smartllm 0.0.8

A unified interface for interacting with multiple Large Language Model providers

SmartLLM

SmartLLM is a unified Python interface for interacting with multiple Large Language Model providers. It provides a consistent API across different LLM providers, handles caching of responses, and supports both synchronous and asynchronous interactions.

Installation

pip install smartllm

Features

• Unified API: Consistent interface for OpenAI, Anthropic, and Perplexity LLMs
• Response Caching: Persistent JSON-based caching of responses to improve performance
• Streaming Support: Real-time streaming of LLM responses (Anthropic only)
• JSON Mode: Structured JSON responses (OpenAI and Anthropic)
• Citations: Access to source information (Perplexity only)
• Asynchronous Execution: Non-blocking request execution via AsyncSmartLLM
• Configurable Parameters: Granular control over temperature, tokens, and other model parameters

Supported Providers

SmartLLM currently supports the following LLM providers:

• OpenAI

  • Models: GPT-4, GPT-3.5 series, and other OpenAI models
  • Features: JSON-structured outputs, token usage information
  • Example: base="openai", model="gpt-4"

• Anthropic

  • Models: Claude models (e.g., claude-3-7-sonnet-20250219)
  • Features: Streaming support, JSON-structured outputs, system prompts
  • Example: base="anthropic", model="claude-3-7-sonnet-20250219"

• Perplexity

  • Models: sonar-small-online, sonar-medium-online, sonar-pro, etc.
  • Features: Web search capabilities, citation information
  • Example: base="perplexity", model="sonar-pro"

Basic Usage

from smartllm import SmartLLM
import os

# Create SmartLLM instance
llm = SmartLLM(
    base="openai",
    model="gpt-4",
    api_key=os.environ.get("OPENAI_API_KEY"),
    prompt="Explain quantum computing in simple terms",
    temperature=0.7
)

# Execute the request
llm.execute()

# Wait for completion
llm.wait_for_completion()

# Check status and get response
if llm.is_completed():
    print(llm.response)
else:
    print(f"Error: {llm.get_error()}")

AsyncSmartLLM - Asynchronous Interface

SmartLLM also provides an asynchronous interface for non-blocking API interactions using AsyncSmartLLM:

import asyncio
import os
from smartllm import AsyncSmartLLM

async def main():
    # Create AsyncSmartLLM instance
    llm = AsyncSmartLLM(
        base="anthropic",
        model="claude-3-7-sonnet-20250219",
        api_key=os.environ.get("ANTHROPIC_API_KEY"),
        prompt="Explain quantum computing in simple terms",
        temperature=0.7
    )

    # Execute the request asynchronously
    await llm.execute()

    # Check status and get response
    if llm.is_completed():
        print(llm.response)
    else:
        print(f"Error: {llm.get_error()}")

# Run the async function
asyncio.run(main())

Async Streaming Support

AsyncSmartLLM also supports streaming responses:

import asyncio
import os
from smartllm import AsyncSmartLLM

# Custom callback for handling streaming chunks
async def print_chunk(chunk: str, accumulated: str) -> None:
    print(chunk, end="", flush=True)

async def main():
    api_key = os.environ.get("ANTHROPIC_API_KEY")
    
    # Enable streaming with stream=True
    llm = AsyncSmartLLM(
        base="anthropic", 
        model="claude-3-7-sonnet-20250219", 
        api_key=api_key,
        prompt="Tell me a short story about a robot learning to paint",
        temperature=0.7, 
        max_output_tokens=1000,
        stream=True
    )

    # Execute with callback
    await llm.execute(callback=print_chunk)
    
    if llm.is_failed():
        print(f"\nError occurred: {llm.get_error()}")
    else:
        print("\n\nFinal response:")
        print(llm.response)

if __name__ == "__main__":
    asyncio.run(main())

Async JSON Mode

import asyncio
import os
from smartllm import AsyncSmartLLM

async def main():
    api_key = os.environ.get("ANTHROPIC_API_KEY")

    json_schema = {
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "topics": {"type": "array", "items": {"type": "string"}},
            "difficulty": {"type": "integer", "minimum": 1, "maximum": 10}
        },
        "required": ["title", "topics", "difficulty"]
    }

    llm = AsyncSmartLLM(
        base="anthropic",
        model="claude-3-7-sonnet-20250219",
        api_key=api_key,
        prompt="Generate information about a quantum computing course",
        json_mode=True,
        json_schema=json_schema
    )

    await llm.execute()

    # Access structured data
    course_info = llm.response  # Returns a Python dictionary
    print(f"Course title: {course_info['title']}")
    print(f"Topics: {', '.join(course_info['topics'])}")
    print(f"Difficulty: {course_info['difficulty']}/10")

if __name__ == "__main__":
    asyncio.run(main())

SmartLLM Class Reference

Constructor

SmartLLM(
    base: str = "",                  # LLM provider ("openai", "anthropic", "perplexity")
    model: str = "",                 # Model identifier
    api_key: str = "",               # API key for the provider
    prompt: Union[str, List[str]] = "", # Single prompt or conversation history
    stream: bool = False,            # Enable streaming (Anthropic only)
    max_input_tokens: Optional[int] = None,  # Max input tokens
    max_output_tokens: Optional[int] = None, # Max output tokens
    output_type: str = "text",       # Output type
    temperature: float = 0.2,        # Temperature for generation
    top_p: float = 0.9,              # Top-p sampling parameter
    frequency_penalty: float = 1.0,  # Frequency penalty
    presence_penalty: float = 0.0,   # Presence penalty
    system_prompt: Optional[str] = None, # System prompt
    search_recency_filter: Optional[str] = None, # Filter for search (Perplexity)
    return_citations: bool = False,  # Include citations (Perplexity)
    json_mode: bool = False,         # Enable JSON mode (OpenAI, Anthropic)
    json_schema: Optional[Dict[str, Any]] = None, # JSON schema
    ttl: int = 7,                    # Cache time-to-live in days
    clear_cache: bool = False        # Clear existing cache
)

AsyncSmartLLM Class Reference

AsyncSmartLLM(
    # Same parameters as SmartLLM above
)

Methods

SmartLLM Methods

execute(callback: Optional[Callable[[str, str], None]] = None) -> SmartLLM
wait_for_completion(timeout: Optional[float] = None) -> bool
is_failed() -> bool
is_completed() -> bool
get_error() -> Optional[str]

AsyncSmartLLM Methods

async execute(callback: Optional[Callable[[str, str], None]] = None) -> AsyncSmartLLM
async generate() -> AsyncSmartLLM
is_failed() -> bool
is_completed() -> bool
get_error() -> Optional[str]

Properties

response: Union[str, Dict[str, Any]]  # The response content or JSON
sources: List[str]  # Citation sources (Perplexity)
usage: Dict[str, int]  # Token usage statistics

Streaming Responses (Anthropic Only)

Synchronous Streaming

from smartllm import SmartLLM
import os

def print_chunk(chunk: str, accumulated: str) -> None:
    print(f"CHUNK: {chunk}")

llm = SmartLLM(
    base="anthropic",
    model="claude-3-7-sonnet-20250219",
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    prompt="Write a short story about a robot learning to paint",
    stream=True  # Enable streaming
)

# Execute with callback
llm.execute(callback=print_chunk)
llm.wait_for_completion()

Asynchronous Streaming

import asyncio
from smartllm import AsyncSmartLLM
import os

async def print_chunk(chunk: str, accumulated: str) -> None:
    print(chunk, end="", flush=True)

async def main():
    llm = AsyncSmartLLM(
        base="anthropic",
        model="claude-3-7-sonnet-20250219",
        api_key=os.environ.get("ANTHROPIC_API_KEY"),
        prompt="Write a short story about a robot learning to paint",
        stream=True  # Enable streaming
    )

    # Execute with callback
    await llm.execute(callback=print_chunk)
    
    print("\n\nFinal response:")
    print(llm.response)

asyncio.run(main())

JSON Mode (OpenAI and Anthropic)

from smartllm import SmartLLM
import os

json_schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "topics": {"type": "array", "items": {"type": "string"}},
        "difficulty": {"type": "integer", "minimum": 1, "maximum": 10}
    },
    "required": ["title", "topics", "difficulty"]
}

llm = SmartLLM(
    base="openai",
    model="gpt-4",
    api_key=os.environ.get("OPENAI_API_KEY"),
    prompt="Generate information about a quantum computing course",
    json_mode=True,
    json_schema=json_schema
)

llm.execute()
llm.wait_for_completion()

# Access structured data
course_info = llm.response  # Returns a Python dictionary
print(f"Course title: {course_info['title']}")
print(f"Topics: {', '.join(course_info['topics'])}")
print(f"Difficulty: {course_info['difficulty']}/10")

Getting Citations (Perplexity Only)

from smartllm import SmartLLM
import os

llm = SmartLLM(
    base="perplexity",
    model="sonar-pro",
    api_key=os.environ.get("PERPLEXITY_API_KEY"),
    prompt="What are the latest advancements in quantum computing?",
    search_recency_filter="week",  # Filter for recent information
    return_citations=True  # Enable citations
)

llm.execute()
llm.wait_for_completion()

# Print the response
print(llm.response)

# Print the sources
print("\nSources:")
for source in llm.sources:
    print(f"- {source}")

Caching Mechanism

SmartLLM uses a persistent JSON-based caching system powered by the Cacherator library. This significantly improves performance by avoiding redundant API calls for identical requests.

Cache Configuration

By default, responses are cached for 7 days. You can customize the cache behavior:

# Set custom time-to-live (TTL) in days
llm = SmartLLM(
    base="openai",
    model="gpt-4",
    api_key=os.environ.get("OPENAI_API_KEY"),
    prompt="Explain quantum computing",
    ttl=30  # Cache results for 30 days
)

# Force clear existing cache
llm = SmartLLM(
    base="openai",
    model="gpt-4",
    api_key=os.environ.get("OPENAI_API_KEY"),
    prompt="Explain quantum computing",
    clear_cache=True  # Ignore any existing cached response
)

How Caching Works

1. Each request is assigned a unique identifier based on:

   • Provider (base)
   • Model
   • Prompt
   • All relevant parameters (temperature, tokens, etc.)

2. Responses are stored in JSON format in the data/llm directory

3. When making an identical request, the cached response is returned instead of making a new API call

4. Cache entries automatically expire after the specified TTL

5. Cache can be manually cleared by setting clear_cache=True

Error Handling

SmartLLM provides robust error handling through state tracking:

llm = SmartLLM(...)
llm.execute()
llm.wait_for_completion()

if llm.is_failed():
    print(f"Request failed: {llm.get_error()}")
elif llm.is_completed():
    print("Request completed successfully")
    print(llm.response)

For AsyncSmartLLM:

llm = AsyncSmartLLM(...)
await llm.execute()

if llm.is_failed():
    print(f"Request failed: {llm.get_error()}")
elif llm.is_completed():
    print("Request completed successfully")
    print(llm.response)

Dependencies

• cacherator: Persistent JSON-based caching
• logorator: Decorator-based logging
• openai>=1.0.0: OpenAI API client
• anthropic>=0.5.0: Anthropic API client
• python-slugify: Utility for creating safe identifiers

License

MIT License