Hi everyone. I've noticed several members asking about integrating BabyAGI with proprietary or internal data sources, which is a fantastic use case. We've just finished a successful pilot project doing exactly that, so I wanted to share our step-by-step approach and some key lessons.
Our goal was to allow our BabyAGI agent to answer questions about internal processes and project histories by connecting it to our company's Confluence wiki. We used the `langchain` framework, which BabyAGI is built upon, to create a retrieval-augmented generation (RAG) pipeline. The core steps were: 1) Loading and chunking our documentation, 2) Creating and persisting a vector store (we used FAISS), and 3) Modifying the BabyAGI agent's task execution chain to include a retrieval step from this store before generating a final answer.
The main challenge wasn't the code, but ensuring the agent's responses were grounded *only* in our provided documents to avoid hallucination. We had to carefully adjust the prompt templates for the agent's execution chain, adding strong instructions to only use the retrieved context and to respond with "I don't have that information" if the context was empty. Another pitfall was chunk size; our initial chunks were too large, leading to vague answers. Smaller, overlapping chunks worked much better.
I'm happy to elaborate on specific parts, like the exact prompt modifications or the chunking strategy. If anyone else has tackled this, I'd love to compare notes on how you handled permissions or updating the knowledge base over time.
— isabel
— isabel
Great post. That prompt engineering step for grounding is so critical and often overlooked. We found that even with those strong "only use the context" instructions, we'd still get subtle fabrications, especially when the retrieved chunks were tangentially related.
Adding a verification chain that scored the final answer's alignment with the retrieved snippets cut down on those by about 90%. Something simple like: "On a scale of 1-5, how directly is the following answer supported by the provided context?" fed back into the agent's loop.
Also, curious about your chunking strategy for Confluence pages. Did you find a sweet spot for chunk size/overlap with all those nested tables and images?
Clean code is not an option, it's a sanity measure.