How can I integrate RAG with an ADK ?

[testercode-oss]

I’ve already exposed a few tools, but I have a use case where an AI needs to read and acknowledge the contents of a PDF document. Adding full RAG for this feels a bit heavy to me.

Is there a way to integrate a PDF document into an ADK as a tool? Or would there be a better or more recommended approach for handling this scenario?

[adk-bot]

Response from ADK Answering Agent (experimental, answer may be inaccurate)

TLDR: You can create a custom FunctionTool that uses a standard Python library to read the PDF file. For a more robust approach, you can use ADK's artifact management to save and load the reference to your PDF document. Full Retrieval-Augmented Generation (RAG) is also a built-in tool type for more complex scenarios.

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Hello! That's an excellent question. You are right that setting up a full RAG system for a single document might be more than you need. The good news is that ADK provides flexible ways to handle this.

Recommended Approach: Custom FunctionTool

The most straightforward way to achieve your goal is to create a custom FunctionTool [1]. This tool would be a simple Python function that:

1. Takes the file path of the PDF as an argument.
2. Uses a standard Python library (like PyMuPDF, pdfplumber, etc.) to open and read the text content from the PDF.
3. Returns the extracted text to the agent, which can then "acknowledge" or summarize it as needed.

Using Artifacts for Better Management

For a more robust and integrated solution, you can leverage ADK's "artifacts" to manage the PDF file. This is a two-step process:

1. Save the PDF reference as an artifact: Instead of passing file paths directly, you can have a tool that saves a reference to your document using context.save_artifact() from the ADK context [2, 3].
2. Load the artifact in your tool: Your custom tool can then use tool_context.load_artifact() to retrieve the reference and process the file [3].

This approach helps in better tracking and management of the data your agent interacts with.

For More Complex Scenarios: Full RAG

As your use case grows, you might find a need for a more powerful retrieval system. ADK does support full RAG capabilities, which you initially mentioned:

• Vector Databases: You can integrate with vector databases like Chroma [4] or Qdrant [5] to build a knowledge base for your agent.
• Vertex AI Search: ADK also supports grounding with Vertex AI Search, allowing your agent to retrieve information from private enterprise documents [6].

This allows you to start simple and scale up to a more advanced RAG architecture as your needs evolve.

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[1] Custom Tools

[2] Artifacts

[3] Context

[4] Chroma

[5] Qdrant

[6] Vertex AI Search Grounding

[testercode-oss]

can you please let me know which version of ADK i need to use and also a code example would be nice