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Use ChromaDBQueryEngine to query Markdown files#

Open In Colab Open on GitHub

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 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 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 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)