如何创建自定义的Memory类#
虽然LangChain中有几种预定义的内存类型，但你很可能想要添加自己的内存类型，以便为你的应用程序提供最佳性能。本笔记将介绍如何实现此操作。
在本笔记中，我们将向ConversationChain添加自定义内存类型。为了添加自定义内存类，我们需要导入基础内存类并对其进行子类化。
from langchain import OpenAI, ConversationChain
from langchain.schema import BaseMemory
from pydantic import BaseModel
from typing import List, Dict, Any
在此示例中，我们将编写一个自定义内存类，该类使用spacy提取实体并将有关它们的信息保存在简单的哈希表中。然后，在对话期间，我们将查看输入文本，提取任何实体，并将有关它们的任何信息放入上下文中。
- 请注意，此实现非常简单和脆弱，可能在生产环境中无用。它的目的是展示您可以添加自定义内存实现。
为此，我们需要使用spacy。
# !pip install spacy
# !python -m spacy download en_core_web_lg
import spacy
nlp = spacy.load('en_core_web_lg')
class SpacyEntityMemory(BaseMemory, BaseModel):
"""Memory class for storing information about entities."""
# Define dictionary to store information about entities.
entities: dict = {}
# Define key to pass information about entities into prompt.
memory_key: str = "entities"
def clear(self):
self.entities = {}
@property
def memory_variables(self) -> List[str]:
"""Define the variables we are providing to the prompt."""
return [self.memory_key]
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load the memory variables, in this case the entity key."""
# Get the input text and run through spacy
doc = nlp(inputs[list(inputs.keys())[0]])
# Extract known information about entities, if they exist.
entities = [self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities]
# Return combined information about entities to put into context.
return {self.memory_key: "\n".join(entities)}
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
# Get the input text and run through spacy
text = inputs[list(inputs.keys())[0]]
doc = nlp(text)
# For each entity that was mentioned, save this information to the dictionary.
for ent in doc.ents:
ent_str = str(ent)
if ent_str in self.entities:
self.entities[ent_str] += f"\n{text}"
else:
self.entities[ent_str] = text
现在我们定义一个提示，以输入有关实体的信息以及用户输入
from langchain.prompts.prompt import PromptTemplate
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.
Relevant entity information:
{entities}
Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(
input_variables=["entities", "input"], template=template
)
现在我们将它们组合在一起！
llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory())
在第一个示例中，没有关于Harrison的先前知识，"相关实体信息"部分为空。
conversation.predict(input="Harrison likes machine learning")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.
Relevant entity information:
Conversation:
Human: Harrison likes machine learning
AI:
> Finished ConversationChain chain.
" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?"
现在在第二个示例中，我们可以看到它提取了有关Harrison的信息。
conversation.predict(input="What do you think Harrison's favorite subject in college was?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.
Relevant entity information:
Harrison likes machine learning
Conversation:
Human: What do you think Harrison's favorite subject in college was?
AI:
> Finished ConversationChain chain.
' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'
再次提醒，此实现方式相当简单且脆弱，可能在生产环境中无用。它的目的是展示您可以添加自定义内存实现。