from typing import Any, Dict, List, Literal, Optional, Union
import boto3
from botocore.client import Config
from botocore.exceptions import UnknownServiceError
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.retrievers import BaseRetriever
from typing_extensions import Annotated
FilterValue = Union[Dict[str, Any], List[Any], int, float, str, bool, None]
Filter = Dict[str, FilterValue]
[docs]class SearchFilter(BaseModel):
"""Filter configuration for retrieval."""
andAll: Optional[List["SearchFilter"]] = None
orAll: Optional[List["SearchFilter"]] = None
equals: Optional[Filter] = None
greaterThan: Optional[Filter] = None
greaterThanOrEquals: Optional[Filter] = None
in_: Optional[Filter] = Field(None, alias="in")
lessThan: Optional[Filter] = None
lessThanOrEquals: Optional[Filter] = None
listContains: Optional[Filter] = None
notEquals: Optional[Filter] = None
notIn: Optional[Filter] = Field(None, alias="notIn")
startsWith: Optional[Filter] = None
stringContains: Optional[Filter] = None
class Config:
allow_population_by_field_name = True
[docs]class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg]
"""Configuration for vector search."""
numberOfResults: int = 4
filter: Optional[SearchFilter] = None
overrideSearchType: Optional[Literal["HYBRID", "SEMANTIC"]] = None
[docs]class RetrievalConfig(BaseModel, extra="allow"): # type: ignore[call-arg]
"""Configuration for retrieval."""
vectorSearchConfiguration: VectorSearchConfig
nextToken: Optional[str] = None
[docs]class AmazonKnowledgeBasesRetriever(BaseRetriever):
"""`Amazon Bedrock Knowledge Bases` retrieval.
See https://aws.amazon.com/bedrock/knowledge-bases for more info.
Args:
knowledge_base_id: Knowledge Base ID.
region_name: The aws region e.g., `us-west-2`.
Fallback to AWS_DEFAULT_REGION env variable or region specified in
~/.aws/config.
credentials_profile_name: The name of the profile in the ~/.aws/credentials
or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default credential profile or, if on an
EC2 instance, credentials from IMDS will be used.
client: boto3 client for bedrock agent runtime.
retrieval_config: Configuration for retrieval.
Example:
.. code-block:: python
from langchain_community.retrievers import AmazonKnowledgeBasesRetriever
retriever = AmazonKnowledgeBasesRetriever(
knowledge_base_id="<knowledge-base-id>",
retrieval_config={
"vectorSearchConfiguration": {
"numberOfResults": 4
}
},
)
"""
knowledge_base_id: str
region_name: Optional[str] = None
credentials_profile_name: Optional[str] = None
endpoint_url: Optional[str] = None
client: Any
retrieval_config: RetrievalConfig
min_score_confidence: Annotated[Optional[float], Field(ge=0.0, le=1.0)]
@root_validator(pre=True)
def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
if values.get("client") is not None:
return values
try:
if values.get("credentials_profile_name"):
session = boto3.Session(profile_name=values["credentials_profile_name"])
else:
# use default credentials
session = boto3.Session()
client_params = {
"config": Config(
connect_timeout=120, read_timeout=120, retries={"max_attempts": 0}
)
}
if values.get("region_name"):
client_params["region_name"] = values["region_name"]
if values.get("endpoint_url"):
client_params["endpoint_url"] = values["endpoint_url"]
values["client"] = session.client("bedrock-agent-runtime", **client_params)
return values
except ImportError:
raise ModuleNotFoundError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except UnknownServiceError as e:
raise ModuleNotFoundError(
"Ensure that you have installed the latest boto3 package "
"that contains the API for `bedrock-runtime-agent`."
) from e
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
def _filter_by_score_confidence(self, docs: List[Document]) -> List[Document]:
"""
Filter out the records that have a score confidence
less than the required threshold.
"""
if not self.min_score_confidence:
return docs
filtered_docs = [
item
for item in docs
if (
item.metadata.get("score") is not None
and item.metadata.get("score", 0.0) >= self.min_score_confidence
)
]
return filtered_docs
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
response = self.client.retrieve(
retrievalQuery={"text": query.strip()},
knowledgeBaseId=self.knowledge_base_id,
retrievalConfiguration=self.retrieval_config.dict(exclude_none=True),
)
results = response["retrievalResults"]
documents = []
for result in results:
content = result["content"]["text"]
result.pop("content")
if "score" not in result:
result["score"] = 0
if "metadata" in result:
result["source_metadata"] = result.pop("metadata")
documents.append(
Document(
page_content=content,
metadata=result,
)
)
return self._filter_by_score_confidence(docs=documents)
