# AmazonKnowledgeBasesRetriever

> **Class** in `langchain_community`

📖 [View in docs](https://reference.langchain.com/python/langchain-community/retrievers/bedrock/AmazonKnowledgeBasesRetriever)

Amazon Bedrock Knowledge Bases retriever.

See https://aws.amazon.com/bedrock/knowledge-bases for more info.

## Signature

```python
AmazonKnowledgeBasesRetriever()
```

## Description

**Setup:**

Install ``langchain-aws``:

.. code-block:: bash

    pip install -U langchain-aws

**Key init 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.

**Instantiate:**

.. code-block:: python

from langchain_community.retrievers import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(
    knowledge_base_id="<knowledge-base-id>",
    retrieval_config={
        "vectorSearchConfiguration": {
            "numberOfResults": 4
        }
    },
)

**Usage:**

.. code-block:: python

query = "..."

retriever.invoke(query)

**Use within a chain:**

.. code-block:: python

from langchain_aws import ChatBedrockConverse
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)

llm = ChatBedrockConverse(
    model_id="anthropic.claude-3-5-sonnet-20240620-v1:0"
)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke("...")

## Extends

- `BaseRetriever`

## Properties

- `knowledge_base_id`
- `region_name`
- `credentials_profile_name`
- `endpoint_url`
- `client`
- `retrieval_config`

## Methods

- [`create_client()`](https://reference.langchain.com/python/langchain-community/retrievers/bedrock/AmazonKnowledgeBasesRetriever/create_client)

## ⚠️ Deprecated

Deprecated since version 0.3.16.

---

[View source on GitHub](https://github.com/langchain-ai/langchain-community/blob/4b280287bd55b99b44db2dd849f02d66c89534d5/libs/community/langchain_community/retrievers/bedrock.py#L22)