当前位置：首页 > news >正文

极客时间：用 FAISS、LangChain 和 Google Colab 模拟 LLM 的短期与长期记忆

news 2025/8/17 0:38:00

每周跟踪AI热点新闻动向和震撼发展想要探索生成式人工智能的前沿进展吗？订阅我们的简报，深入解析最新的技术突破、实际应用案例和未来的趋势。与全球数同行一同，从行业内部的深度分析和实用指南中受益。不要错过这个机会，成为AI领域的领跑者。点击订阅，与未来同行！订阅：https://rengongzhineng.io/

在一场技术实验中，我们在 Google Colab 上利用开源工具，演示了如何在不重新训练模型的前提下，为大型语言模型（LLM）添加“记忆”功能。通过集成 FAISS 向量检索、LangChain 工作流和 Sentence Transformers 嵌入模型，该系统实现了动态知识注入、模拟遗忘、处理记忆冲突以及时间偏好排序等行为。

🎯 实验目标

注入模型从未学过的事实（如：“狗狗 YoYo 喜欢胡萝卜”）
模拟“遗忘”机制
处理前后矛盾的信息（如：“YoYo 现在讨厌胡萝卜”）
实现“时间偏好”逻辑（更倾向于检索最近的信息）

🛠️ 技术配置：LangChain + FAISS + Sentence Transformers

系统构建过程如下：

pythonCopyEditfrom langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
import faiss
import numpy as np
import time# 加载嵌入模型
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
print("Embedding model loaded.")# 创建 FAISS 索引
sample_embedding = embedding_model.embed_query("test")
dimension = np.array(sample_embedding).shape[0]
index = faiss.IndexFlatL2(dimension)
print("FAISS index initialized.")# 初始化 FAISS 向量存储
faiss_store = FAISS(embedding_model.embed_query, index, {}, {})
print("FAISS vector store initialized.")

🧠 添加记忆与查询记忆的函数

pythonCopyEdit# 添加记忆函数
def add_memory(text, reason):metadata = {'reason': reason, 'timestamp': time.time()}doc = Document(page_content=text, metadata=metadata)faiss_store.add_documents([doc])print(f"Added to memory: {text}")# 查询记忆函数
def retrieve_memory(query):retrieved_docs = faiss_store.similarity_search(query, k=1)if retrieved_docs:print(f"Retrieved: {retrieved_docs[0].page_content}")else:print("No relevant information found.")

🧪 实验部分：知识注入、遗忘、矛盾更新、时间偏好

1️⃣ 注入新知识

pythonCopyEditadd_memory("My dog's name is YoYo and he loves carrots.", "initial fact")
retrieve_memory("What does my dog love?")

输出结果：

vbnetCopyEditAdded to memory: My dog's name is YoYo and he loves carrots.
Retrieved: My dog's name is YoYo and he loves carrots.

📌 分析：系统成功存储并检索新事实，表明具备动态注入知识的能力。

2️⃣ 模拟遗忘机制

pythonCopyEdit# 清空 FAISS 索引
faiss_store.index.reset()
print("Memory cleared.")# 尝试再次检索
retrieve_memory("What does my dog love?")

输出结果：

pgsqlCopyEditMemory cleared.
No relevant information found.

📌 分析：通过清空向量索引，系统“遗忘”了原先的信息。

3️⃣ 处理前后矛盾的记忆更新

pythonCopyEditadd_memory("My dog's name is YoYo and he hates carrots.", "contradictory update")
retrieve_memory("What does my dog love?")

输出结果：

vbnetCopyEditAdded to memory: My dog's name is YoYo and he hates carrots.
Retrieved: My dog's name is YoYo and he hates carrots.

📌 分析：系统将新信息覆盖旧记忆，默认以“最近添加”为准，成功处理冲突。

4️⃣ 实现时间偏好（Recency Bias）

pythonCopyEditadd_memory("My dog's name is YoYo and he is indifferent to carrots.", "updated preference")
retrieve_memory("What does my dog feel about carrots?")

输出结果：

vbnetCopyEditAdded to memory: My dog's name is YoYo and he is indifferent to carrots.
Retrieved: My dog's name is YoYo and he is indifferent to carrots.

📌 分析：最新信息被成功检索，说明系统能根据时间排序优先返回更新内容。

📦 完整代码参考（含时间偏好查询与记忆清空）

pythonCopyEditimport time
from uuid import uuid4
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_core.documents import Document
import faiss
import numpy as np# 加载嵌入模型
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
print("Loading embedding model...")# 初始化 FAISS 索引
dimension = np.array(embedding_model.embed_query("test")).shape[0]
index = faiss.IndexFlatL2(dimension)# 初始化文档存储
docstore = InMemoryDocstore()# 初始化索引到文档 ID 映射
index_to_docstore_id = {}# 创建 FAISS 向量存储
print("Initializing FAISS vector store...")
faiss_index = FAISS(embedding_function=embedding_model,index=index,docstore=docstore,index_to_docstore_id=index_to_docstore_id
)# 添加事实到记忆
def add_fact(fact_text, reason):print(f"\n Adding to memory: {fact_text}")metadata = {'reason': reason, 'timestamp': time.time()}doc = Document(page_content=fact_text, metadata=metadata)faiss_index.add_documents([doc])print(f"Fact added with metadata: {metadata}")# 查询记忆
def query_memory(query_text):print(f"\n Querying memory for: '{query_text}'")results = faiss_index.similarity_search(query_text, k=1)if results:top_result = results[0].page_contentprint(f" Retrieved: {top_result}")return top_resultelse:print("No relevant information found.")return None# 清除所有记忆
def clear_memory():print("\n Clearing memory...")faiss_index.index.reset()faiss_index.docstore = InMemoryDocstore()faiss_index.index_to_docstore_id = {}print("Memory cleared.")# 基于时间排序的查询
def query_with_recency(query_text):print(f"\n Querying with recency for: '{query_text}'")results = faiss_index.similarity_search(query_text, k=5)if not results:print("No relevant information found.")return Noneresults.sort(key=lambda x: x.metadata.get('timestamp', 0), reverse=True)top_result = results[0].page_contentprint(f"Retrieved (most recent): {top_result}")return top_result# 实验流程
add_fact("My dog's name is YoYo and he loves carrots.", reason="initial fact")
query_memory("What does my dog love?")
clear_memory()
query_memory("What does my dog love?")
add_fact("My dog's name is YoYo and he hates carrots.", reason="contradictory update")
query_memory("What does my dog love?")
add_fact("My dog's name is YoYo and he is indifferent to carrots.", reason="updated preference")
query_with_recency("What does my dog feel about carrots?")