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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>
This commit is contained in:
Francisco Javier Arceo
GitHub
parent
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[client_with_models-txt=openai/gpt-4o:emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5:dim=768]",
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"request": {
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"headers": {},
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"role": "user",
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"content": "How many experts does the Llama 4 Maverick model have?"
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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"type": "text",
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"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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"type": "text",
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"text": "[1] document_id: file-797509666839, score: 0.019272319661840426, attributes: {'filename': 'test_sequential_file_search.txt', 'chunk_id': '3907d885-d8e7-a72d-1113-f7080454d97c', 'document_id': 'file-797509666839', 'token_count': 19.0, 'metadata_token_count': 11.0} cite as <|file-797509666839|>\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of 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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"content": "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture <|file-797509666839|>."
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"role": "user",
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"content": "Can you tell me more about the architecture?"
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"stream": true,
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"type": "object",
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"properties": {
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"query": {
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"endpoint": "/v1/chat/completions",
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1060
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1627
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1627
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2792
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2792
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2765
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2765
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1645
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1645
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1091
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"display_name": "Gemma 3 27B"
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"owned_by": "google",
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"display_name": "Gemma 3n E4B"
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"object": "model",
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"owned_by": "google",
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"display_name": "Gemma 3n E2B"
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"display_name": "Gemini Flash Latest"
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"owned_by": "google",
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"display_name": "Gemini Flash-Lite Latest"
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"owned_by": "google",
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"display_name": "Gemini 2.5 Flash Preview Sep 2025"
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"created": null,
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"object": "model",
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"owned_by": "google",
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"display_name": "Gemini 2.5 Flash-Lite Preview Sep 2025"
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}
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"created": null,
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"object": "model",
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"owned_by": "google",
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"display_name": "Gemini 3 Pro Preview"
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}
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},
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{
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"__type__": "openai.types.model.Model",
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"__data__": {
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"id": "models/gemini-3-pro-image-preview",
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"created": null,
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"object": "model",
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"owned_by": "google",
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"display_name": "Nano Banana Pro"
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{
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"__type__": "openai.types.model.Model",
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"owned_by": "google",
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"display_name": "Nano Banana Pro"
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"owned_by": "google",
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"display_name": "Gemini Robotics-ER 1.5 Preview"
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"display_name": "Gemini 2.5 Computer Use Preview 10-2025"
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"owned_by": "google",
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"display_name": "Deep Research Pro Preview (Dec-12-2025)"
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"display_name": "Gemini Embedding Experimental"
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"display_name": "Gemini Embedding 001"
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"display_name": "Model that performs Attributed Question Answering."
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"owned_by": "google",
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"display_name": "Imagen 4 (Preview)"
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"owned_by": "google",
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"display_name": "Imagen 4"
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"owned_by": "google",
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"display_name": "Imagen 4 Ultra"
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"object": "model",
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"owned_by": "google",
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"display_name": "Imagen 4 Fast"
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{
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"__type__": "openai.types.model.Model",
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"__data__": {
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"id": "models/veo-2.0-generate-001",
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"created": null,
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"object": "model",
|
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"owned_by": "google",
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"display_name": "Veo 2"
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{
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"__type__": "openai.types.model.Model",
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"__data__": {
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"id": "models/veo-3.0-generate-001",
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"created": null,
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"object": "model",
|
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"owned_by": "google",
|
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"display_name": "Veo 3"
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}
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{
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"__type__": "openai.types.model.Model",
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"__data__": {
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"id": "models/veo-3.0-fast-generate-001",
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"created": null,
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"object": "model",
|
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"owned_by": "google",
|
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"display_name": "Veo 3 fast"
|
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}
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},
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{
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"__type__": "openai.types.model.Model",
|
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"__data__": {
|
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"id": "models/veo-3.1-generate-preview",
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"created": null,
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"object": "model",
|
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"owned_by": "google",
|
||||
"display_name": "Veo 3.1"
|
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}
|
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},
|
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{
|
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"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
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"id": "models/veo-3.1-fast-generate-preview",
|
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"created": null,
|
||||
"object": "model",
|
||||
"owned_by": "google",
|
||||
"display_name": "Veo 3.1 fast"
|
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}
|
||||
},
|
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{
|
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"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "models/gemini-2.5-flash-native-audio-latest",
|
||||
"created": null,
|
||||
"object": "model",
|
||||
"owned_by": "google",
|
||||
"display_name": "Gemini 2.5 Flash Native Audio Latest"
|
||||
}
|
||||
},
|
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{
|
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"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "models/gemini-2.5-flash-native-audio-preview-09-2025",
|
||||
"created": null,
|
||||
"object": "model",
|
||||
"owned_by": "google",
|
||||
"display_name": "Gemini 2.5 Flash Native Audio Preview 09-2025"
|
||||
}
|
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},
|
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{
|
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"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "models/lyria-realtime-exp",
|
||||
"created": null,
|
||||
"object": "model",
|
||||
"owned_by": "google",
|
||||
"display_name": "Lyria Realtime Experimental"
|
||||
}
|
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}
|
||||
],
|
||||
"is_streaming": false
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -90,14 +90,12 @@ class TestVectorStoresValidation:
|
|||
|
||||
async def test_validate_rewrite_query_prompt_missing_placeholder(self):
|
||||
"""Test validation fails when prompt template is missing {query} placeholder."""
|
||||
config = VectorStoresConfig(
|
||||
rewrite_query_params=RewriteQueryParams(
|
||||
prompt="This prompt has no placeholder",
|
||||
),
|
||||
)
|
||||
from pydantic import ValidationError
|
||||
|
||||
with pytest.raises(ValueError, match="'\\{query\\}' placeholder is required"):
|
||||
await validate_vector_stores_config(config, {})
|
||||
with pytest.raises(ValidationError, match=r"prompt must contain \{query\} placeholder"):
|
||||
RewriteQueryParams(
|
||||
prompt="This prompt has no placeholder",
|
||||
)
|
||||
|
||||
|
||||
class TestSafetyConfigValidation:
|
||||
|
|
|
|||
170
tests/unit/core/test_vector_stores_config.py
Normal file
170
tests/unit/core/test_vector_stores_config.py
Normal file
|
|
@ -0,0 +1,170 @@
|
|||
# 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.
|
||||
|
||||
import pytest
|
||||
from pydantic import ValidationError
|
||||
|
||||
from llama_stack.core.datatypes import QualifiedModel, RewriteQueryParams, VectorStoresConfig
|
||||
|
||||
|
||||
class TestVectorStoresConfigValidation:
|
||||
"""Test validation of VectorStoresConfig prompt templates."""
|
||||
|
||||
def test_default_config_is_valid(self):
|
||||
"""Test that default configuration passes all validation."""
|
||||
config = VectorStoresConfig()
|
||||
|
||||
# Verify all sub-configs exist with valid templates
|
||||
assert config.file_search_params.header_template is not None
|
||||
assert config.context_prompt_params.chunk_annotation_template is not None
|
||||
assert config.annotation_prompt_params.chunk_annotation_template is not None
|
||||
|
||||
# Verify required placeholders are present
|
||||
assert "{num_chunks}" in config.file_search_params.header_template
|
||||
assert "knowledge_search" in config.file_search_params.header_template.lower()
|
||||
assert "{chunk.content}" in config.context_prompt_params.chunk_annotation_template
|
||||
assert "{query}" in config.context_prompt_params.context_template
|
||||
|
||||
def test_template_validation_errors(self):
|
||||
"""Test that templates fail validation for common errors."""
|
||||
from llama_stack.core.datatypes import AnnotationPromptParams, ContextPromptParams, FileSearchParams
|
||||
|
||||
# Empty templates fail
|
||||
with pytest.raises(ValidationError, match="must not be empty"):
|
||||
FileSearchParams(header_template="")
|
||||
|
||||
# Missing required placeholders fail
|
||||
with pytest.raises(ValidationError, match="must contain {num_chunks} placeholder"):
|
||||
FileSearchParams(header_template="search found results")
|
||||
|
||||
with pytest.raises(ValidationError, match="must contain 'knowledge_search' keyword"):
|
||||
FileSearchParams(header_template="search found {num_chunks} results")
|
||||
|
||||
with pytest.raises(ValidationError, match="must contain {chunk.content} placeholder"):
|
||||
ContextPromptParams(chunk_annotation_template="Result {index}: some content")
|
||||
|
||||
with pytest.raises(ValidationError, match="must contain {query} placeholder"):
|
||||
ContextPromptParams(context_template="Retrieved results. Use as context.")
|
||||
|
||||
with pytest.raises(ValidationError, match="must contain {file_id} placeholder"):
|
||||
AnnotationPromptParams(chunk_annotation_template="[{index}] {chunk_text}")
|
||||
|
||||
def test_rewrite_query_params_validation(self):
|
||||
"""Test RewriteQueryParams validation."""
|
||||
model = QualifiedModel(provider_id="test", model_id="test-model")
|
||||
|
||||
# Valid config works
|
||||
valid_params = RewriteQueryParams(
|
||||
model=model, prompt="Expand this query: {query}", max_tokens=100, temperature=0.5
|
||||
)
|
||||
assert valid_params.prompt == "Expand this query: {query}"
|
||||
|
||||
# Invalid configurations fail
|
||||
with pytest.raises(ValidationError, match="prompt must contain \\{query\\} placeholder"):
|
||||
RewriteQueryParams(model=model, prompt="No placeholder here")
|
||||
|
||||
with pytest.raises(ValidationError, match="max_tokens must be positive"):
|
||||
RewriteQueryParams(model=model, max_tokens=0)
|
||||
|
||||
with pytest.raises(ValidationError, match="temperature must be between 0.0 and 2.0"):
|
||||
RewriteQueryParams(model=model, temperature=3.0)
|
||||
|
||||
def test_custom_configuration(self):
|
||||
"""Test complete custom configuration."""
|
||||
from llama_stack.core.datatypes import AnnotationPromptParams, ContextPromptParams, FileSearchParams
|
||||
|
||||
config = VectorStoresConfig(
|
||||
default_provider_id="test-provider",
|
||||
default_embedding_model=QualifiedModel(provider_id="test", model_id="embedding-model"),
|
||||
file_search_params=FileSearchParams(
|
||||
header_template="Custom knowledge_search found {num_chunks} items:\nSTART\n", footer_template="END\n"
|
||||
),
|
||||
context_prompt_params=ContextPromptParams(
|
||||
chunk_annotation_template="Item {index}: {chunk.content} | Meta: {metadata}\n",
|
||||
context_template='Results for "{query}": Use carefully.\n',
|
||||
),
|
||||
annotation_prompt_params=AnnotationPromptParams(
|
||||
enable_annotations=False,
|
||||
annotation_instruction_template=" Custom citation format.",
|
||||
chunk_citation_template="[{index}] {metadata_text} --> {file_id}\n{chunk_text}\n",
|
||||
),
|
||||
)
|
||||
|
||||
assert config.default_provider_id == "test-provider"
|
||||
assert "Custom knowledge_search" in config.file_search_params.header_template
|
||||
assert config.annotation_prompt_params.enable_annotations is False
|
||||
|
||||
|
||||
class TestOptionalArchitecture:
|
||||
"""Test optional sub-config architecture and constants fallback."""
|
||||
|
||||
def test_guaranteed_defaults_behavior(self):
|
||||
"""Test that sub-configs are always instantiated with defaults."""
|
||||
# Sub-configs are always instantiated due to default_factory
|
||||
config = VectorStoresConfig()
|
||||
assert config.file_search_params is not None
|
||||
assert config.context_prompt_params is not None
|
||||
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):
|
||||
"""Test that guaranteed defaults match expected constant 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
|
||||
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 context_template == DEFAULT_CONTEXT_TEMPLATE
|
||||
|
||||
# Verify templates can be formatted successfully
|
||||
formatted_header = header_template.format(num_chunks=3)
|
||||
assert "3" in formatted_header
|
||||
assert "knowledge_search" in formatted_header.lower()
|
||||
|
||||
formatted_context = context_template.format(
|
||||
query="test query", annotation_instruction=" Cite sources properly."
|
||||
)
|
||||
assert "test query" in formatted_context
|
||||
|
||||
def test_end_to_end_template_usage(self):
|
||||
"""Test that guaranteed defaults lead to working template output."""
|
||||
# Create config with guaranteed defaults
|
||||
config = VectorStoresConfig()
|
||||
|
||||
header_template = config.file_search_params.header_template
|
||||
chunk_template = config.context_prompt_params.chunk_annotation_template
|
||||
|
||||
# Generate realistic output
|
||||
test_chunks = [
|
||||
{"content": "Paris is the capital of France.", "metadata": {"doc": "geo.pdf"}},
|
||||
{"content": "London is the capital of England.", "metadata": {"doc": "cities.txt"}},
|
||||
]
|
||||
|
||||
header_output = header_template.format(num_chunks=len(test_chunks))
|
||||
chunk_outputs = []
|
||||
for i, chunk_data in enumerate(test_chunks):
|
||||
|
||||
class MockChunk:
|
||||
content = chunk_data["content"]
|
||||
|
||||
chunk_output = chunk_template.format(index=i + 1, chunk=MockChunk(), metadata=chunk_data["metadata"])
|
||||
chunk_outputs.append(chunk_output)
|
||||
|
||||
complete_output = header_output + "".join(chunk_outputs)
|
||||
|
||||
# Verify output is substantial and contains expected content
|
||||
assert len(complete_output) > 100
|
||||
assert "knowledge_search" in complete_output.lower()
|
||||
assert "Paris is the capital" in complete_output
|
||||
assert "London is the capital" in complete_output
|
||||
|
|
@ -8,21 +8,26 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.providers.inline.tool_runtime.rag.config import RagToolRuntimeConfig
|
||||
from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
|
||||
from llama_stack_api import Chunk, ChunkMetadata, QueryChunksResponse, RAGQueryConfig
|
||||
|
||||
|
||||
class TestRagQuery:
|
||||
async def test_query_raises_on_empty_vector_store_ids(self):
|
||||
config = RagToolRuntimeConfig()
|
||||
rag_tool = MemoryToolRuntimeImpl(
|
||||
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
|
||||
config=config, vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
await rag_tool.query(content=MagicMock(), vector_store_ids=[])
|
||||
|
||||
async def test_query_chunk_metadata_handling(self):
|
||||
# Create config with default templates
|
||||
config = RagToolRuntimeConfig()
|
||||
|
||||
rag_tool = MemoryToolRuntimeImpl(
|
||||
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
|
||||
config=config, vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
|
||||
)
|
||||
content = "test query content"
|
||||
vector_store_ids = ["db1"]
|
||||
|
|
@ -33,9 +38,8 @@ class TestRagQuery:
|
|||
source="test_source",
|
||||
metadata_token_count=5,
|
||||
)
|
||||
interleaved_content = MagicMock()
|
||||
chunk = Chunk(
|
||||
content=interleaved_content,
|
||||
content="This is test chunk content from document 1",
|
||||
chunk_id="chunk1",
|
||||
metadata={
|
||||
"key1": "value1",
|
||||
|
|
@ -78,8 +82,11 @@ class TestRagQuery:
|
|||
RAGQueryConfig(mode="wrong_mode")
|
||||
|
||||
async def test_query_adds_vector_store_id_to_chunk_metadata(self):
|
||||
# Create config with default templates
|
||||
config = RagToolRuntimeConfig()
|
||||
|
||||
rag_tool = MemoryToolRuntimeImpl(
|
||||
config=MagicMock(),
|
||||
config=config,
|
||||
vector_io_api=MagicMock(),
|
||||
inference_api=MagicMock(),
|
||||
files_api=MagicMock(),
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue