pip install composio-core

composio add github

from openai import OpenAI
from composio_openai import ComposioToolSet, App

openai_client = OpenAI(api_key="******OPENAIKEY******")

# Initialise the Composio Tool Set
composio_toolset = ComposioToolSet(api_key="**\\*\\***COMPOSIO_API_KEY**\\*\\***")

## Step 4
# Get GitHub tools that are pre-configured
actions = composio_toolset.get_actions(actions=[Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER])

## Step 5
my_task = "Star a repo ComposioHQ/composio on GitHub"

# Create a chat completion request to decide on the action
response = openai_client.chat.completions.create(
model="gpt-4-turbo",
tools=actions, # Passing actions we fetched earlier.
messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": my_task}
  ]
)

npm install composio-core

import { OpenAI } from "openai";
import { OpenAIToolSet } from "composio-core";

const toolset = new OpenAIToolSet({
  apiKey: process.env.COMPOSIO_API_KEY,
});

async function setupUserConnectionIfNotExists(entityId) {
  const entity = await toolset.client.getEntity(entityId);
  const connection = await entity.getConnection('github');

  if (!connection) {
      // If this entity/user hasn't already connected, the account
      const connection = await entity.initiateConnection(appName);
      console.log("Log in via: ", connection.redirectUrl);
      return connection.waitUntilActive(60);
  }

  return connection;
}

async function executeAgent(entityName) {
  const entity = await toolset.client.getEntity(entityName)
  await setupUserConnectionIfNotExists(entity.id);

  const tools = await toolset.get_actions({ actions: ["github_activity_star_repo_for_authenticated_user"] }, entity.id);
  const instruction = "Star a repo ComposioHQ/composio on GitHub"

  const client = new OpenAI({ apiKey: process.env.OPEN_AI_API_KEY })
  const response = await client.chat.completions.create({
      model: "gpt-4-turbo",
      messages: [{
          role: "user",
          content: instruction,
      }],
      tools: tools,
      tool_choice: "auto",
  })

  console.log(response.choices[0].message.tool_calls);
  await toolset.handle_tool_call(response, entity.id);
}

executeGithubAgent("joey")

pip install -U weaviate-client

import weaviate
import weaviate.classes as wvc
import os
import requests
import json

# Best practice: store your credentials in environment variables
wcd_url = os.environ["WCD_DEMO_URL"]
wcd_api_key = os.environ["WCD_DEMO_RO_KEY"]
openai_api_key = os.environ["OPENAI_APIKEY"]

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=wcd_url,                                    # Replace with your Weaviate Cloud URL
    auth_credentials=wvc.init.Auth.api_key(wcd_api_key),    # Replace with your Weaviate Cloud key
    headers={"X-OpenAI-Api-Key": openai_api_key}            # Replace with appropriate header key/value pair for the required API
)

try:
    # ===== define collection =====
    questions = client.collections.create(
        name="Question",
        vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai(),  # If set to "none", you must always provide vectors yourself. It could be any other "text2vec-*" also.
        generative_config=wvc.config.Configure.Generative.openai()  # Ensure the `generative-open` module is used for generative queries.
    )

    # ===== import data =====
    resp = requests.get('https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny.json')
    data = json.loads(resp.text)  # Load data

    question_objs = list()
    for i, d in enumerate(data):
        question_objs.append({
            "answer": d["Answer"],
            "question": d["Question"],
            "category": d["Category"],
        })

    questions = client.collections.get("Question")
    questions.data.insert_many(question_objs)

finally:
    client.close()  # Close client gracefully

try:
    # Replace with your code. Close the client gracefully in the final block.
    questions = client.collections.get("Question")

    response = questions.query.near_text(
        query="biology",
        limit=2
    )

    print(response.objects[0].properties)  # Inspect the first object

finally:
    client.close()  # Close client gracefully

pip install haystack-ai

import os

from haystack import Pipeline, PredefinedPipeline
import urllib.request

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"
urllib.request.urlretrieve("https://www.gutenberg.org/cache/epub/7785/pg7785.txt", "davinci.txt")

indexing_pipeline =  Pipeline.from_template(PredefinedPipeline.INDEXING)
indexing_pipeline.run(data={"sources": ["davinci.txt"]})

rag_pipeline =  Pipeline.from_template(PredefinedPipeline.RAG)

query = "How old was he when he died?"
result = rag_pipeline.run(data={"prompt_builder": {"query":query}, "text_embedder": {"text": query}})
print(result["llm"]["replies"][0])

pip install 'litgpt[all]'

from litgpt import LLM

llm = LLM.load("microsoft/phi-2")
text = llm.generate("Fix the spelling: Every fall, the familly goes to the mountains.")
print(text)
# Corrected Sentence: Every fall, the family goes to the mountains.

pip install -qU portkey-ai openai

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key=OPENAI_API_KEY,
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key=PORTKEY_API_KEY
    )
)

chat_complete = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user",
               "content": "What's a fractal?"}],
)

print(chat_complete.choices[0].message.content)

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key=userdata.get('ANTHROPIC_API_KEY')
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="anthropic",
        api_key=PORTKEY_API_KEY
    ),
)

response = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user",
               "content": "What's a fractal?"}],
    max_tokens= 512
)

pip install agentops

import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init( < INSERT YOUR API KEY HERE >)

...

# (optional: record specific functions)
@agentops.record_action('sample function being record')
def sample_function(...):
    ...

# End of program
agentops.end_session('Success')
# Woohoo You're done 🎉

'pip install arize-phoenix

import phoenix as px
session = px.launch_app()

pip install 'llama-index>=0.10.44'

import phoenix as px
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
import os
from gcsfs import GCSFileSystem
from llama_index.core import (
    Settings,
    VectorStoreIndex,
    StorageContext,
    set_global_handler,
    load_index_from_storage
)
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
import llama_index

# To view traces in Phoenix, you will first have to start a Phoenix server. You can do this by running the following:
session = px.launch_app()

# Initialize LlamaIndex auto-instrumentation
LlamaIndexInstrumentor().instrument()

os.environ["OPENAI_API_KEY"] = "<ENTER_YOUR_OPENAI_API_KEY_HERE>"

# LlamaIndex application initialization may vary 
# depending on your application
Settings.llm = OpenAI(model="gpt-4-turbo-preview")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

# Load your data and create an index. Here we've provided an example of our documentation
file_system = GCSFileSystem(project="public-assets-275721")
index_path = "arize-phoenix-assets/datasets/unstructured/llm/llama-index/arize-docs/index/"
storage_context = StorageContext.from_defaults(
    fs=file_system,
    persist_dir=index_path,
)

index = load_index_from_storage(storage_context)

query_engine = index.as_query_engine()

# Query your LlamaIndex application
query_engine.query("What is the meaning of life?")
query_engine.query("How can I deploy Arize?")

# View the traces in the Phoenix UI
px.active_session().url

pip install vllm

**from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="facebook/opt-125m")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")**

npm install ai

npm install @ai-sdk/openai

import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai'; // Ensure OPENAI_API_KEY environment variable is set

async function main() {
  const { text } = await generateText({
    model: openai('gpt-4-turbo'),
    system: 'You are a friendly assistant!',
    prompt: 'Why is the sky blue?',
  });

  console.log(text);
}

main();

pip install -U langgraph

pip install taipy

**import taipy as tp
import pandas as pd
from taipy import Config, Scope, Gui

# Taipy Scenario & Data Management

# Filtering function - task
def filter_genre(initial_dataset: pd.DataFrame, selected_genre):
    filtered_dataset = initial_dataset[initial_dataset["genres"].str.contains(selected_genre)]
    filtered_data = filtered_dataset.nlargest(7, "Popularity %")
    return filtered_data

# Load the configuration made with Taipy Studio
Config.load("config.toml")
scenario_cfg = Config.scenarios["scenario"]

# Start Taipy Core service
tp.Core().run()

# Create a scenario
scenario = tp.create_scenario(scenario_cfg)

# Taipy User Interface
# Let's add a GUI to our Scenario Management for a full application

# Callback definition - submits scenario with genre selection
def on_genre_selected(state):
    scenario.selected_genre_node.write(state.selected_genre)
    tp.submit(scenario)
    state.df = scenario.filtered_data.read()

# Get the list of genres
genres = [
    "Action", "Adventure", "Animation", "Children", "Comedy", "Fantasy", "IMAX"
    "Romance", "Sci-FI", "Western", "Crime", "Mystery", "Drama", "Horror", "Thriller", "Film-Noir", "War", "Musical", "Documentary"
    ]

# Initialization of variables
df = pd.DataFrame(columns=["Title", "Popularity %"])
selected_genre = "Action"

## Set initial value to Action
def on_init(state):
    on_genre_selected(state)

# User interface definition
my_page = """
# Film recommendation

## Choose your favourite genre
<|{selected_genre}|selector|lov={genres}|on_change=on_genre_selected|dropdown|>

## Here are the top seven picks by popularity
<|{df}|chart|x=Title|y=Popularity %|type=bar|title=Film Popularity|>
"""

Gui(page=my_page).run()**