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https://github.com/agno-agi/agno.git
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Guy Rosin
0e139025c5
## Summary This PR migrates the codebase from psycopg2 to psycopg (v3). Most of the codebase was already using psycopg - what remained was mainly `PostgresTools`, as well as some comments and dependencies. Breaking change: I've changed the related dependency group name from `agno[psycopg2]` to `agno[psycopg]`. ## Type of change - [ ] Bug fix - [ ] New feature - [x] Breaking change - [x] Improvement - [ ] Model update - [ ] Other: --- ## Checklist - [x] Code complies with style guidelines - [x] Ran format/validation scripts (`./scripts/format.sh` and `./scripts/validate.sh`) - [x] Self-review completed - [x] Documentation updated (comments, docstrings) - [x] Examples and guides: Relevant cookbook examples have been included or updated (if applicable) - [x] Tested in clean environment - [x] Tests added/updated (if applicable)
30 lines
1.3 KiB
Python
30 lines
1.3 KiB
Python
"""
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This agent answers questions using knowledge from a PDF stored in a Google Cloud Storage (GCS) bucket.
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Setup Steps:
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1. Install required libraries: agno, google-cloud-storage, psycopg-binary (for PostgreSQL vector DB).
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2. Set up your GCS bucket and upload your PDF file.
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3. For public GCS buckets: No authentication needed, just set the bucket and PDF path.
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4. For private GCS buckets:
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- Grant the service account Storage Object Viewer access to the bucket via Google Cloud Console
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- Export GOOGLE_APPLICATION_CREDENTIALS with the path to your service account JSON before running the script
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5. Update 'bucket_name' and 'blob_name' in the script to your PDF's location.
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6. Run the script to load the knowledge base and ask questions.
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"""
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from agno.agent import Agent
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from agno.knowledge.gcs.pdf import GCSPDFKnowledgeBase
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from agno.vectordb.pgvector import PgVector
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db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
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knowledge_base = GCSPDFKnowledgeBase(
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bucket_name="your-gcs-bucket",
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blob_name="path/to/your.pdf",
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vector_db=PgVector(table_name="recipes", db_url=db_url),
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)
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knowledge_base.load(recreate=False) # Comment out after first run
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agent = Agent(knowledge=knowledge_base, search_knowledge=True)
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agent.print_response("How to make Thai curry?", markdown=True)
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