安装依赖
pip install --upgrade --quiet langchain-core langchain-community langchain-openai
编辑代码
from operator import itemgetter
from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string
from langchain_core.prompts import format_document
from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda
from langchain_openai.chat_models import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain.memory import ConversationBufferMemory
vectorstore = DocArrayInMemorySearch.from_texts(
["wuzikang worked at earth", "sam worked at home", "harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
def _combine_documents(
docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
):
doc_strings = [format_document(doc, document_prompt) for doc in docs]
return document_separator.join(doc_strings)
_inputs = RunnableParallel(
standalone_question=RunnablePassthrough.assign(
chat_history=lambda x: get_buffer_string(x["chat_history"])
)
| CONDENSE_QUESTION_PROMPT
| ChatOpenAI(temperature=0)
| StrOutputParser(),
)
memory = ConversationBufferMemory(
return_messages=True, output_key="answer", input_key="question"
)
loaded_memory = RunnablePassthrough.assign(
chat_history=RunnableLambda(memory.load_memory_variables) | itemgetter("history"),
)
standalone_question = {
"standalone_question": {
"question": lambda x: x["question"],
"chat_history": lambda x: get_buffer_string(x["chat_history"]),
}
| CONDENSE_QUESTION_PROMPT
| ChatOpenAI(temperature=0)
| StrOutputParser(),
}
retrieved_documents = {
"docs": itemgetter("standalone_question") | retriever,
"question": lambda x: x["standalone_question"],
}
final_inputs = {
"context": lambda x: _combine_documents(x["docs"]),
"question": itemgetter("question"),
}
answer = {
"answer": final_inputs | ANSWER_PROMPT | ChatOpenAI(),
"docs": itemgetter("docs"),
}
final_chain = loaded_memory | standalone_question | retrieved_documents | answer
inputs = {"question": "where did sam work?"}
result = final_chain.invoke(inputs)
print(f"result1: {result}")
memory.save_context(inputs, {"answer": result["answer"].content})
memory.load_memory_variables({})
inputs = {"question": "but where did he really work?"}
result2 = final_chain.invoke(inputs)
print(f"result2: {result2}")
运行代码
➜ python3 test06.py
/Users/wuzikang/Desktop/py/langchain_test/own_learn/env/lib/python3.12/site-packages/pydantic/_migration.py:283: UserWarning: `pydantic.error_wrappers:ValidationError` has been moved to `pydantic:ValidationError`.
warnings.warn(f'`{import_path}` has been moved to `{new_location}`.')
result1: {'answer': AIMessage(content='Sam worked at home.', response_metadata={'finish_reason': 'stop', 'logprobs': None}), 'docs': [Document(page_content='sam worked at home'), Document(page_content='wuzikang worked at earth'), Document(page_content='harrison worked at kensho')]}
result2: {'answer': AIMessage(content='Sam actually worked at home.', response_metadata={'finish_reason': 'stop', 'logprobs': None}), 'docs': [Document(page_content='sam worked at home'), Document(page_content='wuzikang worked at earth'), Document(page_content='harrison worked at kensho')]}
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