file_search_basic.py
"""
Google File Search Basic
========================
Cookbook example for `google/gemini/file_search_basic.py`.
"""
from pathlib import Path
from agno.agent import Agent
from agno.models.google import Gemini
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# Create Gemini model
model = Gemini(id="gemini-2.5-flash")
# Create agent with the model
agent = Agent(model=model, markdown=True)
print("Creating File Search store...")
store = model.create_file_search_store(display_name="Basic Demo Store")
print(f"[OK] Created store: {store.name}")
print("\nUploading file to store...")
# Upload a file directly to the File Search store
operation = model.upload_to_file_search_store(
file_path=Path(__file__).parent / "documents" / "sample.txt",
store_name=store.name,
display_name="Sample Document",
)
# Wait for upload to complete
print("Waiting for upload to complete...")
completed_op = model.wait_for_operation(operation)
print("[OK] Upload completed")
# Configure model to use File Search
model.file_search_store_names = [store.name]
# Query the documents
print("\nQuerying documents...")
run = agent.run(
"Can you tell me about the content in the uploaded document? Specifically, what are the main safety guidelines mentioned?"
)
print(f"\nResponse:\n{run.content}")
# Extract and display citations
print("\n" + "=" * 50)
if run.citations and run.citations.raw:
print("Citations:")
print("=" * 50)
# Access grounding metadata directly from citations
grounding_metadata = run.citations.raw.get("grounding_metadata", {})
chunks = grounding_metadata.get("grounding_chunks", []) or []
sources = set()
for chunk in chunks:
if isinstance(chunk, dict):
retrieved_context = chunk.get("retrieved_context")
if isinstance(retrieved_context, dict):
title = retrieved_context.get("title", "Unknown")
sources.add(title)
if sources:
print(f"\nSources ({len(sources)}):")
for i, source in enumerate(sorted(sources), 1):
print(f" [{i}] {source}")
print(f"\nDetailed Citations ({len(chunks)}):")
for i, chunk in enumerate(chunks, 1):
if isinstance(chunk, dict):
retrieved_context = chunk.get("retrieved_context")
if isinstance(retrieved_context, dict):
print(f"\n [{i}] {retrieved_context.get('title', 'Unknown')}")
if retrieved_context.get("uri"):
print(f" URI: {retrieved_context['uri']}")
print(" Type: file_search")
if retrieved_context.get("text"):
text = retrieved_context["text"]
if len(text) > 200:
text = text[:200] + "..."
print(f" Text: {text}")
else:
print("Citations metadata found but no File Search sources detected")
else:
print("No citations found in response")
# Cleanup
print("\n" + "=" * 50)
print("Cleaning up...")
model.delete_file_search_store(store.name)
print("[OK] Store deleted")
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
pass
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno google-genai
3
Export your Google API key
export GOOGLE_API_KEY="your_google_api_key_here"
$Env:GOOGLE_API_KEY="your_google_api_key_here"
4
Run the example
Clone Agno and run the example from the repository root:
git clone https://github.com/agno-agi/agno.git
cd agno
python cookbook/90_models/google/gemini/file_search_basic.py