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https://github.com/hwchase17/langchain
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
import csv
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from langchain.chains.question_answering import load_qa_chain
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from langchain.prompts import PromptTemplate
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from langchain.vectorstores import Chroma
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from langchain_community.embeddings import CohereEmbeddings
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from .chat import chat
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csv_file = open("data/books_with_blurbs.csv", "r")
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csv_reader = csv.reader(csv_file)
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csv_data = list(csv_reader)
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parsed_data = [
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{
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"id": x[0],
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"title": x[1],
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"author": x[2],
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"year": x[3],
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"publisher": x[4],
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"blurb": x[5],
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}
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for x in csv_data
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]
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parsed_data[1]
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embeddings = CohereEmbeddings()
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docsearch = Chroma.from_texts(
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[x["title"] for x in parsed_data], embeddings, metadatas=parsed_data
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).as_retriever()
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prompt_template = """
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{context}
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Use the book reccommendations to suggest books for the user to read.
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Only use the titles of the books, do not make up titles. Format the response as
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a bulleted list prefixed by a relevant message.
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User: {message}"""
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PROMPT = PromptTemplate(
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template=prompt_template, input_variables=["context", "message"]
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)
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book_rec_chain = {
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"input_documents": lambda x: docsearch.get_relevant_documents(x["message"]),
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"message": lambda x: x["message"],
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} | load_qa_chain(chat, chain_type="stuff", prompt=PROMPT)
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