mirror of
https://github.com/hwchase17/langchain
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8e0d5813c2
Import from core instead. Ran: ```bash git grep -l 'from langchain.schema\.output_parser' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.output_parser/from\ langchain_core.output_parsers/g" git grep -l 'from langchain.schema\.messages' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.messages/from\ langchain_core.messages/g" git grep -l 'from langchain.schema\.document' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.document/from\ langchain_core.documents/g" git grep -l 'from langchain.schema\.runnable' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.runnable/from\ langchain_core.runnables/g" git grep -l 'from langchain.schema\.vectorstore' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.vectorstore/from\ langchain_core.vectorstores/g" git grep -l 'from langchain.schema\.language_model' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.language_model/from\ langchain_core.language_models/g" git grep -l 'from langchain.schema\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.embeddings/from\ langchain_core.embeddings/g" git grep -l 'from langchain.schema\.storage' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.storage/from\ langchain_core.stores/g" git checkout master libs/langchain/tests/unit_tests/schema/ make format cd libs/experimental make format cd ../langchain make format ```
44 lines
1.0 KiB
Python
44 lines
1.0 KiB
Python
from langchain.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnableBranch
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from .blurb_matcher import book_rec_chain
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from .chat import chat
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from .library_info import library_info
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from .rag import librarian_rag
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chain = (
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ChatPromptTemplate.from_template(
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"""Given the user message below,
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classify it as either being about `recommendation`, `library` or `other`.
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'{message}'
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Respond with just one word.
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For example, if the message is about a book recommendation,respond with
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`recommendation`.
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"""
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)
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| chat
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| StrOutputParser()
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)
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def extract_op_field(x):
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return x["output_text"]
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branch = RunnableBranch(
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(
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lambda x: "recommendation" in x["topic"].lower(),
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book_rec_chain | extract_op_field,
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),
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(
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lambda x: "library" in x["topic"].lower(),
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{"message": lambda x: x["message"]} | library_info,
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),
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librarian_rag,
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)
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branched_chain = {"topic": chain, "message": lambda x: x["message"]} | branch
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