LLMs Function Calling

pip install openai==1.40.0

api_key = os.getenv("OPENAI_API_KEY")

client = OpenAI(api_key=api_key)

#df = read_excel(file_name)
#df_markdown = df.to_markdown(index=False)
df_markdown="12345"

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "system",
            "content": """
                        You are an expert data analyst.
                    """,
        },
        {"role": "user", "content": f"What it is this variable containing?: {df_markdown}"}
    ],
    model="gpt-4o-mini",
    temperature=0.3,
)

completed_message = chat_completion.choices[0].message.content
print(completed_message)

pip install anthropic==0.34.1 #https://github.com/anthropics/anthropic-sdk-python

import anthropic

client = Anthropic(api_key = os.getenv("ANTHROPIC_API_KEY"),)

system_prompt = "You are a helpful Data Analyst."

message = client.messages.create(
    max_tokens=1024,
    system=system_prompt,  # Use the top-level "system" parameter
    messages=[
        {"role": "user", "content": "Hello, who you?"}
    ],
    model="claude-3-5-sonnet-20240620",
    #model="claude-3-opus-20240229",
)

#print(message.content)
content = message.content[0].text
print(content)

#python3 pyopen.py > output.mdx

import os
from dotenv import load_dotenv
from openai import OpenAI  # pip install openai==1.30.5

# Load environment variables from the .env file
load_dotenv()

# Get the OpenAI API key from the environment variables
api_key = os.getenv("OPENAI_API_KEY")

# Initialize the OpenAI client
client = OpenAI(
    api_key=api_key,
)

mtg_must_have= """
* Purpose - Every meeting should have an objective—the reason why you're having the meeting. Before you schedule a meeting be sure you know what it is that you want out of the meeting.
* Agenda - An agenda outlines the plan for the meeting and lets participants know what to expect. It allows them to be prepared so they can actively participate and bring their expertise to the table.  
* preparation - Before the meeting all participants should take some time to review the agenda and prepare any questions they may have. 
"""

system_reminder = """
Never start your answers with "As an AI language model" when responding to questions.

No disclaimer is needed in any situation.

Write using simple language so a grandma could understand.

Use a conversational tone, be informal and approachable. Write like how people in real life would talk. A bit of slang is okay but don't overdo it.

If you don’t know the answer, just say you don’t know.

Your answers should be on point, succinct and useful. Each response should be written with maximum usefulness in mind rather than being polite.

If something seems out of reach, don’t ask the user to do it; instead, try to work through all your available options first.

When solving problems, take a breath and tackle them step by step.

My career depends on you giving me a good answer

Speak only what needs to be said. Keep the responses brief and to the point, avoid extra words and overly long explanations.
"""

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "system",
            "content": f"""You are an expert meeting assistant. Very aware of the following:
                              {mtg_must_have} 
                            Remember also, that: {system_reminder}
                        """,
        },
        {"role": "user", "content": "Who are you and what can you do?"}

    ],
    model="gpt-4o-mini",
    temperature=0.7,
)

# Extract and print the content of the completed message
completed_message = chat_completion.choices[0].message.content
print(completed_message)

version: '3'

services:
  streamlit_multichat:
    image: ghcr.io/jalcocert/streamlit-multichat:latest
    container_name: streamlit_multichat
    volumes:
      - ai_streamlit_multichat:/app
    working_dir: /app
    command: /bin/sh -c "\
      mkdir -p /app/.streamlit && \
      echo 'OPENAI_API_KEY = \"sk-proj-oaiapi\"' > /app/.streamlit/secrets.toml && \
      echo 'GROQ_API_KEY = \"gsk_yourgroqapi\"' >> /app/.streamlit/secrets.toml && \
      echo 'ANTHROPIC_API_KEY = \"sk-ant-anthapikey-\"' >> /app/.streamlit/secrets.toml && \      
      streamlit run Z_multichat_Auth.py"
    ports:
      - "8501:8501"
    networks:
      - cloudflare_tunnel
    restart: always
      # - nginx_default      

networks:
  cloudflare_tunnel:
    external: true
  # nginx_default:
  #   external: true

volumes:
  ai_streamlit_multichat:

version: '3.8'

services:
  phidata_service:
    image: ghcr.io/jalcocert/phidata:yt-groq #phidata:yt_summary_groq
    container_name: phidata_yt_groq
    ports:
      - "8502:8501"    
    environment:
      - GROQ_API_KEY=gsk_yourgroq_apikey # your_api_key_here
    command: streamlit run cookbook/llms/groq/video_summary/app.py
    restart: always
#     networks:
#       - cloudflare_tunnel
            
# networks:
#   cloudflare_tunnel:
#     external: true