From 5545de0466afe193f2921673fb1509dde1d65350 Mon Sep 17 00:00:00 2001 From: Piyush Jain Date: Fri, 27 Oct 2023 17:02:28 -0700 Subject: [PATCH] Updated the Bedrock rag template (#12462) Updates the bedrock rag template. - Removes pinecone and replaces with FAISS as the vector store - Fixes the environment variables, setting defaults - Adds a `main.py` test file quick sanity testing - Updates README.md with correct instructions --- templates/rag-aws-bedrock/README.md | 18 +- templates/rag-aws-bedrock/main.py | 6 + templates/rag-aws-bedrock/poetry.lock | 1227 ++++------------- templates/rag-aws-bedrock/pyproject.toml | 5 +- .../rag-aws-bedrock/rag_aws_bedrock/chain.py | 68 +- 5 files changed, 294 insertions(+), 1030 deletions(-) create mode 100644 templates/rag-aws-bedrock/main.py diff --git a/templates/rag-aws-bedrock/README.md b/templates/rag-aws-bedrock/README.md index 848a348ede9..d074a2fead9 100644 --- a/templates/rag-aws-bedrock/README.md +++ b/templates/rag-aws-bedrock/README.md @@ -4,26 +4,24 @@ AWS Bedrock is a managed serve that offers a set of foundation models. Here we will use `Anthropic Claude` for text generation and `Amazon Titan` for text embedding. -We will use Pinecode as our vectorstore. +We will use FAISS as our vectorstore. (See [this notebook](https://github.com/aws-samples/amazon-bedrock-workshop/blob/main/03_QuestionAnswering/01_qa_w_rag_claude.ipynb) for additional context on the RAG pipeline.) -(See [this notebook](https://github.com/aws-samples/amazon-bedrock-workshop/blob/58f238a183e7e629c9ae11dd970393af4e64ec44/00_Intro/bedrock_boto3_setup.ipynb#Prerequisites) for additional context on setup.) +Code here uses the `boto3` library to connect with the Bedrock service. See [this page](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration) for setting up and configuring boto3 to work with an AWS account. -## Pinecone +## FAISS -This connects to a hosted Pinecone vectorstore. +You need to install the `faiss-cpu` package to work with the FAISS vector store. -Be sure that you have set a few env variables in `chain.py`: +```bash +pip install faiss-cpu +``` -* `PINECONE_API_KEY` -* `PINECONE_ENV` -* `index_name` ## LLM and Embeddings -Be sure to set AWS enviorment variables: +The code assumes that you are working with the `default` AWS profile and `us-east-1` region. If not, specify these environment variables to reflect the correct region and AWS profile. * `AWS_DEFAULT_REGION` * `AWS_PROFILE` -* `BEDROCK_ASSUME_ROLE` diff --git a/templates/rag-aws-bedrock/main.py b/templates/rag-aws-bedrock/main.py new file mode 100644 index 00000000000..081f5c5d981 --- /dev/null +++ b/templates/rag-aws-bedrock/main.py @@ -0,0 +1,6 @@ +from rag_aws_bedrock.chain import chain + +if __name__ == "__main__": + query = "What is this data about?" + + print(chain.invoke(query)) \ No newline at end of file diff --git a/templates/rag-aws-bedrock/poetry.lock b/templates/rag-aws-bedrock/poetry.lock index 9f0d0140142..9e5b5230821 100644 --- a/templates/rag-aws-bedrock/poetry.lock +++ b/templates/rag-aws-bedrock/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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raise Exception("Missing `PINECONE_API_KEY` environment variable.") - -if os.environ.get("PINECONE_ENVIRONMENT", None) is None: - raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.") - -if os.environ.get("AWS_DEFAULT_REGION", None) is None: - raise Exception("Missing `AWS_DEFAULT_REGION` environment variable.") - -if os.environ.get("AWS_PROFILE", None) is None: - raise Exception("Missing `AWS_PROFILE` environment variable.") - -if os.environ.get("BEDROCK_ASSUME_ROLE", None) is None: - raise Exception("Missing `BEDROCK_ASSUME_ROLE` environment variable.") - -PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") - -### Ingest code - you may need to run this the first time -# Load -# from langchain.document_loaders import WebBaseLoader -# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") -# data = loader.load() - -# # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter -# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) -# all_splits = text_splitter.split_documents(data) - -# # Add to vectorDB -# vectorstore = Pinecone.from_documents( -# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME -# ) -# retriever = vectorstore.as_retriever() +# Get region and profile from env +region = os.environ.get("AWS_DEFAULT_REGION", "us-east-1") +profile = os.environ.get("AWS_PROFILE", "default") # Set LLM and embeddings -boto3_bedrock = bedrock.get_bedrock_client( - assumed_role=os.environ.get("BEDROCK_ASSUME_ROLE", None), - region=os.environ.get("AWS_DEFAULT_REGION", None) +model = Bedrock( + model_id="anthropic.claude-v2", + region_name=region, + credentials_profile_name=profile, + model_kwargs={'max_tokens_to_sample':200} +) +bedrock_embeddings = BedrockEmbeddings( + model_id="amazon.titan-embed-text-v1" ) -model = Bedrock(model_id="anthropic.claude-v2", - client=boto3_bedrock, - model_kwargs={'max_tokens_to_sample':200}) -bedrock_embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1", - client=boto3_bedrock) -# Set vectostore -vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, bedrock_embeddings) +# Add to vectorDB +vectorstore = FAISS.from_texts( + ["harrison worked at kensho"], + embedding=bedrock_embeddings +) +retriever = vectorstore.as_retriever() + +# Get retriever from vectorstore retriever = vectorstore.as_retriever() # RAG prompt @@ -64,10 +39,11 @@ Question: {question} """ prompt = ChatPromptTemplate.from_template(template) + # RAG chain = ( RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) | prompt | model | StrOutputParser() -) +) \ No newline at end of file