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

Browse source 
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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8
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -8,7 +8,6 @@ import asyncio
import base64
import io
import json
import logging
from typing import Any
import faiss
@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import (
HealthResponse,
HealthStatus,
@ -39,7 +39,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import FaissVectorIOConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
@ -83,7 +83,7 @@ class FaissIndex(EmbeddingIndex):
self.index = faiss.deserialize_index(np.load(buffer, allow_pickle=False))
self.chunk_ids = [chunk.chunk_id for chunk in self.chunk_by_index.values()]
except Exception as e:
logger.debug(e, exc_info=True)
log.debug(e, exc_info=True)
raise ValueError(
"Error deserializing Faiss index from storage. If you recently upgraded your Llama Stack, Faiss, "
"or NumPy versions, you may need to delete the index and re-create it again or downgrade versions.\n"
@ -262,7 +262,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
assert self.kvstore is not None
if vector_db_id not in self.cache:
logger.warning(f"Vector DB {vector_db_id} not found")
log.warning(f"Vector DB {vector_db_id} not found")
return
await self.cache[vector_db_id].index.delete()

14
llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import re
import sqlite3
import struct
@ -24,6 +23,7 @@ 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.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
@ -35,7 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import (
VectorDBWithIndex,
)
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
# Specifying search mode is dependent on the VectorIO provider.
VECTOR_SEARCH = "vector"
@ -257,7 +257,7 @@ class SQLiteVecIndex(EmbeddingIndex):
except sqlite3.Error as e:
connection.rollback()
logger.error(f"Error inserting into {self.vector_table}: {e}")
log.error(f"Error inserting into {self.vector_table}: {e}")
raise
finally:
@ -306,7 +306,7 @@ class SQLiteVecIndex(EmbeddingIndex):
try:
chunk = Chunk.model_validate_json(chunk_json)
except Exception as e:
logger.error(f"Error parsing chunk JSON for id {_id}: {e}")
log.error(f"Error parsing chunk JSON for id {_id}: {e}")
continue
chunks.append(chunk)
scores.append(score)
@ -352,7 +352,7 @@ class SQLiteVecIndex(EmbeddingIndex):
try:
chunk = Chunk.model_validate_json(chunk_json)
except Exception as e:
logger.error(f"Error parsing chunk JSON for id {_id}: {e}")
log.error(f"Error parsing chunk JSON for id {_id}: {e}")
continue
chunks.append(chunk)
scores.append(score)
@ -447,7 +447,7 @@ class SQLiteVecIndex(EmbeddingIndex):
connection.commit()
except Exception as e:
connection.rollback()
logger.error(f"Error deleting chunk {chunk_id}: {e}")
log.error(f"Error deleting chunk {chunk_id}: {e}")
raise
finally:
cur.close()
@ -530,7 +530,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
async def unregister_vector_db(self, vector_db_id: str) -> None:
if vector_db_id not in self.cache:
logger.warning(f"Vector DB {vector_db_id} not found")
log.warning(f"Vector DB {vector_db_id} not found")
return
await self.cache[vector_db_id].index.delete()
del self.cache[vector_db_id]