# Use ChromaDBQueryEngine to query Markdown files

This notebook demonstrates the use of the `ChromaDBQueryEngine` 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 `ChromaDBQueryEngine` integrates persistent ChromaDB vector storage with LlamaIndex for efficient document retrieval.

You can create and add this ChromaDBQueryEngine to [DocAgent](https://docs.ag2.ai/latest/docs/user-guide/reference-agents/docagent) to use.

```
%pip install llama-index-vector-stores-chroma==0.4.1
%pip install llama-index==0.12.16
```

### Load LLM configuration

This demonstration requires an `OPENAI_API_KEY` to be in your environment variables. See [our documentation](https://docs.ag2.ai/latest/docs/user-guide/advanced-concepts/llm-configuration-deep-dive) for guidance.

```
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"]
```

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.

```
from autogen.agentchat.contrib.rag.chromadb_query_engine import ChromaDBQueryEngine

query_engine = ChromaDBQueryEngine(
    host="host.docker.internal",  # Change this to the IP of the ChromaDB server
    port=8000,  # Change this to the port of the ChromaDB server
)
```

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

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

Let’s ingest a document and query it.

If you don’t have your documents ingested yet, follow the next two cells. Otherwise skip to the `connect_db` cell.

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

```
input_dir = (
    "/workspaces/ag2/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 = "How much money did Nvidia spend in research and development"
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 = "How much money did Toast earn in 2024?"
answer = query_engine.query(question)
print(answer)
```