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import os
from langchain_community.embeddings import BedrockEmbeddings
from langchain_community.llms.bedrock import Bedrock
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
# 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
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")
# 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
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# RAG
chain = (
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
| prompt
| model
| StrOutputParser()
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
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