Knowledge Bases for Amazon Bedrock
Overviewβ
This will help you getting started with the AmazonKnowledgeBaseRetriever. For detailed documentation of all AmazonKnowledgeBaseRetriever features and configurations head to the API reference.
Knowledge Bases for Amazon Bedrock is a fully managed support for end-to-end RAG workflow provided by Amazon Web Services (AWS). It provides an entire ingestion workflow of converting your documents into embeddings (vector) and storing the embeddings in a specialized vector database. Knowledge Bases for Amazon Bedrock supports popular databases for vector storage, including vector engine for Amazon OpenSearch Serverless, Pinecone, Redis Enterprise Cloud, Amazon Aurora (coming soon), and MongoDB (coming soon).
Integration detailsβ
Retriever | Self-host | Cloud offering | Package | Py support |
---|---|---|---|---|
AmazonKnowledgeBaseRetriever | π (see details below) | β | @langchain/aws | β |
AWS Knowledge Base Retriever can be βself hostedβ in the sense you can run it on your own AWS infrastructure. However it is not possible to run on another cloud provider or on-premises.
Setupβ
In order to use the AmazonKnowledgeBaseRetriever, you need to have an AWS account, where you can manage your indexes and documents. Once youβve setup your account, set the following environment variables:
process.env.AWS_KNOWLEDGE_BASE_ID=your-knowledge-base-id
process.env.AWS_ACCESS_KEY_ID=your-access-key-id
process.env.AWS_SECRET_ACCESS_KEY=your-secret-access-key
If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below:
// process.env.LANGSMITH_API_KEY = "<YOUR API KEY HERE>";
// process.env.LANGSMITH_TRACING = "true";
Installationβ
This retriever lives in the @langchain/aws
package:
- npm
- yarn
- pnpm
npm i @langchain/aws
yarn add @langchain/aws
pnpm add @langchain/aws
Instantiationβ
Now we can instantiate our retriever:
import { AmazonKnowledgeBaseRetriever } from "@langchain/aws";
const retriever = new AmazonKnowledgeBaseRetriever({
topK: 10,
knowledgeBaseId: process.env.AWS_KNOWLEDGE_BASE_ID,
region: "us-east-2",
clientOptions: {
credentials: {
accessKeyId: process.env.AWS_ACCESS_KEY_ID,
secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
},
},
});
Usageβ
const query = "...";
await retriever.invoke(query);
Use within a chainβ
Like other retrievers, AmazonKnowledgeBaseRetriever can be incorporated into LLM applications via chains.
We will need a LLM or chat model:
Pick your chat model:
- OpenAI
- Anthropic
- FireworksAI
- MistralAI
- Groq
- VertexAI
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/openai
yarn add @langchain/openai
pnpm add @langchain/openai
Add environment variables
OPENAI_API_KEY=your-api-key
Instantiate the model
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({
model: "gpt-4o-mini",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/anthropic
yarn add @langchain/anthropic
pnpm add @langchain/anthropic
Add environment variables
ANTHROPIC_API_KEY=your-api-key
Instantiate the model
import { ChatAnthropic } from "@langchain/anthropic";
const llm = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/community
yarn add @langchain/community
pnpm add @langchain/community
Add environment variables
FIREWORKS_API_KEY=your-api-key
Instantiate the model
import { ChatFireworks } from "@langchain/community/chat_models/fireworks";
const llm = new ChatFireworks({
model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/mistralai
yarn add @langchain/mistralai
pnpm add @langchain/mistralai
Add environment variables
MISTRAL_API_KEY=your-api-key
Instantiate the model
import { ChatMistralAI } from "@langchain/mistralai";
const llm = new ChatMistralAI({
model: "mistral-large-latest",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/groq
yarn add @langchain/groq
pnpm add @langchain/groq
Add environment variables
GROQ_API_KEY=your-api-key
Instantiate the model
import { ChatGroq } from "@langchain/groq";
const llm = new ChatGroq({
model: "mixtral-8x7b-32768",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/google-vertexai
yarn add @langchain/google-vertexai
pnpm add @langchain/google-vertexai
Add environment variables
GOOGLE_APPLICATION_CREDENTIALS=credentials.json
Instantiate the model
import { ChatVertexAI } from "@langchain/google-vertexai";
const llm = new ChatVertexAI({
model: "gemini-1.5-flash",
temperature: 0
});
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnablePassthrough,
RunnableSequence,
} from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import type { Document } from "@langchain/core/documents";
const prompt = ChatPromptTemplate.fromTemplate(`
Answer the question based only on the context provided.
Context: {context}
Question: {question}`);
const formatDocs = (docs: Document[]) => {
return docs.map((doc) => doc.pageContent).join("\n\n");
};
// See https://js.langchain.com/v0.2/docs/tutorials/rag
const ragChain = RunnableSequence.from([
{
context: retriever.pipe(formatDocs),
question: new RunnablePassthrough(),
},
prompt,
llm,
new StringOutputParser(),
]);
See our RAG tutorial for more information and examples on RunnableSequence
's like the one above.
await ragChain.invoke("...");
API referenceβ
For detailed documentation of all AmazonKnowledgeBaseRetriever features and configurations head to the API reference.