> ## Documentation Index > Fetch the complete documentation index at: https://docs.ag2.ai/llms.txt > Use this file to discover all available pages before exploring further. # autogen.agentchat.contrib.rag.ChromaDBQueryEngine

ChromaDBQueryEngine

```python theme={null} ChromaDBQueryEngine( host: str | None = 'localhost', port: int | None = 8000, settings: ForwardRef('Settings') | None = None, tenant: str | None = None, database: 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. Collection can be regarded as an abstraction of group of documents in the database.
It expects a Chromadb server to be running and accessible at the specified host and port.
Refer to this [link](https://docs.trychroma.com/production/containers/docker) for running Chromadb in a Docker container.
If the host and port are not provided, the engine will create an in-memory ChromaDB client.
Initializes the ChromaDBQueryEngine with db\_path, metadata, and embedding function and llm.
Parameters: | Name | Description | | -------------------- | ------------------------------------------------------------------------- | | `host` | **Type:** str \| None

**Default:** 'localhost' | | `port` | **Type:** int \| None

**Default:** 8000 | | `settings` | **Type:** ForwardRef('Settings') \| None

**Default:** None | | `tenant` | **Type:** str \| None

**Default:** None | | `database` | **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 ```python theme={null} 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 ```python theme={null} connect_db( self, *args: Any, **kwargs: Any ) -> bool ``` Connect to the database.
It does not overwrite the existing collection in the database.
It takes the following steps, 1. Set up ChromaDB and LlamaIndex storage.
2. Create the llamaIndex vector store index for querying or inserting docs later 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 |
#### get\_collection\_name ```python theme={null} 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 ```python theme={null} 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 overwrites the existing collection in the database.
It takes the following steps, 1. Set up ChromaDB and LlamaIndex storage.
2. insert documents and build indexes 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 ```python theme={null} 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. |