📄 PDF Chatbot

Our PDF Chatbot tool allows you to transform any PDF document into an interactive chatbot that can answer questions based on the document’s content. This tool uses advanced RAG (Retrieval-Augmented Generation) technology to provide accurate, context-aware responses.

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Start creating interactive chatbots from your PDFs

How It Works

The PDF Chatbot processes documents through several sophisticated steps:

Document Processing: Upload your PDF, which is then extracted and chunked into manageable text segments
Vector Embedding: Text chunks are converted into vector representations using Gemini’s models/text-embedding-004
Efficient Indexing: Vectors are indexed using FAISS for lightning-fast similarity search
Intelligent Querying: Questions are answered using Gemini 2.0 Flash LLM based on the most relevant document chunks

The system maintains chat history for contextual understanding and supports dynamic index updates.

API Endpoints

1. PDF to Chunk

This endpoint processes a PDF from a remote URL, extracts and cleans its text, and prepares it for indexing.

// Request
POST /pdf_to_chunk
{
  "user_id": "user123",
  "file_id": "file1",
  "file_url": "https://bucket.s3.amazonaws.com/file1.pdf"
}

// Response
{
  "total_pages": 8,
  "total_chars": 14500,
  "total_chunks": 5,
  "chunks": [
    {
      "chunk_text": "Extracted and cleaned text content...",
      "chunk_chars": 1024,
      "chunk_page": 2,
      "source": "https://bucket.s3.amazonaws.com/file1.pdf",
      "links": ["https://example.com", "https://another-link.com"],
      "unique_id": 9876543210123456
    },
    // Additional chunks...
  ],
  "faiss_index_path": "path/to/faiss/index"
}

2. Chunk to Index

This endpoint stores pre-processed text chunks into a user-specific vector index.

// Request
POST /chunk_to_index
{
  "user_id": "user123",
  "chunk_data": [
    {
      "chunk_text": "example text...",
      "unique_id": 9876543210123456,
      "chunk_chars": 1000,
      "chunk_page": 2,
      "source": "https://example.com",
      "links": []
    }
  ]
}

// Response
{
  "message": "FAISS index created successfully",
  "index_path": "path/to/index"
}

3. Query

This endpoint allows users to ask questions about uploaded documents.

// Request
POST /query
{
  "user_id": "user123",
  "question": "What are the main topics in the document?",
  "chat_history": [
    ["What is the document about?", "It discusses climate change policies."],
    ["What is the impact of climate change?", "It leads to rising temperatures, extreme weather, and sea level rise."]
  ]
}

// Response
{
  "answer": "The document discusses the effects of CO2 emissions and global warming...",
  "chunk_ids": [9876543210123456, 1576543210153056]
}

4. Delete Vectors

This endpoint removes specific vector embeddings from a user’s index.

// Request
POST /delete_vectors
{
  "user_id": "user123",
  "chunk_ids": [9876543210123456, 1576543210153056]
}

// Response
{
  "message": "Vectors deleted successfully",
  "deleted_count": 2
}

Use Cases

Knowledge Base: Create interactive FAQ systems from technical documentation
Educational Content: Transform textbooks or course materials into interactive learning assistants
Legal Documents: Make complex legal documents more accessible through natural language queries
Research Papers: Quickly extract insights from academic papers through conversation

This tool is completely free to use! Start building your PDF chatbot today to make your documents more accessible and interactive.

Tools documentation

Tools

PDF Chatbot

📄 PDF Chatbot

Try PDF Chatbot Now

How It Works

API Endpoints

1. PDF to Chunk

2. Chunk to Index

3. Query

4. Delete Vectors

Use Cases

​📄 PDF Chatbot

Try PDF Chatbot Now

​How It Works

​API Endpoints

​1. PDF to Chunk

​2. Chunk to Index

​3. Query

​4. Delete Vectors

​Use Cases

📄 PDF Chatbot

How It Works

API Endpoints

1. PDF to Chunk

2. Chunk to Index

3. Query

4. Delete Vectors

Use Cases