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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 ```
34 lines
825 B
Python
34 lines
825 B
Python
from langchain import hub
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from langchain.schema import StrOutputParser
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from langchain.utilities import WikipediaAPIWrapper
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from langchain_community.chat_models import ChatAnthropic
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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class Question(BaseModel):
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__root__: str
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wiki = WikipediaAPIWrapper(top_k_results=5)
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prompt = hub.pull("bagatur/chain-of-note-wiki")
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llm = ChatAnthropic(model="claude-2")
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def format_docs(docs):
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return "\n\n".join(
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f"Wikipedia {i+1}:\n{doc.page_content}" for i, doc in enumerate(docs)
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)
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chain = (
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{
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"passages": RunnableLambda(wiki.load) | format_docs,
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"question": RunnablePassthrough(),
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}
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| prompt
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| llm
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| StrOutputParser()
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).with_types(input_type=Question)
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