chore: initial snapshot for gitea/github upload
This commit is contained in:
Your Name
@@ -0,0 +1 @@
|
||||
# S3 Vectors LLM integration
|
||||
@@ -0,0 +1 @@
|
||||
# S3 Vectors vector store integration
|
||||
@@ -0,0 +1,274 @@
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import httpx
|
||||
|
||||
from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
|
||||
from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.types.vector_stores import (
|
||||
VECTOR_STORE_OPENAI_PARAMS,
|
||||
BaseVectorStoreAuthCredentials,
|
||||
VectorStoreIndexEndpoints,
|
||||
VectorStoreResultContent,
|
||||
VectorStoreSearchOptionalRequestParams,
|
||||
VectorStoreSearchResponse,
|
||||
VectorStoreSearchResult,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
else:
|
||||
LiteLLMLoggingObj = Any
|
||||
|
||||
|
||||
class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
|
||||
"""Vector store configuration for AWS S3 Vectors."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
BaseVectorStoreConfig.__init__(self)
|
||||
BaseAWSLLM.__init__(self)
|
||||
|
||||
def get_auth_credentials(
|
||||
self, litellm_params: dict
|
||||
) -> BaseVectorStoreAuthCredentials:
|
||||
return {}
|
||||
|
||||
def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
|
||||
return {
|
||||
"read": [("POST", "/QueryVectors")],
|
||||
"write": [],
|
||||
}
|
||||
|
||||
def get_supported_openai_params(
|
||||
self, model: str
|
||||
) -> List[VECTOR_STORE_OPENAI_PARAMS]:
|
||||
return ["max_num_results"]
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
for param, value in non_default_params.items():
|
||||
if param == "max_num_results":
|
||||
optional_params["maxResults"] = value
|
||||
return optional_params
|
||||
|
||||
def validate_environment(
|
||||
self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
|
||||
) -> dict:
|
||||
headers = headers or {}
|
||||
headers.setdefault("Content-Type", "application/json")
|
||||
return headers
|
||||
|
||||
def get_complete_url(self, api_base: Optional[str], litellm_params: dict) -> str:
|
||||
aws_region_name = litellm_params.get("aws_region_name")
|
||||
if not aws_region_name:
|
||||
raise ValueError("aws_region_name is required for S3 Vectors")
|
||||
return f"https://s3vectors.{aws_region_name}.api.aws"
|
||||
|
||||
def transform_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: Union[str, List[str]],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
api_base: str,
|
||||
litellm_logging_obj: LiteLLMLoggingObj,
|
||||
litellm_params: dict,
|
||||
) -> Tuple[str, Dict]:
|
||||
"""Sync version - generates embedding synchronously."""
|
||||
# For S3 Vectors, vector_store_id should be in format: bucket_name:index_name
|
||||
# If not in that format, try to construct it from litellm_params
|
||||
bucket_name: str
|
||||
index_name: str
|
||||
|
||||
if ":" in vector_store_id:
|
||||
bucket_name, index_name = vector_store_id.split(":", 1)
|
||||
else:
|
||||
# Try to get bucket_name from litellm_params
|
||||
bucket_name_from_params = litellm_params.get("vector_bucket_name")
|
||||
if not bucket_name_from_params or not isinstance(
|
||||
bucket_name_from_params, str
|
||||
):
|
||||
raise ValueError(
|
||||
"vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, "
|
||||
"or vector_bucket_name must be provided in litellm_params"
|
||||
)
|
||||
bucket_name = bucket_name_from_params
|
||||
index_name = vector_store_id
|
||||
|
||||
if isinstance(query, list):
|
||||
query = " ".join(query)
|
||||
|
||||
# Generate embedding for the query
|
||||
embedding_model = litellm_params.get(
|
||||
"embedding_model", "text-embedding-3-small"
|
||||
)
|
||||
|
||||
import litellm as litellm_module
|
||||
|
||||
embedding_response = litellm_module.embedding(
|
||||
model=embedding_model, input=[query]
|
||||
)
|
||||
query_embedding = embedding_response.data[0]["embedding"]
|
||||
|
||||
url = f"{api_base}/QueryVectors"
|
||||
|
||||
request_body: Dict[str, Any] = {
|
||||
"vectorBucketName": bucket_name,
|
||||
"indexName": index_name,
|
||||
"queryVector": {"float32": query_embedding},
|
||||
"topK": vector_store_search_optional_params.get(
|
||||
"max_num_results", 5
|
||||
), # Default to 5
|
||||
"returnDistance": True,
|
||||
"returnMetadata": True,
|
||||
}
|
||||
|
||||
litellm_logging_obj.model_call_details["query"] = query
|
||||
return url, request_body
|
||||
|
||||
async def atransform_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: Union[str, List[str]],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
api_base: str,
|
||||
litellm_logging_obj: LiteLLMLoggingObj,
|
||||
litellm_params: dict,
|
||||
) -> Tuple[str, Dict]:
|
||||
"""Async version - generates embedding asynchronously."""
|
||||
# For S3 Vectors, vector_store_id should be in format: bucket_name:index_name
|
||||
# If not in that format, try to construct it from litellm_params
|
||||
bucket_name: str
|
||||
index_name: str
|
||||
|
||||
if ":" in vector_store_id:
|
||||
bucket_name, index_name = vector_store_id.split(":", 1)
|
||||
else:
|
||||
# Try to get bucket_name from litellm_params
|
||||
bucket_name_from_params = litellm_params.get("vector_bucket_name")
|
||||
if not bucket_name_from_params or not isinstance(
|
||||
bucket_name_from_params, str
|
||||
):
|
||||
raise ValueError(
|
||||
"vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, "
|
||||
"or vector_bucket_name must be provided in litellm_params"
|
||||
)
|
||||
bucket_name = bucket_name_from_params
|
||||
index_name = vector_store_id
|
||||
|
||||
if isinstance(query, list):
|
||||
query = " ".join(query)
|
||||
|
||||
# Generate embedding for the query asynchronously
|
||||
embedding_model = litellm_params.get(
|
||||
"embedding_model", "text-embedding-3-small"
|
||||
)
|
||||
|
||||
import litellm as litellm_module
|
||||
|
||||
embedding_response = await litellm_module.aembedding(
|
||||
model=embedding_model, input=[query]
|
||||
)
|
||||
query_embedding = embedding_response.data[0]["embedding"]
|
||||
|
||||
url = f"{api_base}/QueryVectors"
|
||||
|
||||
request_body: Dict[str, Any] = {
|
||||
"vectorBucketName": bucket_name,
|
||||
"indexName": index_name,
|
||||
"queryVector": {"float32": query_embedding},
|
||||
"topK": vector_store_search_optional_params.get(
|
||||
"max_num_results", 5
|
||||
), # Default to 5
|
||||
"returnDistance": True,
|
||||
"returnMetadata": True,
|
||||
}
|
||||
|
||||
litellm_logging_obj.model_call_details["query"] = query
|
||||
return url, request_body
|
||||
|
||||
def sign_request(
|
||||
self,
|
||||
headers: dict,
|
||||
optional_params: Dict,
|
||||
request_data: Dict,
|
||||
api_base: str,
|
||||
api_key: Optional[str] = None,
|
||||
) -> Tuple[dict, Optional[bytes]]:
|
||||
return self._sign_request(
|
||||
service_name="s3vectors",
|
||||
headers=headers,
|
||||
optional_params=optional_params,
|
||||
request_data=request_data,
|
||||
api_base=api_base,
|
||||
api_key=api_key,
|
||||
)
|
||||
|
||||
def transform_search_vector_store_response(
|
||||
self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
|
||||
) -> VectorStoreSearchResponse:
|
||||
try:
|
||||
response_data = response.json()
|
||||
results: List[VectorStoreSearchResult] = []
|
||||
|
||||
for item in response_data.get("vectors", []) or []:
|
||||
metadata = item.get("metadata", {}) or {}
|
||||
source_text = metadata.get("source_text", "")
|
||||
|
||||
if not source_text:
|
||||
continue
|
||||
|
||||
# Extract file information from metadata
|
||||
chunk_index = metadata.get("chunk_index", "0")
|
||||
file_id = f"s3-vectors-chunk-{chunk_index}"
|
||||
filename = metadata.get("filename", f"document-{chunk_index}")
|
||||
|
||||
# S3 Vectors returns distance, convert to similarity score (0-1)
|
||||
# Lower distance = higher similarity
|
||||
# We'll normalize using 1 / (1 + distance) to get a 0-1 score
|
||||
distance = item.get("distance")
|
||||
score = None
|
||||
if distance is not None:
|
||||
# Convert distance to similarity score between 0 and 1
|
||||
# For cosine distance: similarity = 1 - distance
|
||||
# For euclidean: use 1 / (1 + distance)
|
||||
# Assuming cosine distance here
|
||||
score = max(0.0, min(1.0, 1.0 - float(distance)))
|
||||
|
||||
results.append(
|
||||
VectorStoreSearchResult(
|
||||
score=score,
|
||||
content=[
|
||||
VectorStoreResultContent(text=source_text, type="text")
|
||||
],
|
||||
file_id=file_id,
|
||||
filename=filename,
|
||||
attributes=metadata,
|
||||
)
|
||||
)
|
||||
|
||||
return VectorStoreSearchResponse(
|
||||
object="vector_store.search_results.page",
|
||||
search_query=litellm_logging_obj.model_call_details.get("query", ""),
|
||||
data=results,
|
||||
)
|
||||
except Exception as e:
|
||||
raise self.get_error_class(
|
||||
error_message=str(e),
|
||||
status_code=response.status_code,
|
||||
headers=response.headers,
|
||||
)
|
||||
|
||||
# Vector store creation is not yet implemented
|
||||
def transform_create_vector_store_request(
|
||||
self,
|
||||
vector_store_create_optional_params,
|
||||
api_base: str,
|
||||
) -> Tuple[str, Dict]:
|
||||
raise NotImplementedError
|
||||
|
||||
def transform_create_vector_store_response(self, response: httpx.Response):
|
||||
raise NotImplementedError
|
||||
Reference in New Issue
Block a user