mirror of
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>
11 lines
187 B
Python
11 lines
187 B
Python
from langchain.pydantic_v1 import BaseModel
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from .router import branched_chain
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class ChainInput(BaseModel):
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message: str
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chain = branched_chain.with_types(input_type=ChainInput)
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