autogen.agents.experimental.VectorChromaQueryEngine

VectorChromaQueryEngine

VectorChromaQueryEngine(
    db_path: str | None = None,
    embedding_function: Optional[EmbeddingFunction[Any]] = None,
    metadata: dict[str, Any] | None = None,
    llm: ForwardRef('LLM') | None = None,
    collection_name: str | None = None
)

This engine leverages Chromadb to persist document embeddings in a named collection and LlamaIndex’s VectorStoreIndex to efficiently index and retrieve documents, and generate an answer in response to natural language queries. The Chromadb collection serves as the storage layer, while the collection name uniquely identifies the set of documents within the persistent database.
This implements the autogen.agentchat.contrib.rag.RAGQueryEngine protocol.
Initializes the VectorChromaQueryEngine with db_path, metadata, and embedding function and llm.

Parameters:

Name	Description
db_path	Type: str | None Default: None
embedding_function	Type: Optional[EmbeddingFunction[Any]] Default: None
metadata	Type: dict[str, typing.Any] | None Default: None
llm	Type: ForwardRef(‘LLM’) | None Default: None
collection_name	Type: str | None Default: None

Instance Methods

add_docs

add_docs(
    self,
    new_doc_dir: Path | str | None = None,
    new_doc_paths_or_urls: Sequence[Path | str] | None = None
) -> None

Add additional documents to the existing vector index.
Loads new Docling-parsed Markdown files from a specified directory or a list of file paths and inserts them into the current index for future queries.

Parameters:

Name	Description
new_doc_dir	The directory path from which to load additional documents. If provided, all eligible files in this directory are loaded. Type: pathlib.Path | str | None Default: None
new_doc_paths_or_urls	A list of file paths specifying additional documents to load. Each file should be a Docling-parsed Markdown file. Type: Sequence[pathlib.Path | str] | None Default: None

connect_db

connect_db(
    self,
    *args: Any,
    **kwargs: Any
) -> bool

Establish a connection to the Chromadb database and initialize the collection.

Parameters:

Name	Description
*args	Type: Any
**kwargs	Type: Any

get_collection_name

get_collection_name(self) -> str

Get the name of the collection used by the query engine.
Returns:
The name of the collection.

Returns:

Type	Description
str	The name of the collection.

init_db

init_db(
    self,
    new_doc_dir: Path | str | None = None,
    new_doc_paths_or_urls: Sequence[Path | str] | None = None,
    *args: Any,
    **kwargs: Any
) -> bool

Not required nor implemented for VectorChromaQueryEngine

Parameters:

Name	Description
new_doc_dir	Type: pathlib.Path | str | None Default: None
new_doc_paths_or_urls	Type: Sequence[pathlib.Path | str] | None Default: None
*args	Type: Any
**kwargs	Type: Any

query

query(self, question: str) -> str

Retrieve information from indexed documents by processing a natural language query.

Parameters:

Name	Description
question	A natural language query string used to search the indexed documents. Type: str

Returns:

Type	Description
str	A string containing the response generated by LLM.

validate_query_index

validate_query_index(self) -> None

Ensures an index exists