How aiwithhassan/ai-lawyer-rag-with-deepseek works
<explanation>
1. Identify the Project Type and Purpose
- This is a Python‐based Retrieval‐Augmented Generation (RAG) application with a Streamlit front‐end.
- It ingests documents (e.g., PDFs), builds a FAISS vector database, and runs a three‐phase pipeline:
• Phase 1: Streamlit UI (“frontend.py” or “main.py”)
• Phase 2: Vector database creation (“vector_database.py”)
• Phase 3: RAG pipeline logic (“rag_pipeline.py”)
2. Analyze the File Structure
- Top‑level scripts:
• frontend.py – Streamlit UI for Phase 1
• main.py – an all‑in‑one entry point (Streamlit)
• vector_database.py – builds FAISS vector store from documents
• rag_pipeline.py – retrieval + LLM inference pipeline
- Dependencies: Pipfile / requirements.txt manage Python packages (Streamlit, FAISS, OpenAI SDK, etc.)
- Data store: vectorstore/db_faiss/index.faiss (+ index.pkl)
- Assets: PDF documents used as source knowledge
3. Examine the README
- Describes environment setup (Pipenv, Conda, Pip)
- Explains how to run each phase:
• streamlit run frontend.py (Phase 1)
• python vector_database.py (Phase 2)
• python rag_pipeline.py (Phase 3)
- No existing architecture diagram, so we’ll design one.
4. Diagram Construction
a. Main Components
- User & Browser (end user)
- Streamlit UI (frontend.py or main.py)
- Vector DB Builder (vector_database.py + source PDFs)
- FAISS Vector Store (vectorstore/db_faiss)
- RAG Pipeline (rag_pipeline.py)
- External LLM Service (e.g., OpenAI API)
b. Relationships & Data Flows
1. User enters query into Streamlit UI.
2. UI forwards query to the RAG Pipeline module.
3. Pipeline embeds query (via embedding library), queries FAISS DB for similar docs.
4. Retrieved chunks + query are sent to the LLM API.
5. API returns generated answer, which the UI displays.
6. (Offline) Vector DB Builder ingests PDFs, computes embeddings, and writes to FAISS index.
c. Architectural Patterns
- Modular “phase” separation (ingest → store → query)
- Pipeline pattern in RAG (retrieve → augment → generate)
- Monolithic Python app organized into distinct modules
d. Key Technologies
- Streamlit for UI
- FAISS for vector similarity search
- Python embedding library (e.g., OpenAI embeddings)
- OpenAI or other LLM API for generation
5. Tailoring the Diagram
- Emphasize the frontend/backend boundaries: UI runs in user’s browser (client) vs. backend Python processes.
- Show data persistence in the FAISS store as a long‐lived asset built once per update.
- Highlight extensibility: new document ingestion, swapping out vector DB, or plugging in different LLMs.
6. Diagramming Guidelines
- Label each component clearly (e.g., “Streamlit UI”, “FAISS Index”).
- Use directional arrows to show request/response and data flow:
• Arrow from User → UI → Pipeline → FAISS
• Arrow from FAISS → Pipeline → LLM API → UI → User
• Dashed arrow from “Document Store” → Vector DB Builder → FAISS
- Apply color coding or shapes:
• Blue boxes for user‑facing components (UI)
• Green for compute modules (Pipeline, Builder)
• Orange cylinder for the vector database
• Grey cloud for external services (LLM API)
Remember: be exhaustive in identifying each module and data artifact, but keep the diagram readable by grouping related scripts together. Show both the one‑time ingestion path and the live query path, so the principal engineer can see end‑to‑end flow at a glance.
</explanation>