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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>
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@ -15,6 +15,7 @@ Meta's reference implementation of an agent system that can use tools, access ve
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `persistence` | `AgentPersistenceConfig` | No | | |
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| `vector_stores_config` | `VectorStoresConfig \| None` | No | | Configuration for vector store prompt templates and behavior |
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## Sample Configuration
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@ -10,6 +10,12 @@ title: inline::rag-runtime
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RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `vector_stores_config` | `VectorStoresConfig` | No | `default_provider_id=None default_embedding_model=None rewrite_query_params=None file_search_params=FileSearchParams(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=ContextPromptParams(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=AnnotationPromptParams(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')` | Configuration for vector store prompt templates and behavior |
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## Sample Configuration
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```yaml
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