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llama-stack-mirror/tests/integration/responses/recordings/3ad5c85a4df455be7cc724d789dd9b804be74e28c36069860db48e39e36b080c.json
Francisco Javier Arceo 62005dc1a9
feat: Making static prompt values in Rag/File Search configurable in Vector Store Config (#4368) 
# What does this PR do?

- Enables users to configure prompts used throughout the File Search /
Vector Retrieval
- Configuration is defined in the Vector Stores Config so they can be
modified at runtime
- Backwards compatible, which means the fields are optional and default
to the previously used values

This is the summary of the new options in the `run.yaml`
```yaml
vector_stores:
  file_search_params:
    header_template: 'knowledge_search tool found {num_chunks} chunks:\nBEGIN of knowledge_search tool results.\n'
    footer_template: 'END of knowledge_search tool results.\n'
  context_prompt_params:
    chunk_annotation_template: 'Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n'
    context_template: 'The above results were retrieved to help answer the user\'s query: "{query}". Use them as supporting information only in answering this query.{annotation_instruction}\n'
  annotation_prompt_params:
    enable_annotations: true
    annotation_instruction_template: 'Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format like \'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.\'. Do not add
extra punctuation. Use only the file IDs provided, do not invent new ones.'
    chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>\n{chunk_text}\n'
```

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
Added tests.

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
2025-12-15 11:39:01 -05:00

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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[client_with_models-txt=openai/gpt-4o:emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5:dim=768-llama_experts_pdf]",
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"content": "How many experts does the Llama 4 Maverick model have?"
},
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},
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"text": "[1] document_id: file-156847829497, score: 0.007500205427074304, attributes: {'filename': 'llama_stack_and_models.pdf', 'chunk_id': 'c3556aea-3b73-0278-aa16-ebbdb4c18b18', 'document_id': 'file-156847829497', 'token_count': 98.0, 'metadata_token_count': 11.0} cite as <|file-156847829497|>\n, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. \n"
},
{
"type": "text",
"text": "[2] document_id: file-156847829497, score: 0.0032793158911516916, attributes: {'filename': 'llama_stack_and_models.pdf', 'chunk_id': '16d99c69-8323-27ce-3bd7-7b51dcac2735', 'document_id': 'file-156847829497', 'token_count': 498.0, 'metadata_token_count': 11.0} cite as <|file-156847829497|>\nLlama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. It codifies best \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n \u25cf Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. \u25cf Plugin architecture to support the rich ecosystem of different API implementations in various \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android. \u25cf Standalone applications as examples for how to build production-grade AI applications with Llama \nStack.\n \nLlama Stack Benefits \n\u25cf Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy \nflexible\n \ndeployment\n \nchoices.\n \u25cf Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. \n"
},
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"type": "text",
"text": "END of knowledge_search tool results.\n"
},
{
"type": "text",
"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format like 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'. Do not add extra punctuation. Use only the file IDs provided, do not invent new ones.\n"
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