mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-04 02:03:32 +00:00
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
import os
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.document_loaders import PyPDFLoader
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import MongoDBAtlasVectorSearch
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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 (
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RunnableLambda,
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RunnableParallel,
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RunnablePassthrough,
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)
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from pymongo import MongoClient
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# Set DB
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if os.environ.get("MONGO_URI", None) is None:
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raise Exception("Missing `MONGO_URI` environment variable.")
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MONGO_URI = os.environ["MONGO_URI"]
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DB_NAME = "langchain-test-2"
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COLLECTION_NAME = "test"
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ATLAS_VECTOR_SEARCH_INDEX_NAME = "default"
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client = MongoClient(MONGO_URI)
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db = client[DB_NAME]
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MONGODB_COLLECTION = db[COLLECTION_NAME]
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# Read from MongoDB Atlas Vector Search
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vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
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MONGO_URI,
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DB_NAME + "." + COLLECTION_NAME,
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OpenAIEmbeddings(disallowed_special=()),
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index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
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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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# RAG
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model = ChatOpenAI()
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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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def _ingest(url: str) -> dict:
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loader = PyPDFLoader(url)
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data = loader.load()
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# Split docs
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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docs = text_splitter.split_documents(data)
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# Insert the documents in MongoDB Atlas Vector Search
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_ = MongoDBAtlasVectorSearch.from_documents(
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documents=docs,
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embedding=OpenAIEmbeddings(disallowed_special=()),
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collection=MONGODB_COLLECTION,
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index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
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)
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return {}
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ingest = RunnableLambda(_ingest)
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