mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
c010ec8b71
- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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_community.embeddings import CohereEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain_core.prompts import PromptTemplate
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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.invoke(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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