Simple LLM Call Example

This guide demonstrates how to make a simple LLM call using AiCore's LLM interface.

Prerequisites

1. Python 3.8+
2. AiCore installed (pip install core-for-ai)
3. API key for your chosen LLM provider

Step 1: Import Required Modules

from aicore.llm import Llm
from aicore.llm.config import LlmConfig

Step 2: Configure the LLM

Create a configuration for your LLM provider. Here's an example for OpenAI:

config = LlmConfig(
    provider="openai",
    api_key="your_api_key_here",  # Replace with your actual API key
    model="gpt-4o",              # Model name
    temperature=0.7,             # Creativity level (0-1)
    max_tokens=1000              # Maximum response length
)

Alternatively, you can load configuration from a YAML file:

from aicore.config import Config

config = Config.from_yaml("path/to/config.yml").llm

Example YAML configuration (config.yml):

llm:
  provider: "openai"
  api_key: "your_api_key_here"
  model: "gpt-4o"
  temperature: 0.7
  max_tokens: 1000

Step 3: Initialize the LLM

llm = Llm(config=config)

Step 4: Make a Simple Completion Call

Synchronous Call

response = llm.complete("Explain quantum computing in simple terms")
print(response)

Asynchronous Call

import asyncio

async def get_response():
    response = await llm.acomplete("Explain quantum computing in simple terms")
    print(response)

asyncio.run(get_response())

Step 5: Handle Streaming Responses (Default)

By default, responses are streamed:

response = llm.complete("Tell me a story about AI")
# Response will stream to stdout in real-time

To disable streaming:

response = llm.complete("Tell me a story about AI", stream=False)

Step 6: Using System Prompts

response = llm.complete(
    "Write a poem about technology",
    system_prompt="You are a creative poet specializing in haikus"
)

Step 7: Working with JSON Output

response = llm.complete(
    "List the top 3 programming languages with their main features as JSON",
    json_output=True
)
print(response)  # Returns a parsed dictionary

Step 8: Monitoring Usage

print(llm.usage)  # Shows current session usage

# Example output:
# Total | Cost: $0.0023 | Tokens: 342 | Prompt: 120 | Response: 222

Complete Example

Here's a complete working example:

from aicore.llm import Llm
from aicore.llm.config import LlmConfig

# Configuration
config = LlmConfig(
    provider="openai",
    api_key="your_api_key_here",
    model="gpt-4o",
    temperature=0.7,
    max_tokens=1000
)

# Initialize LLM
llm = Llm(config=config)

# Make completion
response = llm.complete("Explain quantum computing in simple terms")
print(response)

# Print usage
print("\nUsage Stats:")
print(llm.usage)

Troubleshooting

1. Authentication Errors: Verify your API key is correct
2. Model Not Found: Check the model name matches your provider's available models
3. Rate Limits: Implement retry logic or check your provider's quota

For more advanced usage, see the LLM documentation.