🎯 Objective

Build a chatbot that answers user questions based only on the content of an uploaded PDF (e.g., product documentation), using a Retrieval-Augmented Generation (RAG) architecture.

Flowise Link→ https://cloud.flowiseai.com/canvas/38088b33-dd1d-4cc9-a222-08377eb32d14

🏗️ High-Level Architecture (Flowise)

PDF File Upload
   ↓
Text Splitter (internal to PDF node)
   ↓
OpenAI Embeddings (e.g., text-embedding-ada-002)
   ↓
Vector Store (Chroma or Pinecone)
   ↓
Retriever (returns top-matching chunks)
   ↓
Conversational Retrieval QA Chain
      ↙              ↘
  ChatOpenAI       [System Prompt Template]
   ↓
Chat Interface

This flow ensures that the chatbot only answers based on your uploaded content, grounded in real data instead of hallucinating.

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🧩 Components Used & Their Purpose

Component Purpose
PDF File Node Loads and extracts text from uploaded document
Text Splitter Breaks document into chunks so the LLM doesn’t exceed context limits
OpenAI Embeddings Converts each chunk into a numerical vector capturing semantic meaning
Chroma / Pinecone Stores the embeddings and enables fast semantic search
Retriever Searches the vector store for the chunks most relevant to the user's query
Conversational Retrieval QA Chain Coordinates retrieval and answer generation, formats prompt, supports memory
ChatOpenAI The actual LLM that forms the human-readable answer
Chat Interface Allows users to ask questions interactively

🤔 FAQs

1. Why is OpenAI Embeddings used?

Embeddings convert text into vector representations that allow the system to "search by meaning" rather than keywords. When a user asks a question, the system compares the embedding of the query to stored embeddings of document chunks to find the best match.

2. What are Chroma and Pinecone? How are they different?

Both are vector databases:

3. Why do we need the Conversational Retrieval QA Chain?