LLM Operator Implementation in Python
Implementing the LLM Operator pattern in Python involves creating a system with several interacting components as described in your article. Here's a conceptual Python implementation outline, focusing on the core logic and interactions between the Operator LLM, Target LLM Interface, and Knowledge Enhancement Layer.
This outline uses placeholder functions and classes to represent the different components. You would need to replace these with actual implementations using libraries like `langchain`, `fastapi`, `requests`, and specific LLM provider SDKs (e.g., `openai`, `anthropic`).
import os
import json
import requests
from abc import ABC, abstractmethod
# --- Configuration System (Simplified) ---
class Configuration:
def __init__(self):
self.config = {
"operator_llm": {"provider": "openai", "model": "gpt-4"},
"target_llm": {"provider": "anthropic", "model": "claude-3-sonnet"},
"search_tool": {"provider": "serpapi", "api_key": os.getenv("SERPAPI_API_KEY")},
"artifact_storage_path": "./artifacts"
}
def get(self, key):
return self.config.get(key)
# --- Tracing and Logging System (Simplified) ---
class Tracer:
def __init__(self):
self.logs = []
def log(self, message):
self.logs.append(message)
print(message) # Simple console logging
def get_logs(self):
return self.logs
# --- Target LLM Interface (Abstract Base Class) ---
class TargetLLMInterface(ABC):
@abstractmethod
def send_prompt(self, prompt):
pass
# --- Specific Target LLM Implementations ---
class OpenAIInterface(TargetLLMInterface):
def __init__(self, model):
self.model = model
# Assume OpenAI client is initialized elsewhere
def send_prompt(self, prompt):
self.tracer.log(f"Sending prompt to OpenAI ({self.model}): {prompt}")
# Replace with actual OpenAI API call
response = f"OpenAI response to: {prompt}"
self.tracer.log(f"Received response from OpenAI: {response}")
return response
class AnthropicInterface(TargetLLMInterface):
def __init__(self, model):
self.model = model
# Assume Anthropic client is initialized elsewhere
def send_prompt(self, prompt):
self.tracer.log(f"Sending prompt to Anthropic ({self.model}): {prompt}")
# Replace with actual Anthropic API call
response = f"Anthropic response to: {prompt}"
self.tracer.log(f"Received response from Anthropic: {response}")
return response
# --- Knowledge Enhancement Layer (Simplified Search Tool) ---
class SearchTool:
def __init__(self, provider, api_key):
self.provider = provider
self.api_key = api_key
self.tracer = Tracer() # Use the global tracer
def search(self, query):
self.tracer.log(f"Performing web search for: {query}")
# Replace with actual search API call (e.g., SerpAPI)
# For demonstration, return a dummy result
search_results = [
{"title": "Result 1", "link": "http://example.com/1", "snippet": f"Snippet for {query} result 1"},
{"title": "Result 2", "link": "http://example.com/2", "snippet": f"Snippet for {query} result 2"}
]
self.tracer.log(f"Search results: {search_results}")
return search_results
# --- Artifact Management System (Simplified) ---
class ArtifactManager:
def __init__(self, storage_path):
self.storage_path = storage_path
os.makedirs(self.storage_path, exist_ok=True)
self.tracer = Tracer() # Use the global tracer
def save_artifact(self, name, content):
file_path = os.path.join(self.storage_path, f"{name}.txt")
with open(file_path, "w") as f:
f.write(content)
self.tracer.log(f"Artifact saved: {file_path}")
return file_path
# --- Operator LLM (Core Logic) ---
class LLMOperator:
def __init__(self, config, tracer):
self.config = config
self.tracer = tracer
self.target_llm_interface = self._initialize_target_llm()
self.search_tool = self._initialize_search_tool()
self.artifact_manager = self._initialize_artifact_manager()
def _initialize_target_llm(self):
llm_config = self.config.get("target_llm")
provider = llm_config["provider"]
model = llm_config["model"]
if provider == "openai":
return OpenAIInterface(model)
elif provider == "anthropic":
return AnthropicInterface(model)
else:
raise ValueError(f"Unsupported target LLM provider: {provider}")
def _initialize_search_tool(self):
search_config = self.config.get("search_tool")
provider = search_config["provider"]
api_key = search_config["api_key"]
return SearchTool(provider, api_key)
def _initialize_artifact_manager(self):
storage_path = self.config.get("artifact_storage_path")
return ArtifactManager(storage_path)
def analyze_prompt(self, user_prompt):
self.tracer.log(f"Analyzing user prompt: {user_prompt}")
# Use Operator LLM to analyze and potentially enhance the prompt
# For demonstration, just return the original prompt
enhanced_prompt = user_prompt
self.tracer.log(f"Enhanced prompt: {enhanced_prompt}")
return enhanced_prompt
def evaluate_response(self, user_prompt, target_llm_response):
self.tracer.log(f"Evaluating response for prompt '{user_prompt}': {target_llm_response}")
# Use Operator LLM to evaluate the response
# Determine if the response is satisfactory, needs refinement, or requires search
# For demonstration, assume satisfactory if it contains "response"
is_satisfactory = "response" in target_llm_response
self.tracer.log(f"Response satisfactory: {is_satisfactory}")
return is_satisfactory, "Evaluation notes (if any)"
def process_request(self, user_prompt):
self.tracer.log(f"Processing request: {user_prompt}")
enhanced_prompt = self.analyze_prompt(user_prompt)
# Interaction loop
max_iterations = 3
for i in range(max_iterations):
target_llm_response = self.target_llm_interface.send_prompt(enhanced_prompt)
is_satisfactory, evaluation_notes = self.evaluate_response(user_prompt, target_llm_response)
if is_satisfactory:
self.tracer.log("Task completed successfully.")
artifact_name = f"result_{user_prompt[:20].replace(' ', '_')}_{i}"
self.artifact_manager.save_artifact(artifact_name, target_llm_response)
return target_llm_response, self.tracer.get_logs()
else:
self.tracer.log(f"Response not satisfactory. Iteration {i+1}/{max_iterations}. Notes: {evaluation_notes}")
# Decide whether to refine prompt or use search tool
# For demonstration, always refine prompt
enhanced_prompt = f"Refine the previous response: {target_llm_response}. Original request: {user_prompt}"
self.tracer.log(f"Refining prompt for next iteration: {enhanced_prompt}")
self.tracer.log("Max iterations reached without satisfactory result.")
return "Could not achieve a satisfactory result.", self.tracer.get_logs()
# --- User Interface Layer (Simplified Console Interface) ---
class ConsoleUI:
def __init__(self, operator):
self.operator = operator
def run(self):
print("LLM Operator Console Interface")
while True:
user_input = input("Enter your prompt (or 'quit' to exit): ")
if user_input.lower() == 'quit':
break
response, logs = self.operator.process_request(user_input)
print("\n--- Final Response ---")
print(response)
print("\n--- Interaction Logs ---")
for log in logs:
print(log)
print("-" * 20)
# --- Main Execution ---
if __name__ == "__main__":
config = Configuration()
tracer = Tracer()
operator = LLMOperator(config, tracer)
console_ui = ConsoleUI(operator)
console_ui.run()
Explanation:
1. Configuration System: A simple class to hold configuration settings for different components. In a real application, this would likely load from a file or environment variables.
2. Tracing and Logging System: A basic `Tracer` class to record the steps and decisions made by the Operator.
3. Target LLM Interface: An abstract base class `TargetLLMInterface` defines the common interface for interacting with any target LLM. Specific implementations like `OpenAIInterface` and `AnthropicInterface` would handle the details of communicating with those providers.
4. Knowledge Enhancement Layer: A simplified `SearchTool` class represents the integration with an external search service.
5. Artifact Management System: An `ArtifactManager` class handles saving the final outputs.
6. Operator LLM: The core `LLMOperator` class orchestrates the process. It contains methods for:
- `analyze_prompt`: Uses the Operator LLM (conceptually) to refine the user's prompt.
- `evaluate_response`: Uses the Operator LLM (conceptually) to assess the quality of the target LLM's response.
- `process_request`: Implements the main interaction loop, calling the target LLM, evaluating the response, and deciding on the next steps (refine prompt, search, or finish).
7. User Interface Layer: A simple `ConsoleUI` provides a command-line interface for interacting with the Operator.
To make this a functional implementation, you would need to:
- Replace the placeholder logic in `analyze_prompt`, `evaluate_response`, `OpenAIInterface.send_prompt`, `AnthropicInterface.send_prompt`, and `SearchTool.search` with actual API calls using appropriate libraries (e.g., `openai`, `anthropic`, `requests`, `serpapi-python`).
- Implement the logic within `evaluate_response` to make intelligent decisions based on the target LLM's output. This is where the core "agentic" behavior of the Operator LLM would reside.
- Expand the `Configuration` system to handle more detailed settings.
- Implement a more robust `Tracing` system if needed.
- Consider using a framework like `langchain` or `autogen` to manage the complex interactions and state within the `LLMOperator`. These frameworks provide abstractions for chains, agents, and tools that can simplify the implementation of the interaction loop.
- Develop a more sophisticated UI (web-based or desktop) if a console interface is not sufficient.
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