llmyaml 0.1.8

YAML-based LLM configuration and execution

YAMLLM

A Python library for YAML-based LLM configuration and execution.

Installation

pip install yamllm-core

uv add yamllm-core

Quick Start

In order to run a simple query, run a script as follows. NOTE: Printing of the response is not required as this is handles by the query method. This uses the rich library to print the responses in the console.

from yamllm.core.llm import OpenAIGPT, GoogleGemini, DeepSeek, MistralAI
import os
import dotenv

dotenv.load_dotenv()

config_path = "path/to/config.yaml"

# Initialize LLM with config
llm = GoogleGemini(config_path=config_path, api_key=os.environ.get("GOOGLE_API_KEY"))

# Make a query
response = llm.query("Give me some boiler plate pytorch code please")

In order to have an ongoing conversation with the model, run a script as follows.

from yamllm.core.llm import OpenAIGPT, GoogleGemini, DeepSeek, MistralAI
from rich.console import Console
import os
import dotenv

dotenv.load_dotenv()
console = Console()

config_path = "path/to/config.yaml"

llm = GoogleGemini(config_path=config_path, api_key=os.environ.get("GOOGLE_API_KEY"))

while True:
    try:          
        prompt = input("\nHuman: ")
        if prompt.lower() == "exit":
            break
        
        response = llm.query(prompt)
        if response is None:
            continue
        
    except FileNotFoundError as e:
        console.print(f"[red]Configuration file not found:[/red] {e}")
    except ValueError as e:
        console.print(f"[red]Configuration error:[/red] {e}")
    except Exception as e:
        console.print(f"[red]An error occurred:[/red] {str(e)}")

Configuration

YAMLLM uses YAML files for configuration. Set up a .config file to define the parameters for your LLM instance. This file should include settings such as the model type, temperature, maximum tokens, and system prompt.

Example configuration:

  name: "openai"  # supported: openai, google, deepseek, mistral
  model: "gpt-4o-mini"  # model identifier
  api_key: # api key goes here, best practice to put into dotenv
  base_url: # optional: for custom endpoints e.g. "https://generativelanguage.googleapis.com/v1beta/openai/"

# Model Configuration
model_settings:
  temperature: 0.7
  max_tokens: 1000
  top_p: 1.0
  frequency_penalty: 0.0
  presence_penalty: 0.0
  stop_sequences: []
  
# Request Settings
request:
  timeout: 30  # seconds
  retry:
    max_attempts: 3
    initial_delay: 1
    backoff_factor: 2
    
# Context Management
context:
  system_prompt: "You are a helpful assistant, helping me achieve my goals"
  max_context_length: 16000
  memory:
    enabled: true
    max_messages: 10  # number of messages to keep in conversation history
    conversation_db: "yamllm/memory/conversation_history.db"
    vector_store:
      index_path: "yamllm/memory/vector_store/faiss_index.idx"
      metadata_path: "yamllm/memory/vector_store/metadata.pkl"
    
# Output Formatting
output:
  format: "text"  # supported: text, json, markdown
  stream: false

logging:
  level: "INFO"
  file: "yamllm.log"
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

# Tool Management - In development
tools:
  enabled: false
  tool_timeout: 10  # seconds
  tool_list: ['calculator', 'web_search']

# Safety Settings
safety:
  content_filtering: true
  max_requests_per_minute: 60
  sensitive_keywords: []

Place the .config file in your project directory and reference it in your code to initialize the LLM instance.

Features

• YAML-based configuration
• Simple API interface
• Customizable prompt templates
• Error handling and retry logic
• In built memory management in sqlite database for short term memory
• Use of vector database for long term memory based on semantic search
• Choose streaming or non-streamed response

License

MIT License