mirror of
https://github.com/hwchase17/langchain
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79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
import os
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from langchain.embeddings import VertexAIEmbeddings
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from langchain.llms import VertexAI
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from langchain.prompts import PromptTemplate
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from langchain.pydantic_v1 import BaseModel
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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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from langchain.vectorstores import MatchingEngine
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# you need to preate the index first, for example, as described here:
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# https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documents_langchain_matching_engine.ipynb
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expected_variables = [
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"project_id",
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"me_region",
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"gcs_bucket",
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"me_index_id",
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"me_endpoint_id",
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]
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variables = []
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for variable_name in expected_variables:
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variable = os.environ.get(variable_name.upper())
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if not variable:
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raise Exception(f"Missing `{variable_name}` environment variable.")
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variables.append(variable)
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project_id, me_region, gcs_bucket, me_index_id, me_endpoint_id = variables
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vectorstore = MatchingEngine.from_components(
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project_id=project_id,
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region=me_region,
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gcs_bucket_name=gcs_bucket,
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embedding=VertexAIEmbeddings(),
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index_id=me_index_id,
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endpoint_id=me_endpoint_id,
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)
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model = VertexAI()
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template = (
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"SYSTEM: You are an intelligent assistant helping the users with their questions"
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"on research papers.\n\n"
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"Question: {question}\n\n"
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"Strictly Use ONLY the following pieces of context to answer the question at the "
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"end. Think step-by-step and then answer.\n\n"
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"Do not try to make up an answer:\n"
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"- If the answer to the question cannot be determined from the context alone, "
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'say \n"I cannot determine the answer to that."\n'
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'- If the context is empty, just say "I do not know the answer to that."\n\n'
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"=============\n{context}\n=============\n\n"
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"Question: {question}\nHelpful Answer: "
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)
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prompt = PromptTemplate.from_template(template)
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retriever = vectorstore.as_retriever(
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search_type="similarity",
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search_kwargs={
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"k": 10,
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"search_distance": 0.6,
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},
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)
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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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