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Model Manager Module
Supports multiple AI models for semantic analysis of papers
"""
import os
from typing import Dict, List, Optional
import requests
class ModelManager:
"""Manage multiple AI models for semantic paper analysis"""
def __init__(self):
self.current_model = "keyword" # Default to keyword-based
self.model_configs = {
"keyword": {
"name": "Keyword-Based",
"description": "Fast keyword matching without AI models",
"requires_api_key": False,
"cost": "Free",
"speed": "Fastest",
"accuracy": "Basic"
},
"openai": {
"name": "OpenAI GPT",
"description": "GPT-4/GPT-3.5 for semantic understanding",
"requires_api_key": True,
"cost": "Paid",
"speed": "Fast",
"accuracy": "Excellent",
"api_key_env": "OPENAI_API_KEY",
"models": ["gpt-4", "gpt-4-turbo", "gpt-3.5-turbo", "gpt-4o"]
},
"anthropic": {
"name": "Anthropic Claude",
"description": "Claude for advanced semantic analysis",
"requires_api_key": True,
"cost": "Paid",
"speed": "Fast",
"accuracy": "Excellent",
"api_key_env": "ANTHROPIC_API_KEY",
"models": ["claude-3-opus", "claude-3-sonnet", "claude-3-haiku", "claude-3.5-sonnet"]
},
"ollama": {
"name": "Ollama (Local)",
"description": "Local models via Ollama (free, requires installation)",
"requires_api_key": False,
"cost": "Free",
"speed": "Medium",
"accuracy": "Good",
"base_url": "http://localhost:11434",
"models": ["llama2", "llama3", "mistral", "neural-chat", "codellama", "phi3", "gemma", "qwen"]
},
"huggingface": {
"name": "Hugging Face Inference",
"description": "Hugging Face inference API for various models",
"requires_api_key": True,
"cost": "Free tier available",
"speed": "Medium",
"accuracy": "Good",
"api_key_env": "HUGGINGFACE_API_KEY",
"models": [
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-3-8b-chat-hf",
"mistralai/Mistral-7B-Instruct-v0.2",
"google/gemma-7b-it",
"microsoft/Phi-3-mini-4k-instruct",
"Qwen/Qwen2-7B-Instruct"
]
},
"cohere": {
"name": "Cohere Command",
"description": "Cohere's Command models for text analysis",
"requires_api_key": True,
"cost": "Paid (free tier available)",
"speed": "Fast",
"accuracy": "Good",
"api_key_env": "COHERE_API_KEY",
"models": ["command", "command-light", "command-nightly"]
},
"google": {
"name": "Google Gemini",
"description": "Google's Gemini models for semantic understanding",
"requires_api_key": True,
"cost": "Paid (free tier available)",
"speed": "Fast",
"accuracy": "Excellent",
"api_key_env": "GOOGLE_API_KEY",
"models": ["gemini-pro", "gemini-pro-vision", "gemini-1.5-pro"]
},
"together": {
"name": "Together AI",
"description": "Together AI's hosted open-source models",
"requires_api_key": True,
"cost": "Paid (free tier available)",
"speed": "Fast",
"accuracy": "Good",
"api_key_env": "TOGETHER_API_KEY",
"models": [
"meta-llama/Llama-2-70b-chat-hf",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"togethercomputer/RedPajama-INCITE-Chat-7B"
]
},
"replicate": {
"name": "Replicate",
"description": "Replicate's hosted models API",
"requires_api_key": True,
"cost": "Pay-per-use",
"speed": "Variable",
"accuracy": "Good",
"api_key_env": "REPLICATE_API_KEY",
"models": [
"meta/llama-2-70b-chat",
"mistralai/mixtral-8x7b-instruct-v0.1",
"stabilityai/stable-diffusion-xl-base-1.0"
]
},
"deepseek": {
"name": "DeepSeek-R1",
"description": "DeepSeek-R1 with Chain of Thought reasoning for AI intelligence classification",
"requires_api_key": False,
"cost": "Free (Hugging Face Inference)",
"speed": "Medium",
"accuracy": "Excellent",
"reasoning": True,
"model_id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
}
}
def get_available_models(self) -> List[Dict]:
"""Get list of available models with their configurations"""
return [
{
"id": model_id,
"name": config["name"],
"description": config["description"],
"requires_api_key": config["requires_api_key"],
"cost": config["cost"],
"speed": config.get("speed", "N/A"),
"accuracy": config.get("accuracy", "N/A"),
"reasoning": config.get("reasoning", False)
}
for model_id, config in self.model_configs.items()
]
def set_model(self, model_id: str) -> bool:
"""Set the current model for analysis"""
if model_id in self.model_configs:
self.current_model = model_id
return True
return False
def get_current_model(self) -> str:
"""Get the current model ID"""
return self.current_model
def check_api_key(self, model_id: str) -> bool:
"""Check if API key is available for a model"""
config = self.model_configs.get(model_id, {})
if not config.get("requires_api_key", False):
return True
api_key_env = config.get("api_key_env")
if api_key_env:
return bool(os.getenv(api_key_env))
return False
def analyze_paper_semantic(self, paper_data: Dict, model_id: Optional[str] = None) -> Dict:
"""
Analyze a paper using semantic understanding with the specified model
Args:
paper_data: Paper dictionary with title, summary, etc.
model_id: Model to use (defaults to current model)
Returns:
Dictionary with semantic analysis results
"""
model_id = model_id or self.current_model
if model_id == "keyword":
# Fall back to keyword-based analysis
return self._keyword_analysis(paper_data)
elif model_id == "openai":
return self._openai_analysis(paper_data)
elif model_id == "anthropic":
return self._anthropic_analysis(paper_data)
elif model_id == "ollama":
return self._ollama_analysis(paper_data)
elif model_id == "huggingface":
return self._huggingface_analysis(paper_data)
elif model_id == "cohere":
return self._cohere_analysis(paper_data)
elif model_id == "google":
return self._google_analysis(paper_data)
elif model_id == "together":
return self._together_analysis(paper_data)
elif model_id == "replicate":
return self._replicate_analysis(paper_data)
elif model_id == "deepseek":
return self._deepseek_analysis(paper_data)
else:
return self._keyword_analysis(paper_data)
def _keyword_analysis(self, paper_data: Dict) -> Dict:
"""Keyword-based analysis (fallback)"""
return {
"model_used": "keyword",
"semantic_relevance": 0.0,
"key_concepts": [],
"reasoning": "Keyword-based analysis only"
}
def _openai_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using OpenAI API"""
try:
import openai
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
client = openai.OpenAI(api_key=api_key)
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
import json
result = json.loads(response.choices[0].message.content)
result["model_used"] = "openai"
return result
except Exception as e:
print(f"OpenAI analysis error: {e}")
return self._keyword_analysis(paper_data)
def _anthropic_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Anthropic Claude API"""
try:
import anthropic
api_key = os.getenv("ANTHROPIC_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
client = anthropic.Anthropic(api_key=api_key)
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format your response as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = client.messages.create(
model="claude-3-haiku-20240307",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
import json
result = json.loads(response.content[0].text)
result["model_used"] = "anthropic"
return result
except Exception as e:
print(f"Anthropic analysis error: {e}")
return self._keyword_analysis(paper_data)
def _ollama_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using local Ollama model"""
try:
base_url = self.model_configs["ollama"]["base_url"]
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = requests.post(
f"{base_url}/api/generate",
json={
"model": "llama2",
"prompt": prompt,
"stream": False
},
timeout=30
)
if response.status_code == 200:
import json
result = json.loads(response.json()["response"])
result["model_used"] = "ollama"
return result
else:
return self._keyword_analysis(paper_data)
except Exception as e:
print(f"Ollama analysis error: {e}")
return self._keyword_analysis(paper_data)
def _huggingface_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Hugging Face Inference API"""
try:
api_key = os.getenv("HUGGINGFACE_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
model_id = "mistralai/Mistral-7B-Instruct-v0.2"
api_url = f"https://api-inference.huggingface.co/models/{model_id}"
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = requests.post(
api_url,
headers={"Authorization": f"Bearer {api_key}"},
json={"inputs": prompt},
timeout=30
)
if response.status_code == 200:
import json
result = json.loads(response.json()[0]["generated_text"])
result["model_used"] = "huggingface"
return result
else:
return self._keyword_analysis(paper_data)
except Exception as e:
print(f"Hugging Face analysis error: {e}")
return self._keyword_analysis(paper_data)
def _cohere_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Cohere Command API"""
try:
import cohere
api_key = os.getenv("COHERE_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
client = cohere.Client(api_key)
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format your response as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = client.chat(
message=prompt,
model="command"
)
import json
result = json.loads(response.text)
result["model_used"] = "cohere"
return result
except Exception as e:
print(f"Cohere analysis error: {e}")
return self._keyword_analysis(paper_data)
def _google_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Google Gemini API"""
try:
import google.generativeai as genai
api_key = os.getenv("GOOGLE_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
genai.configure(api_key=api_key)
model = genai.GenerativeModel('gemini-pro')
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format your response as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = model.generate_content(prompt)
import json
result = json.loads(response.text)
result["model_used"] = "google"
return result
except Exception as e:
print(f"Google analysis error: {e}")
return self._keyword_analysis(paper_data)
def _together_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Together AI API"""
try:
api_key = os.getenv("TOGETHER_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
api_url = "https://api.together.xyz/v1/chat/completions"
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = requests.post(
api_url,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
json={
"model": model_id,
"messages": [{"role": "user", "content": prompt}]
},
timeout=30
)
if response.status_code == 200:
import json
result = json.loads(response.json()["choices"][0]["message"]["content"])
result["model_used"] = "together"
return result
else:
return self._keyword_analysis(paper_data)
except Exception as e:
print(f"Together AI analysis error: {e}")
return self._keyword_analysis(paper_data)
def _replicate_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using Replicate API"""
try:
api_key = os.getenv("REPLICATE_API_KEY")
if not api_key:
return self._keyword_analysis(paper_data)
# Using a simple text model for analysis
model_id = "meta/llama-2-70b-chat"
api_url = f"https://api.replicate.com/v1/models/{model_id}/predictions"
prompt = f"""
Analyze this AI paper for AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) relevance.
Title: {paper_data.get('title', '')}
Summary: {paper_data.get('summary', '')}
Provide:
1. Semantic relevance score (0-100)
2. Key concepts related to AI intelligence spectrum
3. Brief reasoning
Format as JSON with keys: semantic_relevance, key_concepts, reasoning
"""
response = requests.post(
api_url,
headers={
"Authorization": f"Token {api_key}",
"Content-Type": "application/json"
},
json={
"input": {
"prompt": prompt
}
},
timeout=30
)
if response.status_code == 200:
import json
result = json.loads(response.json()["output"])
result["model_used"] = "replicate"
return result
else:
return self._keyword_analysis(paper_data)
except Exception as e:
print(f"Replicate analysis error: {e}")
return self._keyword_analysis(paper_data)
def _deepseek_analysis(self, paper_data: Dict) -> Dict:
"""Analyze using DeepSeek-R1 with Chain of Thought reasoning"""
try:
from huggingface_hub import InferenceClient
model_id = self.model_configs["deepseek"]["model_id"]
client = InferenceClient(model_id)
system_instruction = (
"You are an AI Research Scientist specializing in AGI, ASI, and ACI taxonomies. "
"Your task is to analyze research paper abstracts and categorize them.\n\n"
"CRITERIA:\n"
"- AGI (Artificial General Intelligence): Focus on cross-domain reasoning, System 2 thinking, and 'generality.'\n"
"- ASI (Artificial Superintelligence): Focus on recursive self-improvement, alignment at scale, and superhuman capabilities.\n"
"- ACI (Artificial Collective Intelligence): Focus on multi-agent systems, swarm intelligence, and human-AI collaboration.\n"
"- Narrow AI: Focus on specific, single-domain optimizations (e.g., just 'faster vision' or 'better LLM weights').\n\n"
"OUTPUT FORMAT (JSON):\n"
"{\n"
' "category": "AGI | ASI | ACI | Narrow AI",\n'
' "confidence_score": 0-100,\n'
' "analysis": "A brief technical justification of why this fits the category based on architectural depth.",\n'
' "aci_potential": "High/Low"\n'
"}\n"
)
# Combine title and summary for analysis
title = paper_data.get('title', '')
summary = paper_data.get('summary', '')
user_input = f"Analyze this abstract: {title}. {summary}"
response = client.chat_completion(
messages=[
{"role": "system", "content": system_instruction},
{"role": "user", "content": user_input}
],
max_tokens=500,
temperature=0.1
)
content = response.choices[0].message.content
# DeepSeek-R1 sometimes includes <think> tags; we strip them for JSON parsing
if "<think>" in content:
content = content.split("<think>")[-1].strip()
import json
result = json.loads(content)
result["model_used"] = "deepseek"
return result
except Exception as e:
print(f"DeepSeek analysis error: {e}")
return self._keyword_analysis(paper_data)
def batch_analyze(self, papers: List[Dict], model_id: Optional[str] = None) -> List[Dict]:
"""
Analyze multiple papers in batch
Args:
papers: List of paper dictionaries
model_id: Model to use
Returns:
List of papers with added semantic analysis
"""
analyzed_papers = []
for paper in papers:
semantic_result = self.analyze_paper_semantic(paper, model_id)
paper['semantic_analysis'] = semantic_result
analyzed_papers.append(paper)
return analyzed_papers
# Test the model manager
if __name__ == "__main__":
manager = ModelManager()
# Test available models
print("Available Models:")
for model in manager.get_available_models():
print(f"- {model['id']}: {model['name']} ({model['cost']})")
# Test keyword analysis
test_paper = {
'title': 'Neural Computers: A New Computing Paradigm',
'summary': 'Researchers propose Neural Computers that unify computation, memory, and I/O in a single learned runtime state, potentially leading to artificial general intelligence.'
}
print("\nTesting keyword analysis:")
result = manager.analyze_paper_semantic(test_paper, "keyword")
print(result)
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