mirror of https://github.com/hwchase17/langchain
Added a rag template for Kendra (#12470)
## Description Adds a rag template for Amazon Kendra with Bedrock. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>pull/12492/head
Piyush Jain
7 months ago
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GitHub
parent
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commit
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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from rag_aws_kendra.chain import chain
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if __name__ == "__main__":
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query = "Does Kendra support table extraction?"
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print(chain.invoke(query))
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[tool.poetry]
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name = "rag_aws_kendra"
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version = "0.0.1"
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description = ""
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authors = []
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.313, <0.1"
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tiktoken = ">=0.5.1"
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boto3 = ">=1.28.57"
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awscli = ">=1.29.57"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.4"
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fastapi = "^0.104.0"
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sse-starlette = "^1.6.5"
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[tool.langserve]
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export_module = "rag_aws_kendra.chain"
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export_attr = "chain"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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import os
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from langchain.llms.bedrock import Bedrock
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from langchain.prompts import ChatPromptTemplate
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from langchain.retrievers import AmazonKendraRetriever
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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# Get region and profile from env
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region = os.environ.get("AWS_DEFAULT_REGION", "us-east-1")
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profile = os.environ.get("AWS_PROFILE", "default")
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kendra_index = os.environ.get("KENDRA_INDEX_ID", None)
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if not kendra_index:
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raise ValueError(
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"No value provided in env variable 'KENDRA_INDEX_ID'. "
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"A Kendra index is required to run this application."
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)
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# Set LLM and embeddings
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model = Bedrock(
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model_id="anthropic.claude-v2",
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region_name=region,
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credentials_profile_name=profile,
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model_kwargs={'max_tokens_to_sample': 200}
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)
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# Create Kendra retriever
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retriever = AmazonKendraRetriever(
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index_id=kendra_index,
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top_k=5,
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region_name=region
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)
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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