> ## 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.MongoDBQueryEngine

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

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

```python theme={null}
MongoDBQueryEngine(
    connection_string: str,
    llm: ForwardRef('LLM') | None = None,
    database_name: str | None = None,
    embedding_function: ForwardRef('BaseEmbedding') | Callable[..., Any] | None = None,
    embedding_model: ForwardRef('BaseEmbedding') | str | None = None,
    collection_name: str | None = None
)
```

A query engine backed by MongoDB Atlas that supports document insertion and querying.<br />This engine initializes a vector database, builds an index from input documents,
and allows querying using the chat engine interface.<br />Attributes: <br />    vector\_db (MongoDBAtlasVectorDB): The MongoDB vector database instance.<br />    vector\_search\_engine (MongoDBAtlasVectorSearch): The vector search engine.<br />    storage\_context (StorageContext): The storage context for the vector store.<br />    index (Optional\[VectorStoreIndex]): The index built from the documents.<br />Initializes a MongoDBQueryEngine instance.<br />

<b>Parameters:</b>

| Name                 | Description                                                                                       |
| -------------------- | ------------------------------------------------------------------------------------------------- |
| `connection_string`  | **Type:** str                                                                                     |
| `llm`                | **Type:** ForwardRef('LLM') \| None<br /><br />**Default:** None                                  |
| `database_name`      | **Type:** str \| None<br /><br />**Default:** None                                                |
| `embedding_function` | **Type:** ForwardRef('BaseEmbedding') \| Callable\[..., Any] \| None<br /><br />**Default:** None |
| `embedding_model`    | **Type:** ForwardRef('BaseEmbedding') \| str \| None<br /><br />**Default:** None                 |
| `collection_name`    | **Type:** str \| 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
```

Adds new documents to the existing vector store index.<br />This method validates that the index exists, loads documents from the specified directory or file paths,
and inserts them into the vector store index.<br />

<b>Parameters:</b>

| Name                    | Description                                                                                                                            |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |
| `new_doc_dir`           | Directory containing new documents.<br /><br />**Type:** pathlib.Path \| str \| None<br /><br />**Default:** None                      |
| `new_doc_paths_or_urls` | List of file paths or URLs for new documents.<br /><br />**Type:** Sequence\[pathlib.Path \| str] \| None<br /><br />**Default:** None |
| `*args`                 | Additional positional arguments.<br /><br />**Type:** Any                                                                              |
| `**kwargs`              | 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
```

Connects to the MongoDB database and initializes the query index from the existing collection.<br />This method verifies the existence of the collection, sets up the database connection,
builds the vector store index, and pings the MongoDB server.<br />Returns: <br />    bool: True if connection is successful; False otherwise.

<b>Parameters:</b>

| Name       | Description   |
| ---------- | ------------- |
| `*args`    | **Type:** Any |
| `**kwargs` | **Type:** Any |

<b>Returns:</b>

| Type | Description                                              |
| ---- | -------------------------------------------------------- |
| bool | bool: True if connection is successful; False otherwise. |

<br />

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

#### get\_collection\_name

```python theme={null}
get_collection_name(self) -> str
```

Retrieves the name of the MongoDB collection.<br />Returns: <br />    str: The collection name.<br />Raises: <br />    ValueError: If the collection name is not set.

<b>Returns:</b>

| Type | Description               |
| ---- | ------------------------- |
| str  | str: The collection name. |

<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
```

Initializes the MongoDB database by creating or overwriting the collection and indexing documents.<br />This method loads documents from a directory or provided file paths, sets up the database (optionally
overwriting any existing collection), builds the vector store index, and inserts the documents.<br />

<b>Parameters:</b>

| Name                    | Description                                                                                                                        |
| ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `new_doc_dir`           | Directory containing documents to be indexed.<br /><br />**Type:** pathlib.Path \| str \| None<br /><br />**Default:** None        |
| `new_doc_paths_or_urls` | List of file paths or URLs for documents.<br /><br />**Type:** Sequence\[pathlib.Path \| str] \| None<br /><br />**Default:** None |
| `*args`                 | Additional positional arguments.<br /><br />**Type:** Any                                                                          |
| `**kwargs`              | Additional keyword arguments.<br /><br />**Type:** Any                                                                             |

<b>Returns:</b>

| Type | Description                                                              |
| ---- | ------------------------------------------------------------------------ |
| bool | bool: True if the database is successfully initialized; False otherwise. |

<br />

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

#### query

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

Queries the indexed documents using the provided question.<br />This method validates that the query index is initialized, creates a query engine from the vector store index,
and executes the query. If the response is empty, a default reply is returned.<br />

<b>Parameters:</b>

| Name       | Description                                  |
| ---------- | -------------------------------------------- |
| `question` | The query question.<br /><br />**Type:** str |
| `*args`    | **Type:** Any                                |
| `**kwargs` | **Type:** Any                                |

<b>Returns:</b>

| Type | Description                                                                      |
| ---- | -------------------------------------------------------------------------------- |
| Any  | Any: The query response as a string, or a default reply if no results are found. |

<br />