> ## 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.LlamaIndexQueryEngine

<h2 id="autogen.agentchat.contrib.rag.LlamaIndexQueryEngine" class="doc doc-heading">
  <code class="doc-symbol doc-symbol-heading doc-symbol-class" />

  <span class="doc doc-object-name doc-class-name">LlamaIndexQueryEngine</span>
</h2>

```python theme={null}
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](https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores/).<br />By default the engine will use OpenAI's GPT-4o model (use the `llm` parameter to change that).<br />Initializes the LlamaIndexQueryEngine with the given vector store.<br />

<b>Parameters:</b>

| Name                | Description                                                                   |
| ------------------- | ----------------------------------------------------------------------------- |
| `vector_store`      | **Type:** BasePydanticVectorStore                                             |
| `llm`               | **Type:** ForwardRef('LLM') \| None<br /><br />**Default:** None              |
| `file_reader_class` | **Type:** type\['SimpleDirectoryReader'] \| None<br /><br />**Default:** None |

### Instance Methods

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### 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.<br />

<b>Parameters:</b>

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

<br />

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### connect\_db

```python theme={null}
connect_db(
    self,
    *args: Any,
    **kwargs: Any
) -> bool
```

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

<b>Parameters:</b>

| Name       | Description                                               |
| ---------- | --------------------------------------------------------- |
| `*args`    | Any additional arguments<br /><br />**Type:** Any         |
| `**kwargs` | Any additional keyword arguments<br /><br />**Type:** Any |

<b>Returns:</b>

| Type | Description                            |
| ---- | -------------------------------------- |
| bool | bool: True if connection is successful |

<br />

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### 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.<br />It takes the following steps: <br />1. Set up LlamaIndex storage context.<br />2. insert documents and build an index upon them.<br />

<b>Parameters:</b>

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

<b>Returns:</b>

| Type | Description                                |
| ---- | ------------------------------------------ |
| bool | bool: True if initialization is successful |

<br />

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### query

```python theme={null}
query(self, question: str) -> str
```

Retrieve information from indexed documents by processing a query using the engine's LLM.<br />

<b>Parameters:</b>

| Name       | Description                                                                                    |
| ---------- | ---------------------------------------------------------------------------------------------- |
| `question` | A natural language query string used to search the indexed documents.<br /><br />**Type:** str |

<b>Returns:</b>

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

<br />