Unlock Conversational AI: Build a File Chatbot with LlamaIndex & A2A Protocol

Want to build a powerful conversational agent that understands documents? This guide explores how to leverage LlamaIndex Workflows and the A2A protocol to create a file chatbot capable of answering questions, providing citations, and maintaining context across multiple turns. Learn how to quickly integrate file parsing, conversational AI, and real-time updates into your applications.

What is the LlamaIndex File Chat Workflow with A2A Protocol?

This sample demonstrates a conversational agent built using LlamaIndex. This system allows users to upload files, have them parsed, and then engage in conversations about the content of those files. The A2A protocol facilitates a standardized way to interact with this agent, enabling clients to send requests and receive real-time status updates.

How Does It Work? A Step-by-Step Breakdown

The agent functions through a well-defined workflow, leveraging LlamaIndex and Google Gemini:

1. File Upload: The client sends a message to the server, including a file attachment.
2. Parsing: The workflow parses the document using LlamaParse, providing updates to the client via real-time streaming.
3. Conversation: The workflow processes the user's query, leveraging the parsed document as context for the LLM (Language Model).
4. Response: The LLM generates a structured response, which is then streamed back to the client along with relevant citations.
5. Context Maintained: The server maintains context for follow-up questions, creating a seamless conversational experience.

The diagram included illustrates the whole process visually.

Key Features That Enhance User Experience

• File Upload: Upload documents directly into the chat for context.
• Multi-turn Conversations: The agent remembers previous interactions for more natural dialogues.
• Real-time Streaming: Get immediate feedback as the agent processes your requests.
• Push Notifications: Stay updated with webhook-based alerts.
• Conversational Memory: The agent maintains context throughout the session.
• LlamaParse Integration: Benefit from accurate file parsing using LlamaParse.

Ready to Get Started? Here's What You Need

Before diving into the code, make sure you have these prerequisites:

• Python: Version 3.12 or higher.
• UV: A fast, modern Python package installer and resolver.
• LLM API Key Access to an LLM ,API key (e.g., Google GenAI).
• LlamaParse API Key: Get a free LlamaParse API key to enable document parsing.

Step-by-Step Setup & Execution

Follow these instructions to set up and run the LlamaIndex file chat agent:

1. Navigate to the Sample Directory:

   cd samples/python/agents/llama_index_file_chat
   

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2. Create an Environment File (.env): Store your API keys securely.

   echo "GOOGLE_API_KEY=your_api_key_here" >> .env
   echo "LLAMA_CLOUD_API_KEY=your_api_key_here" >> .env
   

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3. Run the Agent:

   • Basic run (default port 10010):

     uv run
     

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   • Custom host/port:

     uv run --host 0.0.0.0 --port 8080
     

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4. Run an A2A Client (in a separate terminal):

   cd samples/python
   uv run hosts/cli --agent http://localhost:10010
   

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5. Test the Agent: Download a sample file (e.g., an Arxiv paper available via wget https://arxiv.org/pdf/1706.03762 -O attention.pdf) and interact with the agent:

   What does this file talk about?
   Select a file path to attach? (press enter to skip): ./attention.pdf
   

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Deep Dive into Technical Implementation

The LlamaIndex file chat workflow relies on several key technologies:

• LlamaIndex Workflows: Orchestrates the parsing and chat functionalities.
• Streaming Support: Provides real-time updates during processing.
• Serializable Context: Maintains conversation state for coherent dialogues.
• Push Notification System: Delivers webhook-based updates with JWK authentication.
• A2A Protocol Integration: Ensures compliance with A2A specifications.

Limitations to Consider

• Text-Based Output: Currently only supports text-based responses.
• LlamaParse Credits: LlamaParse offers a limited number of free credits.
• Session-Based Memory: Conversation memory is in-memory and resets upon server restart.
• Scalability: Inserting the entire document into the context window is not scalable for larger files; consider vector databases. For larger document implementation, you'll need to leverage vector DBs for effective Retrieval-Augmented Generation (RAG). LlamaIndex seamlessly integrates with multiple vector and cloud databases.

Exploring Example Interactions

Here are a few example requests and responses to illustrate how the agent works:

• Synchronous Request: Shows a basic request and its corresponding response.
• Multi-turn Example: Demonstrates how the agent handles follow-up questions and maintains context across multiple interactions.
• Streaming Example: Illustrates the real-time streaming of status updates during document parsing and chat processing.

Level Up Your Chatbot Game

Building a file chatbot with LlamaIndex and the A2A protocol opens exciting possibilities for interacting with documents in a conversational way. Leverage this guide to get started and explore the provided resources for further learning! This LlamaIndex file chat system provides a scalable and standardized way to parse and chat about documents.

Further Exploration

• [A2A Protocol Documentation](link to documentation)
• [LlamaIndex Workflow Documentation](link to documentation)
• [LlamaIndex Workflow Examples](link to examples)
• [LlamaParse Documentation](link to documentation)
• [Google Gemini API](link to API)