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62005dc1a9
# 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>
1645 lines
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1645 lines
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{
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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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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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{
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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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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_FzhOmTdZThRndI5rSASPdAqr",
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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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}
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}
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]
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{
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"role": "tool",
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"tool_call_id": "call_FzhOmTdZThRndI5rSASPdAqr",
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"content": [
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{
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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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},
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{
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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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},
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{
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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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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}
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]
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},
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{
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"role": "assistant",
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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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},
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{
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"role": "user",
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"content": "Can you tell me more about the architecture?"
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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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"index": 0,
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"id": "call_VtFoA9FzgtwNWqenbX1PKi1w",
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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 architecture\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"tool_call_id": "call_VtFoA9FzgtwNWqenbX1PKi1w",
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"content": [
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{
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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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},
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{
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"type": "text",
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"text": "[1] document_id: file-797509666839, score: 0.006060444089175898, 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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},
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{
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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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 architecture\". 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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}
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]
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}
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],
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"stream": true,
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"stream_options": {
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"include_usage": true
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},
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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}
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},
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"required": [
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"query"
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]
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}
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}
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}
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]
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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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"response": {
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