Build a RAG-Based PDF Chatbot with Streamlit, FAISS, LangChain & Gemini API (Free!)

Standard LLMs lack access to private, local, or specialized documents. Relying on basic prompt stuffing often exceeds context limits or introduces hallucinations.

Retrieval-Augmented Generation (RAG) overcomes this limitation by dynamically extracting only the most relevant document sections and supplying them directly into the LLM's prompt context.

This guide outlines how to build an end-to-end RAG PDF chatbot using LangChain for orchestration, FAISS (Facebook AI Similarity Search) for high-speed local vector retrieval, Google Gemini for embeddings and generation, and Streamlit for the interactive user interface.

System Architecture & Pipeline Workflow

The application runs entirely in a lightweight Python environment without requiring external cloud vector database instances:

[Phase 1: Ingestion & Vectorization]
[User PDF Upload] ──► [PyPDF / PDF Extractor] ──► [Recursive Text Splitter]
                                                           │
                                                           ▼
                                               [Text Chunks + Overlap]
                                                           │
                                                           ▼
                                              [Google Gemini Embeddings]
                                                           │
                                                           ▼
                                                [Local FAISS Vector Index]

[Phase 2: Retrieval & Chat Inference]
[User Question] ──► [Gemini Query Embedding] ──► [FAISS Similarity Search]
                                                           │
                                                           ▼
                                                 [Top-k Relevant Chunks]
                                                           │
                                                           ▼
                                             [Context-Injected Prompt]
                                                           │
                                                           ▼
                                                [Google Gemini LLM]
                                                           │
                                                           ▼
                                              [Streamlit Chat Response]

Core Pipeline Modules

1. PDF Upload & Text Chunking

• The user uploads one or more PDF files directly through the Streamlit sidebar.

• Text is extracted across pages and processed using RecursiveCharacterTextSplitter.

• Chunk sizes are configured (e.g., 1000 characters with a 200-character overlap) to maintain semantic context across sentence and paragraph boundaries.

2. Embeddings & In-Memory Vector Search (FAISS)

• Text chunks are converted into dense vector representations using Google Gemini's Embedding models (models/embedding-001).

• Vectors are indexed in FAISS, providing microsecond-level similarity lookups across dense vector spaces without recurring database costs.

3. Context Retrieval & Prompt Construction

• Natural language queries are transformed into vector embeddings and matched against the FAISS index using cosine similarity / L2 distance.

• The top-$k$ matching chunks are assembled alongside system constraints into a strict prompt template that forces the model to answer solely based on the uploaded document.

4. Generation via Google Gemini API

• The prompt is sent to Google Gemini (gemini-pro / gemini-flash) to generate concise, grounded answers.

• The response is streamed back to Streamlit's chat interface (st.chat_message) while maintaining conversational session state.

Technology Stack Summary

Key Engineering Takeaways

• Zero-Infrastructure Vector Search: Unlike managed cloud databases, FAISS runs directly within your application process, reducing setup complexity and operational costs.

• Hallucination Guardrails: Grounding Gemini with retrieved FAISS chunks ensures the chatbot avoids unverified general knowledge answers when analyzing specific documents.

• Rapid Prototyping: Combining LangChain's pre-built retrieval abstractions with Streamlit's reactive UI lets developers ship working document Q&A tools in a single script.

For a step-by-step walkthrough of this complete implementation, watch Build a RAG-Based PDF Chatbot with Streamlit, FAISS, LangChain & Gemini API. This video is relevant because it demonstrates the full source code and setup for integrating FAISS, Streamlit, and the Gemini API into a working document chatbot.