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metadata 
title: Sherlock RAG
emoji: 🕵️
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

Sherlock RAG 🕵️

A specialized Chainlit application implementing a Retrieval-Augmented Generation (RAG) pipeline. This assistant is designed to query private project documentation and resumes to provide context-aware answers using high-performance LLMs.

🏗️ Architecture

This project follows a decoupled data-and-code architecture:

  • Application Logic: Hosted on GitHub and deployed to Hugging Face Spaces via Docker.
  • Knowledge Base: Private PDF documents stored in a separate Hugging Face Dataset (jakewatson91/sherlock-rag-docs).
  • Sync Mechanism: The app uses huggingface_hub to sync documents at runtime, bypassing Git LFS limitations and keeping the code repository lightweight.

🛠️ Tech Stack

  • UI/UX: Chainlit
  • Orchestration: LangChain
  • LLM: Moonshot AI (Kimi-k2) via Groq
  • Embeddings: Google Generative AI (text-embedding-004)
  • Data: From HuggingFace Dataset huggingface_hub (Snapshot Download)

🚀 Getting Started

Prerequisites

  • Python 3.11+
  • A Hugging Face Write Token
  • API Keys for:
    • Groq (Moonshot AI)
    • Google Generative AI (Embeddings)

Environment Variables

Create a .env file in the root directory:

HF_TOKEN=your_huggingface_write_token
GROQ_API_KEY=your_groq_api_key
GOOGLE_API_KEY=your_google_api_key