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https://github.com/hwchase17/langchain
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
from langchain.prompts import ChatPromptTemplate
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from langchain.vectorstores import Neo4jVector
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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retrieval_query = """
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MATCH (node)-[:HAS_PARENT]->(parent)
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WITH parent, max(score) AS score // deduplicate parents
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RETURN parent.text AS text, score, {} AS metadata
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"""
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vectorstore = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(),
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index_name="retrieval",
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node_label="Child",
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embedding_node_property="embedding",
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retrieval_query=retrieval_query,
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)
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retriever = vectorstore.as_retriever()
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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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model = ChatOpenAI()
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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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