autogen.agentchat.contrib.rag.LlamaIndexQueryEngine

LlamaIndexQueryEngine

LlamaIndexQueryEngine(
    vector_store: BasePydanticVectorStore,
    llm: ForwardRef('LLM') | None = None,
    file_reader_class: type['SimpleDirectoryReader'] | None = None
)

This engine leverages LlamaIndex’s VectorStoreIndex to efficiently index and retrieve documents, and generate an answer in response to natural language queries. It use any LlamaIndex vector store.
By default the engine will use OpenAI’s GPT-4o model (use the llm parameter to change that).
Initializes the LlamaIndexQueryEngine with the given vector store.

Parameters:

Name	Description
vector_store	Type: BasePydanticVectorStore
llm	Type: ForwardRef(‘LLM’) | None Default: None
file_reader_class	Type: type[‘SimpleDirectoryReader’] | 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,
    *args: Any,
    **kwargs: Any
) -> None

Add new documents to the underlying database and add to the index.

Parameters:

Name	Description
new_doc_dir	A dir of input documents that are used to create the records in database. Type: pathlib.Path | str | None Default: None
new_doc_paths_or_urls	A sequence of input documents that are used to create the records in database. A document can be a Path to a file or a url. Type: Sequence[pathlib.Path | str] | None Default: None
*args	Any additional arguments Type: Any
**kwargs	Any additional keyword arguments Type: Any

connect_db

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

Connect to the database.
It sets up the LlamaIndex storage and create an index from the existing vector store.

Parameters:

Name	Description
*args	Any additional arguments Type: Any
**kwargs	Any additional keyword arguments Type: Any

Returns:

Type	Description
bool	bool: True if connection is successful

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

Initialize the database with the input documents or records.
It takes the following steps:
1. Set up LlamaIndex storage context.
2. insert documents and build an index upon them.

Parameters:

Name	Description
new_doc_dir	a dir of input documents that are used to create the records in database. Type: pathlib.Path | str | None Default: None
new_doc_paths_or_urls	A sequence of input documents that are used to create the records in database. A document can be a Path to a file or a url. Type: Sequence[pathlib.Path | str] | None Default: None
*args	Any additional arguments Type: Any
**kwargs	Any additional keyword arguments Type: Any

Returns:

Type	Description
bool	bool: True if initialization is successful

query

query(self, question: str) -> str

Retrieve information from indexed documents by processing a query using the engine’s LLM.

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.