# langchain-aws
This package contains the LangChain integrations with AWS.
## Installation
```bash
pip install -U langchain-aws
```
All integrations in this package assume that you have the credentials setup to connect with AWS services.
## Authentication
In order to use Amazon Bedrock models, you need to configure AWS credentials. One of the options is to set the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables. More information can be found [here](https://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html).
Alternatively, set the `AWS_BEARER_TOKEN_BEDROCK` environment variable locally for API Key authentication. For additional API key details, refer to [docs](https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys.html).
## Chat Models
`ChatBedrock` class exposes chat models from Bedrock.
```python
from langchain_aws import ChatBedrock
llm = ChatBedrock()
llm.invoke("Sing a ballad of LangChain.")
```
## Embeddings
`BedrockEmbeddings` class exposes embeddings from Bedrock.
```python
from langchain_aws import BedrockEmbeddings
embeddings = BedrockEmbeddings()
embeddings.embed_query("What is the meaning of life?")
```
## LLMs
`BedrockLLM` class exposes LLMs from Bedrock.
```python
from langchain_aws import BedrockLLM
llm = BedrockLLM()
llm.invoke("The meaning of life is")
```
## Retrievers
`AmazonKendraRetriever` class provides a retriever to connect with Amazon Kendra.
```python
from langchain_aws import AmazonKendraRetriever
retriever = AmazonKendraRetriever(
index_id="561be2b6d-9804c7e7-f6a0fbb8-5ccd350"
)
retriever.get_relevant_documents(query="What is the meaning of life?")
```
`AmazonKnowledgeBasesRetriever` class provides a retriever to connect with Amazon Knowledge Bases.
```python
from langchain_aws import AmazonKnowledgeBasesRetriever
retriever = AmazonKnowledgeBasesRetriever(
knowledge_base_id="IAPJ4QPUEU",
retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
)
retriever.get_relevant_documents(query="What is the meaning of life?")
```
## VectorStores
`InMemoryVectorStore` class provides a vectorstore to connect with Amazon MemoryDB.
```python
from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore
vds = InMemoryVectorStore.from_documents(
chunks,
embeddings,
redis_url="rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none",
vector_schema=vector_schema,
index_name=INDEX_NAME,
)
```
## MemoryDB as Retriever
Here we go over different options for using the vector store as a retriever.
There are three different search methods we can use to do retrieval. By default, it will use semantic similarity.
```python
retriever=vds.as_retriever()
```
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"description": "# langchain-aws\n\nThis package contains the LangChain integrations with AWS.\n\n## Installation\n\n```bash\npip install -U langchain-aws\n```\nAll integrations in this package assume that you have the credentials setup to connect with AWS services.\n\n## Authentication\n\nIn order to use Amazon Bedrock models, you need to configure AWS credentials. One of the options is to set the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables. More information can be found [here](https://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html). \nAlternatively, set the `AWS_BEARER_TOKEN_BEDROCK` environment variable locally for API Key authentication. For additional API key details, refer to [docs](https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys.html).\n\n## Chat Models\n\n`ChatBedrock` class exposes chat models from Bedrock.\n\n```python\nfrom langchain_aws import ChatBedrock\n\nllm = ChatBedrock()\nllm.invoke(\"Sing a ballad of LangChain.\")\n```\n\n## Embeddings\n\n`BedrockEmbeddings` class exposes embeddings from Bedrock.\n\n```python\nfrom langchain_aws import BedrockEmbeddings\n\nembeddings = BedrockEmbeddings()\nembeddings.embed_query(\"What is the meaning of life?\")\n```\n\n## LLMs\n`BedrockLLM` class exposes LLMs from Bedrock.\n\n```python\nfrom langchain_aws import BedrockLLM\n\nllm = BedrockLLM()\nllm.invoke(\"The meaning of life is\")\n```\n\n## Retrievers\n`AmazonKendraRetriever` class provides a retriever to connect with Amazon Kendra.\n\n```python\nfrom langchain_aws import AmazonKendraRetriever\n\nretriever = AmazonKendraRetriever(\n index_id=\"561be2b6d-9804c7e7-f6a0fbb8-5ccd350\"\n)\n\nretriever.get_relevant_documents(query=\"What is the meaning of life?\")\n```\n\n`AmazonKnowledgeBasesRetriever` class provides a retriever to connect with Amazon Knowledge Bases.\n\n```python\nfrom langchain_aws import AmazonKnowledgeBasesRetriever\n\nretriever = AmazonKnowledgeBasesRetriever(\n knowledge_base_id=\"IAPJ4QPUEU\",\n retrieval_config={\"vectorSearchConfiguration\": {\"numberOfResults\": 4}},\n)\n\nretriever.get_relevant_documents(query=\"What is the meaning of life?\")\n```\n## VectorStores \n`InMemoryVectorStore` class provides a vectorstore to connect with Amazon MemoryDB.\n\n```python\nfrom langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore\n\nvds = InMemoryVectorStore.from_documents(\n chunks,\n embeddings,\n redis_url=\"rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none\",\n vector_schema=vector_schema,\n index_name=INDEX_NAME,\n )\n```\n\n## MemoryDB as Retriever\n\nHere we go over different options for using the vector store as a retriever.\n\nThere are three different search methods we can use to do retrieval. By default, it will use semantic similarity.\n\n```python\nretriever=vds.as_retriever()\n```\n\n",
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