feat: Enhance Vector Stores config with full configurations (#4397)

# What does this PR do?

Enhances the Vector Stores config with full set of appropriate
configurations
- Add FileIngestionParams, ChunkRetrievalParams, and FileBatchParams
subconfigs
- Update RAG memory, OpenAI vector store mixin, and vector store utils
to use configuration
  - Fix import organization across vector store components
  - Add comprehensive vector stores configuration documentation
  - Update docs navigation to include vector store configuration guide
- Delete `memory/constants.py` and move constant values directly into
Pydantic models

## Test Plan
Tests updated + CI

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

docs/docs/concepts/vector_stores_configuration.mdx

@@ -0,0 +1,261 @@
+# Vector Stores Configuration
+
+## Overview
+
+Llama Stack provides a variety of configuration options for vector stores through the `VectorStoresConfig`. This configuration allows you to customize file processing, chunk retrieval, search behavior, and performance parameters to optimize File Search and your RAG (Retrieval Augmented Generation) applications.
+
+The configuration affects all vector store providers and operations across the entire stack, particularly the OpenAI-compatible vector store APIs.
+
+## Configuration Structure
+
+Vector store configuration is organized into logical subconfigs that group related settings. For example, the yaml below provides an example configuration for the Faiss provider.
+
+```yaml
+vector_stores:
+  default_provider_id: "faiss"
+  default_embedding_model:
+    provider_id: "sentence-transformers"
+    model_id: "all-MiniLM-L6-v2"
+
+  # Query rewriting for enhanced search
+  rewrite_query_params:
+    model:
+      provider_id: "ollama"
+      model_id: "llama3.2:3b-instruct-fp16"
+    prompt: "Rewrite this search query to improve retrieval results by expanding it with relevant synonyms and related terms: {query}"
+    max_tokens: 100
+    temperature: 0.3
+
+  # File processing during file ingestion
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+
+  # Chunk retrieval and ranking during search
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: "rrf"
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+
+  # Batch processing performance settings
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
+
+  # Tool output and prompt formatting
+  file_search_params:
+    header_template: "## Knowledge Search Results\n\nI found {num_chunks} relevant chunks:\n\n"
+    footer_template: "\n---\n\nEnd of search results."
+
+  context_prompt_params:
+    chunk_annotation_template: "**Source {index}:**\n{chunk.content}\n\n"
+    context_template: "Use the above information to answer: {query}"
+
+  annotation_prompt_params:
+    enable_annotations: true
+    annotation_instruction_template: "Cite sources using [Source X] format."
+    chunk_annotation_template: "[Source {index}] {chunk_text} (File: {file_id})"
+```
+
+## Configuration Sections
+
+### File Ingestion Parameters
+
+The `file_ingestion_params` configuration controls how files are processed during ingestion into vector stores when using `client.vector_stores.files.create()`:
+
+#### `file_ingestion_params`
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `default_chunk_size_tokens` | `int` | `512` | Default token count for file/document chunks when not explicitly specified |
+| `default_chunk_overlap_tokens` | `int` | `128` | Number of tokens to overlap between chunks (original default: 512 // 4) |
+
+```yaml
+file_ingestion_params:
+  default_chunk_size_tokens: 512    # Smaller chunks for precision
+  default_chunk_overlap_tokens: 128  # Fixed token overlap for context continuity
+```
+
+**Use Cases:**
+- **Smaller chunks (256-512)**: Better for precise factual retrieval
+- **Larger chunks (800-1200)**: Better for context-heavy applications
+- **Higher overlap (200-300 tokens)**: Reduces context loss at chunk boundaries
+- **Lower overlap (50-100 tokens)**: More efficient storage, faster processing
+
+### Chunk Retrieval Parameters
+
+The `chunk_retrieval_params` controls search behavior and ranking strategies when using `client.vector_stores.search()`:
+
+#### `chunk_retrieval_params`
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `chunk_multiplier` | `int` | `5` | Over-retrieval factor for OpenAI API compatibility (affects all providers) |
+| `max_tokens_in_context` | `int` | `4000` | Maximum tokens allowed in RAG context before truncation |
+| `default_reranker_strategy` | `str` | `"rrf"` | Default ranking strategy: `"rrf"`, `"weighted"`, or `"normalized"` |
+| `rrf_impact_factor` | `float` | `60.0` | Impact factor for Reciprocal Rank Fusion (RRF) reranking |
+| `weighted_search_alpha` | `float` | `0.5` | Alpha weight for weighted search reranking (0.0-1.0) |
+
+```yaml
+chunk_retrieval_params:
+  chunk_multiplier: 5               # Retrieve 5x chunks for reranking
+  max_tokens_in_context: 4000       # Context window limit
+  default_reranker_strategy: "rrf"  # Use RRF for hybrid search
+  rrf_impact_factor: 60.0           # RRF ranking parameter
+  weighted_search_alpha: 0.5        # 50/50 vector/keyword weight
+```
+
+**Ranking Strategies:**
+
+- **RRF (Reciprocal Rank Fusion)**: Combines vector and keyword rankings with configurable impact factor
+- **Weighted**: Linear combination with adjustable alpha (0=keyword only, 1=vector only)
+- **Normalized**: Normalizes scores before combination
+
+### File Batch Parameters
+
+The `file_batch_params` controls performance and concurrency for batch file processing when using `client.vector_stores.file_batches.*`:
+
+#### `file_batch_params`
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `max_concurrent_files_per_batch` | `int` | `3` | Maximum files processed concurrently in file batches |
+| `file_batch_chunk_size` | `int` | `10` | Number of files to process in each batch chunk |
+| `cleanup_interval_seconds` | `int` | `86400` | Interval for cleaning up expired file batches (24 hours) |
+
+```yaml
+file_batch_params:
+  max_concurrent_files_per_batch: 3 # Process 3 files simultaneously
+  file_batch_chunk_size: 10         # Handle 10 files per chunk
+  cleanup_interval_seconds: 86400   # Clean up daily
+```
+
+**Performance Tuning:**
+- **Higher concurrency**: Faster processing, more memory usage
+- **Lower concurrency**: Slower processing, less resource usage
+- **Larger chunk size**: Fewer iterations, more memory per iteration
+- **Smaller chunk size**: More iterations, better memory distribution
+
+## Advanced Configuration
+
+### Default Provider and Model Settings
+
+Set system-wide defaults for vector operations:
+
+```yaml
+vector_stores:
+  default_provider_id: "faiss"  # Default vector store provider
+  default_embedding_model:      # Default embedding model
+    provider_id: "sentence-transformers"
+    model_id: "all-MiniLM-L6-v2"
+```
+
+### Query Rewriting Configuration
+
+Enable intelligent query expansion for better search results:
+
+#### `rewrite_query_params`
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | `QualifiedModel` | LLM model for query rewriting/expansion |
+| `prompt` | `str` | Prompt template (must contain `{query}` placeholder) |
+| `max_tokens` | `int` | Maximum tokens for expansion (1-4096) |
+| `temperature` | `float` | Generation temperature (0.0-2.0) |
+
+```yaml
+rewrite_query_params:
+  model:
+    provider_id: "meta-reference"
+    model_id: "llama3.2"
+  prompt: |
+    Expand this search query with related terms and synonyms for better vector search.
+    Keep the expansion focused and relevant.
+
+    Original query: {query}
+
+    Expanded query:
+  max_tokens: 100
+  temperature: 0.3
+```
+
+**Note**: Query rewriting is optional. Omit this section to disable query expansion.
+
+### Output Formatting Configuration
+
+Customize how search results are formatted for RAG applications:
+
+#### `file_search_params`
+
+```yaml
+file_search_params:
+  header_template: |
+    ## Knowledge Search Results
+
+    I found {num_chunks} relevant chunks from your knowledge base:
+
+  footer_template: |
+
+    ---
+
+    End of search results. Use this information to provide a comprehensive answer.
+```
+
+#### `context_prompt_params`
+
+```yaml
+context_prompt_params:
+  chunk_annotation_template: |
+    **Source {index}:**
+    {chunk.content}
+
+    *Metadata: {metadata}*
+
+  context_template: |
+    Based on the search results above, please answer this question: {query}
+
+    Provide specific details from the sources and cite them appropriately.
+```
+
+#### `annotation_prompt_params`
+
+```yaml
+annotation_prompt_params:
+  enable_annotations: true
+  annotation_instruction_template: |
+    When citing information, use the format [Source X] where X is the source number.
+    Always cite specific sources for factual claims.
+  chunk_annotation_template: |
+    [Source {index}] {chunk_text}
+
+    Source: {file_id}
+```
+
+## Provider-Specific Considerations
+
+### OpenAI-Compatible API
+
+All configuration options affect the OpenAI-compatible vector store API:
+
+- `chunk_multiplier` affects over-retrieval in search operations
+- `file_ingestion_params` control chunking during file attachment
+- `file_batch_params` control batch processing performance
+
+### RAG Tools
+
+The RAG tool runtime respects these configurations:
+
+- Uses `default_chunk_size_tokens` for file insertion
+- Applies `max_tokens_in_context` for context window management
+- Uses formatting templates for tool output
+
+### All Vector Store Providers
+
+These settings apply across all vector store providers:
+
+- **Inline providers**: FAISS, SQLite-vec, Milvus
+- **Remote providers**: ChromaDB, Qdrant, Weaviate, PGVector
+- **Hybrid providers**: Milvus (supports both inline and remote)

docs/docs/providers/tool_runtime/inline_rag-runtime.mdx

@@ -14,7 +14,7 @@ RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunki
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `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 |
+| `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') file_ingestion_params=FileIngestionParams(default_chunk_size_tokens=512, default_chunk_overlap_tokens=128) chunk_retrieval_params=ChunkRetrievalParams(chunk_multiplier=5, max_tokens_in_context=4000, default_reranker_strategy='rrf', rrf_impact_factor=60.0, weighted_search_alpha=0.5) file_batch_params=FileBatchParams(max_concurrent_files_per_batch=3, file_batch_chunk_size=10, cleanup_interval_seconds=86400)` | Configuration for vector store prompt templates and behavior |
 
 ## Sample Configuration
 

docs/sidebars.ts

@@ -41,6 +41,15 @@ const sidebars: SidebarsConfig = {
             'concepts/apis/api_leveling',
           ],
         },
+        {
+          type: 'category',
+          label: 'Vector Stores',
+          collapsed: true,
+          items: [
+            'concepts/file_operations_vector_stores',
+            'concepts/vector_stores_configuration',
+          ],
+        },
         'concepts/distributions',
         'concepts/resources',
       ],

src/llama_stack/core/datatypes.py

@@ -18,15 +18,6 @@ from llama_stack.core.storage.datatypes import (
     StorageConfig,
 )
 from llama_stack.log import LoggingConfig
-from llama_stack.providers.utils.memory.constants import (
-    DEFAULT_ANNOTATION_INSTRUCTION_TEMPLATE,
-    DEFAULT_CHUNK_ANNOTATION_TEMPLATE,
-    DEFAULT_CHUNK_WITH_SOURCES_TEMPLATE,
-    DEFAULT_CONTEXT_TEMPLATE,
-    DEFAULT_FILE_SEARCH_FOOTER_TEMPLATE,
-    DEFAULT_FILE_SEARCH_HEADER_TEMPLATE,
-    DEFAULT_QUERY_REWRITE_PROMPT,
-)
 from llama_stack_api import (
     Api,
     Benchmark,
@@ -367,7 +358,7 @@ class RewriteQueryParams(BaseModel):
         description="LLM model for query rewriting/expansion in vector search.",
     )
     prompt: str = Field(
-        default=DEFAULT_QUERY_REWRITE_PROMPT,
+        default="Expand this query with relevant synonyms and related terms. Return only the improved query, no explanations:\n\n{query}\n\nImproved query:",
         description="Prompt template for query rewriting. Use {query} as placeholder for the original query.",
     )
     max_tokens: int = Field(
@@ -407,11 +398,11 @@ class FileSearchParams(BaseModel):
     """Configuration for file search tool output formatting."""
 
     header_template: str = Field(
-        default=DEFAULT_FILE_SEARCH_HEADER_TEMPLATE,
+        default="knowledge_search tool found {num_chunks} chunks:\nBEGIN of knowledge_search tool results.\n",
         description="Template for the header text shown before search results. Available placeholders: {num_chunks} number of chunks found.",
     )
     footer_template: str = Field(
-        default=DEFAULT_FILE_SEARCH_FOOTER_TEMPLATE,
+        default="END of knowledge_search tool results.\n",
         description="Template for the footer text shown after search results.",
     )
 
@@ -433,11 +424,11 @@ class ContextPromptParams(BaseModel):
     """Configuration for LLM prompt content and chunk formatting."""
 
     chunk_annotation_template: str = Field(
-        default=DEFAULT_CHUNK_ANNOTATION_TEMPLATE,
+        default="Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
         description="Template for formatting individual chunks in search results. Available placeholders: {index} 1-based chunk index, {chunk.content} chunk content, {metadata} chunk metadata dict.",
     )
     context_template: str = Field(
-        default=DEFAULT_CONTEXT_TEMPLATE,
+        default='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',
         description="Template for explaining the search results to the model. Available placeholders: {query} user's query, {num_chunks} number of chunks.",
     )
 
@@ -470,11 +461,11 @@ class AnnotationPromptParams(BaseModel):
         description="Whether to include annotation information in results.",
     )
     annotation_instruction_template: str = Field(
-        default=DEFAULT_ANNOTATION_INSTRUCTION_TEMPLATE,
+        default="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.",
         description="Instructions for how the model should cite sources. Used when enable_annotations is True.",
     )
     chunk_annotation_template: str = Field(
-        default=DEFAULT_CHUNK_WITH_SOURCES_TEMPLATE,
+        default="[{index}] {metadata_text} cite as <|{file_id}|>\n{chunk_text}\n",
         description="Template for chunks with annotation information. Available placeholders: {index} 1-based chunk index, {metadata_text} formatted metadata, {file_id} document identifier, {chunk_text} chunk content.",
     )
 
@@ -499,6 +490,61 @@ class AnnotationPromptParams(BaseModel):
         return v
 
 
+class FileIngestionParams(BaseModel):
+    """Configuration for file processing during ingestion."""
+
+    default_chunk_size_tokens: int = Field(
+        default=512,
+        description="Default chunk size for RAG tool operations when not specified",
+    )
+    default_chunk_overlap_tokens: int = Field(
+        default=128,
+        description="Default overlap in tokens between chunks (original default: 512 // 4 = 128)",
+    )
+
+
+class ChunkRetrievalParams(BaseModel):
+    """Configuration for chunk retrieval and ranking during search."""
+
+    chunk_multiplier: int = Field(
+        default=5,
+        description="Multiplier for OpenAI API over-retrieval (affects all providers)",
+    )
+    max_tokens_in_context: int = Field(
+        default=4000,
+        description="Maximum tokens allowed in RAG context before truncation",
+    )
+    default_reranker_strategy: str = Field(
+        default="rrf",
+        description="Default reranker when not specified: 'rrf', 'weighted', or 'normalized'",
+    )
+    rrf_impact_factor: float = Field(
+        default=60.0,
+        description="Impact factor for RRF (Reciprocal Rank Fusion) reranking",
+    )
+    weighted_search_alpha: float = Field(
+        default=0.5,
+        description="Alpha weight for weighted search reranking (0.0-1.0)",
+    )
+
+
+class FileBatchParams(BaseModel):
+    """Configuration for file batch processing."""
+
+    max_concurrent_files_per_batch: int = Field(
+        default=3,
+        description="Maximum files processed concurrently in file batches",
+    )
+    file_batch_chunk_size: int = Field(
+        default=10,
+        description="Number of files to process in each batch chunk",
+    )
+    cleanup_interval_seconds: int = Field(
+        default=86400,  # 24 hours
+        description="Interval for cleaning up expired file batches (seconds)",
+    )
+
+
 class VectorStoresConfig(BaseModel):
     """Configuration for vector stores in the stack."""
 
@@ -527,6 +573,19 @@ class VectorStoresConfig(BaseModel):
         description="Configuration for source annotation and attribution features.",
     )
 
+    file_ingestion_params: FileIngestionParams = Field(
+        default_factory=FileIngestionParams,
+        description="Configuration for file processing during ingestion.",
+    )
+    chunk_retrieval_params: ChunkRetrievalParams = Field(
+        default_factory=ChunkRetrievalParams,
+        description="Configuration for chunk retrieval and ranking during search.",
+    )
+    file_batch_params: FileBatchParams = Field(
+        default_factory=FileBatchParams,
+        description="Configuration for file batch processing.",
+    )
+
 
 class SafetyConfig(BaseModel):
     """Configuration for default moderations model."""

src/llama_stack/core/utils/config.py

@@ -11,6 +11,9 @@ def redact_sensitive_fields(data: dict[str, Any]) -> dict[str, Any]:
     """Redact sensitive information from config before printing."""
     sensitive_patterns = ["api_key", "api_token", "password", "secret", "token"]
 
+    # Specific configuration field names that should NOT be redacted despite containing "token"
+    safe_token_fields = ["chunk_size_tokens", "max_tokens", "default_chunk_overlap_tokens"]
+
     def _redact_value(v: Any) -> Any:
         if isinstance(v, dict):
             return _redact_dict(v)
@@ -21,7 +24,10 @@ def redact_sensitive_fields(data: dict[str, Any]) -> dict[str, Any]:
     def _redact_dict(d: dict[str, Any]) -> dict[str, Any]:
         result = {}
         for k, v in d.items():
-            if any(pattern in k.lower() for pattern in sensitive_patterns):
+            # Don't redact if it's a safe field
+            if any(safe_field in k.lower() for safe_field in safe_token_fields):
+                result[k] = _redact_value(v)
+            elif any(pattern in k.lower() for pattern in sensitive_patterns):
                 result[k] = "********"
             else:
                 result[k] = _redact_value(v)

src/llama_stack/distributions/ci-tests/config.yaml

@@ -296,19 +296,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/ci-tests/run-with-postgres-store.yaml

@@ -305,19 +305,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter-gpu/config.yaml

@@ -299,19 +299,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml

@@ -308,19 +308,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter/config.yaml

@@ -296,19 +296,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter/run-with-postgres-store.yaml

@@ -305,19 +305,32 @@ vector_stores:
       '
     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}
+      query. {annotation_instruction}
 
       '
   annotation_prompt_params:
     enable_annotations: true
-    annotation_instruction_template: ' Cite sources immediately at the end of sentences
+    annotation_instruction_template: Cite sources immediately at the end of sentences
-      before punctuation, using `<|file-id|>` format like ''This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.''.
+      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.'
+      new ones.
     chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|>
 
       {chunk_text}
 
       '
+  file_ingestion_params:
+    default_chunk_size_tokens: 512
+    default_chunk_overlap_tokens: 128
+  chunk_retrieval_params:
+    chunk_multiplier: 5
+    max_tokens_in_context: 4000
+    default_reranker_strategy: rrf
+    rrf_impact_factor: 60.0
+    weighted_search_alpha: 0.5
+  file_batch_params:
+    max_concurrent_files_per_batch: 3
+    file_batch_chunk_size: 10
+    cleanup_interval_seconds: 86400
 safety:
   default_shield_id: llama-guard

src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py

@@ -11,11 +11,8 @@ from typing import Any
 
 from opentelemetry import trace
 
+from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.memory.constants import (
-    DEFAULT_ANNOTATION_INSTRUCTION_TEMPLATE,
-    DEFAULT_CHUNK_WITH_SOURCES_TEMPLATE,
-)
 from llama_stack_api import (
     ImageContentItem,
     OpenAIChatCompletionContentPartImageParam,
@@ -175,8 +172,10 @@ class ToolExecutor:
                 self.vector_stores_config.annotation_prompt_params.annotation_instruction_template
             )
         else:
-            chunk_annotation_template = DEFAULT_CHUNK_WITH_SOURCES_TEMPLATE
+            # Use defaults from VectorStoresConfig when annotations disabled
-            annotation_instruction_template = DEFAULT_ANNOTATION_INSTRUCTION_TEMPLATE
+            default_config = VectorStoresConfig()
+            chunk_annotation_template = default_config.annotation_prompt_params.chunk_annotation_template
+            annotation_instruction_template = default_config.annotation_prompt_params.annotation_instruction_template
 
         content_items = []
         content_items.append(TextContentItem(text=header_template.format(num_chunks=len(search_results))))

src/llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -116,8 +116,10 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime):
         self,
         documents: list[RAGDocument],
         vector_store_id: str,
-        chunk_size_in_tokens: int = 512,
+        chunk_size_in_tokens: int | None = None,
     ) -> None:
+        if chunk_size_in_tokens is None:
+            chunk_size_in_tokens = self.config.vector_stores_config.file_ingestion_params.default_chunk_size_tokens
         if not documents:
             return
 
@@ -145,10 +147,11 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime):
                     log.error(f"Failed to upload file for document {doc.document_id}: {e}")
                     continue
 
+                overlap_tokens = self.config.vector_stores_config.file_ingestion_params.default_chunk_overlap_tokens
                 chunking_strategy = VectorStoreChunkingStrategyStatic(
                     static=VectorStoreChunkingStrategyStaticConfig(
                         max_chunk_size_tokens=chunk_size_in_tokens,
-                        chunk_overlap_tokens=chunk_size_in_tokens // 4,
+                        chunk_overlap_tokens=overlap_tokens,
                     )
                 )
 
@@ -180,7 +183,9 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime):
                 "No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
             )
 
-        query_config = query_config or RAGQueryConfig()
+        query_config = query_config or RAGQueryConfig(
+            max_tokens_in_context=self.config.vector_stores_config.chunk_retrieval_params.max_tokens_in_context
+        )
         query = await generate_rag_query(
             query_config.query_generator_config,
             content,
@@ -319,7 +324,9 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime):
         if query_config:
             query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
         else:
-            query_config = RAGQueryConfig()
+            query_config = RAGQueryConfig(
+                max_tokens_in_context=self.config.vector_stores_config.chunk_retrieval_params.max_tokens_in_context
+            )
 
         query = kwargs["query"]
         result = await self.query(

src/llama_stack/providers/utils/memory/__init__.py

@@ -4,6 +4,4 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from .constants import DEFAULT_QUERY_REWRITE_PROMPT
+__all__ = []
-
-__all__ = ["DEFAULT_QUERY_REWRITE_PROMPT"]

src/llama_stack/providers/utils/memory/constants.py

@@ -1,22 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-# Default prompt template for query rewriting in vector search
-DEFAULT_QUERY_REWRITE_PROMPT = "Expand this query with relevant synonyms and related terms. Return only the improved query, no explanations:\n\n{query}\n\nImproved query:"
-
-# Default templates for file search tool output formatting
-DEFAULT_FILE_SEARCH_HEADER_TEMPLATE = (
-    "knowledge_search tool found {num_chunks} chunks:\nBEGIN of knowledge_search tool results.\n"
-)
-DEFAULT_FILE_SEARCH_FOOTER_TEMPLATE = "END of knowledge_search tool results.\n"
-
-# Default templates for LLM prompt content and chunk formatting
-DEFAULT_CHUNK_ANNOTATION_TEMPLATE = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
-DEFAULT_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'
-
-# Default templates for source annotation and attribution features
-DEFAULT_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."
-DEFAULT_CHUNK_WITH_SOURCES_TEMPLATE = "[{index}] {metadata_text} cite as <|{file_id}|>\n{chunk_text}\n"

src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -15,6 +15,7 @@ from typing import Annotated, Any
 from fastapi import Body
 from pydantic import TypeAdapter
 
+from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.id_generation import generate_object_id
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.memory.vector_store import (
@@ -59,10 +60,6 @@ EMBEDDING_DIMENSION = 768
 logger = get_logger(name=__name__, category="providers::utils")
 
 # Constants for OpenAI vector stores
-CHUNK_MULTIPLIER = 5
-FILE_BATCH_CLEANUP_INTERVAL_SECONDS = 24 * 60 * 60  # 1 day in seconds
-MAX_CONCURRENT_FILES_PER_BATCH = 3  # Maximum concurrent file processing within a batch
-FILE_BATCH_CHUNK_SIZE = 10  # Process files in chunks of this size
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_stores:{VERSION}::"
@@ -85,11 +82,13 @@ class OpenAIVectorStoreMixin(ABC):
         self,
         files_api: Files | None = None,
         kvstore: KVStore | None = None,
+        vector_stores_config: VectorStoresConfig | None = None,
     ):
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
         self.files_api = files_api
         self.kvstore = kvstore
+        self.vector_stores_config = vector_stores_config or VectorStoresConfig()
         self._last_file_batch_cleanup_time = 0
         self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._vector_store_locks: dict[str, asyncio.Lock] = {}
@@ -619,7 +618,7 @@ class OpenAIVectorStoreMixin(ABC):
                 else 0.0
             )
             params = {
-                "max_chunks": max_num_results * CHUNK_MULTIPLIER,
+                "max_chunks": max_num_results * self.vector_stores_config.chunk_retrieval_params.chunk_multiplier,
                 "score_threshold": score_threshold,
                 "mode": search_mode,
             }
@@ -1072,7 +1071,10 @@ class OpenAIVectorStoreMixin(ABC):
 
         # Run cleanup if needed (throttled to once every 1 day)
         current_time = int(time.time())
-        if current_time - self._last_file_batch_cleanup_time >= FILE_BATCH_CLEANUP_INTERVAL_SECONDS:
+        if (
+            current_time - self._last_file_batch_cleanup_time
+            >= self.vector_stores_config.file_batch_params.cleanup_interval_seconds
+        ):
             logger.info("Running throttled cleanup of expired file batches")
             asyncio.create_task(self._cleanup_expired_file_batches())
             self._last_file_batch_cleanup_time = current_time
@@ -1089,7 +1091,7 @@ class OpenAIVectorStoreMixin(ABC):
         batch_info: dict[str, Any],
     ) -> None:
         """Process files with controlled concurrency and chunking."""
-        semaphore = asyncio.Semaphore(MAX_CONCURRENT_FILES_PER_BATCH)
+        semaphore = asyncio.Semaphore(self.vector_stores_config.file_batch_params.max_concurrent_files_per_batch)
 
         async def process_single_file(file_id: str) -> tuple[str, bool]:
             """Process a single file with concurrency control."""
@@ -1108,12 +1110,13 @@ class OpenAIVectorStoreMixin(ABC):
 
         # Process files in chunks to avoid creating too many tasks at once
         total_files = len(file_ids)
-        for chunk_start in range(0, total_files, FILE_BATCH_CHUNK_SIZE):
+        chunk_size = self.vector_stores_config.file_batch_params.file_batch_chunk_size
-            chunk_end = min(chunk_start + FILE_BATCH_CHUNK_SIZE, total_files)
+        for chunk_start in range(0, total_files, chunk_size):
+            chunk_end = min(chunk_start + chunk_size, total_files)
             chunk = file_ids[chunk_start:chunk_end]
 
-            chunk_num = chunk_start // FILE_BATCH_CHUNK_SIZE + 1
+            chunk_num = chunk_start // chunk_size + 1
-            total_chunks = (total_files + FILE_BATCH_CHUNK_SIZE - 1) // FILE_BATCH_CHUNK_SIZE
+            total_chunks = (total_files + chunk_size - 1) // chunk_size
             logger.info(
                 f"Processing chunk {chunk_num} of {total_chunks} ({len(chunk)} files, {chunk_start + 1}-{chunk_end} of {total_files} total files)"
             )

src/llama_stack/providers/utils/memory/vector_store.py

@@ -17,6 +17,7 @@ import numpy as np
 from numpy.typing import NDArray
 from pydantic import BaseModel
 
+from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.log import get_logger
 from llama_stack.models.llama.llama3.tokenizer import Tokenizer
 from llama_stack.providers.utils.inference.prompt_adapter import (
@@ -262,6 +263,7 @@ class VectorStoreWithIndex:
     vector_store: VectorStore
     index: EmbeddingIndex
     inference_api: Api.inference
+    vector_stores_config: VectorStoresConfig | None = None
 
     async def insert_chunks(
         self,
@@ -294,6 +296,8 @@ class VectorStoreWithIndex:
         query: InterleavedContent,
         params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
+        config = self.vector_stores_config or VectorStoresConfig()
+
         if params is None:
             params = {}
         k = params.get("max_chunks", 3)
@@ -302,19 +306,25 @@ class VectorStoreWithIndex:
 
         ranker = params.get("ranker")
         if ranker is None:
-            reranker_type = RERANKER_TYPE_RRF
+            reranker_type = (
-            reranker_params = {"impact_factor": 60.0}
+                RERANKER_TYPE_RRF
+                if config.chunk_retrieval_params.default_reranker_strategy == "rrf"
+                else config.chunk_retrieval_params.default_reranker_strategy
+            )
+            reranker_params = {"impact_factor": config.chunk_retrieval_params.rrf_impact_factor}
         else:
-            strategy = ranker.get("strategy", "rrf")
+            strategy = ranker.get("strategy", config.chunk_retrieval_params.default_reranker_strategy)
             if strategy == "weighted":
                 weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
                 reranker_type = RERANKER_TYPE_WEIGHTED
-                reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
+                reranker_params = {
+                    "alpha": weights[0] if len(weights) > 0 else config.chunk_retrieval_params.weighted_search_alpha
+                }
             elif strategy == "normalized":
                 reranker_type = RERANKER_TYPE_NORMALIZED
             else:
                 reranker_type = RERANKER_TYPE_RRF
-                k_value = ranker.get("params", {}).get("k", 60.0)
+                k_value = ranker.get("params", {}).get("k", config.chunk_retrieval_params.rrf_impact_factor)
                 reranker_params = {"impact_factor": k_value}
 
         query_string = interleaved_content_as_str(query)

tests/unit/core/routers/test_vector_io.py

@@ -156,7 +156,6 @@ async def test_query_rewrite_functionality():
     from unittest.mock import MagicMock
 
     from llama_stack.core.datatypes import QualifiedModel, RewriteQueryParams, VectorStoresConfig
-    from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_REWRITE_PROMPT
     from llama_stack_api import VectorStoreSearchResponsePage
 
     mock_routing_table = Mock()
@@ -197,7 +196,7 @@ async def test_query_rewrite_functionality():
 
     # Verify default prompt is used
     prompt_text = chat_call_args.messages[0].content
-    expected_prompt = DEFAULT_QUERY_REWRITE_PROMPT.format(query="test query")
+    expected_prompt = "Expand this query with relevant synonyms and related terms. Return only the improved query, no explanations:\n\ntest query\n\nImproved query:"
     assert prompt_text == expected_prompt
 
     # Verify routing table was called with rewritten query and rewrite_query=False

tests/unit/core/test_vector_stores_config.py

@@ -110,22 +110,23 @@ class TestOptionalArchitecture:
         assert config.annotation_prompt_params is not None
         assert "{num_chunks}" in config.file_search_params.header_template
 
-    def test_guaranteed_defaults_match_constants(self):
+    def test_guaranteed_defaults_have_expected_values(self):
-        """Test that guaranteed defaults match expected constant values."""
+        """Test that guaranteed defaults have expected hardcoded values."""
-        from llama_stack.providers.utils.memory.constants import (
-            DEFAULT_CONTEXT_TEMPLATE,
-            DEFAULT_FILE_SEARCH_HEADER_TEMPLATE,
-        )
-
         # Create config with guaranteed defaults
         config = VectorStoresConfig()
 
-        # Verify defaults match constants
+        # Verify defaults have expected values
         header_template = config.file_search_params.header_template
         context_template = config.context_prompt_params.context_template
 
-        assert header_template == DEFAULT_FILE_SEARCH_HEADER_TEMPLATE
+        assert (
-        assert context_template == DEFAULT_CONTEXT_TEMPLATE
+            header_template
+            == "knowledge_search tool found {num_chunks} chunks:\nBEGIN of knowledge_search tool results.\n"
+        )
+        assert (
+            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'
+        )
 
         # Verify templates can be formatted successfully
         formatted_header = header_template.format(num_chunks=3)

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -1091,13 +1091,11 @@ async def test_max_concurrent_files_per_batch(vector_io_adapter):
     # Give time for the semaphore logic to start processing files
     await asyncio.sleep(0.2)
 
-    # Verify that only MAX_CONCURRENT_FILES_PER_BATCH files are processing concurrently
+    # Verify that only max_concurrent_files_per_batch files are processing concurrently
     # The semaphore in _process_files_with_concurrency should limit this
-    from llama_stack.providers.utils.memory.openai_vector_store_mixin import MAX_CONCURRENT_FILES_PER_BATCH
+    max_concurrent_files = vector_io_adapter.vector_stores_config.file_batch_params.max_concurrent_files_per_batch
 
-    assert active_files == MAX_CONCURRENT_FILES_PER_BATCH, (
+    assert active_files == max_concurrent_files, f"Expected {max_concurrent_files} active files, got {active_files}"
-        f"Expected {MAX_CONCURRENT_FILES_PER_BATCH} active files, got {active_files}"
-    )
 
     # Verify batch is in progress
     assert batch.status == "in_progress"