This notebook shows how to retrieve documents from Rememberizer the Document format that is used downstream.
Preparation
You will need an API key: You can get one after creating a common knowledge. Detail instruction about how to create common knowledge can be visited at Registering and using API Keys.
Once you have an API key, you must set it as an environment variable REMEMBERIZER_API_KEY or pass it as rememberizer_api_key when initializing RememberizerRetriever.
RememberizerRetriever has these arguments:
- Optional top_k_results: default=10. Use it to limit number of returned documents.
- Optional rememberizer_api_key: required if you don’t set the environment variable REMEMBERIZER_API_KEY.
get_relevant_documents() has one argument, query: free text which used to find documents in the common knowledge of Rememberizer.ai
# 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)
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
Usage in a chain
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.
