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56 lines
1.6 KiB
Markdown
56 lines
1.6 KiB
Markdown
# RAG Sample PDFs for agent_template_backend
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These PDF files are synthetic, searchable sample documents created to validate the RAG embedding and retrieval flow of `agent_template_backend`.
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## Files
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- `01_billing_agent_invoice_policy.pdf` - sample knowledge for `billing_agent`
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- `02_orders_agent_lifecycle_policy.pdf` - sample knowledge for `orders_agent`
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- `03_product_agent_catalog_policy.pdf` - sample knowledge for `product_agent`
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- `04_support_agent_sla_policy.pdf` - sample knowledge for `support_agent`
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- `05_business_context_rag_flow.pdf` - sample knowledge about BusinessContext, identity.yaml and MCP parameter mapping
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## How to use
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Copy the PDF files to the backend documentation directory:
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```bash
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mkdir -p agent_template_backend/docs/rag_samples
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cp *.pdf agent_template_backend/docs/rag_samples/
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```
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For a local smoke test, use:
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```env
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VECTOR_STORE_PROVIDER=sqlite
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EMBEDDING_PROVIDER=mock
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SQLITE_DB_PATH=./data/agent_framework.db
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RAG_TOP_K=4
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```
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Then run:
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```bash
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python scripts/generate_rag_embeddings.py \
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--docs-dir ./agent_template_backend/docs/rag_samples \
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--namespace default
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```
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For production-like semantic embeddings with OCI Generative AI, use:
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```env
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VECTOR_STORE_PROVIDER=autonomous
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EMBEDDING_PROVIDER=oci
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OCI_COMPARTMENT_ID=ocid1.compartment.oc1..xxxx
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OCI_REGION=us-chicago-1
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OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
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```
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## Suggested retrieval test questions
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- What is a prorated charge?
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- When can the OrdersAgent open an exchange request?
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- Which SKU represents the AI Agents book?
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- What is the target response for a critical support ticket?
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- How does BusinessContext map customer_key to MCP tool parameters?
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