Build a RAG Based PDF Chatbot with Fundamentals of RAG

Standard Large Language Models provide general answers, but they lack visibility into private documents, proprietary data, and internal documentation. Fine-tuning models on static files is expensive and cumbersome.

Retrieval-Augmented Generation (RAG) solves this by dynamically supplying relevant document sections directly into the model's context window.

This tutorial outlines how to build a RAG PDF Chatbot using LangChain for orchestration, ChromaDB for persistent vector storage, and Google Gemini for embedding generation and final reasoning.

System Workflow & Pipeline Architecture

The application is structured into two core phases: document ingestion and semantic query retrieval.

[Phase 1: Ingestion]
[Input PDF Document] ──► [LangChain PyPDFLoader] ──► [RecursiveCharacterTextSplitter]
                                                              │
                                                              ▼
                                                     [Text Chunks + Overlap]
                                                              │
                                                              ▼
                                                   [Google Gemini Embeddings]
                                                              │
                                                              ▼
                                                   [ChromaDB Vector Store]

[Phase 2: Retrieval & Generation]
[User Question] ──► [Gemini Query Embedding] ──► [ChromaDB Similarity Search]
                                                              │
                                                              ▼
                                                    [Top-k Document Chunks]
                                                              │
                                                              ▼
                                                [Context + Prompt Assembly]
                                                              │
                                                              ▼
                                                    [Google Gemini Pro LLM]
                                                              │
                                                              ▼
                                                    [Grounded AI Response]

1. Document Loading: The PDF is parsed using LangChain's document loaders (PyPDFLoader or PyMuPDFLoader) to extract raw text and metadata.

2. Text Chunking: The document text is segmented into smaller, overlapping windows using RecursiveCharacterTextSplitter. Overlap ensures continuity across chunk boundaries so context is not truncated mid-sentence.

3. Vector Embeddings & Storage: Chunks are vectorized using Google Gemini's embedding models and stored locally in ChromaDB collections for high-performance cosine similarity lookup.

4. Contextual Retrieval: When a user asks a question, ChromaDB queries the indexed vector space and returns the top-$k$ most relevant text chunks.

5. Prompt Injection & Synthesis: The retrieved context chunks and the user prompt are passed to Google Gemini, which synthesizes an answer grounded strictly in the source PDF.

Core Tech Stack

Key Takeaway

By decoupling vector search from LLM generation, RAG ensures that your chatbot provides accurate, verifiable, and hallucination-free answers without requiring custom model training. ChromaDB offers a lightweight, zero-configuration local vector database, while Google Gemini delivers large context windows and rapid reasoning.