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juan-calvo-datatonic
545b76b0fd
- **Description:** This is a template demonstrating how to utilize Google Vertex AI Search in conjunction with ChatVertexAI()
51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import os
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from langchain.chat_models import ChatVertexAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.pydantic_v1 import BaseModel
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from langchain.retrievers import GoogleVertexAISearchRetriever
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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# Get region and profile from env
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project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_ID")
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data_store_id = os.environ.get("DATA_STORE_ID")
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model_type = os.environ.get("MODEL_TYPE")
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if not data_store_id:
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raise ValueError(
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"No value provided in env variable 'DATA_STORE_ID'. "
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"A data store is required to run this application."
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)
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# Set LLM and embeddings
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model = ChatVertexAI(model_name=model_type, temperature=0.0)
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# Create Kendra retriever
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retriever = GoogleVertexAISearchRetriever(
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project_id=project_id, search_engine_id=data_store_id
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)
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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