import json
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.retrievers import BaseRetriever
from pydantic import BaseModel, ConfigDict, Field, model_validator
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
model_config = ConfigDict(
populate_by_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: Optional configuration for retrieval specified as a
Python object (RetrievalConfig) or as a dictionary
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: Optional[Union[RetrievalConfig, Dict[str, Any]]] = None
min_score_confidence: Annotated[
Optional[float], Field(ge=0.0, le=1.0, default=None)
]
@model_validator(mode="before")
@classmethod
def create_client(cls, values: Dict[str, Any]) -> 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]:
"""
Get relevant document from a KnowledgeBase
:param query: the user's query
:param run_manager: The callback handler to use
:return: List of relevant documents
"""
retrieve_request: Dict[str, Any] = self._get_retrieve_request(query)
response = self.client.retrieve(**retrieve_request)
results = response["retrievalResults"]
documents: List[
Document
] = AmazonKnowledgeBasesRetriever._retrieval_results_to_documents(results)
return self._filter_by_score_confidence(docs=documents)
def _get_retrieve_request(self, query: str) -> Dict[str, Any]:
"""
Build a Retrieve request
:param query:
:return:
"""
request: Dict[str, Any] = {
"retrievalQuery": {"text": query.strip()},
"knowledgeBaseId": self.knowledge_base_id,
}
if self.retrieval_config:
request["retrievalConfiguration"] = self.retrieval_config.model_dump(
exclude_none=True, by_alias=True
)
return request
@staticmethod
def _retrieval_results_to_documents(
results: List[Dict[str, Any]],
) -> List[Document]:
"""
Convert the Retrieve API results to LangChain Documents
:param results: Retrieve API results list
:return: List of LangChain Documents
"""
documents = []
for result in results:
content = AmazonKnowledgeBasesRetriever._get_content_from_result(result)
result["type"] = result.get("content", {}).get("type", "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 documents
@staticmethod
def _get_content_from_result(result: Dict[str, Any]) -> Optional[str]:
"""
Convert the content from one Retrieve API result to string
:param result: Retrieve API search result
:return: string representation of the content attribute
"""
if not result:
raise ValueError("Invalid search result")
content: dict = result.get("content")
if not content:
raise ValueError(
"Invalid search result, content is missing from the result"
)
if not content.get("type"):
return content.get("text")
if content["type"] == "TEXT":
return content.get("text")
elif content["type"] == "IMAGE":
return content.get("byteContent")
elif content["type"] == "ROW":
row: Optional[List[dict]] = content.get("row", [])
return json.dumps(row if row else [])
else:
# future proofing this class to prevent code breaks if new types
# are introduced
return None
