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# +-------------------------------------------------------------+
#
# Add Bedrock Knowledge Base Context to your LLM calls
#
# +-------------------------------------------------------------+
# Thank you users! We ❤️ you! - Krrish & Ishaan
import json
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from litellm._logging import verbose_logger, verbose_proxy_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.custom_prompt_management import CustomPromptManagement
from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
httpxSpecialProvider,
)
from litellm.types.integrations.rag.bedrock_knowledgebase import (
BedrockKBContent,
BedrockKBGuardrailConfiguration,
BedrockKBRequest,
BedrockKBResponse,
BedrockKBRetrievalConfiguration,
BedrockKBRetrievalQuery,
BedrockKBRetrievalResult,
)
from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import StandardCallbackDynamicParams
else:
StandardCallbackDynamicParams = Any
class BedrockKnowledgeBaseHook(CustomPromptManagement, BaseAWSLLM):
CONTENT_PREFIX_STRING = "Context: \n\n"
def __init__(
self,
**kwargs,
):
self.async_handler = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
# store kwargs as optional_params
self.optional_params = kwargs
super().__init__(**kwargs)
BaseAWSLLM.__init__(self)
async def async_get_chat_completion_prompt(
self,
model: str,
messages: List[AllMessageValues],
non_default_params: dict,
prompt_id: Optional[str],
prompt_variables: Optional[dict],
dynamic_callback_params: StandardCallbackDynamicParams,
) -> Tuple[str, List[AllMessageValues], dict]:
"""
Retrieves the context from the Bedrock Knowledge Base and appends it to the messages.
"""
vector_store_ids = non_default_params.pop("vector_store_ids", None)
if vector_store_ids:
for vector_store_id in vector_store_ids:
response = await self.make_bedrock_kb_retrieve_request(
knowledge_base_id=vector_store_id,
query=self._get_kb_query_from_messages(messages),
)
verbose_logger.debug(f"Bedrock Knowledge Base Response: {response}")
context_message = (
self.get_chat_completion_message_from_bedrock_kb_response(response)
)
if context_message is not None:
messages.append(context_message)
return model, messages, non_default_params
def _get_kb_query_from_messages(self, messages: List[AllMessageValues]) -> str:
"""
Uses the text `content` field of the last message in the list of messages
"""
if len(messages) == 0:
return ""
last_message = messages[-1]
last_message_content = last_message.get("content", None)
if last_message_content is None:
return ""
if isinstance(last_message_content, str):
return last_message_content
elif isinstance(last_message_content, list):
return "\n".join([item.get("text", "") for item in last_message_content])
return ""
def _prepare_request(
self,
credentials: Any,
data: BedrockKBRequest,
optional_params: dict,
aws_region_name: str,
api_base: str,
extra_headers: Optional[dict] = None,
) -> Any:
"""
Prepare a signed AWS request.
Args:
credentials: AWS credentials
data: Request data
optional_params: Additional parameters
aws_region_name: AWS region name
api_base: Base API URL
extra_headers: Additional headers
Returns:
AWSRequest: A signed AWS request
"""
try:
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
encoded_data = json.dumps(data).encode("utf-8")
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
request = AWSRequest(
method="POST", url=api_base, data=encoded_data, headers=headers
)
sigv4.add_auth(request)
if extra_headers is not None and "Authorization" in extra_headers:
# prevent sigv4 from overwriting the auth header
request.headers["Authorization"] = extra_headers["Authorization"]
return request.prepare()
async def make_bedrock_kb_retrieve_request(
self,
knowledge_base_id: str,
query: str,
guardrail_id: Optional[str] = None,
guardrail_version: Optional[str] = None,
next_token: Optional[str] = None,
retrieval_configuration: Optional[BedrockKBRetrievalConfiguration] = None,
) -> BedrockKBResponse:
"""
Make a Bedrock Knowledge Base retrieve request.
Args:
knowledge_base_id (str): The unique identifier of the knowledge base to query
query (str): The query text to search for
guardrail_id (Optional[str]): The guardrail ID to apply
guardrail_version (Optional[str]): The version of the guardrail to apply
next_token (Optional[str]): Token for pagination
retrieval_configuration (Optional[BedrockKBRetrievalConfiguration]): Configuration for the retrieval process
Returns:
BedrockKBRetrievalResponse: A typed response object containing the retrieval results
"""
from fastapi import HTTPException
credentials = self.get_credentials()
aws_region_name = self._get_aws_region_name(
optional_params=self.optional_params
)
# Prepare request data
request_data: BedrockKBRequest = BedrockKBRequest(
retrievalQuery=BedrockKBRetrievalQuery(text=query),
)
if next_token:
request_data["nextToken"] = next_token
if retrieval_configuration:
request_data["retrievalConfiguration"] = retrieval_configuration
if guardrail_id and guardrail_version:
request_data["guardrailConfiguration"] = BedrockKBGuardrailConfiguration(
guardrailId=guardrail_id, guardrailVersion=guardrail_version
)
verbose_logger.debug(
f"Request Data: {json.dumps(request_data, indent=4, default=str)}"
)
# Prepare the request
api_base = f"https://bedrock-agent-runtime.{aws_region_name}.amazonaws.com/knowledgebases/{knowledge_base_id}/retrieve"
prepared_request = self._prepare_request(
credentials=credentials,
data=request_data,
optional_params=self.optional_params,
aws_region_name=aws_region_name,
api_base=api_base,
)
verbose_proxy_logger.debug(
"Bedrock Knowledge Base request body: %s, url %s, headers: %s",
request_data,
prepared_request.url,
prepared_request.headers,
)
response = await self.async_handler.post(
url=prepared_request.url,
data=prepared_request.body, # type: ignore
headers=prepared_request.headers, # type: ignore
)
verbose_proxy_logger.debug("Bedrock Knowledge Base response: %s", response.text)
if response.status_code == 200:
response_data = response.json()
return BedrockKBResponse(**response_data)
else:
verbose_proxy_logger.error(
"Bedrock Knowledge Base: error in response. Status code: %s, response: %s",
response.status_code,
response.text,
)
raise HTTPException(
status_code=response.status_code,
detail={
"error": "Error calling Bedrock Knowledge Base",
"response": response.text,
},
)
@staticmethod
def should_use_prompt_management_hook(non_default_params: Dict) -> bool:
if non_default_params.get("vector_store_ids", None):
return True
return False
@staticmethod
def get_initialized_custom_logger(
non_default_params: Dict,
) -> Optional[CustomLogger]:
from litellm.litellm_core_utils.litellm_logging import (
_init_custom_logger_compatible_class,
)
if BedrockKnowledgeBaseHook.should_use_prompt_management_hook(
non_default_params
):
return _init_custom_logger_compatible_class(
logging_integration="bedrock_knowledgebase_hook",
internal_usage_cache=None,
llm_router=None,
)
return None
@staticmethod
def get_chat_completion_message_from_bedrock_kb_response(
response: BedrockKBResponse,
) -> Optional[ChatCompletionUserMessage]:
"""
Retrieves the context from the Bedrock Knowledge Base response and returns a ChatCompletionUserMessage object.
"""
retrieval_results: Optional[List[BedrockKBRetrievalResult]] = response.get(
"retrievalResults", None
)
if retrieval_results is None:
return None
# string to combine the context from the knowledge base
context_string: str = BedrockKnowledgeBaseHook.CONTENT_PREFIX_STRING
for retrieval_result in retrieval_results:
retrieval_result_content: Optional[BedrockKBContent] = (
retrieval_result.get("content", None) or {}
)
if retrieval_result_content is None:
continue
retrieval_result_text: Optional[str] = retrieval_result_content.get(
"text", None
)
if retrieval_result_text is None:
continue
context_string += retrieval_result_text
message = ChatCompletionUserMessage(
role="user",
content=context_string,
)
return message
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