mirror of
https://github.com/hwchase17/langchain
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fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from operator import itemgetter
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from typing import List, Optional, Tuple
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from langchain.schema import BaseMessage, format_document
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores.elasticsearch import ElasticsearchStore
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from .connection import es_connection_details
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from .prompts import CONDENSE_QUESTION_PROMPT, DOCUMENT_PROMPT, LLM_CONTEXT_PROMPT
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# Setup connecting to Elasticsearch
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vectorstore = ElasticsearchStore(
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**es_connection_details,
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embedding=HuggingFaceEmbeddings(
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model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
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),
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index_name="workplace-search-example",
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)
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retriever = vectorstore.as_retriever()
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# Set up LLM to user
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llm = ChatOpenAI(temperature=0)
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def _combine_documents(
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docs, document_prompt=DOCUMENT_PROMPT, document_separator="\n\n"
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):
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doc_strings = [format_document(doc, document_prompt) for doc in docs]
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return document_separator.join(doc_strings)
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def _format_chat_history(chat_history: List[Tuple]) -> str:
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buffer = ""
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for dialogue_turn in chat_history:
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human = "Human: " + dialogue_turn[0]
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ai = "Assistant: " + dialogue_turn[1]
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buffer += "\n" + "\n".join([human, ai])
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return buffer
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class ChainInput(BaseModel):
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chat_history: Optional[List[BaseMessage]] = Field(
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description="Previous chat messages."
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)
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question: str = Field(..., description="The question to answer.")
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_inputs = RunnableParallel(
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standalone_question=RunnablePassthrough.assign(
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chat_history=lambda x: _format_chat_history(x["chat_history"])
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)
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| CONDENSE_QUESTION_PROMPT
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| llm
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| StrOutputParser(),
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)
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_context = {
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"context": itemgetter("standalone_question") | retriever | _combine_documents,
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"question": lambda x: x["standalone_question"],
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}
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chain = _inputs | _context | LLM_CONTEXT_PROMPT | llm | StrOutputParser()
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chain = chain.with_types(input_type=ChainInput)
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