9 open-source gems to become the ultimate developer🔥 🚀

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}
  ]
)

pip3 install llmware

"""This example demonstrates the various ways to retrieve data from libraries:
      1. Create a sample library (e.g., UN Resolutions)
      2. Execute a Text Query with Author Name Filter
      3. Display Results
"""

import os
from llmware.library import Library
from llmware.retrieval import Query
from llmware.setup import Setup

def create_un_500_sample_library(library_name):

    library = Library().create_new_library(library_name)
    sample_files_path = Setup().load_sample_files(over_write=False)
    ingestion_folder_path = os.path.join(sample_files_path, "UN-Resolutions-500")
    parsing_output = library.add_files(ingestion_folder_path)

    return library

def text_retrieval_by_author(library, query_text, author):

    #   create a Query instance and pass the previously created Library object
    query = Query(library)

    #   set the keys that should be returned in the results
    query.query_result_return_keys = ["file_source", "page_num", "text", "author_or_speaker"]

    #   perform a text query by author
    query_results = query.text_query_by_author_or_speaker(query_text, author)

    #   display the results
    for i, result in enumerate(query_results):

        file_source = result["file_source"]
        page_num = result["page_num"]
        author = result["author_or_speaker"]
        text = result["text"]

        # shortening for display purpose only
        if len(text) > 150:  text = text[0:150] + " ... "

        print (f"\n> Top result for '{query_text}': {file_source} (page {page_num}), Author: {author}:\nText:{text}")

    return query_results

if __name__ == "__main__":

    library = create_un_500_sample_library("lib_author_filter_1")
    qr_output = text_retrieval_by_author(library=library, query_text='United Nations', author='Andrea Chambers')

npm i @copilotkit/react-core @copilotkit/react-ui

"use client";

import { CopilotKit } from "@copilotkit/react-core";
import { CopilotSidebar } from "@copilotkit/react-ui";
import "@copilotkit/react-ui/styles.css";

export default function RootLayout({children}) {
  return (
    <CopilotKit publicApiKey="<your-public-api-key>">
      <CopilotSidebar>
        {children}
      </CopilotSidebar>
    </CopilotKit>
  );
}

import { useCopilotReadable } from "@copilotkit/react-core";

export function YourComponent() {
  const { employees } = useEmployees();

  // Define Copilot readable state
  useCopilotReadable({
    description: "List of available users",
    value: users,
  });

  return (
    <>...</>
  );
}

import { useCopilotReadable, useCopilotAction } from "@copilotkit/react-core";

export function YourComponent() {
  const { employees, selectEmployee } = useEmployees();

  // Define Copilot readable state
  useCopilotReadable({
    description: "List of available users",
    value: users,
  });

  // Define Copilot action
  useCopilotAction({
    name: "Select an employee",
    description: "Select an employee from the list",
    parameters: [
      {
        name: "employeeId",
        type: "string",
        description: "The ID of the employee to select",
        required: true,
      }
    ],
    handler: async ({ employeeId }) => selectEmployee(employeeId),
  });

  return (
    <>...</>
  );
}

pip install julep

from julep import Client
from pprint import pprint
import textwrap
import os

base_url = os.environ.get("JULEP_API_URL")
api_key = os.environ.get("JULEP_API_KEY")

client = Client(api_key=api_key, base_url=base_url)

#create agent
agent = client.agents.create(
    name="Jessica"
    model="gpt-4",
    tools=[]    # Tools defined here
)
#create a user
user = client.users.create(
    name="Anon",
    about="Average nerdy tech bro/girl spending 8 hours a day on a laptop,
)
#create a session
situation_prompt = """You are Jessica. You're a stuck-up Cali teenager.
You complain about everything. You live in Bel-Air, Los Angeles and
drag yourself to Curtis High School when necessary.
"""
session = client.sessions.create(
    user_id=user.id, agent_id=agent.id, situation=situation_prompt
)
#start a conversation

user_msg = "hey. what do you think of Starbucks?"
response = client.sessions.chat(
    session_id=session.id,
    messages=[
        {
            "role": "user",
            "content": user_msg,
            "name": "Anon",
        }
    ],
    recall=True,
    remember=True,
)

print("\n".join(textwrap.wrap(response.response[0][0].content, width=100)))

pip install traceloop-sdk

from traceloop.sdk import Traceloop

Traceloop.init() 

Traceloop.init(disable_batch=True)

pip install taipy

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

# Defining the helper functions

# 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()

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

# 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

# The main script
if __name__ == "__main__":
    # Taipy Scenario & Data Management

    # 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 complete application

    # Get 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"

    # 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()

import { task } from "@trigger.dev/sdk/v3";
// Generate an image using OpenAI Dall-E 3
export const generateContent = task({
  id: "generate-content",
  retry: {
    maxAttempts: 3,
  },
  run: async ({ theme, description }: Payload) => {
    const textResult = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: generateTextPrompt(theme, description),
    });

    if (!textResult.choices[0]) {
      throw new Error("No content, retrying…");
    }

    const imageResult = await openai.images.generate({
      model: "dall-e-3",
      prompt: generateImagePrompt(theme, description),
    });

    if (!imageResult.data[0]) {
      throw new Error("No image, retrying…");
    }

    return {
      text: textResult.choices[0],
      image: imageResult.data[0].url,
    };
  },
});

pip install --upgrade pymilvus==v2.4.4

pip install pymilvus[model]

from pymilvus import MilvusClient

client = MilvusClient("http://localhost:19530")

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus",
    db_name="default"
)

client = MilvusClient(
    uri="http://localhost:19530",
    token="user:password", # replace this with your token
    db_name="default"
)