Doc Chat Studio: Building a Production-Grade RAG AI with LangChain, FAISS & Streamlit

In recent years, Retrieval-Augmented Generation (RAG) has emerged as one of the most powerful architectural patterns for enterprise AI applications. Instead of relying solely on a large language model’s memory, RAG grounds responses in your own documents, ensuring accuracy, transparency, and trust.

In this blog, we’ll deep-dive into Doc Chat Studio, an AI-powered document chat application built using Python, LangChain, FAISS, HuggingFace embeddings, and Streamlit. This project demonstrates how to design a multi-step, agentic RAG pipeline that supports real-world document formats like PDFs, DOCX, Markdown, and text files.

🚀 What Is Doc Chat Studio?

Doc Chat Studio is an interactive AI application that allows users to:

Upload multiple documents (PDF, Word, Markdown, TXT)
Index them using semantic embeddings
Ask natural language questions
Receive context-aware, source-cited answers
Maintain conversation memory across multiple questions

Unlike basic chatbots, this app combines semantic search + agentic reasoning, making it suitable for enterprise knowledge bases, internal documentation, and research workflows.

🧠 High-Level Architecture

At a high level, the application consists of four major layers:

User Interface (Streamlit)
Document Processing Pipeline
Vector Search (FAISS)
Agentic RAG Reasoning with LangChain


User → Streamlit UI → Document Upload
     → Text Chunking → Embeddings → FAISS
     → Retriever → Agentic LLM Chains
     → Final Answer with Sources

🎨 Modern Streamlit UI

The application uses custom CSS injection to provide a clean, modern UI:

Gradient-styled cards
Pill-shaped document badges
Chat bubbles with timestamps
Tab-based layout (Documents | Chat)

This makes the app feel more like a polished SaaS product than a demo.

📂 Document Ingestion & Processing

Doc Chat Studio supports multiple file formats:

PDF (via pypdf)
DOCX (via python-docx)
Markdown & TXT

Text Chunking Strategy

Documents are split using:

RecursiveCharacterTextSplitter
Chunk size: 1000 characters
Overlap: 200 characters

This ensures:

Better semantic recall
Reduced hallucinations
Higher retrieval accuracy

Each chunk is stored with metadata:


{
  "source": "filename.pdf",
  "chunk": 3
}

🔎 Semantic Search with FAISS

For vector search, the app uses:

HuggingFace all-MiniLM-L6-v2 embeddings
LangChain FAISS VectorStore

FAISS enables:

In-memory similarity search
Low latency retrieval
Scalable indexing for large document sets

A retriever fetches the top-k most relevant chunks for every question.

🧠 Agentic RAG: Multi-Step Reasoning with LangChain

One of the most powerful aspects of Doc Chat Studio is its agentic RAG pipeline.

Instead of a single prompt → response flow, the app uses a SequentialChain with three reasoning steps:

🔹 Step 1: Summarization

Checks conversation history first
Reuses previous answers when possible
Avoids redundant document searches

🔹 Step 2: Analysis

Determines whether the summary fully answers the question
Identifies gaps that require document grounding

🔹 Step 3: Final Answer Generation

Produces a structured, user-friendly response
Adds explicit source citations
Ensures answers are grounded in documents or prior chat history

This approach mirrors agentic AI behavior, where the system plans, evaluates, and executes reasoning steps dynamically.

💬 Conversation Memory

The app uses ConversationBufferMemory to:

Persist multi-turn conversations
Allow follow-up questions
Prevent repeated answers
Improve coherence over time

This makes interactions feel natural and contextual—similar to enterprise copilots.

🛡️ Fallback & Offline Safety

Doc Chat Studio is resilient by design:

If OpenAI API keys are missing:
- The app falls back to retrieved document snippets
If FAISS is unavailable:
- It gracefully degrades without crashing
If no relevant context exists:
- The assistant responds transparently:
  
  “The requested information is not available in the provided context.”

This is critical for enterprise-grade reliability.

🧪 Supported Models

Primary LLM: gpt-4o
Fallback LLM: gpt-4o-mini
Embeddings: HuggingFace MiniLM (local, cost-efficient)

The design allows easy replacement with:

Azure OpenAI
Local LLMs
Other embedding models

🎯 Why This Project Matters

Doc Chat Studio is more than a demo—it demonstrates real-world AI architecture best practices:

✔ RAG over hallucination
✔ Agentic reasoning instead of single prompts
✔ Source-grounded answers
✔ Enterprise-ready UI & UX
✔ Extensible and modular design

This architecture can be reused for:

Internal knowledge bases
Compliance document analysis
Technical documentation assistants
Research copilots

🚀 What’s Next?

Potential enhancements include:

Persistent vector storage (disk-based FAISS)
User authentication
Role-based access control
Document versioning
Deployment on Azure / AWS
Local LLM support (Ollama, LLaMA)

📌 Final Thoughts

Doc Chat Studio showcases how modern AI systems should be built—grounded, explainable, and agentic. By combining LangChain, FAISS, and Streamlit, it provides a blueprint for building scalable, trustworthy AI assistants powered by your own data.

If you’re exploring RAG, agentic AI, or enterprise GenAI, this PoC is an excellent reference implementation. Github: https://github.com/srinik16/aiassistant

FAQSFUSION