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Adding the example I build for the Cohere hackathon. It can: use a vector database to reccommend books <img width="840" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/96543a18-217b-4445-ab4b-950c7cced915"> Use a prompt template to provide information about the library <img width="834" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/996c8e0f-cab0-4213-bcc9-9baf84f1494b"> Use Cohere RAG to provide grounded results <img width="822" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/7bb4a883-5316-41a9-9d2e-19fd49a43dcb"> --------- 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.embeddings import CohereEmbeddings
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from langchain.prompts import PromptTemplate
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from langchain.vectorstores import Chroma
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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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