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2443e85533
docs: fix milvus import problem update milvus-rag template with milvus-lite Signed-off-by: ChengZi <chen.zhang@zilliz.com>
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from langchain_milvus.vectorstores import Milvus
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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# Example for document loading (from url), splitting, and creating vectorstore
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# Setting the URI as a local file, e.g.`./milvus.db`, is the most convenient method,
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# as it automatically utilizes Milvus Lite to store all data in this file.
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#
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# If you have large scale of data such as more than a million docs,
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# we recommend setting up a more performant Milvus server on docker or kubernetes.
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# (https://milvus.io/docs/quickstart.md)
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# When using this setup, please use the server URI,
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# e.g.`http://localhost:19530`, as your URI.
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URI = "./milvus.db"
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"""
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# Load
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from langchain_community.document_loaders import WebBaseLoader
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loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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data = loader.load()
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# Split
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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all_splits = text_splitter.split_documents(data)
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# Add to vectorDB
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vectorstore = Milvus.from_documents(documents=all_splits,
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collection_name="rag_milvus",
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embedding=OpenAIEmbeddings(),
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drop_old=True,
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connection_args={"uri": URI},
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)
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retriever = vectorstore.as_retriever()
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"""
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# Embed a single document as a test
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vectorstore = Milvus.from_texts(
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["harrison worked at kensho"],
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collection_name="rag_milvus",
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embedding=OpenAIEmbeddings(),
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drop_old=True,
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connection_args={"uri": URI},
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)
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retriever = vectorstore.as_retriever()
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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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# LLM
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model = ChatOpenAI()
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# RAG chain
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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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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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