Developed and Enhanced FAISS Query Toolkit

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📅 2025-02-10 — Session: Developed and Enhanced FAISS Query Toolkit

🕒 18:20–19:40
🏷️ Labels: FAISS, Langchain, Querying, Text Processing, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to develop and enhance a querying toolkit for a FAISS knowledge base using LangChain, and to address various technical challenges encountered during the process.

Key Activities

  • Building a Query Toolkit: Developed a querying toolkit for a FAISS knowledge base, detailing various query functions and their implementations using LangChain.
  • Error Resolution: Resolved an ImportError related to the LanguageTextSplitter class in the LangChain library by updating packages, installing necessary dependencies, and modifying import statements.
  • Dynamic Text Splitter Implementation: Implemented a dynamic text splitter function to handle various formats such as Markdown, Python, HTML, JSON, and LaTeX, enhancing text processing capabilities.
  • MIME Type Handling: Updated functions to handle MIME types in text processing, mapping MIME types to appropriate text splitters and improving chunk processing.
  • Handling spaCy Model Error: Addressed the error caused by the absence of the en_core_web_sm model in spaCy, providing solutions for installation and code modification.
  • Enhancements to process_chunks Function: Modified the process_chunks function to count and display the total number of characters and words for each text chunk processed.
  • Data Storage Structure Overview: Outlined a structured map for data storage, detailing the organization of files, chunks, embedded chunks, and file hashes.
  • Designing Querying Needs: Outlined essential querying needs across AI-driven workflows, detailing specific query types, triggers, and implementation strategies.

Achievements

  • Successfully developed and enhanced the querying toolkit for FAISS using LangChain.
  • Resolved critical errors and improved text processing and querying capabilities.
  • Established a comprehensive data storage structure and outlined querying strategies for AI workflows.

Pending Tasks

  • Further testing and validation of the querying toolkit in real-world scenarios.
  • Continuous monitoring and improvement of text processing functions to handle new file formats and errors.

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