📅 2025-02-10 — Session: Implemented and Debugged FAISS and LangChain Systems
🕒 15:30–17:55
🏷️ Labels: Langchain, FAISS, Embedding, Debugging, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance and debug the LangChain and FAISS systems for efficient text chunking, embedding, and retrieval processes.
Key Activities
- Reviewed LangChain text chunking tools and integrated dynamic text splitters to optimize text processing pipelines.
- Implemented a reset function for the chunking system to manage file directories and metadata.
- Optimized AI retrieval strategies focusing on vector economics and smart querying.
- Integrated AI-directed filtering using SelfQueryRetriever to improve retrieval accuracy.
- Debugged FAISS load issues, focusing on file path errors and ensuring compatibility with LangChain.
- Implemented incremental embedding functions to manage vector stores efficiently, reducing redundant processing and managing costs.
- Diagnosed and fixed JSON structure mismatches in the
load_jsonfunction to handle metadata robustly.
Achievements
- Successfully integrated and debugged LangChain’s dynamic text splitters and FAISS systems.
- Enhanced retrieval accuracy and efficiency through AI-directed filtering and optimized embedding processes.
- Resolved FAISS load errors and JSON structure mismatches, ensuring robust data management.
Pending Tasks
- Further optimization of embedding calls and retrieval strategies to enhance performance and reduce costs.