ops120/wechat-local-viewer

What it solves

This project provides a local, private interface for browsing and searching through exported WeChat chat history. It solves the problem of navigating large volumes of chat data (up to hundreds of thousands of messages) that are otherwise difficult to manage or search within the official application.

How it works

Users provide their own decrypted WeChat database files (contacts, sessions, and messages) in a specific directory. The tool uses a Python-based ETL (Extract, Transform, Load) process to ingest this data into a local SQLite database with FTS5 (Full-Text Search) indexing. A FastAPI backend serves this data to a Vue.js frontend, allowing for fast searching, filtering, and exporting of chat records. It also integrates with OpenAI-compatible LLM APIs to provide summarization and Q&A capabilities based on the local chat data.

Who it’s for

It is designed for users who have technical proficiency to handle their own data decryption and wish to perform advanced analysis, searching, or AI-assisted summarization of their personal chat history in a completely offline, private environment.

Highlights

• Local-First Architecture: All data processing and storage occur on the user's machine with no external dependencies or data exfiltration.
• High-Performance Search: Utilizes SQLite FTS5 for near-instantaneous full-text search across large message datasets.
• LLM Integration: Supports streaming responses from OpenAI-compatible services (like DeepSeek or Ollama) to summarize or answer questions about chat history.
• Data Portability: Allows exporting contacts and session data into CSV or JSON formats.
• Zero-Configuration Setup: Includes a simple batch script for one-click startup on Windows.