diff --git a/libs/partners/databricks/.gitignore b/libs/partners/databricks/.gitignore deleted file mode 100644 index bee8a64b79a99..0000000000000 --- a/libs/partners/databricks/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/libs/partners/databricks/LICENSE b/libs/partners/databricks/LICENSE deleted file mode 100644 index fc0602feecdd6..0000000000000 --- a/libs/partners/databricks/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 LangChain, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/libs/partners/databricks/Makefile b/libs/partners/databricks/Makefile deleted file mode 100644 index 91babddc1619d..0000000000000 --- a/libs/partners/databricks/Makefile +++ /dev/null @@ -1,62 +0,0 @@ -.PHONY: all format lint test tests integration_tests docker_tests help extended_tests - -# Default target executed when no arguments are given to make. -all: help - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ -integration_test integration_tests: TEST_FILE = tests/integration_tests/ - - -# unit tests are run with the --disable-socket flag to prevent network calls -test tests: - poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) - -# integration tests are run without the --disable-socket flag to allow network calls -integration_test integration_tests: - poetry run pytest $(TEST_FILE) - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/databricks --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_databricks -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - poetry run ruff check . - poetry run ruff format $(PYTHON_FILES) --diff - poetry run ruff check --select I $(PYTHON_FILES) - mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) - -format format_diff: - poetry run ruff format $(PYTHON_FILES) - poetry run ruff check --select I --fix $(PYTHON_FILES) - -spell_check: - poetry run codespell --toml pyproject.toml - -spell_fix: - poetry run codespell --toml pyproject.toml -w - -check_imports: $(shell find langchain_databricks -name '*.py') - poetry run python ./scripts/check_imports.py $^ - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'check_imports - check imports' - @echo 'format - run code formatters' - @echo 'lint - run linters' - @echo 'test - run unit tests' - @echo 'tests - run unit tests' - @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/databricks/README.md b/libs/partners/databricks/README.md deleted file mode 100644 index acba5c7707e68..0000000000000 --- a/libs/partners/databricks/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# langchain-databricks - -This package contains the LangChain integration with Databricks - -## Installation - -```bash -pip install -U langchain-databricks -``` - -And you should configure credentials by setting the following environment variables: - -* TODO: fill this out - -## Chat Models - -`ChatDatabricks` class exposes chat models from Databricks. - -```python -from langchain_databricks import ChatDatabricks - -llm = ChatDatabricks() -llm.invoke("Sing a ballad of LangChain.") -``` \ No newline at end of file diff --git a/libs/partners/databricks/langchain_databricks/__init__.py b/libs/partners/databricks/langchain_databricks/__init__.py deleted file mode 100644 index 7d3130780e014..0000000000000 --- a/libs/partners/databricks/langchain_databricks/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from importlib import metadata - -from langchain_databricks.chat_models import ChatDatabricks -from langchain_databricks.embeddings import DatabricksEmbeddings -from langchain_databricks.vectorstores import DatabricksVectorSearch - -try: - __version__ = metadata.version(__package__) -except metadata.PackageNotFoundError: - # Case where package metadata is not available. - __version__ = "" -del metadata # optional, avoids polluting the results of dir(__package__) - -__all__ = [ - "ChatDatabricks", - "DatabricksEmbeddings", - "DatabricksVectorSearch", - "__version__", -] diff --git a/libs/partners/databricks/langchain_databricks/chat_models.py b/libs/partners/databricks/langchain_databricks/chat_models.py deleted file mode 100644 index 2528e97668983..0000000000000 --- a/libs/partners/databricks/langchain_databricks/chat_models.py +++ /dev/null @@ -1,556 +0,0 @@ -"""Databricks chat models.""" - -import json -import logging -from typing import ( - Any, - Callable, - Dict, - Iterator, - List, - Literal, - Mapping, - Optional, - Sequence, - Type, - Union, -) - -from langchain_core.callbacks import CallbackManagerForLLMRun -from langchain_core.language_models import BaseChatModel -from langchain_core.language_models.base import LanguageModelInput -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, - BaseMessageChunk, - ChatMessage, - ChatMessageChunk, - FunctionMessage, - HumanMessage, - HumanMessageChunk, - SystemMessage, - SystemMessageChunk, - ToolMessage, - ToolMessageChunk, -) -from langchain_core.messages.tool import tool_call_chunk -from langchain_core.output_parsers.openai_tools import ( - make_invalid_tool_call, - parse_tool_call, -) -from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - PrivateAttr, -) -from langchain_core.runnables import Runnable -from langchain_core.tools import BaseTool -from langchain_core.utils.function_calling import convert_to_openai_tool - -from langchain_databricks.utils import get_deployment_client - -logger = logging.getLogger(__name__) - - -class ChatDatabricks(BaseChatModel): - """Databricks chat model integration. - - Setup: - Install ``langchain-databricks``. - - .. code-block:: bash - - pip install -U langchain-databricks - - If you are outside Databricks, set the Databricks workspace hostname and personal access token to environment variables: - - .. code-block:: bash - - export DATABRICKS_HOSTNAME="https://your-databricks-workspace" - export DATABRICKS_TOKEN="your-personal-access-token" - - Key init args — completion params: - endpoint: str - Name of Databricks Model Serving endpoint to query. - target_uri: str - The target URI to use. Defaults to ``databricks``. - temperature: float - Sampling temperature. Higher values make the model more creative. - n: Optional[int] - The number of completion choices to generate. - stop: Optional[List[str]] - List of strings to stop generation at. - max_tokens: Optional[int] - Max number of tokens to generate. - extra_params: Optional[Dict[str, Any]] - Any extra parameters to pass to the endpoint. - - Instantiate: - .. code-block:: python - - from langchain_databricks import ChatDatabricks - llm = ChatDatabricks( - endpoint="databricks-meta-llama-3-1-405b-instruct", - temperature=0, - max_tokens=500, - ) - - Invoke: - .. code-block:: python - - messages = [ - ("system", "You are a helpful translator. Translate the user sentence to French."), - ("human", "I love programming."), - ] - llm.invoke(messages) - - .. code-block:: python - - AIMessage( - content="J'adore la programmation.", - response_metadata={ - 'prompt_tokens': 32, - 'completion_tokens': 9, - 'total_tokens': 41 - }, - id='run-64eebbdd-88a8-4a25-b508-21e9a5f146c5-0' - ) - - Stream: - .. code-block:: python - - for chunk in llm.stream(messages): - print(chunk) - - .. code-block:: python - - content='J' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content="'" id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content='ad' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content='ore' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content=' la' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content=' programm' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content='ation' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content='.' id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - content='' response_metadata={'finish_reason': 'stop'} id='run-609b8f47-e580-4691-9ee4-e2109f53155e' - - .. code-block:: python - - stream = llm.stream(messages) - full = next(stream) - for chunk in stream: - full += chunk - full - - .. code-block:: python - - AIMessageChunk( - content="J'adore la programmation.", - response_metadata={ - 'finish_reason': 'stop' - }, - id='run-4cef851f-6223-424f-ad26-4a54e5852aa5' - ) - - Async: - .. code-block:: python - - await llm.ainvoke(messages) - - # stream: - # async for chunk in llm.astream(messages) - - # batch: - # await llm.abatch([messages]) - - .. code-block:: python - - AIMessage( - content="J'adore la programmation.", - response_metadata={ - 'prompt_tokens': 32, - 'completion_tokens': 9, - 'total_tokens': 41 - }, - id='run-e4bb043e-772b-4e1d-9f98-77ccc00c0271-0' - ) - - Tool calling: - .. code-block:: python - - from langchain_core.pydantic_v1 import BaseModel, Field - - class GetWeather(BaseModel): - '''Get the current weather in a given location''' - - location: str = Field(..., description="The city and state, e.g. San Francisco, CA") - - class GetPopulation(BaseModel): - '''Get the current population in a given location''' - - location: str = Field(..., description="The city and state, e.g. San Francisco, CA") - - llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) - ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?") - ai_msg.tool_calls - - .. code-block:: python - - [ - { - 'name': 'GetWeather', - 'args': { - 'location': 'Los Angeles, CA' - }, - 'id': 'call_ea0a6004-8e64-4ae8-a192-a40e295bfa24', - 'type': 'tool_call' - } - ] - - To use tool calls, your model endpoint must support ``tools`` parameter. See [Function calling on Databricks](https://python.langchain.com/v0.2/docs/integrations/chat/databricks/#function-calling-on-databricks) for more information. - - """ # noqa: E501 - - endpoint: str - """Name of Databricks Model Serving endpoint to query.""" - target_uri: str = "databricks" - """The target URI to use. Defaults to ``databricks``.""" - temperature: float = 0.0 - """Sampling temperature. Higher values make the model more creative.""" - n: int = 1 - """The number of completion choices to generate.""" - stop: Optional[List[str]] = None - """List of strings to stop generation at.""" - max_tokens: Optional[int] = None - """The maximum number of tokens to generate.""" - extra_params: dict = Field(default_factory=dict) - """Any extra parameters to pass to the endpoint.""" - _client: Any = PrivateAttr() - - def __init__(self, **kwargs: Any): - super().__init__(**kwargs) - self._client = get_deployment_client(self.target_uri) - - @property - def _default_params(self) -> Dict[str, Any]: - params: Dict[str, Any] = { - "target_uri": self.target_uri, - "endpoint": self.endpoint, - "temperature": self.temperature, - "n": self.n, - "stop": self.stop, - "max_tokens": self.max_tokens, - "extra_params": self.extra_params, - } - return params - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - data = self._prepare_inputs(messages, stop, **kwargs) - resp = self._client.predict(endpoint=self.endpoint, inputs=data) - return self._convert_response_to_chat_result(resp) - - def _prepare_inputs( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - **kwargs: Any, - ) -> Dict[str, Any]: - data: Dict[str, Any] = { - "messages": [_convert_message_to_dict(msg) for msg in messages], - "temperature": self.temperature, - "n": self.n, - **self.extra_params, - **kwargs, - } - if stop := self.stop or stop: - data["stop"] = stop - if self.max_tokens is not None: - data["max_tokens"] = self.max_tokens - - return data - - def _convert_response_to_chat_result( - self, response: Mapping[str, Any] - ) -> ChatResult: - generations = [ - ChatGeneration( - message=_convert_dict_to_message(choice["message"]), - generation_info=choice.get("usage", {}), - ) - for choice in response["choices"] - ] - usage = response.get("usage", {}) - return ChatResult(generations=generations, llm_output=usage) - - def _stream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[ChatGenerationChunk]: - data = self._prepare_inputs(messages, stop, **kwargs) - first_chunk_role = None - for chunk in self._client.predict_stream(endpoint=self.endpoint, inputs=data): - if chunk["choices"]: - choice = chunk["choices"][0] - - chunk_delta = choice["delta"] - if first_chunk_role is None: - first_chunk_role = chunk_delta.get("role") - - chunk_message = _convert_dict_to_message_chunk( - chunk_delta, first_chunk_role - ) - - generation_info = {} - if finish_reason := choice.get("finish_reason"): - generation_info["finish_reason"] = finish_reason - if logprobs := choice.get("logprobs"): - generation_info["logprobs"] = logprobs - - chunk = ChatGenerationChunk( - message=chunk_message, generation_info=generation_info or None - ) - - if run_manager: - run_manager.on_llm_new_token( - chunk.text, chunk=chunk, logprobs=logprobs - ) - - yield chunk - else: - # Handle the case where choices are empty if needed - continue - - def bind_tools( - self, - tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], - *, - tool_choice: Optional[ - Union[dict, str, Literal["auto", "none", "required", "any"], bool] - ] = None, - **kwargs: Any, - ) -> Runnable[LanguageModelInput, BaseMessage]: - """Bind tool-like objects to this chat model. - - Assumes model is compatible with OpenAI tool-calling API. - - Args: - tools: A list of tool definitions to bind to this chat model. - Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic - models, callables, and BaseTools will be automatically converted to - their schema dictionary representation. - tool_choice: Which tool to require the model to call. - Options are: - name of the tool (str): calls corresponding tool; - "auto": automatically selects a tool (including no tool); - "none": model does not generate any tool calls and instead must - generate a standard assistant message; - "required": the model picks the most relevant tool in tools and - must generate a tool call; - - or a dict of the form: - {"type": "function", "function": {"name": <>}}. - **kwargs: Any additional parameters to pass to the - :class:`~langchain.runnable.Runnable` constructor. - """ - formatted_tools = [convert_to_openai_tool(tool) for tool in tools] - if tool_choice: - if isinstance(tool_choice, str): - # tool_choice is a tool/function name - if tool_choice not in ("auto", "none", "required"): - tool_choice = { - "type": "function", - "function": {"name": tool_choice}, - } - elif isinstance(tool_choice, dict): - tool_names = [ - formatted_tool["function"]["name"] - for formatted_tool in formatted_tools - ] - if not any( - tool_name == tool_choice["function"]["name"] - for tool_name in tool_names - ): - raise ValueError( - f"Tool choice {tool_choice} was specified, but the only " - f"provided tools were {tool_names}." - ) - else: - raise ValueError( - f"Unrecognized tool_choice type. Expected str, bool or dict. " - f"Received: {tool_choice}" - ) - kwargs["tool_choice"] = tool_choice - return super().bind(tools=formatted_tools, **kwargs) - - @property - def _llm_type(self) -> str: - """Return type of chat model.""" - return "chat-databricks" - - -### Conversion function to convert Pydantic models to dictionaries and vice versa. ### - - -def _convert_message_to_dict(message: BaseMessage) -> dict: - message_dict = {"content": message.content} - - # OpenAI supports "name" field in messages. - if (name := message.name or message.additional_kwargs.get("name")) is not None: - message_dict["name"] = name - - if id := message.id: - message_dict["id"] = id - - if isinstance(message, ChatMessage): - return {"role": message.role, **message_dict} - elif isinstance(message, HumanMessage): - return {"role": "user", **message_dict} - elif isinstance(message, AIMessage): - if tool_calls := _get_tool_calls_from_ai_message(message): - message_dict["tool_calls"] = tool_calls # type: ignore[assignment] - # If tool calls present, content null value should be None not empty string. - message_dict["content"] = message_dict["content"] or None # type: ignore[assignment] - return {"role": "assistant", **message_dict} - elif isinstance(message, SystemMessage): - return {"role": "system", **message_dict} - elif isinstance(message, ToolMessage): - return { - "role": "tool", - "tool_call_id": message.tool_call_id, - **message_dict, - } - elif ( - isinstance(message, FunctionMessage) - or "function_call" in message.additional_kwargs - ): - raise ValueError( - "Function messages are not supported by Databricks. Please" - " create a feature request at https://github.com/mlflow/mlflow/issues." - ) - else: - raise ValueError(f"Got unknown message type: {type(message)}") - - -def _get_tool_calls_from_ai_message(message: AIMessage) -> List[Dict]: - tool_calls = [ - { - "type": "function", - "id": tc["id"], - "function": { - "name": tc["name"], - "arguments": json.dumps(tc["args"]), - }, - } - for tc in message.tool_calls - ] - - invalid_tool_calls = [ - { - "type": "function", - "id": tc["id"], - "function": { - "name": tc["name"], - "arguments": tc["args"], - }, - } - for tc in message.invalid_tool_calls - ] - - if tool_calls or invalid_tool_calls: - return tool_calls + invalid_tool_calls - - # Get tool calls from additional kwargs if present. - return [ - { - k: v - for k, v in tool_call.items() # type: ignore[union-attr] - if k in {"id", "type", "function"} - } - for tool_call in message.additional_kwargs.get("tool_calls", []) - ] - - -def _convert_dict_to_message(_dict: Dict) -> BaseMessage: - role = _dict["role"] - content = _dict.get("content") - content = content if content is not None else "" - - if role == "user": - return HumanMessage(content=content) - elif role == "system": - return SystemMessage(content=content) - elif role == "assistant": - additional_kwargs: Dict = {} - tool_calls = [] - invalid_tool_calls = [] - if raw_tool_calls := _dict.get("tool_calls"): - additional_kwargs["tool_calls"] = raw_tool_calls - for raw_tool_call in raw_tool_calls: - try: - tool_calls.append(parse_tool_call(raw_tool_call, return_id=True)) - except Exception as e: - invalid_tool_calls.append( - make_invalid_tool_call(raw_tool_call, str(e)) - ) - return AIMessage( - content=content, - additional_kwargs=additional_kwargs, - id=_dict.get("id"), - tool_calls=tool_calls, - invalid_tool_calls=invalid_tool_calls, - ) - else: - return ChatMessage(content=content, role=role) - - -def _convert_dict_to_message_chunk( - _dict: Mapping[str, Any], default_role: str -) -> BaseMessageChunk: - role = _dict.get("role", default_role) - content = _dict.get("content") - content = content if content is not None else "" - - if role == "user": - return HumanMessageChunk(content=content) - elif role == "system": - return SystemMessageChunk(content=content) - elif role == "tool": - return ToolMessageChunk( - content=content, tool_call_id=_dict["tool_call_id"], id=_dict.get("id") - ) - elif role == "assistant": - additional_kwargs: Dict = {} - tool_call_chunks = [] - if raw_tool_calls := _dict.get("tool_calls"): - additional_kwargs["tool_calls"] = raw_tool_calls - try: - tool_call_chunks = [ - tool_call_chunk( - name=tc["function"].get("name"), - args=tc["function"].get("arguments"), - id=tc.get("id"), - index=tc["index"], - ) - for tc in raw_tool_calls - ] - except KeyError: - pass - return AIMessageChunk( - content=content, - additional_kwargs=additional_kwargs, - id=_dict.get("id"), - tool_call_chunks=tool_call_chunks, - ) - else: - return ChatMessageChunk(content=content, role=role) diff --git a/libs/partners/databricks/langchain_databricks/embeddings.py b/libs/partners/databricks/langchain_databricks/embeddings.py deleted file mode 100644 index 52113763e5d3f..0000000000000 --- a/libs/partners/databricks/langchain_databricks/embeddings.py +++ /dev/null @@ -1,91 +0,0 @@ -from typing import Any, Dict, Iterator, List - -from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, PrivateAttr - -from langchain_databricks.utils import get_deployment_client - - -class DatabricksEmbeddings(Embeddings, BaseModel): - """Databricks embedding model integration. - - Setup: - Install ``langchain-databricks``. - - .. code-block:: bash - - pip install -U langchain-databricks - - If you are outside Databricks, set the Databricks workspace - hostname and personal access token to environment variables: - - .. code-block:: bash - - export DATABRICKS_HOSTNAME="https://your-databricks-workspace" - export DATABRICKS_TOKEN="your-personal-access-token" - - Key init args — completion params: - endpoint: str - Name of Databricks Model Serving endpoint to query. - target_uri: str - The target URI to use. Defaults to ``databricks``. - query_params: Dict[str, str] - The parameters to use for queries. - documents_params: Dict[str, str] - The parameters to use for documents. - - Instantiate: - .. code-block:: python - from langchain_databricks import DatabricksEmbeddings - embed = DatabricksEmbeddings( - endpoint="databricks-bge-large-en", - ) - - Embed single text: - .. code-block:: python - input_text = "The meaning of life is 42" - embed.embed_query(input_text) - - .. code-block:: python - [ - 0.01605224609375, - -0.0298309326171875, - ... - ] - - """ - - endpoint: str - """The endpoint to use.""" - target_uri: str = "databricks" - """The parameters to use for queries.""" - query_params: Dict[str, Any] = {} - """The parameters to use for documents.""" - documents_params: Dict[str, Any] = {} - """The target URI to use.""" - _client: Any = PrivateAttr() - - def __init__(self, **kwargs: Any): - super().__init__(**kwargs) - self._client = get_deployment_client(self.target_uri) - - def embed_documents(self, texts: List[str]) -> List[List[float]]: - return self._embed(texts, params=self.documents_params) - - def embed_query(self, text: str) -> List[float]: - return self._embed([text], params=self.query_params)[0] - - def _embed(self, texts: List[str], params: Dict[str, str]) -> List[List[float]]: - embeddings: List[List[float]] = [] - for txt in _chunk(texts, 20): - resp = self._client.predict( - endpoint=self.endpoint, - inputs={"input": txt, **params}, # type: ignore[arg-type] - ) - embeddings.extend(r["embedding"] for r in resp["data"]) - return embeddings - - -def _chunk(texts: List[str], size: int) -> Iterator[List[str]]: - for i in range(0, len(texts), size): - yield texts[i : i + size] diff --git a/libs/partners/databricks/langchain_databricks/py.typed b/libs/partners/databricks/langchain_databricks/py.typed deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/databricks/langchain_databricks/utils.py b/libs/partners/databricks/langchain_databricks/utils.py deleted file mode 100644 index 33e160a05bedc..0000000000000 --- a/libs/partners/databricks/langchain_databricks/utils.py +++ /dev/null @@ -1,101 +0,0 @@ -from typing import Any, List, Union -from urllib.parse import urlparse - -import numpy as np - - -def get_deployment_client(target_uri: str) -> Any: - if (target_uri != "databricks") and (urlparse(target_uri).scheme != "databricks"): - raise ValueError( - "Invalid target URI. The target URI must be a valid databricks URI." - ) - - try: - from mlflow.deployments import get_deploy_client # type: ignore[import-untyped] - - return get_deploy_client(target_uri) - except ImportError as e: - raise ImportError( - "Failed to create the client. " - "Please run `pip install mlflow` to install " - "required dependencies." - ) from e - - -# Utility function for Maximal Marginal Relevance (MMR) reranking. -# Copied from langchain_community/vectorstores/utils.py to avoid cross-dependency -Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] - - -def maximal_marginal_relevance( - query_embedding: np.ndarray, - embedding_list: list, - lambda_mult: float = 0.5, - k: int = 4, -) -> List[int]: - """Calculate maximal marginal relevance. - - Args: - query_embedding: Query embedding. - embedding_list: List of embeddings to select from. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5. - k: Number of Documents to return. Defaults to 4. - - Returns: - List of indices of embeddings selected by maximal marginal relevance. - """ - if min(k, len(embedding_list)) <= 0: - return [] - if query_embedding.ndim == 1: - query_embedding = np.expand_dims(query_embedding, axis=0) - similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] - most_similar = int(np.argmax(similarity_to_query)) - idxs = [most_similar] - selected = np.array([embedding_list[most_similar]]) - while len(idxs) < min(k, len(embedding_list)): - best_score = -np.inf - idx_to_add = -1 - similarity_to_selected = cosine_similarity(embedding_list, selected) - for i, query_score in enumerate(similarity_to_query): - if i in idxs: - continue - redundant_score = max(similarity_to_selected[i]) - equation_score = ( - lambda_mult * query_score - (1 - lambda_mult) * redundant_score - ) - if equation_score > best_score: - best_score = equation_score - idx_to_add = i - idxs.append(idx_to_add) - selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) - return idxs - - -def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: - """Row-wise cosine similarity between two equal-width matrices. - - Raises: - ValueError: If the number of columns in X and Y are not the same. - """ - if len(X) == 0 or len(Y) == 0: - return np.array([]) - - X = np.array(X) - Y = np.array(Y) - if X.shape[1] != Y.shape[1]: - raise ValueError( - "Number of columns in X and Y must be the same. X has shape" - f"{X.shape} " - f"and Y has shape {Y.shape}." - ) - - X_norm = np.linalg.norm(X, axis=1) - Y_norm = np.linalg.norm(Y, axis=1) - # Ignore divide by zero errors run time warnings as those are handled below. - with np.errstate(divide="ignore", invalid="ignore"): - similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) - similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 - return similarity diff --git a/libs/partners/databricks/langchain_databricks/vectorstores.py b/libs/partners/databricks/langchain_databricks/vectorstores.py deleted file mode 100644 index 7359dcf9ab50e..0000000000000 --- a/libs/partners/databricks/langchain_databricks/vectorstores.py +++ /dev/null @@ -1,837 +0,0 @@ -from __future__ import annotations - -import asyncio -import json -import logging -import uuid -from enum import Enum -from functools import partial -from typing import ( - Any, - Callable, - Dict, - Iterable, - List, - Optional, - Tuple, - Type, -) - -import numpy as np -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.vectorstores import VST, VectorStore - -from langchain_databricks.utils import maximal_marginal_relevance - -logger = logging.getLogger(__name__) - - -class IndexType(str, Enum): - DIRECT_ACCESS = "DIRECT_ACCESS" - DELTA_SYNC = "DELTA_SYNC" - - -_DIRECT_ACCESS_ONLY_MSG = "`%s` is only supported for direct-access index." -_NON_MANAGED_EMB_ONLY_MSG = ( - "`%s` is not supported for index with Databricks-managed embeddings." -) - - -class DatabricksVectorSearch(VectorStore): - """Databricks vector store integration. - - Setup: - Install ``langchain-databricks`` and ``databricks-vectorsearch`` python packages. - - .. code-block:: bash - - pip install -U langchain-databricks databricks-vectorsearch - - If you don't have a Databricks Vector Search endpoint already, you can create one by following the instructions here: https://docs.databricks.com/en/generative-ai/create-query-vector-search.html - - If you are outside Databricks, set the Databricks workspace - hostname and personal access token to environment variables: - - .. code-block:: bash - - export DATABRICKS_HOSTNAME="https://your-databricks-workspace" - export DATABRICKS_TOKEN="your-personal-access-token" - - Key init args — indexing params: - - endpoint: The name of the Databricks Vector Search endpoint. - index_name: The name of the index to use. Format: "catalog.schema.index". - embedding: The embedding model. - Required for direct-access index or delta-sync index - with self-managed embeddings. - text_column: The name of the text column to use for the embeddings. - Required for direct-access index or delta-sync index - with self-managed embeddings. - Make sure the text column specified is in the index. - columns: The list of column names to get when doing the search. - Defaults to ``[primary_key, text_column]``. - - Instantiate: - - `DatabricksVectorSearch` supports two types of indexes: - - * **Delta Sync Index** automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes. - - * **Direct Vector Access Index** supports direct read and write of vectors and metadata. The user is responsible for updating this table using the REST API or the Python SDK. - - Also for delta-sync index, you can choose to use Databricks-managed embeddings or self-managed embeddings (via LangChain embeddings classes). - - If you are using a delta-sync index with Databricks-managed embeddings: - - .. code-block:: python - - from langchain_databricks.vectorstores import DatabricksVectorSearch - - vector_store = DatabricksVectorSearch( - endpoint="", - index_name="" - ) - - If you are using a direct-access index or a delta-sync index with self-managed embeddings, - you also need to provide the embedding model and text column in your source table to - use for the embeddings: - - .. code-block:: python - - from langchain_openai import OpenAIEmbeddings - - vector_store = DatabricksVectorSearch( - endpoint="", - index_name="", - embedding=OpenAIEmbeddings(), - text_column="document_content" - ) - - Add Documents: - .. code-block:: python - from langchain_core.documents import Document - - document_1 = Document(page_content="foo", metadata={"baz": "bar"}) - document_2 = Document(page_content="thud", metadata={"bar": "baz"}) - document_3 = Document(page_content="i will be deleted :(") - documents = [document_1, document_2, document_3] - ids = ["1", "2", "3"] - vector_store.add_documents(documents=documents, ids=ids) - - Delete Documents: - .. code-block:: python - vector_store.delete(ids=["3"]) - - .. note:: - - The `delete` method is only supported for direct-access index. - - Search: - .. code-block:: python - results = vector_store.similarity_search(query="thud",k=1) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - .. code-block:: python - * thud [{'id': '2'}] - - .. note: - - By default, similarity search only returns the primary key and text column. - If you want to retrieve the custom metadata associated with the document, - pass the additional columns in the `columns` parameter when initializing the vector store. - - .. code-block:: python - - vector_store = DatabricksVectorSearch( - endpoint="", - index_name="", - columns=["baz", "bar"], - ) - - vector_store.similarity_search(query="thud",k=1) - # Output: * thud [{'bar': 'baz', 'baz': None, 'id': '2'}] - - Search with filter: - .. code-block:: python - results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - .. code-block:: python - * thud [{'id': '2'}] - - Search with score: - .. code-block:: python - results = vector_store.similarity_search_with_score(query="qux",k=1) - for doc, score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - .. code-block:: python - * [SIM=0.748804] foo [{'id': '1'}] - - Async: - .. code-block:: python - # add documents - await vector_store.aadd_documents(documents=documents, ids=ids) - # delete documents - await vector_store.adelete(ids=["3"]) - # search - results = vector_store.asimilarity_search(query="thud",k=1) - # search with score - results = await vector_store.asimilarity_search_with_score(query="qux",k=1) - for doc,score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - .. code-block:: python - * [SIM=0.748807] foo [{'id': '1'}] - - Use as Retriever: - .. code-block:: python - retriever = vector_store.as_retriever( - search_type="mmr", - search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, - ) - retriever.invoke("thud") - .. code-block:: python - [Document(metadata={'id': '2'}, page_content='thud')] - """ # noqa: E501 - - def __init__( - self, - endpoint: str, - index_name: str, - embedding: Optional[Embeddings] = None, - text_column: Optional[str] = None, - columns: Optional[List[str]] = None, - ): - try: - from databricks.vector_search.client import ( # type: ignore[import] - VectorSearchClient, - ) - except ImportError as e: - raise ImportError( - "Could not import databricks-vectorsearch python package. " - "Please install it with `pip install databricks-vectorsearch`." - ) from e - - self.index = VectorSearchClient().get_index(endpoint, index_name) - self._index_details = IndexDetails(self.index) - - _validate_embedding(embedding, self._index_details) - self._embeddings = embedding - self._text_column = _validate_and_get_text_column( - text_column, self._index_details - ) - self._columns = _validate_and_get_return_columns( - columns or [], self._text_column, self._index_details - ) - self._primary_key = self._index_details.primary_key - - @property - def embeddings(self) -> Optional[Embeddings]: - """Access the query embedding object if available.""" - return self._embeddings - - @classmethod - def from_texts( - cls: Type[VST], - texts: List[str], - embedding: Embeddings, - metadatas: Optional[List[Dict]] = None, - **kwargs: Any, - ) -> VST: - raise NotImplementedError( - "`from_texts` is not supported. " - "Use `add_texts` to add to existing direct-access index." - ) - - def add_texts( - self, - texts: Iterable[str], - metadatas: Optional[List[Dict]] = None, - ids: Optional[List[Any]] = None, - **kwargs: Any, - ) -> List[str]: - """Add texts to the index. - - .. note:: - - This method is only supported for a direct-access index. - - Args: - texts: List of texts to add. - metadatas: List of metadata for each text. Defaults to None. - ids: List of ids for each text. Defaults to None. - If not provided, a random uuid will be generated for each text. - - Returns: - List of ids from adding the texts into the index. - """ - if self._index_details.is_delta_sync_index(): - raise NotImplementedError(_DIRECT_ACCESS_ONLY_MSG % "add_texts") - - # Wrap to list if input texts is a single string - if isinstance(texts, str): - texts = [texts] - texts = list(texts) - vectors = self._embeddings.embed_documents(texts) # type: ignore[union-attr] - ids = ids or [str(uuid.uuid4()) for _ in texts] - metadatas = metadatas or [{} for _ in texts] - - updates = [ - { - self._primary_key: id_, - self._text_column: text, - self._index_details.embedding_vector_column["name"]: vector, - **metadata, - } - for text, vector, id_, metadata in zip(texts, vectors, ids, metadatas) - ] - - upsert_resp = self.index.upsert(updates) - if upsert_resp.get("status") in ("PARTIAL_SUCCESS", "FAILURE"): - failed_ids = upsert_resp.get("result", dict()).get( - "failed_primary_keys", [] - ) - if upsert_resp.get("status") == "FAILURE": - logger.error("Failed to add texts to the index.") - else: - logger.warning("Some texts failed to be added to the index.") - return [id_ for id_ in ids if id_ not in failed_ids] - - return ids - - async def aadd_texts( - self, - texts: Iterable[str], - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> List[str]: - return await asyncio.get_running_loop().run_in_executor( - None, partial(self.add_texts, **kwargs), texts, metadatas - ) - - def delete(self, ids: Optional[List[Any]] = None, **kwargs: Any) -> Optional[bool]: - """Delete documents from the index. - - .. note:: - - This method is only supported for a direct-access index. - - Args: - ids: List of ids of documents to delete. - - Returns: - True if successful. - """ - if self._index_details.is_delta_sync_index(): - raise NotImplementedError(_DIRECT_ACCESS_ONLY_MSG % "delete") - - if ids is None: - raise ValueError("ids must be provided.") - self.index.delete(ids) - return True - - def similarity_search( - self, - query: str, - k: int = 4, - filter: Optional[Dict[str, Any]] = None, - *, - query_type: Optional[str] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs most similar to query. - - Args: - query: Text to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - - Returns: - List of Documents most similar to the embedding. - """ - docs_with_score = self.similarity_search_with_score( - query=query, - k=k, - filter=filter, - query_type=query_type, - **kwargs, - ) - return [doc for doc, _ in docs_with_score] - - async def asimilarity_search( - self, query: str, k: int = 4, **kwargs: Any - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search, query, k=k, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) - - def similarity_search_with_score( - self, - query: str, - k: int = 4, - filter: Optional[Dict[str, Any]] = None, - *, - query_type: Optional[str] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Return docs most similar to query, along with scores. - - Args: - query: Text to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - - Returns: - List of Documents most similar to the embedding and score for each. - """ - if self._index_details.is_databricks_managed_embeddings(): - query_text = query - query_vector = None - else: - # The value for `query_text` needs to be specified only for hybrid search. - if query_type is not None and query_type.upper() == "HYBRID": - query_text = query - else: - query_text = None - query_vector = self._embeddings.embed_query(query) # type: ignore[union-attr] - - search_resp = self.index.similarity_search( - columns=self._columns, - query_text=query_text, - query_vector=query_vector, - filters=filter, - num_results=k, - query_type=query_type, - ) - return self._parse_search_response(search_resp) - - def _select_relevance_score_fn(self) -> Callable[[float], float]: - """ - Databricks Vector search uses a normalized score 1/(1+d) where d - is the L2 distance. Hence, we simply return the identity function. - """ - return lambda score: score - - async def asimilarity_search_with_score( - self, *args: Any, **kwargs: Any - ) -> List[Tuple[Document, float]]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search_with_score, *args, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) - - def similarity_search_by_vector( - self, - embedding: List[float], - k: int = 4, - filter: Optional[Any] = None, - *, - query_type: Optional[str] = None, - query: Optional[str] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs most similar to embedding vector. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - - Returns: - List of Documents most similar to the embedding. - """ - if self._index_details.is_databricks_managed_embeddings(): - raise NotImplementedError( - _NON_MANAGED_EMB_ONLY_MSG % "similarity_search_by_vector" - ) - - docs_with_score = self.similarity_search_by_vector_with_score( - embedding=embedding, - k=k, - filter=filter, - query_type=query_type, - query=query, - **kwargs, - ) - return [doc for doc, _ in docs_with_score] - - async def asimilarity_search_by_vector( - self, embedding: List[float], k: int = 4, **kwargs: Any - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) - - def similarity_search_by_vector_with_score( - self, - embedding: List[float], - k: int = 4, - filter: Optional[Any] = None, - *, - query_type: Optional[str] = None, - query: Optional[str] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Return docs most similar to embedding vector, along with scores. - - .. note:: - - This method is not supported for index with Databricks-managed embeddings. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - - Returns: - List of Documents most similar to the embedding and score for each. - """ - if self._index_details.is_databricks_managed_embeddings(): - raise NotImplementedError( - _NON_MANAGED_EMB_ONLY_MSG % "similarity_search_by_vector_with_score" - ) - - if query_type is not None and query_type.upper() == "HYBRID": - if query is None: - raise ValueError( - "A value for `query` must be specified for hybrid search." - ) - query_text = query - else: - if query is not None: - raise ValueError( - ( - "Cannot specify both `embedding` and " - '`query` unless `query_type="HYBRID"' - ) - ) - query_text = None - - search_resp = self.index.similarity_search( - columns=self._columns, - query_vector=embedding, - query_text=query_text, - filters=filter, - num_results=k, - query_type=query_type, - ) - return self._parse_search_response(search_resp) - - def max_marginal_relevance_search( - self, - query: str, - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - filter: Optional[Dict[str, Any]] = None, - *, - query_type: Optional[str] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance. - - Maximal marginal relevance optimizes for similarity to query AND diversity - among selected documents. - - .. note:: - - This method is not supported for index with Databricks-managed embeddings. - - Args: - query: Text to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - fetch_k: Number of Documents to fetch to pass to MMR algorithm. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - Returns: - List of Documents selected by maximal marginal relevance. - """ - if self._index_details.is_databricks_managed_embeddings(): - raise NotImplementedError( - _NON_MANAGED_EMB_ONLY_MSG % "max_marginal_relevance_search" - ) - - query_vector = self._embeddings.embed_query(query) # type: ignore[union-attr] - docs = self.max_marginal_relevance_search_by_vector( - query_vector, - k, - fetch_k, - lambda_mult=lambda_mult, - filter=filter, - query_type=query_type, - ) - return docs - - async def amax_marginal_relevance_search( - self, - query: str, - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - **kwargs: Any, - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial( - self.max_marginal_relevance_search, - query, - k=k, - fetch_k=fetch_k, - lambda_mult=lambda_mult, - **kwargs, - ) - return await asyncio.get_event_loop().run_in_executor(None, func) - - def max_marginal_relevance_search_by_vector( - self, - embedding: List[float], - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - filter: Optional[Any] = None, - *, - query_type: Optional[str] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance. - - Maximal marginal relevance optimizes for similarity to query AND diversity - among selected documents. - - .. note:: - - This method is not supported for index with Databricks-managed embeddings. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - fetch_k: Number of Documents to fetch to pass to MMR algorithm. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5. - filter: Filters to apply to the query. Defaults to None. - query_type: The type of this query. Supported values are "ANN" and "HYBRID". - Returns: - List of Documents selected by maximal marginal relevance. - """ - if self._index_details.is_databricks_managed_embeddings(): - raise NotImplementedError( - _NON_MANAGED_EMB_ONLY_MSG % "max_marginal_relevance_search_by_vector" - ) - - embedding_column = self._index_details.embedding_vector_column["name"] - search_resp = self.index.similarity_search( - columns=list(set(self._columns + [embedding_column])), - query_text=None, - query_vector=embedding, - filters=filter, - num_results=fetch_k, - query_type=query_type, - ) - - embeddings_result_index = ( - search_resp.get("manifest").get("columns").index({"name": embedding_column}) - ) - embeddings = [ - doc[embeddings_result_index] - for doc in search_resp.get("result").get("data_array") - ] - - mmr_selected = maximal_marginal_relevance( - np.array(embedding, dtype=np.float32), - embeddings, - k=k, - lambda_mult=lambda_mult, - ) - - ignore_cols: List = ( - [embedding_column] if embedding_column not in self._columns else [] - ) - candidates = self._parse_search_response(search_resp, ignore_cols=ignore_cols) - selected_results = [r[0] for i, r in enumerate(candidates) if i in mmr_selected] - return selected_results - - async def amax_marginal_relevance_search_by_vector( - self, - embedding: List[float], - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - **kwargs: Any, - ) -> List[Document]: - raise NotImplementedError - - def _parse_search_response( - self, search_resp: Dict, ignore_cols: Optional[List[str]] = None - ) -> List[Tuple[Document, float]]: - """Parse the search response into a list of Documents with score.""" - if ignore_cols is None: - ignore_cols = [] - - columns = [ - col["name"] - for col in search_resp.get("manifest", dict()).get("columns", []) - ] - docs_with_score = [] - for result in search_resp.get("result", dict()).get("data_array", []): - doc_id = result[columns.index(self._primary_key)] - text_content = result[columns.index(self._text_column)] - ignore_cols = [self._primary_key, self._text_column] + ignore_cols - metadata = { - col: value - for col, value in zip(columns[:-1], result[:-1]) - if col not in ignore_cols - } - metadata[self._primary_key] = doc_id - score = result[-1] - doc = Document(page_content=text_content, metadata=metadata) - docs_with_score.append((doc, score)) - return docs_with_score - - -def _validate_and_get_text_column( - text_column: Optional[str], index_details: IndexDetails -) -> str: - if index_details.is_databricks_managed_embeddings(): - index_source_column: str = index_details.embedding_source_column["name"] - # check if input text column matches the source column of the index - if text_column is not None: - raise ValueError( - f"The index '{index_details.name}' has the source column configured as " - f"'{index_source_column}'. Do not pass the `text_column` parameter." - ) - return index_source_column - else: - if text_column is None: - raise ValueError("The `text_column` parameter is required for this index.") - return text_column - - -def _validate_and_get_return_columns( - columns: List[str], text_column: str, index_details: IndexDetails -) -> List[str]: - """ - Get a list of columns to retrieve from the index. - - If the index is direct-access index, validate the given columns against the schema. - """ - # add primary key column and source column if not in columns - if index_details.primary_key not in columns: - columns.append(index_details.primary_key) - if text_column and text_column not in columns: - columns.append(text_column) - - # Validate specified columns are in the index - if index_details.is_direct_access_index() and ( - index_schema := index_details.schema - ): - if missing_columns := [c for c in columns if c not in index_schema]: - raise ValueError( - "Some columns specified in `columns` are not " - f"in the index schema: {missing_columns}" - ) - return columns - - -def _validate_embedding( - embedding: Optional[Embeddings], index_details: IndexDetails -) -> None: - if index_details.is_databricks_managed_embeddings(): - if embedding is not None: - raise ValueError( - f"The index '{index_details.name}' uses Databricks-managed embeddings. " - "Do not pass the `embedding` parameter when initializing vector store." - ) - else: - if not embedding: - raise ValueError( - "The `embedding` parameter is required for a direct-access index " - "or delta-sync index with self-managed embedding." - ) - _validate_embedding_dimension(embedding, index_details) - - -def _validate_embedding_dimension( - embeddings: Embeddings, index_details: IndexDetails -) -> None: - """validate if the embedding dimension matches with the index's configuration.""" - if index_embedding_dimension := index_details.embedding_vector_column.get( - "embedding_dimension" - ): - # Infer the embedding dimension from the embedding function.""" - actual_dimension = len(embeddings.embed_query("test")) - if actual_dimension != index_embedding_dimension: - raise ValueError( - f"The specified embedding model's dimension '{actual_dimension}' does " - f"not match with the index configuration '{index_embedding_dimension}'." - ) - - -class IndexDetails: - """An utility class to store the configuration details of an index.""" - - def __init__(self, index: Any): - self._index_details = index.describe() - - @property - def name(self) -> str: - return self._index_details["name"] - - @property - def schema(self) -> Optional[Dict]: - if self.is_direct_access_index(): - schema_json = self.index_spec.get("schema_json") - if schema_json is not None: - return json.loads(schema_json) - return None - - @property - def primary_key(self) -> str: - return self._index_details["primary_key"] - - @property - def index_spec(self) -> Dict: - return ( - self._index_details.get("delta_sync_index_spec", {}) - if self.is_delta_sync_index() - else self._index_details.get("direct_access_index_spec", {}) - ) - - @property - def embedding_vector_column(self) -> Dict: - if vector_columns := self.index_spec.get("embedding_vector_columns"): - return vector_columns[0] - return {} - - @property - def embedding_source_column(self) -> Dict: - if source_columns := self.index_spec.get("embedding_source_columns"): - return source_columns[0] - return {} - 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-[metadata] -lock-version = "2.0" -python-versions = ">=3.8.1,<3.12" -content-hash = "857f47603d9dd6fe8882c7525613a54a54ee459a9ee012f3d19e510c5477f3db" diff --git a/libs/partners/databricks/pyproject.toml b/libs/partners/databricks/pyproject.toml deleted file mode 100644 index 22b6554cfd094..0000000000000 --- a/libs/partners/databricks/pyproject.toml +++ /dev/null @@ -1,100 +0,0 @@ -[tool.poetry] -name = "langchain-databricks" -version = "0.1.0" -description = "An integration package connecting Databricks and LangChain" -authors = [] -readme = "README.md" -repository = "https://github.com/langchain-ai/langchain" -license = "MIT" - -[tool.poetry.urls] -"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/databricks" -"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22databricks%3D%3D0%22&expanded=true" - -[tool.poetry.dependencies] -# TODO: Replace <3.12 to <4.0 once https://github.com/mlflow/mlflow/commit/04370119fcc1b2ccdbcd9a50198ab00566d58cd2 is released -python = ">=3.8.1,<3.12" -langchain-core = "^0.2.0" -mlflow = ">=2.9" - 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-[tool.poetry.group.dev.dependencies] -langchain-core = { path = "../../core", develop = true } - -[tool.ruff.lint] -select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort - "T201", # print -] - -[tool.mypy] -disallow_untyped_defs = "True" - -[tool.coverage.run] -omit = ["tests/*"] - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" - -[tool.pytest.ini_options] -# --strict-markers will raise errors on unknown marks. -# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks -# -# https://docs.pytest.org/en/7.1.x/reference/reference.html -# --strict-config any warnings encountered while parsing the `pytest` -# section of the configuration file raise errors. -# -# https://github.com/tophat/syrupy -addopts = "--strict-markers --strict-config --durations=5" -# Registering custom markers. -# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers -markers = [ - "compile: mark placeholder test used to compile integration tests without running them", -] -asyncio_mode = "auto" diff --git a/libs/partners/databricks/scripts/check_imports.py b/libs/partners/databricks/scripts/check_imports.py deleted file mode 100644 index 58a460c149353..0000000000000 --- a/libs/partners/databricks/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -import sys -import traceback -from importlib.machinery import SourceFileLoader - -if __name__ == "__main__": - files = sys.argv[1:] - has_failure = False - for file in files: - try: - SourceFileLoader("x", file).load_module() - except Exception: - has_failure = True - print(file) # noqa: T201 - traceback.print_exc() - print() # noqa: T201 - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/databricks/scripts/check_pydantic.sh b/libs/partners/databricks/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/databricks/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/databricks/scripts/lint_imports.sh b/libs/partners/databricks/scripts/lint_imports.sh deleted file mode 100755 index 19ccec1480c01..0000000000000 --- a/libs/partners/databricks/scripts/lint_imports.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -set -eu - -# Initialize a variable to keep track of errors -errors=0 - -# make sure not importing from langchain, langchain_experimental, or langchain_community -git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) - -# Decide on an exit status based on the errors -if [ "$errors" -gt 0 ]; then - exit 1 -else - exit 0 -fi diff --git a/libs/partners/databricks/tests/__init__.py b/libs/partners/databricks/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/databricks/tests/integration_tests/__init__.py b/libs/partners/databricks/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/databricks/tests/integration_tests/test_compile.py b/libs/partners/databricks/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0fbd..0000000000000 --- a/libs/partners/databricks/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest - - -@pytest.mark.compile -def test_placeholder() -> None: - """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/databricks/tests/unit_tests/__init__.py b/libs/partners/databricks/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/databricks/tests/unit_tests/test_chat_models.py b/libs/partners/databricks/tests/unit_tests/test_chat_models.py deleted file mode 100644 index 118d3022fcd01..0000000000000 --- a/libs/partners/databricks/tests/unit_tests/test_chat_models.py +++ /dev/null @@ -1,321 +0,0 @@ -"""Test chat model integration.""" - -import json -from typing import Generator -from unittest import mock - -import mlflow # type: ignore # noqa: F401 -import pytest -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, - ChatMessage, - ChatMessageChunk, - FunctionMessage, - HumanMessage, - HumanMessageChunk, - SystemMessage, - SystemMessageChunk, - ToolMessageChunk, -) -from langchain_core.messages.tool import ToolCallChunk -from langchain_core.pydantic_v1 import BaseModel, Field - -from langchain_databricks.chat_models import ( - ChatDatabricks, - _convert_dict_to_message, - _convert_dict_to_message_chunk, - _convert_message_to_dict, -) - -_MOCK_CHAT_RESPONSE = { - "id": "chatcmpl_id", - "object": "chat.completion", - "created": 1721875529, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "To calculate the result of 36939 multiplied by 8922.4, " - "I get:\n\n36939 x 8922.4 = 329,511,111.6", - }, - "finish_reason": "stop", - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 36, "total_tokens": 66}, -} - -_MOCK_STREAM_RESPONSE = [ - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "36939"}, - "finish_reason": None, - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 20, "total_tokens": 50}, - }, - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "x"}, - "finish_reason": None, - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 22, "total_tokens": 52}, - }, - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "8922.4"}, - "finish_reason": None, - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 24, "total_tokens": 54}, - }, - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": " = "}, - "finish_reason": None, - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 28, "total_tokens": 58}, - }, - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "329,511,111.6"}, - "finish_reason": None, - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 30, "total_tokens": 60}, - }, - { - "id": "chatcmpl_bb1fce87-f14e-4ae1-ac22-89facc74898a", - "object": "chat.completion.chunk", - "created": 1721877054, - "model": "meta-llama-3.1-70b-instruct-072424", - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": ""}, - "finish_reason": "stop", - "logprobs": None, - } - ], - "usage": {"prompt_tokens": 30, "completion_tokens": 36, "total_tokens": 66}, - }, -] - - -@pytest.fixture(autouse=True) -def mock_client() -> Generator: - client = mock.MagicMock() - client.predict.return_value = _MOCK_CHAT_RESPONSE - client.predict_stream.return_value = _MOCK_STREAM_RESPONSE - with mock.patch("mlflow.deployments.get_deploy_client", return_value=client): - yield - - -@pytest.fixture -def llm() -> ChatDatabricks: - return ChatDatabricks( - endpoint="databricks-meta-llama-3-70b-instruct", target_uri="databricks" - ) - - -def test_chat_mlflow_predict(llm: ChatDatabricks) -> None: - res = llm.invoke( - [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "36939 * 8922.4"}, - ] - ) - assert res.content == _MOCK_CHAT_RESPONSE["choices"][0]["message"]["content"] # type: ignore[index] - - -def test_chat_mlflow_stream(llm: ChatDatabricks) -> None: - res = llm.stream( - [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "36939 * 8922.4"}, - ] - ) - for chunk, expected in zip(res, _MOCK_STREAM_RESPONSE): - assert chunk.content == expected["choices"][0]["delta"]["content"] # type: ignore[index] - - -def test_chat_mlflow_bind_tools(llm: ChatDatabricks) -> None: - class GetWeather(BaseModel): - """Get the current weather in a given location""" - - location: str = Field( - ..., description="The city and state, e.g. San Francisco, CA" - ) - - class GetPopulation(BaseModel): - """Get the current population in a given location""" - - location: str = Field( - ..., description="The city and state, e.g. San Francisco, CA" - ) - - llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) - response = llm_with_tools.invoke( - "Which city is hotter today and which is bigger: LA or NY?" - ) - assert isinstance(response, AIMessage) - - -### Test data conversion functions ### - - -@pytest.mark.parametrize( - ("role", "expected_output"), - [ - ("user", HumanMessage("foo")), - ("system", SystemMessage("foo")), - ("assistant", AIMessage("foo")), - ("any_role", ChatMessage(content="foo", role="any_role")), - ], -) -def test_convert_message(role: str, expected_output: BaseMessage) -> None: - message = {"role": role, "content": "foo"} - result = _convert_dict_to_message(message) - assert result == expected_output - - # convert back - dict_result = _convert_message_to_dict(result) - assert dict_result == message - - -def test_convert_message_with_tool_calls() -> None: - ID = "call_fb5f5e1a-bac0-4422-95e9-d06e6022ad12" - tool_calls = [ - { - "id": ID, - "type": "function", - "function": { - "name": "main__test__python_exec", - "arguments": '{"code": "result = 36939 * 8922.4"}', - }, - } - ] - message_with_tools = { - "role": "assistant", - "content": None, - "tool_calls": tool_calls, - "id": ID, - } - result = _convert_dict_to_message(message_with_tools) - expected_output = AIMessage( - content="", - additional_kwargs={"tool_calls": tool_calls}, - id=ID, - tool_calls=[ - { - "name": tool_calls[0]["function"]["name"], # type: ignore[index] - "args": json.loads(tool_calls[0]["function"]["arguments"]), # type: ignore[index] - "id": ID, - "type": "tool_call", - } - ], - ) - assert result == expected_output - - # convert back - dict_result = _convert_message_to_dict(result) - assert dict_result == message_with_tools - - -@pytest.mark.parametrize( - ("role", "expected_output"), - [ - ("user", HumanMessageChunk(content="foo")), - ("system", SystemMessageChunk(content="foo")), - ("assistant", AIMessageChunk(content="foo")), - ("any_role", ChatMessageChunk(content="foo", role="any_role")), - ], -) -def test_convert_message_chunk(role: str, expected_output: BaseMessage) -> None: - delta = {"role": role, "content": "foo"} - result = _convert_dict_to_message_chunk(delta, "default_role") - assert result == expected_output - - # convert back - dict_result = _convert_message_to_dict(result) - assert dict_result == delta - - -def test_convert_message_chunk_with_tool_calls() -> None: - delta_with_tools = { - "role": "assistant", - "content": None, - "tool_calls": [{"index": 0, "function": {"arguments": " }"}}], - } - result = _convert_dict_to_message_chunk(delta_with_tools, "role") - expected_output = AIMessageChunk( - content="", - additional_kwargs={"tool_calls": delta_with_tools["tool_calls"]}, - id=None, - tool_call_chunks=[ToolCallChunk(name=None, args=" }", id=None, index=0)], - ) - assert result == expected_output - - -def test_convert_tool_message_chunk() -> None: - delta = { - "role": "tool", - "content": "foo", - "tool_call_id": "tool_call_id", - "id": "some_id", - } - result = _convert_dict_to_message_chunk(delta, "default_role") - expected_output = ToolMessageChunk( - content="foo", id="some_id", tool_call_id="tool_call_id" - ) - assert result == expected_output - - # convert back - dict_result = _convert_message_to_dict(result) - assert dict_result == delta - - -def test_convert_message_to_dict_function() -> None: - with pytest.raises(ValueError, match="Function messages are not supported"): - _convert_message_to_dict(FunctionMessage(content="", name="name")) diff --git a/libs/partners/databricks/tests/unit_tests/test_embeddings.py b/libs/partners/databricks/tests/unit_tests/test_embeddings.py deleted file mode 100644 index 655add03fe543..0000000000000 --- a/libs/partners/databricks/tests/unit_tests/test_embeddings.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Test Together AI embeddings.""" - -from typing import Any, Dict, Generator -from unittest import mock - -import pytest -from mlflow.deployments import BaseDeploymentClient # type: ignore[import-untyped] - -from langchain_databricks import DatabricksEmbeddings - - -def _mock_embeddings(endpoint: str, inputs: Dict[str, Any]) -> Dict[str, Any]: - return { - "object": "list", - "data": [ - { - "object": "embedding", - "embedding": list(range(1536)), - "index": 0, - } - for _ in inputs["input"] - ], - "model": "text-embedding-3-small", - "usage": {"prompt_tokens": 8, "total_tokens": 8}, - } - - -@pytest.fixture -def mock_client() -> Generator: - client = mock.MagicMock() - client.predict.side_effect = _mock_embeddings - with mock.patch("mlflow.deployments.get_deploy_client", return_value=client): - yield client - - -@pytest.fixture -def embeddings() -> DatabricksEmbeddings: - return DatabricksEmbeddings( - endpoint="text-embedding-3-small", - documents_params={"fruit": "apple"}, - query_params={"fruit": "banana"}, - ) - - -def test_embed_documents( - mock_client: BaseDeploymentClient, embeddings: DatabricksEmbeddings -) -> None: - documents = ["foo"] * 30 - output = embeddings.embed_documents(documents) - assert len(output) == 30 - assert len(output[0]) == 1536 - assert mock_client.predict.call_count == 2 - assert all( - call_arg[1]["inputs"]["fruit"] == "apple" - for call_arg in mock_client().predict.call_args_list - ) - - -def test_embed_query( - mock_client: BaseDeploymentClient, embeddings: DatabricksEmbeddings -) -> None: - query = "foo bar" - output = embeddings.embed_query(query) - assert len(output) == 1536 - mock_client.predict.assert_called_once() - assert mock_client.predict.call_args[1] == { - "endpoint": "text-embedding-3-small", - "inputs": {"input": [query], "fruit": "banana"}, - } diff --git a/libs/partners/databricks/tests/unit_tests/test_imports.py b/libs/partners/databricks/tests/unit_tests/test_imports.py deleted file mode 100644 index dfcdfaa1ded84..0000000000000 --- a/libs/partners/databricks/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,12 +0,0 @@ -from langchain_databricks import __all__ - -EXPECTED_ALL = [ - "ChatDatabricks", - "DatabricksEmbeddings", - "DatabricksVectorSearch", - "__version__", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/databricks/tests/unit_tests/test_vectorstore.py b/libs/partners/databricks/tests/unit_tests/test_vectorstore.py deleted file mode 100644 index ed8654e787036..0000000000000 --- a/libs/partners/databricks/tests/unit_tests/test_vectorstore.py +++ /dev/null @@ -1,629 +0,0 @@ -import uuid -from typing import Any, Dict, Generator, List, Optional, Set -from unittest import mock -from unittest.mock import MagicMock, patch - -import pytest -from langchain_core.embeddings import Embeddings - -from langchain_databricks.vectorstores import DatabricksVectorSearch - -INPUT_TEXTS = ["foo", "bar", "baz"] -DEFAULT_VECTOR_DIMENSION = 4 - - -class FakeEmbeddings(Embeddings): - """Fake embeddings functionality for testing.""" - - def __init__(self, dimension: int = DEFAULT_VECTOR_DIMENSION): - super().__init__() - self.dimension = dimension - - def embed_documents(self, embedding_texts: List[str]) -> List[List[float]]: - """Return simple embeddings.""" - return [ - [float(1.0)] * (self.dimension - 1) + [float(i)] - for i in range(len(embedding_texts)) - ] - - def embed_query(self, text: str) -> List[float]: - """Return simple embeddings.""" - return [float(1.0)] * (self.dimension - 1) + [float(0.0)] - - -EMBEDDING_MODEL = FakeEmbeddings() - - -### Dummy similarity_search() Response ### -EXAMPLE_SEARCH_RESPONSE = { - "manifest": { - "column_count": 3, - "columns": [ - {"name": "id"}, - {"name": "text"}, - {"name": "text_vector"}, - {"name": "score"}, - ], - }, - "result": { - "row_count": len(INPUT_TEXTS), - "data_array": sorted( - [ - [str(uuid.uuid4()), s, e, 0.5] - for s, e in zip( - INPUT_TEXTS, EMBEDDING_MODEL.embed_documents(INPUT_TEXTS) - ) - ], - key=lambda x: x[2], # type: ignore - reverse=True, - ), - }, - "next_page_token": "", -} - - -### Dummy Indices #### - -ENDPOINT_NAME = "test-endpoint" -DIRECT_ACCESS_INDEX = "test-direct-access-index" -DELTA_SYNC_INDEX = "test-delta-sync-index" -DELTA_SYNC_SELF_MANAGED_EMBEDDINGS_INDEX = "test-delta-sync-self-managed-index" -ALL_INDEX_NAMES = { - DIRECT_ACCESS_INDEX, - DELTA_SYNC_INDEX, - DELTA_SYNC_SELF_MANAGED_EMBEDDINGS_INDEX, -} - -INDEX_DETAILS = { - DELTA_SYNC_INDEX: { - "name": DELTA_SYNC_INDEX, - "endpoint_name": ENDPOINT_NAME, - "index_type": "DELTA_SYNC", - "primary_key": "id", - "delta_sync_index_spec": { - "source_table": "ml.llm.source_table", - "pipeline_type": "CONTINUOUS", - "embedding_source_columns": [ - { - "name": "text", - "embedding_model_endpoint_name": "openai-text-embedding", - } - ], - }, - }, - DELTA_SYNC_SELF_MANAGED_EMBEDDINGS_INDEX: { - "name": DELTA_SYNC_SELF_MANAGED_EMBEDDINGS_INDEX, - "endpoint_name": ENDPOINT_NAME, - "index_type": "DELTA_SYNC", - "primary_key": "id", - "delta_sync_index_spec": { - "source_table": "ml.llm.source_table", - "pipeline_type": "CONTINUOUS", - "embedding_vector_columns": [ - { - "name": "text_vector", - "embedding_dimension": DEFAULT_VECTOR_DIMENSION, - } - ], - }, - }, - DIRECT_ACCESS_INDEX: { - "name": DIRECT_ACCESS_INDEX, - "endpoint_name": ENDPOINT_NAME, - "index_type": "DIRECT_ACCESS", - "primary_key": "id", - "direct_access_index_spec": { - "embedding_vector_columns": [ - { - "name": "text_vector", - "embedding_dimension": DEFAULT_VECTOR_DIMENSION, - } - ], - "schema_json": f"{{" - f'"{"id"}": "int", ' - f'"feat1": "str", ' - f'"feat2": "float", ' - f'"text": "string", ' - f'"{"text_vector"}": "array"' - f"}}", - }, - }, -} - - -@pytest.fixture(autouse=True) -def mock_vs_client() -> Generator: - def _get_index(endpoint: str, index_name: str) -> MagicMock: - from databricks.vector_search.client import VectorSearchIndex # type: ignore - - if endpoint != ENDPOINT_NAME: - raise ValueError(f"Unknown endpoint: {endpoint}") - - index = MagicMock(spec=VectorSearchIndex) - index.describe.return_value = INDEX_DETAILS[index_name] - index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE - return index - - mock_client = MagicMock() - mock_client.get_index.side_effect = _get_index - with mock.patch( - "databricks.vector_search.client.VectorSearchClient", - return_value=mock_client, - ): - yield - - -def init_vector_search( - index_name: str, columns: Optional[List[str]] = None -) -> DatabricksVectorSearch: - kwargs: Dict[str, Any] = { - "endpoint": ENDPOINT_NAME, - "index_name": index_name, - "columns": columns, - } - if index_name != DELTA_SYNC_INDEX: - kwargs.update( - { - "embedding": EMBEDDING_MODEL, - "text_column": "text", - } - ) - return DatabricksVectorSearch(**kwargs) # type: ignore[arg-type] - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES) -def test_init(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - assert vectorsearch.index.describe() == INDEX_DETAILS[index_name] - - -def test_init_fail_text_column_mismatch() -> None: - with pytest.raises(ValueError, match=f"The index '{DELTA_SYNC_INDEX}' has"): - DatabricksVectorSearch( - endpoint=ENDPOINT_NAME, - index_name=DELTA_SYNC_INDEX, - text_column="some_other_column", - ) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_init_fail_no_text_column(index_name: str) -> None: - with pytest.raises(ValueError, match="The `text_column` parameter is required"): - DatabricksVectorSearch( - endpoint=ENDPOINT_NAME, - index_name=index_name, - embedding=EMBEDDING_MODEL, - ) - - -def test_init_fail_columns_not_in_schema() -> None: - columns = ["some_random_column"] - with pytest.raises(ValueError, match="Some columns specified in `columns`"): - init_vector_search(DIRECT_ACCESS_INDEX, columns=columns) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_init_fail_no_embedding(index_name: str) -> None: - with pytest.raises(ValueError, match="The `embedding` parameter is required"): - DatabricksVectorSearch( - endpoint=ENDPOINT_NAME, - index_name=index_name, - text_column="text", - ) - - -def test_init_fail_embedding_already_specified_in_source() -> None: - with pytest.raises(ValueError, match=f"The index '{DELTA_SYNC_INDEX}' uses"): - DatabricksVectorSearch( - endpoint=ENDPOINT_NAME, - index_name=DELTA_SYNC_INDEX, - embedding=EMBEDDING_MODEL, - ) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_init_fail_embedding_dim_mismatch(index_name: str) -> None: - with pytest.raises( - ValueError, match="embedding model's dimension '1000' does not match" - ): - DatabricksVectorSearch( - endpoint=ENDPOINT_NAME, - index_name=index_name, - text_column="text", - embedding=FakeEmbeddings(1000), - ) - - -def test_from_texts_not_supported() -> None: - with pytest.raises(NotImplementedError, match="`from_texts` is not supported"): - DatabricksVectorSearch.from_texts(INPUT_TEXTS, EMBEDDING_MODEL) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DIRECT_ACCESS_INDEX}) -def test_add_texts_not_supported_for_delta_sync_index(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - with pytest.raises( - NotImplementedError, - match="`add_texts` is only supported for direct-access index.", - ): - vectorsearch.add_texts(INPUT_TEXTS) - - -def is_valid_uuid(val: str) -> bool: - try: - uuid.UUID(str(val)) - return True - except ValueError: - return False - - -def test_add_texts() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - ids = [idx for idx, i in enumerate(INPUT_TEXTS)] - vectors = EMBEDDING_MODEL.embed_documents(INPUT_TEXTS) - - added_ids = vectorsearch.add_texts(INPUT_TEXTS, ids=ids) - vectorsearch.index.upsert.assert_called_once_with( - [ - { - "id": id_, - "text": text, - "text_vector": vector, - } - for text, vector, id_ in zip(INPUT_TEXTS, vectors, ids) - ] - ) - assert len(added_ids) == len(INPUT_TEXTS) - assert added_ids == ids - - -def test_add_texts_handle_single_text() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - vectors = EMBEDDING_MODEL.embed_documents(INPUT_TEXTS) - - added_ids = vectorsearch.add_texts(INPUT_TEXTS[0]) - vectorsearch.index.upsert.assert_called_once_with( - [ - { - "id": id_, - "text": text, - "text_vector": vector, - } - for text, vector, id_ in zip(INPUT_TEXTS, vectors, added_ids) - ] - ) - assert len(added_ids) == 1 - assert is_valid_uuid(added_ids[0]) - - -def test_add_texts_with_default_id() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - vectors = EMBEDDING_MODEL.embed_documents(INPUT_TEXTS) - - added_ids = vectorsearch.add_texts(INPUT_TEXTS) - vectorsearch.index.upsert.assert_called_once_with( - [ - { - "id": id_, - "text": text, - "text_vector": vector, - } - for text, vector, id_ in zip(INPUT_TEXTS, vectors, added_ids) - ] - ) - assert len(added_ids) == len(INPUT_TEXTS) - assert all([is_valid_uuid(id_) for id_ in added_ids]) - - -def test_add_texts_with_metadata() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - vectors = EMBEDDING_MODEL.embed_documents(INPUT_TEXTS) - metadatas = [{"feat1": str(i), "feat2": i + 1000} for i in range(len(INPUT_TEXTS))] - - added_ids = vectorsearch.add_texts(INPUT_TEXTS, metadatas=metadatas) - vectorsearch.index.upsert.assert_called_once_with( - [ - { - "id": id_, - "text": text, - "text_vector": vector, - **metadata, # type: ignore[arg-type] - } - for text, vector, id_, metadata in zip( - INPUT_TEXTS, vectors, added_ids, metadatas - ) - ] - ) - assert len(added_ids) == len(INPUT_TEXTS) - assert all([is_valid_uuid(id_) for id_ in added_ids]) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_embeddings_property(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - assert vectorsearch.embeddings == EMBEDDING_MODEL - - -def test_delete() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - vectorsearch.delete(["some id"]) - vectorsearch.index.delete.assert_called_once_with(["some id"]) - - -def test_delete_fail_no_ids() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - with pytest.raises(ValueError, match="ids must be provided."): - vectorsearch.delete() - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DIRECT_ACCESS_INDEX}) -def test_delete_not_supported_for_delta_sync_index(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - with pytest.raises( - NotImplementedError, match="`delete` is only supported for direct-access" - ): - vectorsearch.delete(["some id"]) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES) -@pytest.mark.parametrize("query_type", [None, "ANN"]) -def test_similarity_search(index_name: str, query_type: Optional[str]) -> None: - vectorsearch = init_vector_search(index_name) - query = "foo" - filters = {"some filter": True} - limit = 7 - - search_result = vectorsearch.similarity_search( - query, k=limit, filter=filters, query_type=query_type - ) - if index_name == DELTA_SYNC_INDEX: - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_text=query, - query_vector=None, - filters=filters, - num_results=limit, - query_type=query_type, - ) - else: - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_text=None, - query_vector=EMBEDDING_MODEL.embed_query(query), - filters=filters, - num_results=limit, - query_type=query_type, - ) - assert len(search_result) == len(INPUT_TEXTS) - assert sorted([d.page_content for d in search_result]) == sorted(INPUT_TEXTS) - assert all(["id" in d.metadata for d in search_result]) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES) -def test_similarity_search_hybrid(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - query = "foo" - filters = {"some filter": True} - limit = 7 - - search_result = vectorsearch.similarity_search( - query, k=limit, filter=filters, query_type="HYBRID" - ) - if index_name == DELTA_SYNC_INDEX: - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_text=query, - query_vector=None, - filters=filters, - num_results=limit, - query_type="HYBRID", - ) - else: - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_text=query, - query_vector=EMBEDDING_MODEL.embed_query(query), - filters=filters, - num_results=limit, - query_type="HYBRID", - ) - assert len(search_result) == len(INPUT_TEXTS) - assert sorted([d.page_content for d in search_result]) == sorted(INPUT_TEXTS) - assert all(["id" in d.metadata for d in search_result]) - - -def test_similarity_search_both_filter_and_filters_passed() -> None: - vectorsearch = init_vector_search(DIRECT_ACCESS_INDEX) - query = "foo" - filter = {"some filter": True} - filters = {"some other filter": False} - - vectorsearch.similarity_search(query, filter=filter, filters=filters) - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_vector=EMBEDDING_MODEL.embed_query(query), - # `filter` should prevail over `filters` - filters=filter, - num_results=4, - query_text=None, - query_type=None, - ) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -@pytest.mark.parametrize( - "columns, expected_columns", - [ - (None, {"id"}), - (["id", "text", "text_vector"], {"text_vector", "id"}), - ], -) -def test_mmr_search( - index_name: str, columns: Optional[List[str]], expected_columns: Set[str] -) -> None: - vectorsearch = init_vector_search(index_name, columns=columns) - - query = INPUT_TEXTS[0] - filters = {"some filter": True} - limit = 1 - - search_result = vectorsearch.max_marginal_relevance_search( - query, k=limit, filters=filters - ) - assert [doc.page_content for doc in search_result] == [INPUT_TEXTS[0]] - assert [set(doc.metadata.keys()) for doc in search_result] == [expected_columns] - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_mmr_parameters(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - - query = INPUT_TEXTS[0] - limit = 1 - fetch_k = 3 - lambda_mult = 0.25 - filters = {"some filter": True} - - with patch( - "langchain_databricks.vectorstores.maximal_marginal_relevance" - ) as mock_mmr: - mock_mmr.return_value = [2] - retriever = vectorsearch.as_retriever( - search_type="mmr", - search_kwargs={ - "k": limit, - "fetch_k": fetch_k, - "lambda_mult": lambda_mult, - "filter": filters, - }, - ) - search_result = retriever.invoke(query) - - mock_mmr.assert_called_once() - assert mock_mmr.call_args[1]["lambda_mult"] == lambda_mult - assert vectorsearch.index.similarity_search.call_args[1]["num_results"] == fetch_k - assert vectorsearch.index.similarity_search.call_args[1]["filters"] == filters - assert len(search_result) == limit - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES) -@pytest.mark.parametrize("threshold", [0.4, 0.5, 0.8]) -def test_similarity_score_threshold(index_name: str, threshold: float) -> None: - query = INPUT_TEXTS[0] - limit = len(INPUT_TEXTS) - - vectorsearch = init_vector_search(index_name) - retriever = vectorsearch.as_retriever( - search_type="similarity_score_threshold", - search_kwargs={"k": limit, "score_threshold": threshold}, - ) - search_result = retriever.invoke(query) - if threshold <= 0.5: - assert len(search_result) == len(INPUT_TEXTS) - else: - assert len(search_result) == 0 - - -def test_standard_params() -> None: - vectorstore = init_vector_search(DIRECT_ACCESS_INDEX) - retriever = vectorstore.as_retriever() - ls_params = retriever._get_ls_params() - assert ls_params == { - "ls_retriever_name": "vectorstore", - "ls_vector_store_provider": "DatabricksVectorSearch", - "ls_embedding_provider": "FakeEmbeddings", - } - - vectorstore = init_vector_search(DELTA_SYNC_INDEX) - retriever = vectorstore.as_retriever() - ls_params = retriever._get_ls_params() - assert ls_params == { - "ls_retriever_name": "vectorstore", - "ls_vector_store_provider": "DatabricksVectorSearch", - } - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -@pytest.mark.parametrize("query_type", [None, "ANN"]) -def test_similarity_search_by_vector( - index_name: str, query_type: Optional[str] -) -> None: - vectorsearch = init_vector_search(index_name) - query_embedding = EMBEDDING_MODEL.embed_query("foo") - filters = {"some filter": True} - limit = 7 - - search_result = vectorsearch.similarity_search_by_vector( - query_embedding, k=limit, filter=filters, query_type=query_type - ) - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_vector=query_embedding, - filters=filters, - num_results=limit, - query_type=query_type, - query_text=None, - ) - assert len(search_result) == len(INPUT_TEXTS) - assert sorted([d.page_content for d in search_result]) == sorted(INPUT_TEXTS) - assert all(["id" in d.metadata for d in search_result]) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES - {DELTA_SYNC_INDEX}) -def test_similarity_search_by_vector_hybrid(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - query_embedding = EMBEDDING_MODEL.embed_query("foo") - filters = {"some filter": True} - limit = 7 - - search_result = vectorsearch.similarity_search_by_vector( - query_embedding, k=limit, filter=filters, query_type="HYBRID", query="foo" - ) - vectorsearch.index.similarity_search.assert_called_once_with( - columns=["id", "text"], - query_vector=query_embedding, - filters=filters, - num_results=limit, - query_type="HYBRID", - query_text="foo", - ) - assert len(search_result) == len(INPUT_TEXTS) - assert sorted([d.page_content for d in search_result]) == sorted(INPUT_TEXTS) - assert all(["id" in d.metadata for d in search_result]) - - -@pytest.mark.parametrize("index_name", ALL_INDEX_NAMES) -def test_similarity_search_empty_result(index_name: str) -> None: - vectorsearch = init_vector_search(index_name) - vectorsearch.index.similarity_search.return_value = { - "manifest": { - "column_count": 3, - "columns": [ - {"name": "id"}, - {"name": "text"}, - {"name": "score"}, - ], - }, - "result": { - "row_count": 0, - "data_array": [], - }, - "next_page_token": "", - } - - search_result = vectorsearch.similarity_search("foo") - assert len(search_result) == 0 - - -def test_similarity_search_by_vector_not_supported_for_managed_embedding() -> None: - vectorsearch = init_vector_search(DELTA_SYNC_INDEX) - query_embedding = EMBEDDING_MODEL.embed_query("foo") - filters = {"some filter": True} - limit = 7 - - with pytest.raises( - NotImplementedError, match="`similarity_search_by_vector` is not supported" - ): - vectorsearch.similarity_search_by_vector( - query_embedding, k=limit, filters=filters - )