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77 lines
1.6 KiB
Markdown
77 lines
1.6 KiB
Markdown
# Files Retrieval Agent
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A sample agent that demonstrates using `FilesRetrieval` with the
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`gemini-embedding-2-preview` embedding model for retrieval-augmented
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generation (RAG) over local files.
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## What it does
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This agent indexes local text files from the `data/` directory using
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`FilesRetrieval` (backed by LlamaIndex's `VectorStoreIndex` and Google's
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`gemini-embedding-2-preview` embedding model), then answers user questions
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by retrieving relevant documents before generating a response.
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## Prerequisites
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- Python 3.10+
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- `google-genai >= 1.64.0` (required for `gemini-embedding-2-preview`
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support via the Vertex AI `embedContent` endpoint)
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- `llama-index-embeddings-google-genai >= 0.3.0`
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Install dependencies:
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```bash
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uv sync --all-extras
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```
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## Authentication
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Configure one of the following:
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**Google AI API:**
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```bash
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export GOOGLE_API_KEY="your-api-key"
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```
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**Vertex AI:**
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```bash
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export GOOGLE_GENAI_USE_ENTERPRISE=1
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export GOOGLE_CLOUD_PROJECT="your-project-id"
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export GOOGLE_CLOUD_LOCATION="us-central1"
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```
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Note: `gemini-embedding-2-preview` is currently only available in
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`us-central1`.
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## Usage
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```bash
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cd contributing/samples
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# Interactive CLI
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adk run files_retrieval_agent
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# Web UI
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adk web .
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```
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## Example queries
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- "What agent types does ADK support?"
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- "How does FilesRetrieval work?"
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- "What tools are available in ADK?"
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## File structure
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```
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files_retrieval_agent/
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├── __init__.py
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├── agent.py # Agent definition with FilesRetrieval tool
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├── data/
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│ ├── adk_overview.txt # ADK architecture overview
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│ └── tools_guide.txt # ADK tools documentation
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└── README.md
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```
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