Model Configuration
Configure LLM models from different providers for your applications.
Model Selection
OpenAI Models
from openai import OpenAI
client = OpenAI()
# GPT-4o (recommended)
response = client.chat.completions.create(
model="gpt-4o",
messages=messages,
temperature=0.7
)
# GPT-4o-mini (faster, cheaper)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=messages
)
Anthropic Models
from anthropic import Anthropic
client = Anthropic()
# Claude 3.5 Sonnet
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=4096,
messages=messages
)
Google Models
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-pro')
response = model.generate_content(prompt)
Common Parameters
Controls randomness (0.0 = deterministic, 1.0 = creative)
Maximum tokens in response
Nucleus sampling threshold
Framework Integration
Agno
from agno import Agent, OpenAI, Anthropic, Gemini
# OpenAI
agent = Agent(model=OpenAI(id="gpt-4o"))
# Anthropic
agent = Agent(model=Anthropic(id="claude-3-5-sonnet-20241022"))
# Gemini
agent = Agent(model=Gemini(id="gemini-1.5-pro"))
LangChain
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatOpenAI(model="gpt-4o")
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
Local Models (Ollama)
from agno import Agent, Ollama
# Run Llama 3.2 locally
agent = Agent(model=Ollama(id="llama3.2"))
Install Ollama from ollama.ai for local model support.
API Keys
Set up provider API keys
Local RAG
Use local models with Ollama