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fix: S3_VECTORS_DEFAULT_DIMENSION

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This commit is contained in:
Ishaan Jaffer 2026-01-27 14:36:41 -08:00
parent a3d795be95
commit 7aa11ecf4a
2 changed files with 22 additions and 7 deletions
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7
litellm/constants.py
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@ -1327,6 +1327,13 @@ COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int(
DEFAULT_CHUNK_SIZE = int(os.getenv("DEFAULT_CHUNK_SIZE", 1000))
DEFAULT_CHUNK_OVERLAP = int(os.getenv("DEFAULT_CHUNK_OVERLAP", 200))
########################### S3 Vectors RAG Constants ###########################
S3_VECTORS_DEFAULT_DIMENSION = int(os.getenv("S3_VECTORS_DEFAULT_DIMENSION", 1024))
S3_VECTORS_DEFAULT_DISTANCE_METRIC = str(
os.getenv("S3_VECTORS_DEFAULT_DISTANCE_METRIC", "cosine")
)
S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS = ["source_text"]
########################### Microsoft SSO Constants ###########################
MICROSOFT_USER_EMAIL_ATTRIBUTE = str(
os.getenv("MICROSOFT_USER_EMAIL_ATTRIBUTE", "userPrincipalName")

22
litellm/rag/ingestion/s3_vectors_ingestion.py
View file

@ -21,6 +21,11 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import litellm
from litellm._logging import verbose_logger
from litellm.constants import (
S3_VECTORS_DEFAULT_DIMENSION,
S3_VECTORS_DEFAULT_DISTANCE_METRIC,
S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS,
)
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
from litellm.llms.custom_httpx.http_handler import (
@ -47,8 +52,8 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
Configuration:
- vector_bucket_name: S3 vector bucket name (required)
- index_name: Vector index name (auto-creates if not provided)
- dimension: Vector dimension (default: 1024)
- distance_metric: "cosine" or "euclidean" (default: cosine)
- dimension: Vector dimension (default: S3_VECTORS_DEFAULT_DIMENSION)
- distance_metric: "cosine" or "euclidean" (default: S3_VECTORS_DEFAULT_DISTANCE_METRIC)
- non_filterable_metadata_keys: List of metadata keys to exclude from filtering
"""
@ -63,9 +68,12 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
# Extract config
self.vector_bucket_name = self.vector_store_config["vector_bucket_name"]
self.index_name = self.vector_store_config.get("index_name")
self.distance_metric = self.vector_store_config.get("distance_metric", "cosine")
self.distance_metric = self.vector_store_config.get(
"distance_metric", S3_VECTORS_DEFAULT_DISTANCE_METRIC
)
self.non_filterable_metadata_keys = self.vector_store_config.get(
"non_filterable_metadata_keys", ["source_text"]
"non_filterable_metadata_keys",
S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS,
)
# Get dimension from config (will be auto-detected on first use if not provided)
@ -97,7 +105,7 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
the output dimension of the embedding model.
"""
if not self.embedding_config or "model" not in self.embedding_config:
return 1024
return S3_VECTORS_DEFAULT_DIMENSION
try:
model_name = self.embedding_config["model"]
@ -122,10 +130,10 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
except Exception as e:
verbose_logger.warning(
f"Could not auto-detect dimension from embedding model: {e}. "
"Using default dimension of 1024."
f"Using default dimension of {S3_VECTORS_DEFAULT_DIMENSION}."
)
return 1024
return S3_VECTORS_DEFAULT_DIMENSION
def _get_dimension_from_config(self) -> Optional[int]:
"""