Remove

docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_05_25_23.json

@@ -26228,7 +26228,7 @@
       "timestampEdited": null,
       "callEndedTimestamp": null,
       "isPinned": false,
-      "content": "i.e. \n```python\nindex = GPTChromaIndex(documents, chroma_collection=chroma_collection)\n```\nthen\n```python\nresponse = index.query(\"What did the author do growing up?\")\n```\n\nwe get \n```\nValueError: chroma_collection cannot be None.\n```\n\nSince we check it here:\n```\nclass GPTChromaIndexQuery(BaseGPTVectorStoreIndexQuery[ChromaIndexStruct]):    \"\"\"GPTChromaIndex query.\"\"\"    def __init__(        self,        index_struct: ChromaIndexStruct,        embed_model: Optional[BaseEmbedding] = None,        similarity_top_k: int = 1,        chroma_collection: Optional[Any] = None,        **kwargs: Any,    ) -> None:        \"\"\"Initialize params.\"\"\"        super().__init__(            index_struct=index_struct,            embed_model=embed_model,            similarity_top_k=similarity_top_k,            **kwargs,        )        import_err_msg = (            \"`chromadb` package not found, please run `pip install chromadb`\"        )        try:            import chromadb        except ImportError:            raise ValueError(import_err_msg)        if chroma_collection is None:            raise ValueError(\"chroma_collection cannot be None.\")\n```\n\nWhat am I missing ?",
+      "content": "i.e. \n```python\nindex = GPTChromaIndex(documents, chroma_collection=chroma_collection)\n```\nthen\n```python\nresponse = index.query(\"What did the author do growing up?\")\n```\n\nwe get \n```\nValueError: chroma_collection cannot be None.\n```\n\nSince we check it here:\n```\nclass GPTChromaIndexQuery(BaseGPTVectorStoreIndexQuery[ChromaIndexStruct]):    \"\"\"GPTChromaIndex query.\"\"\"    def __init__(        self,        index_struct: ChromaIndexStruct,        embed_model: Optional[BaseEmbedding] = None,        similarity_top_k: int = 1,        chroma_collection: Optional[Any] = None,        **kwargs: Any    ) -> None:        \"\"\"Initialize params.\"\"\"        super().__init__(            index_struct=index_struct,            embed_model=embed_model,            similarity_top_k=similarity_top_k,            **kwargs,        )        import_err_msg = (            \"`chromadb` package not found, please run `pip install chromadb`\"        )        try:            import chromadb        except ImportError:            raise ValueError(import_err_msg)        if chroma_collection is None:            raise ValueError(\"chroma_collection cannot be None.\")\n```\n\nWhat am I missing ?",
       "author": {
         "id": "1065787112475066409",
         "name": "chroma_anton",

docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_06_02_23.json

@@ -26228,7 +26228,7 @@
       "timestampEdited": null,
       "callEndedTimestamp": null,
       "isPinned": false,
-      "content": "i.e. \n```python\nindex = GPTChromaIndex(documents, chroma_collection=chroma_collection)\n```\nthen\n```python\nresponse = index.query(\"What did the author do growing up?\")\n```\n\nwe get \n```\nValueError: chroma_collection cannot be None.\n```\n\nSince we check it here:\n```\nclass GPTChromaIndexQuery(BaseGPTVectorStoreIndexQuery[ChromaIndexStruct]):    \"\"\"GPTChromaIndex query.\"\"\"    def __init__(        self,        index_struct: ChromaIndexStruct,        embed_model: Optional[BaseEmbedding] = None,        similarity_top_k: int = 1,        chroma_collection: Optional[Any] = None,        **kwargs: Any,    ) -> None:        \"\"\"Initialize params.\"\"\"        super().__init__(            index_struct=index_struct,            embed_model=embed_model,            similarity_top_k=similarity_top_k,            **kwargs,        )        import_err_msg = (            \"`chromadb` package not found, please run `pip install chromadb`\"        )        try:            import chromadb        except ImportError:            raise ValueError(import_err_msg)        if chroma_collection is None:            raise ValueError(\"chroma_collection cannot be None.\")\n```\n\nWhat am I missing ?",
+      "content": "i.e. \n```python\nindex = GPTChromaIndex(documents, chroma_collection=chroma_collection)\n```\nthen\n```python\nresponse = index.query(\"What did the author do growing up?\")\n```\n\nwe get \n```\nValueError: chroma_collection cannot be None.\n```\n\nSince we check it here:\n```\nclass GPTChromaIndexQuery(BaseGPTVectorStoreIndexQuery[ChromaIndexStruct]):    \"\"\"GPTChromaIndex query.\"\"\"    def __init__(        self,        index_struct: ChromaIndexStruct,        embed_model: Optional[BaseEmbedding] = None,        similarity_top_k: int = 1,        chroma_collection: Optional[Any] = None,        **kwargs: Any    ) -> None:        \"\"\"Initialize params.\"\"\"        super().__init__(            index_struct=index_struct,            embed_model=embed_model,            similarity_top_k=similarity_top_k,            **kwargs,        )        import_err_msg = (            \"`chromadb` package not found, please run `pip install chromadb`\"        )        try:            import chromadb        except ImportError:            raise ValueError(import_err_msg)        if chroma_collection is None:            raise ValueError(\"chroma_collection cannot be None.\")\n```\n\nWhat am I missing ?",
       "author": {
         "id": "1065787112475066409",
         "name": "chroma_anton",

docs/docs/examples/embeddings/custom_embeddings.ipynb

@@ -86,7 +86,7 @@
     "        self,\n",
     "        instructor_model_name: str = \"hkunlp/instructor-large\",\n",
     "        instruction: str = \"Represent a document for semantic search:\",\n",
-    "        **kwargs: Any,\n",
+    "        **kwargs: Any\n",
     "    ) -> None:\n",
     "        self._model = INSTRUCTOR(instructor_model_name)\n",
     "        self._instruction = instruction\n",

docs/docs/examples/low_level/vector_store.ipynb

@@ -207,9 +207,7 @@
     "        pass\n",
     "\n",
     "    def query(\n",
-    "        self,\n",
-    "        query: VectorStoreQuery,\n",
-    "        **kwargs: Any,\n",
+    "        self, query: VectorStoreQuery, **kwargs: Any\n",
     "    ) -> VectorStoreQueryResult:\n",
     "        \"\"\"Get nodes for response.\"\"\"\n",
     "        pass\n",
@@ -473,9 +471,7 @@
     "    \"\"\"Implements semantic/dense search.\"\"\"\n",
     "\n",
     "    def query(\n",
-    "        self,\n",
-    "        query: VectorStoreQuery,\n",
-    "        **kwargs: Any,\n",
+    "        self, query: VectorStoreQuery, **kwargs: Any\n",
     "    ) -> VectorStoreQueryResult:\n",
     "        \"\"\"Get nodes for response.\"\"\"\n",
     "\n",
@@ -568,9 +564,7 @@
     "    \"\"\"Implements Metadata Filtering.\"\"\"\n",
     "\n",
     "    def query(\n",
-    "        self,\n",
-    "        query: VectorStoreQuery,\n",
-    "        **kwargs: Any,\n",
+    "        self, query: VectorStoreQuery, **kwargs: Any\n",
     "    ) -> VectorStoreQueryResult:\n",
     "        \"\"\"Get nodes for response.\"\"\"\n",
     "        # 1. First filter by metadata\n",

docs/docs/module_guides/evaluating/usage_pattern.md

@@ -12,7 +12,7 @@ All of the evaluation modules in LlamaIndex implement the `BaseEvaluator` class,
         query: Optional[str] = None,
         contexts: Optional[Sequence[str]] = None,
         response: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
 ```
 
@@ -23,7 +23,7 @@ def evaluate_response(
     self,
     query: Optional[str] = None,
     response: Optional[Response] = None,
-    **kwargs: Any,
+    **kwargs: Any
 ) -> EvaluationResult:
 ```
 

docs/docs/module_guides/models/embeddings.md

@@ -164,7 +164,7 @@ class InstructorEmbeddings(BaseEmbedding):
         self,
         instructor_model_name: str = "hkunlp/instructor-large",
         instruction: str = "Represent the Computer Science documentation or question:",
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         self._model = INSTRUCTOR(instructor_model_name)
         self._instruction = instruction

llama-index-core/llama_index/core/agent/react/base.py

@@ -105,7 +105,7 @@ def from_tools(
         handle_reasoning_failure_fn: Optional[
             Callable[[CallbackManager, Exception], ToolOutput]
         ] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> "ReActAgent":
         """Convenience constructor method from set of BaseTools (Optional).
 

llama-index-core/llama_index/core/agent/runner/base.py

@@ -247,9 +247,7 @@ def __init__(
 
     @staticmethod
     def from_llm(
-        tools: Optional[List[BaseTool]] = None,
-        llm: Optional[LLM] = None,
-        **kwargs: Any,
+        tools: Optional[List[BaseTool]] = None, llm: Optional[LLM] = None, **kwargs: Any
     ) -> "AgentRunner":
         from llama_index.core.agent import ReActAgent
 

llama-index-core/llama_index/core/base/embeddings/base.py

@@ -295,10 +295,7 @@ async def aget_text_embedding(self, text: str) -> Embedding:
 
     @dispatcher.span
     def get_text_embedding_batch(
-        self,
-        texts: List[str],
-        show_progress: bool = False,
-        **kwargs: Any,
+        self, texts: List[str], show_progress: bool = False, **kwargs: Any
     ) -> List[Embedding]:
         """Get a list of text embeddings, with batching."""
         dispatch_event = dispatcher.get_dispatch_event()

llama-index-core/llama_index/core/callbacks/base_handler.py

@@ -28,7 +28,7 @@ def on_event_start(
         payload: Optional[Dict[str, Any]] = None,
         event_id: str = "",
         parent_id: str = "",
-        **kwargs: Any,
+        **kwargs: Any
     ) -> str:
         """Run when an event starts and return id of event."""
 
@@ -38,7 +38,7 @@ def on_event_end(
         event_type: CBEventType,
         payload: Optional[Dict[str, Any]] = None,
         event_id: str = "",
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Run when an event ends."""
 

llama-index-core/llama_index/core/chat_engine/context.py

@@ -70,7 +70,7 @@ def from_defaults(
         node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
         context_template: Optional[str] = None,
         llm: Optional[LLM] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> "ContextChatEngine":
         """Initialize a ContextChatEngine from default parameters."""
         llm = llm or llm_from_settings_or_context(Settings, service_context)

llama-index-core/llama_index/core/chat_engine/simple.py

@@ -49,7 +49,7 @@ def from_defaults(
         llm: Optional[LLM] = None,
         # deprecated
         service_context: Optional[ServiceContext] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> "SimpleChatEngine":
         """Initialize a SimpleChatEngine from default parameters."""
         llm = llm or llm_from_settings_or_context(Settings, service_context)

llama-index-core/llama_index/core/evaluation/base.py

@@ -51,7 +51,7 @@ def evaluate(
         query: Optional[str] = None,
         response: Optional[str] = None,
         contexts: Optional[Sequence[str]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         """Run evaluation with query string, retrieved contexts,
         and generated response string.
@@ -74,7 +74,7 @@ async def aevaluate(
         query: Optional[str] = None,
         response: Optional[str] = None,
         contexts: Optional[Sequence[str]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         """Run evaluation with query string, retrieved contexts,
         and generated response string.
@@ -88,7 +88,7 @@ def evaluate_response(
         self,
         query: Optional[str] = None,
         response: Optional[Response] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         """Run evaluation with query string and generated Response object.
 
@@ -109,7 +109,7 @@ async def aevaluate_response(
         self,
         query: Optional[str] = None,
         response: Optional[Response] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         """Run evaluation with query string and generated Response object.
 

llama-index-core/llama_index/core/evaluation/correctness.py

@@ -125,7 +125,7 @@ async def aevaluate(
         contexts: Optional[Sequence[str]] = None,
         reference: Optional[str] = None,
         sleep_time_in_seconds: int = 0,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         del kwargs  # Unused
         del contexts  # Unused

llama-index-core/llama_index/core/evaluation/eval_utils.py

@@ -37,10 +37,7 @@ async def aget_responses(
     return await asyncio_mod.gather(*tasks)
 
 
-def get_responses(
-    *args: Any,
-    **kwargs: Any,
-) -> List[str]:
+def get_responses(*args: Any, **kwargs: Any) -> List[str]:
     """Get responses.
 
     Sync version of aget_responses.

llama-index-core/llama_index/core/evaluation/guideline.py

@@ -94,7 +94,7 @@ async def aevaluate(
         response: Optional[str] = None,
         contexts: Optional[Sequence[str]] = None,
         sleep_time_in_seconds: int = 0,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         """Evaluate whether the query and response pair passes the guidelines."""
         del contexts  # Unused

llama-index-core/llama_index/core/evaluation/pairwise.py

@@ -243,7 +243,7 @@ async def aevaluate(
         second_response: Optional[str] = None,
         reference: Optional[str] = None,
         sleep_time_in_seconds: int = 0,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> EvaluationResult:
         del kwargs  # Unused
         del contexts  # Unused

llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py

@@ -39,7 +39,7 @@ def __init__(
         metrics: Sequence[BaseRetrievalMetric],
         retriever: BaseRetriever,
         node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Init params."""
         super().__init__(
@@ -89,7 +89,7 @@ def __init__(
         metrics: Sequence[BaseRetrievalMetric],
         retriever: BaseRetriever,
         node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Init params."""
         super().__init__(

llama-index-core/llama_index/core/extractors/interface.py

@@ -99,7 +99,7 @@ async def aprocess_nodes(
         nodes: List[BaseNode],
         excluded_embed_metadata_keys: Optional[List[str]] = None,
         excluded_llm_metadata_keys: Optional[List[str]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> List[BaseNode]:
         """Post process nodes parsed from documents.
 
@@ -137,7 +137,7 @@ def process_nodes(
         nodes: List[BaseNode],
         excluded_embed_metadata_keys: Optional[List[str]] = None,
         excluded_llm_metadata_keys: Optional[List[str]] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> List[BaseNode]:
         return asyncio.run(
             self.aprocess_nodes(

llama-index-core/llama_index/core/indices/composability/graph.py

@@ -57,7 +57,7 @@ def from_indices(
         index_summaries: Optional[Sequence[str]] = None,
         service_context: Optional[ServiceContext] = None,
         storage_context: Optional[StorageContext] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> "ComposableGraph":  # type: ignore
         """Create composable graph using this index class as the root."""
         service_context = service_context or ServiceContext.from_defaults()

llama-index-core/llama_index/core/indices/document_summary/retrievers.py

@@ -52,7 +52,7 @@ def __init__(
         callback_manager: Optional[CallbackManager] = None,
         object_map: Optional[dict] = None,
         verbose: bool = False,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         self._index = index
         self._choice_select_prompt = (
@@ -135,7 +135,7 @@ def __init__(
         callback_manager: Optional[CallbackManager] = None,
         object_map: Optional[dict] = None,
         verbose: bool = False,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Init params."""
         self._index = index

llama-index-core/llama_index/core/indices/empty/base.py

@@ -35,7 +35,7 @@ def __init__(
         index_struct: Optional[EmptyIndexStruct] = None,
         # deprecated
         service_context: Optional[ServiceContext] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Initialize params."""
         super().__init__(

llama-index-core/llama_index/core/indices/empty/retrievers.py

@@ -25,7 +25,7 @@ def __init__(
         index: EmptyIndex,
         input_prompt: Optional[BasePromptTemplate] = None,
         callback_manager: Optional[CallbackManager] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Initialize params."""
         self._index = index

llama-index-core/llama_index/core/indices/keyword_table/rake_base.py

@@ -30,7 +30,7 @@ def as_retriever(
         retriever_mode: Union[
             str, KeywordTableRetrieverMode
         ] = KeywordTableRetrieverMode.RAKE,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> BaseRetriever:
         return super().as_retriever(retriever_mode=retriever_mode, **kwargs)
 

llama-index-core/llama_index/core/indices/keyword_table/simple_base.py

@@ -36,7 +36,7 @@ def as_retriever(
         retriever_mode: Union[
             str, KeywordTableRetrieverMode
         ] = KeywordTableRetrieverMode.SIMPLE,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> BaseRetriever:
         return super().as_retriever(retriever_mode=retriever_mode, **kwargs)
 

llama-index-core/llama_index/core/indices/loading.py

@@ -10,9 +10,7 @@
 
 
 def load_index_from_storage(
-    storage_context: StorageContext,
-    index_id: Optional[str] = None,
-    **kwargs: Any,
+    storage_context: StorageContext, index_id: Optional[str] = None, **kwargs: Any
 ) -> BaseIndex:
     """Load index from storage context.
 
@@ -83,9 +81,7 @@ def load_indices_from_storage(
 
 
 def load_graph_from_storage(
-    storage_context: StorageContext,
-    root_id: str,
-    **kwargs: Any,
+    storage_context: StorageContext, root_id: str, **kwargs: Any
 ) -> ComposableGraph:
     """Load composable graph from storage context.
 

llama-index-core/llama_index/core/indices/managed/base.py

@@ -35,7 +35,7 @@ def __init__(
         show_progress: bool = False,
         # deprecated
         service_context: Optional[ServiceContext] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Initialize params."""
         super().__init__(
@@ -90,7 +90,7 @@ def from_documents(
         transformations: Optional[List[TransformComponent]] = None,
         # deprecated
         service_context: Optional[ServiceContext] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> IndexType:
         """Build an index from a sequence of documents."""
         raise NotImplementedError(

llama-index-core/llama_index/core/indices/multi_modal/retriever.py

@@ -61,7 +61,7 @@ def __init__(
         doc_ids: Optional[List[str]] = None,
         sparse_top_k: Optional[int] = None,
         callback_manager: Optional[CallbackManager] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Initialize params."""
         self._index = index

llama-index-core/llama_index/core/indices/struct_store/base.py

@@ -46,7 +46,7 @@ def __init__(
         service_context: Optional[ServiceContext] = None,
         schema_extract_prompt: Optional[BasePromptTemplate] = None,
         output_parser: Optional[OUTPUT_PARSER_TYPE] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> None:
         """Initialize params."""
         self.schema_extract_prompt = (

llama-index-core/llama_index/core/indices/struct_store/container_builder.py

@@ -110,10 +110,7 @@ def derive_index_from_context(
         for table_name, context_str in full_context_dict.items():
             doc = Document(text=context_str, metadata={"table_name": table_name})
             context_docs.append(doc)
-        return index_cls.from_documents(
-            documents=context_docs,
-            **index_kwargs,
-        )
+        return index_cls.from_documents(documents=context_docs, **index_kwargs)
 
     def query_index_for_context(
         self,