Open AI tutorial

Open AI tutorial

https://platform.openai.com/api-keys

https://platform.openai.com/docs/libraries

Install open AI

jupyter labjupyter server list

pip install openai

Colab

Load env file

import os
from dotenv
import load_dotenvload_dotenv()MY_ENV_VAR = os.getenv('MY_ENV_VAR')

.env file

MY_ENV_VAR="This is my env var content."

Set openAI key

openai.api_key = key

Model of Chat GPT

Famous model is gpt-3.5-turbo

Error code: 429 - {'error': {'message':
'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}

https://platform.openai.com/account/limits

First promp API send to Chat GPT

First, increase the balance in Chatgpt.

pip install openai

from openai
import OpenAI
import oskey = "sk-Fh ..."prompt = 'what is the capital of USA?'client = OpenAI(
    api_key = key)chat_completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": prompt}])

print(chat_completion)

ChatCompletion(id='chatcmpl-9aYk3coc90XEJ5NrPNhFQAZZW0uJe', choices=[Choice(finish_reason='stop',
index=0, logprobs=None,
message=ChatCompletionMessage(content='The capital of the United States of America is Washington, D.C.', role='assistant', function_call=None, tool_calls=None))], created=1705392343, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=14, prompt_tokens=14, total_tokens=28))

How you can have your own ChatGPT chatbot?

List of AI assistant

https://newsletter.theresanaiforthat.com/

Q&A with RAG

One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or RAG.

LLMs can reason about wide-ranging topics, but their knowledge is limited to the public data up to a specific point in time that they were trained on. If you want to build AI applications that can reason about private data or data introduced after a model’s cutoff date, you need to augment the knowledge of the model with the specific information it needs. The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG).

Conversing with LLMs is a great way to demonstrate their capabilities. Adding chat history and external context can exponentially increase the complexity of the conversation. In this example, we’ll show how to use Runnables to construct a conversational QA system that can answer questions, remember previous chats, and utilize external context.

The first step is to load our context (in this example we’ll use the State Of The Union speech from 2022). This is also a good place to instantiate our retriever, and memory classes.

Python quick start

RAG retrieval augmented generation

https://www.youtube.com/watch?v=tcqEUSNCn8I

1. prepare your data
2. load your data into python
3. make chunk of data
4. using chroma database to store data
5. store data in sqlite

Private GPT

LLM

Pinecone vector database is a vector-based database that offers high-performance search and similarity matching. It can deal with high-dimensional vector data at a higher scale, easy integration, and faster query results.

pinecone.io

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