_src/api/openapi/generate_openapi_specs.py new file mode 100644 index 0000000000..bb74de9542 --- /dev/null +++ b/docs/v1.2.0/_src/api/openapi/generate_openapi_specs.py @@ -0,0 +1,31 @@ +import json +from pathlib import Path +import os +import sys +import shutil + +sys.path.append("../../../../") + +rest_path = Path("../../../../rest_api").absolute() +pipeline_path = str(rest_path / "pipeline" / "pipeline_empty.yaml") +app_path = str(rest_path / "application.py") +print(f"Loading OpenAPI specs from {app_path} with pipeline at {pipeline_path}") + +os.environ["PIPELINE_YAML_PATH"] = pipeline_path + +from rest_api.application import get_openapi_specs, haystack_version + +# Generate the openapi specs +specs = get_openapi_specs() + +# Dump the specs into a JSON file +with open("openapi.json", "w") as f: + json.dump(specs, f, indent=4) + +# Remove rc versions of the specs from the folder +for specs_file in os.listdir(): + if os.path.isfile(specs_file) and "rc" in specs_file and Path(specs_file).suffix == ".json": + os.remove(specs_file) + +# Add versioned copy +shutil.copy("openapi.json", f"openapi-{haystack_version}.json") diff --git a/docs/v1.2.0/_src/api/openapi/openapi-1.1.0.json b/docs/v1.2.0/_src/api/openapi/openapi-1.1.0.json new file mode 100644 index 0000000000..f5fcee5b74 --- /dev/null +++ b/docs/v1.2.0/_src/api/openapi/openapi-1.1.0.json @@ -0,0 +1,834 @@ +{ + "openapi": "3.0.2", + "info": { + "title": "Haystack REST API", + "version": "1.1.0" + }, + "paths": { + "/initialized": { + "get": { + "tags": [ + "search" + ], + "summary": "Check Status", + "description": "This endpoint can be used during startup to understand if the\nserver is ready to take any requests, or is still loading.\n\nThe recommended approach is to call this endpoint with a short timeout,\nlike 500ms, and in case of no reply, consider the server busy.", + "operationId": "check_status_initialized_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/hs_version": { + "get": { + "tags": [ + "search" + ], + "summary": "Haystack Version", + "description": "Get the running Haystack version.", + "operationId": "haystack_version_hs_version_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/query": { + "post": { + "tags": [ + "search" + ], + "summary": "Query", + "description": "This endpoint receives the question as a string and allows the requester to set\nadditional parameters that will be passed on to the Haystack pipeline.", + "operationId": "query_query_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback", + "description": "This endpoint allows the API user to retrieve all the feedback that has been submitted\nthrough the `POST /feedback` endpoint.", + "operationId": "get_feedback_feedback_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + }, + "post": { + "tags": [ + "feedback" + ], + "summary": "Post Feedback", + "description": "This endpoint allows the API user to submit feedback on an answer for a particular query.\n\nFor example, the user can send feedback on whether the answer was correct and\nwhether the right snippet was identified as the answer.\n\nInformation submitted through this endpoint is used to train the underlying QA model.", + "operationId": "post_feedback_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "title": "Feedback", + "anyOf": [ + { + "$ref": "#/components/schemas/LabelSerialized" + }, + { + "$ref": "#/components/schemas/CreateLabelSerialized" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "tags": [ + "feedback" + ], + "summary": "Delete Feedback", + "description": "This endpoint allows the API user to delete all the\nfeedback that has been sumbitted through the\n`POST /feedback` endpoint", + "operationId": "delete_feedback_feedback_delete", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/eval-feedback": { + "post": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback Metrics", + "description": "This endpoint returns basic accuracy metrics based on user feedback,\ne.g., the ratio of correct answers or correctly identified documents.\nYou can filter the output by document or label.\n\nExample:\n\n`curl --location --request POST 'http://127.0.0.1:8000/eval-doc-qa-feedback' --header 'Content-Type: application/json' --data-raw '{ \"filters\": {\"document_id\": [\"XRR3xnEBCYVTkbTystOB\"]} }'`", + "operationId": "get_feedback_metrics_eval_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/export-feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Export Feedback", + "description": "This endpoint returns JSON output in the SQuAD format for question/answer pairs\nthat were marked as \"relevant\" by user feedback through the `POST /feedback` endpoint.\n\nThe context_size param can be used to limit response size for large documents.", + "operationId": "export_feedback_export_feedback_get", + "parameters": [ + { + "required": false, + "schema": { + "title": "Context Size", + "type": "integer", + "default": 100000 + }, + "name": "context_size", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Full Document Context", + "type": "boolean", + "default": true + }, + "name": "full_document_context", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Only Positive Labels", + "type": "boolean", + "default": false + }, + "name": "only_positive_labels", + "in": "query" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/file-upload": { + "post": { + "tags": [ + "file-upload" + ], + "summary": "Upload File", + "description": "You can use this endpoint to upload a file for indexing\n(see https://haystack.deepset.ai/guides/rest-api#indexing-documents-in-the-haystack-rest-api-document-store).", + "operationId": "upload_file_file_upload_post", + "requestBody": { + "content": { + "multipart/form-data": { + "schema": { + "$ref": "#/components/schemas/Body_upload_file_file_upload_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/get_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Get Documents", + "description": "This endpoint allows you to retrieve documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "get_documents_documents_get_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Get Documents Documents Get By Filters Post", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + } + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/delete_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Delete Documents", + "description": "This endpoint allows you to delete documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "delete_documents_documents_delete_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Delete Documents Documents Delete By Filters Post", + "type": "boolean" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "AnswerSerialized": { + "title": "AnswerSerialized", + "required": [ + "answer" + ], + "type": "object", + "properties": { + "answer": { + "title": "Answer", + "type": "string" + }, + "type": { + "title": "Type", + "enum": [ + "generative", + "extractive", + "other" + ], + "type": "string", + "default": "extractive" + }, + "score": { + "title": "Score", + "type": "number" + }, + "context": { + "title": "Context", + "type": "string" + }, + "offsets_in_document": { + "title": "Offsets In Document", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "offsets_in_context": { + "title": "Offsets In Context", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "document_id": { + "title": "Document Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + } + } + }, + "Body_upload_file_file_upload_post": { + "title": "Body_upload_file_file_upload_post", + "required": [ + "files" + ], + "type": "object", + "properties": { + "files": { + "title": "Files", + "type": "array", + "items": { + "type": "string", + "format": "binary" + } + }, + "meta": { + "title": "Meta", + "type": "string", + "default": "null" + }, + "remove_numeric_tables": { + "title": "Remove Numeric Tables" + }, + "valid_languages": { + "title": "Valid Languages" + }, + "clean_whitespace": { + "title": "Clean Whitespace" + }, + "clean_empty_lines": { + "title": "Clean Empty Lines" + }, + "clean_header_footer": { + "title": "Clean Header Footer" + }, + "split_by": { + "title": "Split By" + }, + "split_length": { + "title": "Split Length" + }, + "split_overlap": { + "title": "Split Overlap" + }, + "split_respect_sentence_boundary": { + "title": "Split Respect Sentence Boundary" + } + } + }, + "CreateLabelSerialized": { + "title": "CreateLabelSerialized", + "required": [ + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + }, + "additionalProperties": false + }, + "DocumentSerialized": { + "title": "DocumentSerialized", + "required": [ + "content", + "content_type", + "id", + "meta" + ], + "type": "object", + "properties": { + "content": { + "title": "Content", + "type": "string" + }, + "content_type": { + "title": "Content Type", + "enum": [ + "text", + "table", + "image" + ], + "type": "string" + }, + "id": { + "title": "Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "score": { + "title": "Score", + "type": "number" + }, + "embedding": { + "title": "Embedding", + "type": "array", + "items": { + "type": "number" + } + }, + "id_hash_keys": { + "title": "Id Hash Keys", + "type": "array", + "items": { + "type": "string" + } + } + } + }, + "FilterRequest": { + "title": "FilterRequest", + "type": "object", + "properties": { + "filters": { + "title": "Filters", + "type": "object", + "additionalProperties": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "type": "string" + } + } + ] + } + } + } + }, + "HTTPValidationError": { + "title": "HTTPValidationError", + "type": "object", + "properties": { + "detail": { + "title": "Detail", + "type": "array", + "items": { + "$ref": "#/components/schemas/ValidationError" + } + } + } + }, + "LabelSerialized": { + "title": "LabelSerialized", + "required": [ + "id", + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + } + }, + "QueryRequest": { + "title": "QueryRequest", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + }, + "debug": { + "title": "Debug", + "type": "boolean", + "default": false + } + }, + "additionalProperties": false + }, + "QueryResponse": { + "title": "QueryResponse", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "answers": { + "title": "Answers", + "type": "array", + "items": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "default": [] + }, + "documents": { + "title": "Documents", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "default": [] + }, + "_debug": { + "title": " Debug", + "type": "object" + } + } + }, + "Span": { + "title": "Span", + "required": [ + "start", + "end" + ], + "type": "object", + "properties": { + "start": { + "title": "Start", + "type": "integer" + }, + "end": { + "title": "End", + "type": "integer" + } + } + }, + "ValidationError": { + "title": "ValidationError", + "required": [ + "loc", + "msg", + "type" + ], + "type": "object", + "properties": { + "loc": { + "title": "Location", + "type": "array", + "items": { + "type": "string" + } + }, + "msg": { + "title": "Message", + "type": "string" + }, + "type": { + "title": "Error Type", + "type": "string" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/api/openapi/openapi-1.2.0.json b/docs/v1.2.0/_src/api/openapi/openapi-1.2.0.json new file mode 100644 index 0000000000..aa3a5e2a0a --- /dev/null +++ b/docs/v1.2.0/_src/api/openapi/openapi-1.2.0.json @@ -0,0 +1,834 @@ +{ + "openapi": "3.0.2", + "info": { + "title": "Haystack REST API", + "version": "1.2.0" + }, + "paths": { + "/initialized": { + "get": { + "tags": [ + "search" + ], + "summary": "Check Status", + "description": "This endpoint can be used during startup to understand if the\nserver is ready to take any requests, or is still loading.\n\nThe recommended approach is to call this endpoint with a short timeout,\nlike 500ms, and in case of no reply, consider the server busy.", + "operationId": "check_status_initialized_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/hs_version": { + "get": { + "tags": [ + "search" + ], + "summary": "Haystack Version", + "description": "Get the running Haystack version.", + "operationId": "haystack_version_hs_version_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/query": { + "post": { + "tags": [ + "search" + ], + "summary": "Query", + "description": "This endpoint receives the question as a string and allows the requester to set\nadditional parameters that will be passed on to the Haystack pipeline.", + "operationId": "query_query_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback", + "description": "This endpoint allows the API user to retrieve all the feedback that has been submitted\nthrough the `POST /feedback` endpoint.", + "operationId": "get_feedback_feedback_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + }, + "post": { + "tags": [ + "feedback" + ], + "summary": "Post Feedback", + "description": "This endpoint allows the API user to submit feedback on an answer for a particular query.\n\nFor example, the user can send feedback on whether the answer was correct and\nwhether the right snippet was identified as the answer.\n\nInformation submitted through this endpoint is used to train the underlying QA model.", + "operationId": "post_feedback_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "title": "Feedback", + "anyOf": [ + { + "$ref": "#/components/schemas/LabelSerialized" + }, + { + "$ref": "#/components/schemas/CreateLabelSerialized" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "tags": [ + "feedback" + ], + "summary": "Delete Feedback", + "description": "This endpoint allows the API user to delete all the\nfeedback that has been sumbitted through the\n`POST /feedback` endpoint", + "operationId": "delete_feedback_feedback_delete", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/eval-feedback": { + "post": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback Metrics", + "description": "This endpoint returns basic accuracy metrics based on user feedback,\ne.g., the ratio of correct answers or correctly identified documents.\nYou can filter the output by document or label.\n\nExample:\n\n`curl --location --request POST 'http://127.0.0.1:8000/eval-doc-qa-feedback' --header 'Content-Type: application/json' --data-raw '{ \"filters\": {\"document_id\": [\"XRR3xnEBCYVTkbTystOB\"]} }'`", + "operationId": "get_feedback_metrics_eval_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/export-feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Export Feedback", + "description": "This endpoint returns JSON output in the SQuAD format for question/answer pairs\nthat were marked as \"relevant\" by user feedback through the `POST /feedback` endpoint.\n\nThe context_size param can be used to limit response size for large documents.", + "operationId": "export_feedback_export_feedback_get", + "parameters": [ + { + "required": false, + "schema": { + "title": "Context Size", + "type": "integer", + "default": 100000 + }, + "name": "context_size", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Full Document Context", + "type": "boolean", + "default": true + }, + "name": "full_document_context", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Only Positive Labels", + "type": "boolean", + "default": false + }, + "name": "only_positive_labels", + "in": "query" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/file-upload": { + "post": { + "tags": [ + "file-upload" + ], + "summary": "Upload File", + "description": "You can use this endpoint to upload a file for indexing\n(see https://haystack.deepset.ai/guides/rest-api#indexing-documents-in-the-haystack-rest-api-document-store).", + "operationId": "upload_file_file_upload_post", + "requestBody": { + "content": { + "multipart/form-data": { + "schema": { + "$ref": "#/components/schemas/Body_upload_file_file_upload_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/get_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Get Documents", + "description": "This endpoint allows you to retrieve documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "get_documents_documents_get_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Get Documents Documents Get By Filters Post", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + } + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/delete_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Delete Documents", + "description": "This endpoint allows you to delete documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "delete_documents_documents_delete_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Delete Documents Documents Delete By Filters Post", + "type": "boolean" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "AnswerSerialized": { + "title": "AnswerSerialized", + "required": [ + "answer" + ], + "type": "object", + "properties": { + "answer": { + "title": "Answer", + "type": "string" + }, + "type": { + "title": "Type", + "enum": [ + "generative", + "extractive", + "other" + ], + "type": "string", + "default": "extractive" + }, + "score": { + "title": "Score", + "type": "number" + }, + "context": { + "title": "Context", + "type": "string" + }, + "offsets_in_document": { + "title": "Offsets In Document", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "offsets_in_context": { + "title": "Offsets In Context", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "document_id": { + "title": "Document Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + } + } + }, + "Body_upload_file_file_upload_post": { + "title": "Body_upload_file_file_upload_post", + "required": [ + "files" + ], + "type": "object", + "properties": { + "files": { + "title": "Files", + "type": "array", + "items": { + "type": "string", + "format": "binary" + } + }, + "meta": { + "title": "Meta", + "type": "string", + "default": "null" + }, + "remove_numeric_tables": { + "title": "Remove Numeric Tables" + }, + "valid_languages": { + "title": "Valid Languages" + }, + "clean_whitespace": { + "title": "Clean Whitespace" + }, + "clean_empty_lines": { + "title": "Clean Empty Lines" + }, + "clean_header_footer": { + "title": "Clean Header Footer" + }, + "split_by": { + "title": "Split By" + }, + "split_length": { + "title": "Split Length" + }, + "split_overlap": { + "title": "Split Overlap" + }, + "split_respect_sentence_boundary": { + "title": "Split Respect Sentence Boundary" + } + } + }, + "CreateLabelSerialized": { + "title": "CreateLabelSerialized", + "required": [ + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + }, + "additionalProperties": false + }, + "DocumentSerialized": { + "title": "DocumentSerialized", + "required": [ + "content", + "content_type", + "id", + "meta" + ], + "type": "object", + "properties": { + "content": { + "title": "Content", + "type": "string" + }, + "content_type": { + "title": "Content Type", + "enum": [ + "text", + "table", + "image" + ], + "type": "string" + }, + "id": { + "title": "Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "score": { + "title": "Score", + "type": "number" + }, + "embedding": { + "title": "Embedding", + "type": "array", + "items": { + "type": "number" + } + }, + "id_hash_keys": { + "title": "Id Hash Keys", + "type": "array", + "items": { + "type": "string" + } + } + } + }, + "FilterRequest": { + "title": "FilterRequest", + "type": "object", + "properties": { + "filters": { + "title": "Filters", + "type": "object", + "additionalProperties": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "type": "string" + } + } + ] + } + } + } + }, + "HTTPValidationError": { + "title": "HTTPValidationError", + "type": "object", + "properties": { + "detail": { + "title": "Detail", + "type": "array", + "items": { + "$ref": "#/components/schemas/ValidationError" + } + } + } + }, + "LabelSerialized": { + "title": "LabelSerialized", + "required": [ + "id", + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + } + }, + "QueryRequest": { + "title": "QueryRequest", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + }, + "debug": { + "title": "Debug", + "type": "boolean", + "default": false + } + }, + "additionalProperties": false + }, + "QueryResponse": { + "title": "QueryResponse", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "answers": { + "title": "Answers", + "type": "array", + "items": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "default": [] + }, + "documents": { + "title": "Documents", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "default": [] + }, + "_debug": { + "title": " Debug", + "type": "object" + } + } + }, + "Span": { + "title": "Span", + "required": [ + "start", + "end" + ], + "type": "object", + "properties": { + "start": { + "title": "Start", + "type": "integer" + }, + "end": { + "title": "End", + "type": "integer" + } + } + }, + "ValidationError": { + "title": "ValidationError", + "required": [ + "loc", + "msg", + "type" + ], + "type": "object", + "properties": { + "loc": { + "title": "Location", + "type": "array", + "items": { + "type": "string" + } + }, + "msg": { + "title": "Message", + "type": "string" + }, + "type": { + "title": "Error Type", + "type": "string" + } + } + } + } + } +} diff --git a/docs/v1.2.0/_src/api/openapi/openapi.json b/docs/v1.2.0/_src/api/openapi/openapi.json new file mode 100644 index 0000000000..12ddf0ba0a --- /dev/null +++ b/docs/v1.2.0/_src/api/openapi/openapi.json @@ -0,0 +1,834 @@ +{ + "openapi": "3.0.2", + "info": { + "title": "Haystack REST API", + "version": "1.2.0rc0" + }, + "paths": { + "/initialized": { + "get": { + "tags": [ + "search" + ], + "summary": "Check Status", + "description": "This endpoint can be used during startup to understand if the\nserver is ready to take any requests, or is still loading.\n\nThe recommended approach is to call this endpoint with a short timeout,\nlike 500ms, and in case of no reply, consider the server busy.", + "operationId": "check_status_initialized_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/hs_version": { + "get": { + "tags": [ + "search" + ], + "summary": "Haystack Version", + "description": "Get the running Haystack version.", + "operationId": "haystack_version_hs_version_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/query": { + "post": { + "tags": [ + "search" + ], + "summary": "Query", + "description": "This endpoint receives the question as a string and allows the requester to set\nadditional parameters that will be passed on to the Haystack pipeline.", + "operationId": "query_query_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback", + "description": "This endpoint allows the API user to retrieve all the feedback that has been submitted\nthrough the `POST /feedback` endpoint.", + "operationId": "get_feedback_feedback_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + }, + "post": { + "tags": [ + "feedback" + ], + "summary": "Post Feedback", + "description": "This endpoint allows the API user to submit feedback on an answer for a particular query.\n\nFor example, the user can send feedback on whether the answer was correct and\nwhether the right snippet was identified as the answer.\n\nInformation submitted through this endpoint is used to train the underlying QA model.", + "operationId": "post_feedback_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "title": "Feedback", + "anyOf": [ + { + "$ref": "#/components/schemas/LabelSerialized" + }, + { + "$ref": "#/components/schemas/CreateLabelSerialized" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "tags": [ + "feedback" + ], + "summary": "Delete Feedback", + "description": "This endpoint allows the API user to delete all the\nfeedback that has been sumbitted through the\n`POST /feedback` endpoint", + "operationId": "delete_feedback_feedback_delete", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/eval-feedback": { + "post": { + "tags": [ + "feedback" + ], + "summary": "Get Feedback Metrics", + "description": "This endpoint returns basic accuracy metrics based on user feedback,\ne.g., the ratio of correct answers or correctly identified documents.\nYou can filter the output by document or label.\n\nExample:\n\n`curl --location --request POST 'http://127.0.0.1:8000/eval-doc-qa-feedback' --header 'Content-Type: application/json' --data-raw '{ \"filters\": {\"document_id\": [\"XRR3xnEBCYVTkbTystOB\"]} }'`", + "operationId": "get_feedback_metrics_eval_feedback_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/export-feedback": { + "get": { + "tags": [ + "feedback" + ], + "summary": "Export Feedback", + "description": "This endpoint returns JSON output in the SQuAD format for question/answer pairs\nthat were marked as \"relevant\" by user feedback through the `POST /feedback` endpoint.\n\nThe context_size param can be used to limit response size for large documents.", + "operationId": "export_feedback_export_feedback_get", + "parameters": [ + { + "required": false, + "schema": { + "title": "Context Size", + "type": "integer", + "default": 100000 + }, + "name": "context_size", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Full Document Context", + "type": "boolean", + "default": true + }, + "name": "full_document_context", + "in": "query" + }, + { + "required": false, + "schema": { + "title": "Only Positive Labels", + "type": "boolean", + "default": false + }, + "name": "only_positive_labels", + "in": "query" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/file-upload": { + "post": { + "tags": [ + "file-upload" + ], + "summary": "Upload File", + "description": "You can use this endpoint to upload a file for indexing\n(see https://haystack.deepset.ai/guides/rest-api#indexing-documents-in-the-haystack-rest-api-document-store).", + "operationId": "upload_file_file_upload_post", + "requestBody": { + "content": { + "multipart/form-data": { + "schema": { + "$ref": "#/components/schemas/Body_upload_file_file_upload_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/get_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Get Documents", + "description": "This endpoint allows you to retrieve documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "get_documents_documents_get_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Get Documents Documents Get By Filters Post", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + } + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/documents/delete_by_filters": { + "post": { + "tags": [ + "document" + ], + "summary": "Delete Documents", + "description": "This endpoint allows you to delete documents contained in your document store.\nYou can filter the documents to delete by metadata (like the document's name),\nor provide an empty JSON object to clear the document store.\n\nExample of filters:\n`'{\"filters\": {{\"name\": [\"some\", \"more\"], \"category\": [\"only_one\"]}}'`\n\nTo get all documents you should provide an empty dict, like:\n`'{\"filters\": {}}'`", + "operationId": "delete_documents_documents_delete_by_filters_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FilterRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "title": "Response Delete Documents Documents Delete By Filters Post", + "type": "boolean" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "AnswerSerialized": { + "title": "AnswerSerialized", + "required": [ + "answer" + ], + "type": "object", + "properties": { + "answer": { + "title": "Answer", + "type": "string" + }, + "type": { + "title": "Type", + "enum": [ + "generative", + "extractive", + "other" + ], + "type": "string", + "default": "extractive" + }, + "score": { + "title": "Score", + "type": "number" + }, + "context": { + "title": "Context", + "type": "string" + }, + "offsets_in_document": { + "title": "Offsets In Document", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "offsets_in_context": { + "title": "Offsets In Context", + "type": "array", + "items": { + "$ref": "#/components/schemas/Span" + } + }, + "document_id": { + "title": "Document Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + } + } + }, + "Body_upload_file_file_upload_post": { + "title": "Body_upload_file_file_upload_post", + "required": [ + "files" + ], + "type": "object", + "properties": { + "files": { + "title": "Files", + "type": "array", + "items": { + "type": "string", + "format": "binary" + } + }, + "meta": { + "title": "Meta", + "type": "string", + "default": "null" + }, + "remove_numeric_tables": { + "title": "Remove Numeric Tables" + }, + "valid_languages": { + "title": "Valid Languages" + }, + "clean_whitespace": { + "title": "Clean Whitespace" + }, + "clean_empty_lines": { + "title": "Clean Empty Lines" + }, + "clean_header_footer": { + "title": "Clean Header Footer" + }, + "split_by": { + "title": "Split By" + }, + "split_length": { + "title": "Split Length" + }, + "split_overlap": { + "title": "Split Overlap" + }, + "split_respect_sentence_boundary": { + "title": "Split Respect Sentence Boundary" + } + } + }, + "CreateLabelSerialized": { + "title": "CreateLabelSerialized", + "required": [ + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + }, + "additionalProperties": false + }, + "DocumentSerialized": { + "title": "DocumentSerialized", + "required": [ + "content", + "content_type", + "id", + "meta" + ], + "type": "object", + "properties": { + "content": { + "title": "Content", + "type": "string" + }, + "content_type": { + "title": "Content Type", + "enum": [ + "text", + "table", + "image" + ], + "type": "string" + }, + "id": { + "title": "Id", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "score": { + "title": "Score", + "type": "number" + }, + "embedding": { + "title": "Embedding", + "type": "array", + "items": { + "type": "number" + } + }, + "id_hash_keys": { + "title": "Id Hash Keys", + "type": "array", + "items": { + "type": "string" + } + } + } + }, + "FilterRequest": { + "title": "FilterRequest", + "type": "object", + "properties": { + "filters": { + "title": "Filters", + "type": "object", + "additionalProperties": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "type": "string" + } + } + ] + } + } + } + }, + "HTTPValidationError": { + "title": "HTTPValidationError", + "type": "object", + "properties": { + "detail": { + "title": "Detail", + "type": "array", + "items": { + "$ref": "#/components/schemas/ValidationError" + } + } + } + }, + "LabelSerialized": { + "title": "LabelSerialized", + "required": [ + "id", + "query", + "document", + "is_correct_answer", + "is_correct_document", + "origin" + ], + "type": "object", + "properties": { + "id": { + "title": "Id", + "type": "string" + }, + "query": { + "title": "Query", + "type": "string" + }, + "document": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "is_correct_answer": { + "title": "Is Correct Answer", + "type": "boolean" + }, + "is_correct_document": { + "title": "Is Correct Document", + "type": "boolean" + }, + "origin": { + "title": "Origin", + "enum": [ + "user-feedback", + "gold-label" + ], + "type": "string" + }, + "answer": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "no_answer": { + "title": "No Answer", + "type": "boolean" + }, + "pipeline_id": { + "title": "Pipeline Id", + "type": "string" + }, + "created_at": { + "title": "Created At", + "type": "string" + }, + "updated_at": { + "title": "Updated At", + "type": "string" + }, + "meta": { + "title": "Meta", + "type": "object" + }, + "filters": { + "title": "Filters", + "type": "object" + } + } + }, + "QueryRequest": { + "title": "QueryRequest", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + }, + "debug": { + "title": "Debug", + "type": "boolean", + "default": false + } + }, + "additionalProperties": false + }, + "QueryResponse": { + "title": "QueryResponse", + "required": [ + "query" + ], + "type": "object", + "properties": { + "query": { + "title": "Query", + "type": "string" + }, + "answers": { + "title": "Answers", + "type": "array", + "items": { + "$ref": "#/components/schemas/AnswerSerialized" + }, + "default": [] + }, + "documents": { + "title": "Documents", + "type": "array", + "items": { + "$ref": "#/components/schemas/DocumentSerialized" + }, + "default": [] + }, + "_debug": { + "title": " Debug", + "type": "object" + } + } + }, + "Span": { + "title": "Span", + "required": [ + "start", + "end" + ], + "type": "object", + "properties": { + "start": { + "title": "Start", + "type": "integer" + }, + "end": { + "title": "End", + "type": "integer" + } + } + }, + "ValidationError": { + "title": "ValidationError", + "required": [ + "loc", + "msg", + "type" + ], + "type": "object", + "properties": { + "loc": { + "title": "Location", + "type": "array", + "items": { + "type": "string" + } + }, + "msg": { + "title": "Message", + "type": "string" + }, + "type": { + "title": "Error Type", + "type": "string" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/api/pydoc/answer-generator.yml b/docs/v1.2.0/_src/api/pydoc/answer-generator.yml new file mode 100644 index 0000000000..e9529c36dd --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/answer-generator.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/answer_generator] + modules: ['base', 'transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: generator.md diff --git a/docs/v1.2.0/_src/api/pydoc/crawler.yml b/docs/v1.2.0/_src/api/pydoc/crawler.yml new file mode 100644 index 0000000000..4f76c9c41b --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/crawler.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/connector] + modules: ['crawler'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: crawler.md diff --git a/docs/v1.2.0/_src/api/pydoc/document-classifier.yml b/docs/v1.2.0/_src/api/pydoc/document-classifier.yml new file mode 100644 index 0000000000..8e6cb9b4b1 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/document-classifier.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/document_classifier] + modules: ['base', 'transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: document_classifier.md diff --git a/docs/v1.2.0/_src/api/pydoc/document-store.yml b/docs/v1.2.0/_src/api/pydoc/document-store.yml new file mode 100644 index 0000000000..8fd230b3bc --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/document-store.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/document_stores] + modules: ['base', 'elasticsearch', 'memory', 'sql', 'faiss', 'milvus', 'weaviate', 'graphdb', 'deepsetcloud'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: document_store.md diff --git a/docs/v1.2.0/_src/api/pydoc/evaluation.yml b/docs/v1.2.0/_src/api/pydoc/evaluation.yml new file mode 100644 index 0000000000..0535869faf --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/evaluation.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/evaluator] + modules: ['evaluator'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: evaluation.md diff --git a/docs/v1.2.0/_src/api/pydoc/extractor.yml b/docs/v1.2.0/_src/api/pydoc/extractor.yml new file mode 100644 index 0000000000..578bb7ad89 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/extractor.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/extractor] + modules: ['entity'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: extractor.md diff --git a/docs/v1.2.0/_src/api/pydoc/file-classifier.yml b/docs/v1.2.0/_src/api/pydoc/file-classifier.yml new file mode 100644 index 0000000000..7c4a0f3b5a --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/file-classifier.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/file_classifier] + modules: ['file_type'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: file_classifier.md diff --git a/docs/v1.2.0/_src/api/pydoc/file-converters.yml b/docs/v1.2.0/_src/api/pydoc/file-converters.yml new file mode 100644 index 0000000000..372d2c4b64 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/file-converters.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/file_converter] + modules: ['base', 'docx', 'image', 'markdown', 'pdf', 'tika', 'txt'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: file_converter.md diff --git a/docs/v1.2.0/_src/api/pydoc/other.yml b/docs/v1.2.0/_src/api/pydoc/other.yml new file mode 100644 index 0000000000..31703fc31d --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/other.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/other] + modules: ['docs2answers', 'join_docs'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: other.md diff --git a/docs/v1.2.0/_src/api/pydoc/pipelines.yml b/docs/v1.2.0/_src/api/pydoc/pipelines.yml new file mode 100644 index 0000000000..9bd4db2d09 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/pipelines.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/pipelines] + modules: ['base', 'standard_pipelines'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: pipelines.md diff --git a/docs/v1.2.0/_src/api/pydoc/preprocessor.yml b/docs/v1.2.0/_src/api/pydoc/preprocessor.yml new file mode 100644 index 0000000000..745aa56ebc --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/preprocessor.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/preprocessor] + modules: ['base', 'preprocessor'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: preprocessor.md diff --git a/docs/v1.2.0/_src/api/pydoc/primitives.yml b/docs/v1.2.0/_src/api/pydoc/primitives.yml new file mode 100644 index 0000000000..a2153d6a90 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/primitives.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/] + modules: ['schema'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: primitives.md diff --git a/docs/v1.2.0/_src/api/pydoc/query-classifier.yml b/docs/v1.2.0/_src/api/pydoc/query-classifier.yml new file mode 100644 index 0000000000..03ff3ce5ad --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/query-classifier.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/query_classifier] + modules: ['base', 'sklearn', 'transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: query_classifier.md diff --git a/docs/v1.2.0/_src/api/pydoc/question-generator.yml b/docs/v1.2.0/_src/api/pydoc/question-generator.yml new file mode 100644 index 0000000000..ac7cf42eb1 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/question-generator.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/question_generator] + modules: ['question_generator'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: question_generator.md diff --git a/docs/v1.2.0/_src/api/pydoc/ranker.yml b/docs/v1.2.0/_src/api/pydoc/ranker.yml new file mode 100644 index 0000000000..04134ebe1a --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/ranker.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/ranker] + modules: ['base', 'sentence_transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: ranker.md diff --git a/docs/v1.2.0/_src/api/pydoc/reader.yml b/docs/v1.2.0/_src/api/pydoc/reader.yml new file mode 100644 index 0000000000..8d02c51947 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/reader.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/reader] + modules: ['base', 'farm', 'transformers', 'table'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: reader.md diff --git a/docs/v1.2.0/_src/api/pydoc/retriever.yml b/docs/v1.2.0/_src/api/pydoc/retriever.yml new file mode 100644 index 0000000000..97b552c690 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/retriever.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/retriever] + modules: ['base', 'sparse', 'dense', 'text2sparql'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: retriever.md diff --git a/docs/v1.2.0/_src/api/pydoc/summarizer.yml b/docs/v1.2.0/_src/api/pydoc/summarizer.yml new file mode 100644 index 0000000000..b237e273bc --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/summarizer.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/summarizer] + modules: ['base', 'transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: summarizer.md diff --git a/docs/v1.2.0/_src/api/pydoc/translator.yml b/docs/v1.2.0/_src/api/pydoc/translator.yml new file mode 100644 index 0000000000..00d1356522 --- /dev/null +++ b/docs/v1.2.0/_src/api/pydoc/translator.yml @@ -0,0 +1,20 @@ +loaders: + - type: python + search_path: [../../../../haystack/nodes/translator] + modules: ['base', 'transformers'] + ignore_when_discovered: ['__init__'] +processors: + - type: filter + expression: not name.startswith('_') and default() + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: markdown + descriptive_class_title: false + descriptive_module_title: true + add_method_class_prefix: false + add_member_class_prefix: false + filename: translator.md diff --git a/docs/v1.2.0/_src/benchmarks/farm_per_component.html b/docs/v1.2.0/_src/benchmarks/farm_per_component.html new file mode 100644 index 0000000000..6a9d3d5cea --- /dev/null +++ b/docs/v1.2.0/_src/benchmarks/farm_per_component.html @@ -0,0 +1,48 @@ + + + + + + + +

+ + + diff --git a/docs/v1.2.0/_src/benchmarks/reader_performance.json b/docs/v1.2.0/_src/benchmarks/reader_performance.json new file mode 100644 index 0000000000..be935fe271 --- /dev/null +++ b/docs/v1.2.0/_src/benchmarks/reader_performance.json @@ -0,0 +1,44 @@ +{ + "chart_type": "BarChart", + "title": "Reader Performance", + "subtitle": "Time and Accuracy Benchmarks", + "description": "Performance benchmarks of different Readers that can be used off-the-shelf in Haystack. Some models are geared towards speed, while others are more performance-focused. Accuracy is measured as F1 score and speed as passages/sec (with passages of 384 tokens). Each Reader is benchmarked using the SQuAD v2.0 development set, which contains 11866 question answer pairs. When tokenized using the BERT tokenizer and split using a sliding window approach, these become 12350 passages that are passed into the model. We set max_seq_len=384 and doc_stride=128. These benchmarking tests are run using an AWS p3.2xlarge instance with a Nvidia V100 GPU with this script. Please note that we are using the FARMReader class rather than the TransformersReader class. Also, the F1 measure that is reported here is in fact calculated on token level, rather than word level as is done in the official SQuAD script.", + "bars": "horizontal", + "columns": [ + "Model", + "F1", + "Speed (passages/sec)" + ], + "data": [ + { + "F1": 82.58860575299658, + "Speed": 125.81040525892848, + "Model": "RoBERTa" + }, + { + "F1": 78.87858491007042, + "Speed": 260.6443097981493, + "Model": "MiniLM" + }, + { + "F1": 74.31182400443286, + "Speed": 121.08066567525722, + "Model": "BERT base" + }, + { + "F1": 83.26306774734308, + "Speed": 42.21949937744112, + "Model": "BERT large" + }, + { + "F1": 84.50422699207468, + "Speed": 42.07400844838985, + "Model": "XLM-RoBERTa" + }, + { + "F1": 42.31925844723574, + "Speed": 222.91207128366702, + "Model": "DistilBERT" + } + ] +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/benchmarks/retriever_map.json b/docs/v1.2.0/_src/benchmarks/retriever_map.json new file mode 100644 index 0000000000..51e0687cf3 --- /dev/null +++ b/docs/v1.2.0/_src/benchmarks/retriever_map.json @@ -0,0 +1,204 @@ +{ + "chart_type": "LineChart", + "title": "Retriever Accuracy", + "subtitle": "mAP at different number of docs", + "description": "Here you can see how the mean avg. precision (mAP) of the retriever decays as the number of documents increases. The set up is the same as the above querying benchmark except that a varying number of negative documents are used to fill the document store.", + "columns": [ + "n_docs", + "BM25 / Elasticsearch", + "DPR / Elasticsearch", + "DPR / FAISS (flat)", + "DPR / FAISS (HNSW)", + "DPR / Milvus (flat)", + "DPR / Milvus (HNSW)", + "Sentence Transformers / Elasticsearch" + ], + "axis": [ + { + "x": "Number of docs", + "y": "mAP" + } + ], + "data": [ + { + "model": "DPR / Elasticsearch", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 10000, + "map": 89.87097014904354 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 10000, + "map": 66.26543444531747 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 1000, + "map": 90.06638620360428 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 10000, + "map": 87.11255142468549 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 10000, + "map": 89.87097014904354 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 10000, + "map": 89.51337675393017 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 10000, + "map": 89.87097014904354 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 10000, + "map": 88.24421129104469 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 100000, + "map": 86.54606328368976 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 500000, + "map": 80.86137228234091 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 100000, + "map": 56.25299537353825 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 500000, + "map": 45.595090262466535 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 100000, + "map": 82.74686664920836 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 500000, + "map": 76.49564526892904 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 100000, + "map": 86.54606328368973 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 500000, + "map": 80.86137228234091 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 100000, + "map": 84.33419639513305 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 500000, + "map": 75.73062475537202 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 100000, + "map": 86.54606328368973 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 500000, + "map": 80.86137228234091 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 100000, + "map": 81.63864883662649 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 500000, + "map": 73.57986207906387 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 1000, + "map": 74.20444712972909 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 1000, + "map": 92.95105322830891 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 10000, + "map": 89.8709701490436 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 100000, + "map": 86.54014997282701 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 1000, + "map": 92.76308330349686 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 10000, + "map": 89.00403653862938 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 100000, + "map": 85.7342431384476 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 500000, + "map": 80.85588135082547 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 500000, + "map": 77.5426462347698 + } + ] +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/benchmarks/retriever_performance.json b/docs/v1.2.0/_src/benchmarks/retriever_performance.json new file mode 100644 index 0000000000..dbb9340481 --- /dev/null +++ b/docs/v1.2.0/_src/benchmarks/retriever_performance.json @@ -0,0 +1,88 @@ +{ + "chart_type": "BarChart", + "title": "Retriever Performance", + "subtitle": "Time and Accuracy Benchmarks", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "bars": "horizontal", + "columns": [ + "Model", + "mAP", + "Index Speed (docs/sec)", + "Query Speed (queries/sec)" + ], + "series": { + "s0": "map", + "s1": "time", + "s2": "time" + }, + "axes": { + "label": "map", + "time_side": "top", + "time_label": "seconds" + }, + "data": [ + { + "model": "DPR / Elasticsearch", + "n_docs": 100000, + "index_speed": 71.36964873196698, + "query_speed": 5.192368815242574, + "map": 86.54606328368976 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 100000, + "index_speed": 485.5602670200369, + "query_speed": 103.0884393334727, + "map": 56.25299537353825 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 100000, + "index_speed": 119.52937722555107, + "query_speed": 6.385621466857457, + "map": 82.74686664920836 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 100000, + "index_speed": 100.01184910084558, + "query_speed": 6.6270933964840415, + "map": 86.54606328368973 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 100000, + "index_speed": 89.90389306648805, + "query_speed": 39.7839528511866, + "map": 84.33419639513305 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 100000, + "index_speed": 116.00982709720004, + "query_speed": 28.57264344960955, + "map": 86.54606328368973 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 100000, + "index_speed": 115.61076852516383, + "query_speed": 38.80526238789059, + "map": 81.63864883662649 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 100000, + "index_speed": 70.05381128388427, + "query_speed": 15.306895223372484, + "map": 86.54014997282701 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 100000, + "index_speed": 70.31004397719536, + "query_speed": 24.95733865947408, + "map": 85.7342431384476 + } + ] +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/benchmarks/retriever_speed.json b/docs/v1.2.0/_src/benchmarks/retriever_speed.json new file mode 100644 index 0000000000..7877d2a358 --- /dev/null +++ b/docs/v1.2.0/_src/benchmarks/retriever_speed.json @@ -0,0 +1,204 @@ +{ + "chart_type": "LineChart", + "title": "Retriever Speed", + "subtitle": "Query Speed at different number of docs", + "description": "Here you can see how the query speed of different Retriever / DocumentStore combinations scale as the number of documents increases. The set up is the same as the above querying benchmark except that a varying number of negative documents are used to fill the document store.", + "columns": [ + "n_docs", + "BM25 / Elasticsearch", + "DPR / Elasticsearch", + "DPR / FAISS (flat)", + "DPR / FAISS (HNSW)", + "DPR / Milvus (flat)", + "DPR / Milvus (HNSW)", + "Sentence Transformers / Elasticsearch" + ], + "axis": [ + { + "x": "Number of docs", + "y": "Queries/sec" + } + ], + "data": [ + { + "model": "DPR / Elasticsearch", + "n_docs": 1000, + "query_speed": 34.22768858415144 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 10000, + "query_speed": 22.197089725786853 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 10000, + "query_speed": 127.11481826852273 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 1000, + "query_speed": 47.51341215808855 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 10000, + "query_speed": 29.74515869340777 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 1000, + "query_speed": 42.49634272581313 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 10000, + "query_speed": 27.684040507849826 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 1000, + "query_speed": 43.36685860983961 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 10000, + "query_speed": 41.819147130090286 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 1000, + "query_speed": 41.12204778755844 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 10000, + "query_speed": 37.86882443918513 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 1000, + "query_speed": 41.14803671045185 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 10000, + "query_speed": 40.072871546542935 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 100000, + "query_speed": 5.192368815242574 + }, + { + "model": "DPR / Elasticsearch", + "n_docs": 500000, + "query_speed": 1.0337466563959614 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 100000, + "query_speed": 103.0884393334727 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 500000, + "query_speed": 78.95037031647355 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 100000, + "query_speed": 6.385621466857457 + }, + { + "model": "Sentence Transformers / Elasticsearch", + "n_docs": 500000, + "query_speed": 1.4175454254854258 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 100000, + "query_speed": 6.6270933964840415 + }, + { + "model": "DPR / FAISS (flat)", + "n_docs": 500000, + "query_speed": 1.5394964631878052 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 100000, + "query_speed": 39.7839528511866 + }, + { + "model": "DPR / FAISS (HNSW)", + "n_docs": 500000, + "query_speed": 39.84177061191119 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 100000, + "query_speed": 28.57264344960955 + }, + { + "model": "DPR / Milvus (flat)", + "n_docs": 500000, + "query_speed": 15.645867393099733 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 100000, + "query_speed": 38.80526238789059 + }, + { + "model": "DPR / Milvus (HNSW)", + "n_docs": 500000, + "query_speed": 37.15717318924075 + }, + { + "model": "BM25 / Elasticsearch", + "n_docs": 1000, + "query_speed": 282.95914917837337 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 1000, + "query_speed": 29.061163356184426 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 10000, + "query_speed": 24.834414667596725 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 100000, + "query_speed": 15.306895223372484 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 1000, + "query_speed": 29.10621389658101 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 10000, + "query_speed": 26.92417300437131 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 100000, + "query_speed": 24.95733865947408 + }, + { + "model": "DPR / OpenSearch (flat)", + "n_docs": 500000, + "query_speed": 11.33271222977541 + }, + { + "model": "DPR / OpenSearch (HNSW)", + "n_docs": 500000, + "query_speed": 24.13921492357397 + } + ] +} \ No newline at end of file diff --git a/docs/v1.2.0/_src/conf.py b/docs/v1.2.0/_src/conf.py new file mode 100644 index 0000000000..53da501e81 --- /dev/null +++ b/docs/v1.2.0/_src/conf.py @@ -0,0 +1,92 @@ +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + + +# -- Project information ----------------------------------------------------- + +project = "Haystack" +copyright = "2020, deepset" +author = "deepset" + +# The full version, including alpha/beta/rc tags + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "IPython.sphinxext.ipython_console_highlighting", + "sphinx_rtd_theme", + "sphinx_tabs.tabs", + "sphinx_copybutton", + "nbsphinx", + "sphinx.ext.autosectionlabel", + "sphinx_markdown_builder", + "recommonmark", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["../templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["build/*"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# This fixes weird spacing between bullet points in lists +html4_writer = True + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["../static"] + +# -- Added configuration ----------------------------------------------------- + +# Define master file which is by default contents.rst +master_doc = "index" + +# Logo for the title +html_logo = "img/logo.png" + +# Custom css +# html_context = {"css_files":["_static/custom.css"]} + +# Additional layouts +html_additional_pages = {"index": "pages/index.html"} + +# The file extensions of source files. +source_suffix = { + ".rst": "restructuredtext", + ".txt": "restructuredtext", + ".md": "markdown", +} + +# -- Add autodocs for __init__() methods ------------------------------------- + + +def skip(app, what, name, obj, would_skip, options): + if name == "__init__": + return False + return would_skip + + +def setup(app): + # Custom css + app.add_stylesheet("rtd_theme.css") + app.connect("autodoc-skip-member", skip) diff --git 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