# Use MongoDBQueryEngine to query Markdown files

This notebook demonstrates the use of the `MongoDBQueryEngine` for retrieval-augmented question answering over documents. It shows how to set up the engine with Docling parsed Markdown files, and execute natural language queries against the indexed data.

The `MongoDBQueryEngine` integrates cloud MongoDB Atlas vector storage with LlamaIndex for efficient document retrieval.

```
%pip install llama-index==0.12.16
%pip install llama-index-vector-stores-mongodb==0.6.0
%pip install llama-index-embeddings-huggingface==0.5.2
```

### Set up Open AI key for query engine retrieval

```
import os

import autogen

config_list = autogen.config_list_from_json(env_or_file="../OAI_CONFIG_LIST")

assert len(config_list) > 0
print("models to use: ", [config_list[i]["model"] for i in range(len(config_list))])

# Put the OpenAI API key into the environment
os.environ["OPENAI_API_KEY"] = config_list[0]["api_key"]
```

### Set up Mongo DB instance

To use this notebook, you will need to have a MongoDB Atlas environment. For this notebook, we use a docker instance. please refer [MongoDB: Create a Local Atlas Deployment with Docker](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-deploy-docker/) for more info.

Some info that you need to get include: - MongoDB Connection String (URI)

```
from autogen.agentchat.contrib.rag.mongodb_query_engine import MongoDBQueryEngine

query_engine = MongoDBQueryEngine(
    connection_string="mongodb://localhost:27017/?directConnection=true",
    database_name="vector_db",
    collection_name="test_collection",
)
```

Here we can see the default collection name in the vector store, this is where all documents will be ingested. When creating the `MongoDBQueryEngine` you can specify a `collection_name` and `database_name` to ingest into.

```
print(query_engine.get_collection_name())
```

### Initialize DB and ingest documents

Let’s ingest a document and query it.

`init_db` will overwrite the existing collection with the same name.

```
input_dir = "../test/agents/experimental/document_agent/pdf_parsed/"  # Update to match your input directory
```

```
input_docs = [input_dir + "nvidia_10k_2024.md"]  # Update to match your input documents
query_engine.init_db(new_doc_paths_or_urls=input_docs)
```

If the given collection already has the document you need, you can use `connect_db` to avoid overwriting the existing collection.

```
query_engine.connect_db()
```

```
question = "What is Nvidia's revenue in 2024?"
answer = query_engine.query(question)
print(answer)
```

Great, we got the data we needed. Now, let’s add another document.

```
new_docs = [input_dir + "Toast_financial_report.md"]
query_engine.add_docs(new_doc_paths_or_urls=new_docs)
```

And query again from the same database but this time for another corporate entity.

```
question = "What is the trading symbol for Toast"
answer = query_engine.query(question)
print(answer)
```