使用SQLite存储的实体记忆

在这个演示中，我们将创建一个简单的对话链，它使用基于SqliteEntityStore的ConversationEntityMemory。

from langchain.chains import ConversationChain
from langchain.llms import OpenAI
from langchain.memory import ConversationEntityMemory
from langchain.memory.entity import SQLiteEntityStore
from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE

entity_store = SQLiteEntityStore()
llm = OpenAI(temperature=0)
memory = ConversationEntityMemory(llm=llm, entity_store=entity_store)
conversation = ConversationChain(
    llm=llm,
    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
    memory=memory,
    verbose=True,
)

Notice the usage of EntitySqliteStore as parameter to entity_store on the memory property.

conversation.run("Deven & Sam are working on a hackathon project")

> Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.

You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.

Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.

Context:
{'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'}

Current conversation:

Last line:
Human: Deven & Sam are working on a hackathon project
You:

> Finished chain.





' That sounds like a great project! What kind of project are they working on?'

conversation.memory.entity_store.get("Deven")

'Deven is working on a hackathon project with Sam.'

conversation.memory.entity_store.get("Sam")

'Sam is working on a hackathon project with Deven.'