Knowledge Graph RAG with Local LLM

I have updated former knowledge-graph-rag with new langchain version using Neo4j and Azure service.

pip install -r requirements.txt

The pipeline

pipeline.py -> main script to run the pipeline.

1. It extracts text from PDFs in the files folder.
2. Sends the text to the LLM to extract entities and relationships.

• To use a I needed to build a custom chat_prompt, as pointed out in this StackOverflow topic.
• I chose to also build my own Pydantic class and examples, instead of using the library's default, to align the model to the crime-related theme.

3. Inserts into the Neo4J database the extracted entities and relationships.

After running the pipeline script, check out the Neo4J database at http://localhost:7474/browser/:

MATCH (n)-[r]->(m)
RETURN n, r, m

You should see all the entities and relationships extracted from the PDFs.

The Graph RAG

graph_rag.py -> main script to run the Graph RAG Q&A.

1. It queries the Neo4J database with a natural language question.
2. It returns the answer in natural language based on the result of the query.

Right now you need to write the questions using the same words as the entities and relationships in the database. I'm working on a way to make the questions more flexible...