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feat: add auto-generated CI documentation pre-commit hook (#2890)

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Our CI is entirely undocumented, this commit adds a README.md file with
a table of the current CI and what is does

---------

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg 2025-07-25 11:57:01 -04:00 • committed by Mustafa Elbehery
parent 7f834339ba
commit b381ed6d64
93 changed files with 495 additions and 477 deletions
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16
llama_stack/providers/remote/vector_io/milvus/milvus.py
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@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import os
from typing import Any
@ -21,6 +20,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
from llama_stack.providers.utils.kvstore import kvstore_impl
@ -34,7 +34,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"
@ -68,7 +68,7 @@ class MilvusIndex(EmbeddingIndex):
)
if not await asyncio.to_thread(self.client.has_collection, self.collection_name):
logger.info(f"Creating new collection {self.collection_name} with nullable sparse field")
log.info(f"Creating new collection {self.collection_name} with nullable sparse field")
# Create schema for vector search
schema = self.client.create_schema()
schema.add_field(
@ -147,7 +147,7 @@ class MilvusIndex(EmbeddingIndex):
data=data,
)
except Exception as e:
logger.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
log.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
raise e
async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
@ -203,7 +203,7 @@ class MilvusIndex(EmbeddingIndex):
return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
except Exception as e:
logger.error(f"Error performing BM25 search: {e}")
log.error(f"Error performing BM25 search: {e}")
# Fallback to simple text search
return await self._fallback_keyword_search(query_string, k, score_threshold)
@ -247,7 +247,7 @@ class MilvusIndex(EmbeddingIndex):
self.client.delete, collection_name=self.collection_name, filter=f'chunk_id == "{chunk_id}"'
)
except Exception as e:
logger.error(f"Error deleting chunk {chunk_id} from Milvus collection {self.collection_name}: {e}")
log.error(f"Error deleting chunk {chunk_id} from Milvus collection {self.collection_name}: {e}")
raise
@ -288,10 +288,10 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
)
self.cache[vector_db.identifier] = index
if isinstance(self.config, RemoteMilvusVectorIOConfig):
logger.info(f"Connecting to Milvus server at {self.config.uri}")
log.info(f"Connecting to Milvus server at {self.config.uri}")
self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
else:
logger.info(f"Connecting to Milvus Lite at: {self.config.db_path}")
log.info(f"Connecting to Milvus Lite at: {self.config.db_path}")
uri = os.path.expanduser(self.config.db_path)
self.client = MilvusClient(uri=uri)