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https://github.com/hwchase17/langchain
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7e4dbb26a8
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>
28 lines
785 B
Python
28 lines
785 B
Python
from langchain.prompts import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from .chat import chat
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librarian_prompt = ChatPromptTemplate.from_messages(
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[
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SystemMessagePromptTemplate.from_template(
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"""
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You are a librarian at cohere community library. Your job is to
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help recommend people books to read based on their interests and
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preferences. You also give information about the library.
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The library opens at 8am and closes at 9pm daily. It is closed on
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Sundays.
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Please answer the following message:
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"""
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),
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HumanMessagePromptTemplate.from_template("{message}"),
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]
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)
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library_info = librarian_prompt | chat
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