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feat: use mps generated correlation ID

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This commit is contained in:
Abhishek Kumar 2026-06-09 18:24:40 +05:30
parent 91ac460799
commit 3336c6e794
30 changed files with 453 additions and 89 deletions
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15
api/services/workflow/tools/knowledge_base.py
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@ -29,6 +29,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider: Optional[str] = None,
embeddings_endpoint: Optional[str] = None,
embeddings_api_version: Optional[str] = None,
correlation_id: Optional[str] = None,
tracing_context=None,
) -> Dict[str, Any]:
"""Retrieve relevant information from the knowledge base using vector similarity search.
@ -75,6 +76,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
# Create span with parent context
@ -115,6 +117,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
# Add result metadata to span
@ -192,6 +195,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
else:
# Tracing is disabled - perform retrieval without tracing
@ -206,6 +210,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
@ -220,6 +225,7 @@ async def _perform_retrieval(
embeddings_provider: Optional[str] = None,
embeddings_endpoint: Optional[str] = None,
embeddings_api_version: Optional[str] = None,
correlation_id: Optional[str] = None,
) -> Dict[str, Any]:
"""Internal function to perform the actual retrieval operation.
@ -272,11 +278,20 @@ async def _perform_retrieval(
api_version=embeddings_api_version or "2024-02-15-preview",
)
else:
default_headers = None
if (
embeddings_provider == ServiceProviders.DOGRAH.value
and correlation_id
):
default_headers = {
"X-Dograh-Correlation-Id": correlation_id,
}
embedding_service = OpenAIEmbeddingService(
db_client=db_client,
api_key=embeddings_api_key,
model_id=embeddings_model or "text-embedding-3-small",
base_url=embeddings_base_url,
default_headers=default_headers,
)
results = await embedding_service.search_similar_chunks(