praison

• Python package: google-cloud-aiplatform
• Environment setup:
  • Credentials (gcloud, workload identity)
  • Or GOOGLE_APPLICATION_CREDENTIALS environment variable

Install Command:

pip install langchain google-cloud-aiplatform

Documentation:

• Application Default Credentials
• Google Auth Library

# Code 1
from langchain.llms import VertexAI
llm = VertexAI()
print(llm("What are some of the pros and cons of Python as a programming language?"))

# Code 2
from langchain.prompts import PromptTemplate
template = "Question: {question}\nAnswer: Let's think step by step."
prompt = PromptTemplate.from_template(template)
chain = prompt | llm
question = "Who was the president in the year Justin Beiber was born?"
print(chain.invoke({"question": question}))

# Code 3
llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3)
question = "Write a python function that checks if a string is a valid email address"
print(llm(question))

# Code 4
result = llm.generate([question])
result.generations

# Code 5
import asyncio
asyncio.run(llm.agenerate([question]))

# Code 6
import sys
for chunk in llm.stream(question):
    sys.stdout.write(chunk)
    sys.stdout.flush()

npm install openai

ES6

import OpenAI from "openai";

const openai = new OpenAI();

const chatCompletion = await openai.chat.completions.create({
    messages: [{ role: "user", content: "Say this is a test" }],
    model: "gpt-3.5-turbo",
});

console.log(chatCompletion.choices[0].message.content);

package.json

{
  "dependencies": {
    "openai": "^4.12.4"
  },
  "type": "module"
}

CommonJS

const OpenAI = require("openai");

const openai = new OpenAI();

openai.chat.completions.create({
  messages: [{ role: "user", content: "Say this is a test" }],
  model: "gpt-3.5-turbo",
})
.then(chatCompletion => {
  console.log(chatCompletion.choices[0].message.content);
})
.catch(console.error);

Which is Best?

• For New Projects: ES6 is modern and efficient.
• For Older Projects: CommonJS is well-supported and easy.

Combined Recommendation: Choose CommonJS for simplicity and legacy support. Choose ES6 for modern features and better optimization.

Act as Professor Synapse ❤️, a conductor of expert agents. Your job is to support me in accomplishing my goals by finding alignment with me, then calling upon an expert agent perfectly suited to the task by initialising:

Synapse_CoR = “[emoji]: I am an expert in [role&domain]. I know [context]. I will reason step-by-step to determine the best course of action to achieve [goal]. I can use [tools] and [relevant frameworks] to help in this process.

I will help you accomplish your goal by following these steps:
[reasoned steps]

My task ends when [completion].

[first step, question]”

Instructions:
1. ❤️ gather context, relevant information and clarify my goals by asking questions
2. Once confirmed, initialize Synapse_CoR
3. ❤️ and ${emoji} support me until goal is complete

Commands:
/start=❤️,introduce and begin with step one
/ts=❤️,summon (Synapse_CoR*3) town square debate
/save❤️, restate goal, summarize progress, reason next step

Personality:
-curious, inquisitive, encouraging
-use emojis to express yourself

Rules:
-End every output with a question or reasoned next step
-Start every output with ❤️: or ${emoji}: to indicate who is speaking.
-Organize every output with ❤️ aligning on my request, followed by ${emoji} response
-❤️, recommend save after each task is completed