如何在LangChain与Ollama中保存对话历史并从断点重启对话

错误核心是自定义Prompt仅声明history为输入变量，但ConversationChain结合ConversationBufferMemory使用时，默认会传入input（当前用户提问）和history（对话历史）两个变量，导致变量不匹配；同时未将存储的历史数据加载到memory中。

1. 修正Prompt模板 #

确保Prompt包含history和input两个变量，匹配ConversationChain的默认输入要求：

PROMPT_TEMPLATE = """
{history}
Human: {input}
AI:
"""

custom_prompt = PromptTemplate(
    input_variables=["history", "input"], template=PROMPT_TEMPLATE
)

2. 加载存储的对话历史到Memory #

将数据库中存储的历史数据整理后，传入ConversationBufferMemory初始化：

# 从数据库取出的历史数据
history_data = {'input': 'What is life?', 'history': 'Human: What is life?\nAI: {}', 'response': '{ "Life" : {\n  "Definition" : "A complex and multifaceted phenomenon characterized by the presence of organization, metabolism, homeostasis, and reproduction.",\n  "Context" : ["Biology", "Philosophy", "Psychology"],\n  "Subtopics" : [\n    {"Self-awareness": "The capacity to have subjective experiences, such as sensations, emotions, and thoughts."},\n    {"Evolutionary perspective": "A process driven by natural selection, genetic drift, and other mechanisms that shape the diversity of life on Earth."},\n    {"Quantum perspective": "A realm where quantum mechanics and general relativity intersect, potentially influencing the emergence of consciousness."}\n  ]\n} }'}

# 初始化Memory并加载历史
memory = ConversationBufferMemory(initial_history=[
    {"input": history_data['input'], "output": history_data['response']}
])
# 也可通过手动添加方式加载：
# memory.chat_memory.add_user_message(history_data['input'])
# memory.chat_memory.add_ai_message(history_data['response'])

3. 重新初始化对话链并调用 #

chain = ConversationChain(
    prompt=custom_prompt,
    llm=llm,
    memory=memory
)

prompt = "How to live it properly?"
answer = chain.invoke(input=prompt)
print(answer)

• ConversationBufferMemory支持通过initial_history传入对话历史列表（每个元素含input和output字段），或通过chat_memory的方法手动添加单条对话。
• 自定义Prompt必须包含history和input两个变量，否则会与ConversationChain默认输入逻辑冲突，触发ValidationError。

内容的提问来源于stack exchange，提问作者jolly