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Rememberizer

Rememberizer 是一个知识增强服务，由 SkyDeck AI Inc. 为 AI 应用程序创建。

本笔记本展示了如何从 Rememberizer 中检索文档并将其转换为下游使用的 Document 格式。

准备

您需要一个 API 密钥：在 https://rememberizer.ai 上创建 common knowledge 后即可获得。获取 API 密钥后，您必须将其设置为环境变量 REMEMBERIZER_API_KEY，或者在初始化 RememberizerRetriever 时将其作为 rememberizer_api_key 传递。

RememberizerRetriever 具有以下参数：

  • 可选的 top_k_results：默认为 10。用于限制返回文档的数量。
  • 可选的 rememberizer_api_key：如果未设置环境变量 REMEMBERIZER_API_KEY，则此参数为必填项。

get_relevant_documents() 具有一个参数 query：用于在 Rememberizer.ai 的 common knowledge 中查找文档的自由文本。

示例

基本用法​

# Setup API key
from getpass import getpass

REMEMBERIZER_API_KEY = getpass()
import os

from langchain_community.retrievers import RememberizerRetriever

os.environ["REMEMBERIZER_API_KEY"] = REMEMBERIZER_API_KEY
retriever = RememberizerRetriever(top_k_results=5)
API Reference:RememberizerRetriever
docs = retriever.get_relevant_documents(query="How does Large Language Models works?")
docs[0].metadata  # meta-information of the Document
{'id': 13646493,
 'document_id': '17s3LlMbpkTk0ikvGwV0iLMCj-MNubIaP',
 'name': 'What is a large language model (LLM)_ _ Cloudflare.pdf',
 'type': 'application/pdf',
 'path': '/langchain/What is a large language model (LLM)_ _ Cloudflare.pdf',
 'url': 'https://drive.google.com/file/d/17s3LlMbpkTk0ikvGwV0iLMCj-MNubIaP/view',
 'size': 337089,
 'created_time': '',
 'modified_time': '',
 'indexed_on': '2024-04-04T03:36:28.886170Z',
 'integration': {'id': 347, 'integration_type': 'google_drive'}}
print(docs[0].page_content[:400])  # a content of the Document
before, or contextualized in new ways. on some level they " understand " semantics in that they can associate words and concepts by their meaning, having seen them grouped together in that way millions or billions of times. how developers can quickly start building their own llms to build llm applications, developers need easy access to multiple data sets, and they need places for those data sets

在链中使用

OPENAI_API_KEY = getpass()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model_name="gpt-3.5-turbo")
qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
questions = [
    "What is RAG?",
    "How does Large Language Models works?",
]
chat_history = []

for question in questions:
    result = qa.invoke({"question": question, "chat_history": chat_history})
    chat_history.append((question, result["answer"]))
    print(f"-> **Question**: {question} \n")
    print(f"**Answer**: {result['answer']} \n")
-> **Question**: What is RAG? 

**Answer**: RAG stands for Retrieval-Augmented Generation. It is an AI framework that retrieves facts from an external knowledge base to enhance the responses generated by Large Language Models (LLMs) by providing up-to-date and accurate information. This framework helps users understand the generative process of LLMs and ensures that the model has access to reliable information sources. 

-> **Question**: How does Large Language Models works? 

**Answer**: Large Language Models (LLMs) work by analyzing massive data sets of language to comprehend and generate human language text. They are built on machine learning, specifically deep learning, which involves training a program to recognize features of data without human intervention. LLMs use neural networks, specifically transformer models, to understand context in human language, making them better at interpreting language even in vague or new contexts. Developers can quickly start building their own LLMs by accessing multiple data sets and using services like Cloudflare's Vectorize and Cloudflare Workers AI platform.