# priv/python/dspy_bridge.py
import dspy
from base_bridge import BaseBridge
from typing import Dict, Any, Optional
import uuid

class DSPyBridge(BaseBridge):
    """Bridge implementation for DSPy framework"""
    
    def _initialize_framework(self) -> None:
        """Initialize DSPy-specific components"""
        self.programs = {}
        self.lm_configured = False
        
    def _register_handlers(self) -> Dict[str, Callable]:
        """Register DSPy-specific command handlers"""
        return {
            # Common handlers from base
            'ping': self.ping,
            'get_stats': self.get_stats,
            'get_info': self.get_info,
            'cleanup': self.cleanup,
            
            # DSPy-specific handlers
            'configure_lm': self.configure_lm,
            'create_program': self.create_program,
            'execute_program': self.execute_program,
            'create_signature': self.create_signature,
            'list_programs': self.list_programs,
            'delete_program': self.delete_program
        }
    
    def get_framework_info(self) -> Dict[str, Any]:
        """Return DSPy framework information"""
        return {
            'name': 'dspy',
            'version': dspy.__version__,
            'capabilities': [
                'signatures',
                'programs',
                'language_models',
                'chain_of_thought',
                'retrieval'
            ],
            'supported_models': [
                'gemini',
                'openai',
                'anthropic',
                'cohere'
            ]
        }
    
    def configure_lm(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Configure DSPy language model"""
        lm_type = args.get('type', 'gemini')
        
        if lm_type == 'gemini':
            lm = dspy.Google(
                model=args.get('model', 'gemini-1.5-flash'),
                api_key=args.get('api_key'),
                temperature=args.get('temperature', 0.7)
            )
        elif lm_type == 'openai':
            lm = dspy.OpenAI(
                model=args.get('model', 'gpt-4'),
                api_key=args.get('api_key'),
                temperature=args.get('temperature', 0.7)
            )
        else:
            raise ValueError(f"Unsupported LM type: {lm_type}")
        
        dspy.settings.configure(lm=lm)
        self.lm_configured = True
        
        return {
            'status': 'configured',
            'lm_type': lm_type,
            'model': args.get('model')
        }
    
    def create_signature(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Create a DSPy signature dynamically"""
        signature_config = args['signature']
        
        # Create signature class
        class_name = signature_config['name']
        fields = {}
        
        # Add input fields
        for field_name, field_config in signature_config.get('inputs', {}).items():
            fields[field_name] = dspy.InputField(
                desc=field_config.get('description', ''),
                prefix=field_config.get('prefix'),
                format=field_config.get('format')
            )
        
        # Add output fields
        for field_name, field_config in signature_config.get('outputs', {}).items():
            fields[field_name] = dspy.OutputField(
                desc=field_config.get('description', ''),
                prefix=field_config.get('prefix'),
                format=field_config.get('format')
            )
        
        # Create signature class
        signature_class = type(class_name, (dspy.Signature,), fields)
        
        # Store for later use
        signature_id = str(uuid.uuid4())
        self.signatures[signature_id] = signature_class
        
        return {'signature_id': signature_id}
    
    def create_program(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Create a DSPy program"""
        if not self.lm_configured:
            raise RuntimeError("Language model not configured")
        
        signature = args.get('signature')
        program_type = args.get('type', 'predict')
        
        if program_type == 'predict':
            if isinstance(signature, str):
                # Use stored signature
                signature_class = self.signatures.get(signature)
                if not signature_class:
                    raise ValueError(f"Signature not found: {signature}")
            else:
                # Create inline signature
                signature_class = self._create_signature_class(signature)
            
            program = dspy.Predict(signature_class)
            
        elif program_type == 'chain_of_thought':
            program = dspy.ChainOfThought(signature_class)
            
        elif program_type == 'retrieve':
            # Retrieval-augmented program
            retriever = self._create_retriever(args.get('retriever_config', {}))
            program = dspy.Retrieve(retriever)
            
        else:
            raise ValueError(f"Unsupported program type: {program_type}")
        
        # Store program
        program_id = str(uuid.uuid4())
        self.programs[program_id] = program
        
        return {
            'program_id': program_id,
            'type': program_type
        }
    
    def execute_program(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a DSPy program"""
        program_id = args['program_id']
        inputs = args['inputs']
        
        program = self.programs.get(program_id)
        if not program:
            raise ValueError(f"Program not found: {program_id}")
        
        # Execute program
        result = program(**inputs)
        
        # Extract outputs
        outputs = {}
        for key, value in result.items():
            if hasattr(value, '__dict__'):
                outputs[key] = str(value)
            else:
                outputs[key] = value
        
        return {
            'outputs': outputs,
            'program_id': program_id
        }
    
    def list_programs(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """List all programs"""
        return {
            'programs': list(self.programs.keys()),
            'count': len(self.programs)
        }
    
    def delete_program(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Delete a program"""
        program_id = args['program_id']
        
        if program_id in self.programs:
            del self.programs[program_id]
            return {'status': 'deleted', 'program_id': program_id}
        else:
            raise ValueError(f"Program not found: {program_id}")
    
    def cleanup(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Cleanup resources"""
        program_count = len(self.programs)
        self.programs.clear()
        self.signatures.clear()
        
        return {
            'status': 'cleaned',
            'programs_cleared': program_count
        }


# Main entry point
if __name__ == "__main__":
    import sys
    
    # Check if running as pool worker
    mode = "standalone"
    worker_id = None
    
    if len(sys.argv) > 1 and sys.argv[1] == "--pool-worker":
        mode = "pool_worker"
        if len(sys.argv) > 2:
            worker_id = sys.argv[2]
    
    # Create and run bridge
    bridge = DSPyBridge(mode=mode, worker_id=worker_id)
    bridge.run()

defmodule DSPex.Adapters.DSPyAdapter do
  @moduledoc """
  Adapter for DSPy ML framework
  """
  
  use DSPex.Adapters.BaseMLAdapter
  
  alias DSPex.PythonBridge.SessionPoolV2
  
  # DSPy-specific types
  @type signature :: %{
    name: String.t(),
    inputs: map(),
    outputs: map()
  }
  
  @type program_type :: :predict | :chain_of_thought | :retrieve
  
  # Implement required callbacks
  
  @impl true
  def get_framework_info do
    call_bridge("get_info", %{})
  end
  
  @impl true
  def validate_environment do
    # Check for required API keys
    case System.get_env("GEMINI_API_KEY") do
      nil -> {:error, "GEMINI_API_KEY environment variable not set"}
      _ -> :ok
    end
  end
  
  @impl true
  def initialize(options) do
    # Configure default LM if API key is available
    if api_key = System.get_env("GEMINI_API_KEY") do
      configure_lm(%{
        type: "gemini",
        model: Keyword.get(options, :model, "gemini-1.5-flash"),
        api_key: api_key,
        temperature: Keyword.get(options, :temperature, 0.7)
      })
    end
    
    {:ok, %{initialized: true}}
  end
  
  # DSPy-specific functions
  
  @doc """
  Configure the language model for DSPy
  """
  def configure_lm(config, options \\ []) do
    call_bridge("configure_lm", config, options)
  end
  
  @doc """
  Create a DSPy signature
  """
  def create_signature(signature, options \\ []) do
    call_bridge("create_signature", %{signature: signature}, options)
  end
  
  @doc """
  Create a DSPy program from a signature
  """
  def create_program(signature, type \\ :predict, options \\ []) do
    args = %{
      signature: signature,
      type: type
    }
    
    call_bridge("create_program", args, options)
  end
  
  @doc """
  Execute a DSPy program
  """
  def execute_program(program_id, inputs, options \\ []) do
    args = %{
      program_id: program_id,
      inputs: inputs
    }
    
    with {:ok, result} <- call_bridge("execute_program", args, options) do
      {:ok, result["outputs"]}
    end
  end
  
  @doc """
  List all programs
  """
  def list_programs(options \\ []) do
    call_bridge("list_programs", %{}, options)
  end
  
  @doc """
  Delete a program
  """
  def delete_program(program_id, options \\ []) do
    call_bridge("delete_program", %{program_id: program_id}, options)
  end
  
  # High-level convenience functions
  
  @doc """
  Create and execute a program in one call
  """
  def predict(signature, inputs, options \\ []) do
    with {:ok, %{"program_id" => program_id}} <- create_program(signature, :predict, options),
         {:ok, outputs} <- execute_program(program_id, inputs, options),
         {:ok, _} <- delete_program(program_id, options) do
      {:ok, outputs}
    end
  end
  
  @doc """
  Chain of thought reasoning
  """
  def chain_of_thought(signature, inputs, options \\ []) do
    with {:ok, %{"program_id" => program_id}} <- create_program(signature, :chain_of_thought, options),
         {:ok, outputs} <- execute_program(program_id, inputs, options),
         {:ok, _} <- delete_program(program_id, options) do
      {:ok, outputs}
    end
  end
end

# priv/python/langchain_bridge.py
from langchain import __version__ as langchain_version
from langchain.chat_models import ChatOpenAI, ChatAnthropic
from langchain.chains import LLMChain, ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate, ChatPromptTemplate
from langchain.agents import initialize_agent, Tool
from langchain.tools import DuckDuckGoSearchRun
from base_bridge import BaseBridge
from typing import Dict, Any, Optional
import uuid

class LangChainBridge(BaseBridge):
    """Bridge implementation for LangChain framework"""
    
    def _initialize_framework(self) -> None:
        """Initialize LangChain-specific components"""
        self.chains = {}
        self.agents = {}
        self.memories = {}
        self.tools = {}
        self.llm = None
        
    def _register_handlers(self) -> Dict[str, Callable]:
        """Register LangChain-specific command handlers"""
        return {
            # Common handlers
            'ping': self.ping,
            'get_stats': self.get_stats,
            'get_info': self.get_info,
            'cleanup': self.cleanup,
            
            # LangChain-specific handlers
            'configure_llm': self.configure_llm,
            'create_chain': self.create_chain,
            'execute_chain': self.execute_chain,
            'create_agent': self.create_agent,
            'execute_agent': self.execute_agent,
            'create_memory': self.create_memory,
            'add_tool': self.add_tool,
            'list_chains': self.list_chains,
            'list_agents': self.list_agents,
            'delete_chain': self.delete_chain,
            'delete_agent': self.delete_agent
        }
    
    def get_framework_info(self) -> Dict[str, Any]:
        """Return LangChain framework information"""
        return {
            'name': 'langchain',
            'version': langchain_version,
            'capabilities': [
                'chains',
                'agents',
                'tools',
                'memory',
                'prompts',
                'streaming'
            ],
            'supported_models': [
                'openai',
                'anthropic',
                'huggingface',
                'cohere'
            ]
        }
    
    def configure_llm(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Configure LangChain LLM"""
        llm_type = args.get('type', 'openai')
        
        if llm_type == 'openai':
            self.llm = ChatOpenAI(
                model_name=args.get('model', 'gpt-4'),
                temperature=args.get('temperature', 0.7),
                openai_api_key=args.get('api_key'),
                streaming=args.get('streaming', False)
            )
        elif llm_type == 'anthropic':
            self.llm = ChatAnthropic(
                model=args.get('model', 'claude-2'),
                temperature=args.get('temperature', 0.7),
                anthropic_api_key=args.get('api_key')
            )
        else:
            raise ValueError(f"Unsupported LLM type: {llm_type}")
        
        return {
            'status': 'configured',
            'llm_type': llm_type,
            'model': args.get('model')
        }
    
    def create_chain(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Create a LangChain chain"""
        if not self.llm:
            raise RuntimeError("LLM not configured")
        
        chain_type = args.get('type', 'llm')
        
        if chain_type == 'llm':
            # Basic LLM chain
            prompt = PromptTemplate(
                input_variables=args.get('input_variables', ['input']),
                template=args.get('template', '{input}')
            )
            chain = LLMChain(llm=self.llm, prompt=prompt)
            
        elif chain_type == 'conversation':
            # Conversation chain with memory
            memory_id = args.get('memory_id')
            if memory_id:
                memory = self.memories.get(memory_id)
            else:
                memory = ConversationBufferMemory()
            
            chain = ConversationChain(
                llm=self.llm,
                memory=memory,
                verbose=args.get('verbose', False)
            )
            
        else:
            raise ValueError(f"Unsupported chain type: {chain_type}")
        
        # Store chain
        chain_id = str(uuid.uuid4())
        self.chains[chain_id] = chain
        
        return {
            'chain_id': chain_id,
            'type': chain_type
        }
    
    def execute_chain(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a LangChain chain"""
        chain_id = args['chain_id']
        inputs = args['inputs']
        
        chain = self.chains.get(chain_id)
        if not chain:
            raise ValueError(f"Chain not found: {chain_id}")
        
        # Execute chain
        result = chain.run(**inputs)
        
        return {
            'output': result,
            'chain_id': chain_id
        }
    
    def create_agent(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Create a LangChain agent"""
        if not self.llm:
            raise RuntimeError("LLM not configured")
        
        agent_type = args.get('type', 'zero-shot-react-description')
        tool_ids = args.get('tool_ids', [])
        
        # Gather tools
        tools = []
        for tool_id in tool_ids:
            if tool_id in self.tools:
                tools.append(self.tools[tool_id])
        
        # Create agent
        agent = initialize_agent(
            tools=tools,
            llm=self.llm,
            agent=agent_type,
            verbose=args.get('verbose', False)
        )
        
        # Store agent
        agent_id = str(uuid.uuid4())
        self.agents[agent_id] = agent
        
        return {
            'agent_id': agent_id,
            'type': agent_type,
            'tools': tool_ids
        }
    
    def execute_agent(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a LangChain agent"""
        agent_id = args['agent_id']
        input_text = args['input']
        
        agent = self.agents.get(agent_id)
        if not agent:
            raise ValueError(f"Agent not found: {agent_id}")
        
        # Execute agent
        result = agent.run(input_text)
        
        return {
            'output': result,
            'agent_id': agent_id
        }
    
    def create_memory(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Create a memory instance"""
        memory_type = args.get('type', 'buffer')
        
        if memory_type == 'buffer':
            memory = ConversationBufferMemory()
        else:
            raise ValueError(f"Unsupported memory type: {memory_type}")
        
        memory_id = str(uuid.uuid4())
        self.memories[memory_id] = memory
        
        return {
            'memory_id': memory_id,
            'type': memory_type
        }
    
    def add_tool(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Add a tool for agents"""
        tool_type = args.get('type')
        
        if tool_type == 'search':
            tool = Tool(
                name="Search",
                func=DuckDuckGoSearchRun().run,
                description="Search the web for information"
            )
        else:
            # Custom tool
            tool = Tool(
                name=args.get('name'),
                func=lambda x: f"Mock result for: {x}",  # Would be actual implementation
                description=args.get('description')
            )
        
        tool_id = str(uuid.uuid4())
        self.tools[tool_id] = tool
        
        return {
            'tool_id': tool_id,
            'name': tool.name
        }
    
    def list_chains(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """List all chains"""
        return {
            'chains': list(self.chains.keys()),
            'count': len(self.chains)
        }
    
    def list_agents(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """List all agents"""
        return {
            'agents': list(self.agents.keys()),
            'count': len(self.agents)
        }
    
    def delete_chain(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Delete a chain"""
        chain_id = args['chain_id']
        
        if chain_id in self.chains:
            del self.chains[chain_id]
            return {'status': 'deleted', 'chain_id': chain_id}
        else:
            raise ValueError(f"Chain not found: {chain_id}")
    
    def delete_agent(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Delete an agent"""
        agent_id = args['agent_id']
        
        if agent_id in self.agents:
            del self.agents[agent_id]
            return {'status': 'deleted', 'agent_id': agent_id}
        else:
            raise ValueError(f"Agent not found: {agent_id}")
    
    def cleanup(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Cleanup resources"""
        chains_count = len(self.chains)
        agents_count = len(self.agents)
        
        self.chains.clear()
        self.agents.clear()
        self.memories.clear()
        self.tools.clear()
        
        return {
            'status': 'cleaned',
            'chains_cleared': chains_count,
            'agents_cleared': agents_count
        }


if __name__ == "__main__":
    import sys
    
    mode = "standalone"
    worker_id = None
    
    if len(sys.argv) > 1 and sys.argv[1] == "--pool-worker":
        mode = "pool_worker"
        if len(sys.argv) > 2:
            worker_id = sys.argv[2]
    
    bridge = LangChainBridge(mode=mode, worker_id=worker_id)
    bridge.run()

defmodule DSPex.Adapters.LangChainAdapter do
  @moduledoc """
  Adapter for LangChain framework
  """
  
  use DSPex.Adapters.BaseMLAdapter
  
  # LangChain-specific types
  @type chain_type :: :llm | :conversation | :sequential
  @type agent_type :: :"zero-shot-react-description" | :"conversational-react-description"
  @type memory_type :: :buffer | :window | :summary
  
  @impl true
  def get_framework_info do
    call_bridge("get_info", %{})
  end
  
  @impl true
  def validate_environment do
    # Check for at least one API key
    cond do
      System.get_env("OPENAI_API_KEY") -> :ok
      System.get_env("ANTHROPIC_API_KEY") -> :ok
      true -> {:error, "No LLM API key found (OPENAI_API_KEY or ANTHROPIC_API_KEY)"}
    end
  end
  
  @impl true
  def initialize(options) do
    # Configure default LLM if API key is available
    llm_config = 
      cond do
        api_key = System.get_env("OPENAI_API_KEY") ->
          %{
            type: "openai",
            model: Keyword.get(options, :model, "gpt-4"),
            api_key: api_key,
            temperature: Keyword.get(options, :temperature, 0.7),
            streaming: Keyword.get(options, :streaming, false)
          }
          
        api_key = System.get_env("ANTHROPIC_API_KEY") ->
          %{
            type: "anthropic",
            model: Keyword.get(options, :model, "claude-2"),
            api_key: api_key,
            temperature: Keyword.get(options, :temperature, 0.7)
          }
          
        true ->
          nil
      end
    
    if llm_config do
      configure_llm(llm_config)
    end
    
    {:ok, %{initialized: true}}
  end
  
  # LangChain-specific functions
  
  @doc """
  Configure the LLM for LangChain
  """
  def configure_llm(config, options \\ []) do
    call_bridge("configure_llm", config, options)
  end
  
  @doc """
  Create a LangChain chain
  """
  def create_chain(type, config, options \\ []) do
    args = Map.merge(config, %{type: type})
    call_bridge("create_chain", args, options)
  end
  
  @doc """
  Execute a chain
  """
  def execute_chain(chain_id, inputs, options \\ []) do
    args = %{
      chain_id: chain_id,
      inputs: inputs
    }
    
    with {:ok, result} <- call_bridge("execute_chain", args, options) do
      {:ok, result["output"]}
    end
  end
  
  @doc """
  Create an agent with tools
  """
  def create_agent(type, tool_ids, config \\ %{}, options \\ []) do
    args = Map.merge(config, %{
      type: type,
      tool_ids: tool_ids
    })
    
    call_bridge("create_agent", args, options)
  end
  
  @doc """
  Execute an agent
  """
  def execute_agent(agent_id, input, options \\ []) do
    args = %{
      agent_id: agent_id,
      input: input
    }
    
    with {:ok, result} <- call_bridge("execute_agent", args, options) do
      {:ok, result["output"]}
    end
  end
  
  @doc """
  Create a memory instance
  """
  def create_memory(type \\ :buffer, options \\ []) do
    call_bridge("create_memory", %{type: type}, options)
  end
  
  @doc """
  Add a tool for agents
  """
  def add_tool(name, description, type \\ :custom, options \\ []) do
    args = %{
      name: name,
      description: description,
      type: type
    }
    
    call_bridge("add_tool", args, options)
  end
  
  @doc """
  List all chains
  """
  def list_chains(options \\ []) do
    call_bridge("list_chains", %{}, options)
  end
  
  @doc """
  List all agents
  """
  def list_agents(options \\ []) do
    call_bridge("list_agents", %{}, options)
  end
  
  # High-level convenience functions
  
  @doc """
  Simple question-answering
  """
  def ask(question, options \\ []) do
    with {:ok, %{"chain_id" => chain_id}} <- create_chain(:llm, %{
           template: "{input}",
           input_variables: ["input"]
         }, options),
         {:ok, answer} <- execute_chain(chain_id, %{input: question}, options),
         {:ok, _} <- delete_chain(chain_id, options) do
      {:ok, answer}
    end
  end
  
  @doc """
  Conversational chat with memory
  """
  def chat(session_id, message, options \\ []) do
    # Use session_id to maintain conversation memory
    options = Keyword.put(options, :session_id, session_id)
    
    # Check if conversation chain exists in session
    case get_session_data(session_id, :chain_id) do
      nil ->
        # Create new conversation chain
        with {:ok, %{"memory_id" => memory_id}} <- create_memory(:buffer, options),
             {:ok, %{"chain_id" => chain_id}} <- create_chain(:conversation, %{
               memory_id: memory_id
             }, options) do
          
          # Store in session
          put_session_data(session_id, :chain_id, chain_id)
          put_session_data(session_id, :memory_id, memory_id)
          
          # Execute
          execute_chain(chain_id, %{input: message}, options)
        end
        
      chain_id ->
        # Use existing chain
        execute_chain(chain_id, %{input: message}, options)
    end
  end
  
  @doc """
  Research agent with web search
  """
  def research(topic, options \\ []) do
    with {:ok, %{"tool_id" => search_tool}} <- add_tool(
           "Web Search",
           "Search the web for information",
           :search,
           options
         ),
         {:ok, %{"agent_id" => agent_id}} <- create_agent(
           :"zero-shot-react-description",
           [search_tool],
           %{verbose: true},
           options
         ),
         {:ok, result} <- execute_agent(
           agent_id,
           "Research the following topic and provide a summary: #{topic}",
           options
         ),
         {:ok, _} <- delete_agent(agent_id, options) do
      {:ok, result}
    end
  end
  
  # Helper functions
  
  defp get_session_data(session_id, key) do
    # Would integrate with DSPex session management
    DSPex.PythonBridge.SessionStore.get(session_id, key)
  end
  
  defp put_session_data(session_id, key, value) do
    DSPex.PythonBridge.SessionStore.put(session_id, key, value)
  end
  
  defp delete_chain(chain_id, options) do
    call_bridge("delete_chain", %{chain_id: chain_id}, options)
  end
  
  defp delete_agent(agent_id, options) do
    call_bridge("delete_agent", %{agent_id: agent_id}, options)
  end
end

# priv/python/custom_ml_bridge.py
from base_bridge import BaseBridge
from typing import Dict, Any, Optional, Callable
import uuid

class CustomMLBridge(BaseBridge):
    """
    Template for creating custom ML framework bridges.
    
    This example shows how to integrate a hypothetical ML framework
    that deals with custom models and predictions.
    """
    
    def _initialize_framework(self) -> None:
        """Initialize your framework-specific components"""
        # Example: Initialize model registry, connections, etc.
        self.models = {}
        self.predictions = {}
        self.datasets = {}
        
        # Initialize your ML framework here
        # import your_ml_framework
        # self.framework = your_ml_framework.initialize()
    
    def _register_handlers(self) -> Dict[str, Callable]:
        """Register your framework-specific command handlers"""
        return {
            # Required common handlers
            'ping': self.ping,
            'get_stats': self.get_stats,
            'get_info': self.get_info,
            'cleanup': self.cleanup,
            
            # Your framework-specific handlers
            'load_model': self.load_model,
            'train_model': self.train_model,
            'predict': self.predict,
            'evaluate': self.evaluate,
            'save_model': self.save_model,
            'load_dataset': self.load_dataset,
            'preprocess': self.preprocess,
            'list_models': self.list_models,
            'delete_model': self.delete_model,
            
            # Add more handlers as needed
        }
    
    def get_framework_info(self) -> Dict[str, Any]:
        """Return information about your framework"""
        return {
            'name': 'custom_ml',
            'version': '1.0.0',  # Your framework version
            'capabilities': [
                'model_training',
                'prediction',
                'evaluation',
                'preprocessing',
                'model_persistence'
            ],
            'supported_models': [
                'linear_regression',
                'random_forest',
                'neural_network',
                'custom_model'
            ],
            'requirements': [
                'numpy',
                'scikit-learn',
                'your-ml-library'
            ]
        }
    
    def load_model(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Load a pre-trained model"""
        model_path = args.get('path')
        model_type = args.get('type', 'auto')
        
        # Example implementation
        try:
            # Load your model here
            # model = your_framework.load_model(model_path, model_type)
            
            # For demo purposes, create a mock model
            model = {
                'type': model_type,
                'path': model_path,
                'loaded_at': datetime.utcnow().isoformat()
            }
            
            model_id = str(uuid.uuid4())
            self.models[model_id] = model
            
            return {
                'model_id': model_id,
                'type': model_type,
                'status': 'loaded'
            }
            
        except Exception as e:
            raise RuntimeError(f"Failed to load model: {str(e)}")
    
    def train_model(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Train a new model"""
        model_type = args.get('type')
        dataset_id = args.get('dataset_id')
        hyperparameters = args.get('hyperparameters', {})
        
        # Validate dataset exists
        if dataset_id not in self.datasets:
            raise ValueError(f"Dataset not found: {dataset_id}")
        
        dataset = self.datasets[dataset_id]
        
        # Train model
        # model = your_framework.train(
        #     model_type=model_type,
        #     data=dataset,
        #     **hyperparameters
        # )
        
        # Mock implementation
        model = {
            'type': model_type,
            'dataset_id': dataset_id,
            'hyperparameters': hyperparameters,
            'trained_at': datetime.utcnow().isoformat(),
            'metrics': {
                'accuracy': 0.95,
                'loss': 0.05
            }
        }
        
        model_id = str(uuid.uuid4())
        self.models[model_id] = model
        
        return {
            'model_id': model_id,
            'metrics': model['metrics'],
            'status': 'trained'
        }
    
    def predict(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Make predictions using a model"""
        model_id = args.get('model_id')
        inputs = args.get('inputs')
        options = args.get('options', {})
        
        # Validate model exists
        if model_id not in self.models:
            raise ValueError(f"Model not found: {model_id}")
        
        model = self.models[model_id]
        
        # Make predictions
        # predictions = model.predict(inputs, **options)
        
        # Mock implementation
        predictions = {
            'values': [0.8, 0.2] if isinstance(inputs, list) else 0.8,
            'confidence': 0.95,
            'model_id': model_id
        }
        
        # Store prediction for tracking
        prediction_id = str(uuid.uuid4())
        self.predictions[prediction_id] = {
            'model_id': model_id,
            'inputs': inputs,
            'outputs': predictions,
            'timestamp': datetime.utcnow().isoformat()
        }
        
        return {
            'prediction_id': prediction_id,
            'predictions': predictions['values'],
            'confidence': predictions['confidence']
        }
    
    def evaluate(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Evaluate model performance"""
        model_id = args.get('model_id')
        dataset_id = args.get('dataset_id')
        metrics = args.get('metrics', ['accuracy', 'precision', 'recall'])
        
        # Validate inputs
        if model_id not in self.models:
            raise ValueError(f"Model not found: {model_id}")
        if dataset_id not in self.datasets:
            raise ValueError(f"Dataset not found: {dataset_id}")
        
        # Evaluate model
        # results = your_framework.evaluate(
        #     model=self.models[model_id],
        #     data=self.datasets[dataset_id],
        #     metrics=metrics
        # )
        
        # Mock implementation
        results = {
            'accuracy': 0.94,
            'precision': 0.92,
            'recall': 0.96,
            'f1_score': 0.94
        }
        
        return {
            'model_id': model_id,
            'dataset_id': dataset_id,
            'metrics': results
        }
    
    def save_model(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Save a model to disk"""
        model_id = args.get('model_id')
        path = args.get('path')
        format = args.get('format', 'native')
        
        if model_id not in self.models:
            raise ValueError(f"Model not found: {model_id}")
        
        # Save model
        # your_framework.save_model(
        #     model=self.models[model_id],
        #     path=path,
        #     format=format
        # )
        
        return {
            'model_id': model_id,
            'path': path,
            'format': format,
            'status': 'saved'
        }
    
    def load_dataset(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Load a dataset for training/evaluation"""
        source = args.get('source')
        format = args.get('format', 'csv')
        options = args.get('options', {})
        
        # Load dataset
        # dataset = your_framework.load_data(
        #     source=source,
        #     format=format,
        #     **options
        # )
        
        # Mock implementation
        dataset = {
            'source': source,
            'format': format,
            'shape': (1000, 10),
            'features': ['feature1', 'feature2', '...'],
            'loaded_at': datetime.utcnow().isoformat()
        }
        
        dataset_id = str(uuid.uuid4())
        self.datasets[dataset_id] = dataset
        
        return {
            'dataset_id': dataset_id,
            'shape': dataset['shape'],
            'features': dataset['features']
        }
    
    def preprocess(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Preprocess data"""
        dataset_id = args.get('dataset_id')
        operations = args.get('operations', [])
        
        if dataset_id not in self.datasets:
            raise ValueError(f"Dataset not found: {dataset_id}")
        
        # Apply preprocessing
        # processed_data = your_framework.preprocess(
        #     data=self.datasets[dataset_id],
        #     operations=operations
        # )
        
        # Create new dataset with processed data
        processed_dataset = {
            'original_id': dataset_id,
            'operations': operations,
            'shape': (950, 12),  # Mock: some rows removed, features added
            'processed_at': datetime.utcnow().isoformat()
        }
        
        new_dataset_id = str(uuid.uuid4())
        self.datasets[new_dataset_id] = processed_dataset
        
        return {
            'dataset_id': new_dataset_id,
            'original_dataset_id': dataset_id,
            'operations_applied': operations,
            'shape': processed_dataset['shape']
        }
    
    def list_models(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """List all loaded models"""
        model_list = []
        for model_id, model in self.models.items():
            model_list.append({
                'id': model_id,
                'type': model.get('type'),
                'created_at': model.get('trained_at') or model.get('loaded_at')
            })
        
        return {
            'models': model_list,
            'count': len(model_list)
        }
    
    def delete_model(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Delete a model from memory"""
        model_id = args.get('model_id')
        
        if model_id in self.models:
            del self.models[model_id]
            return {'status': 'deleted', 'model_id': model_id}
        else:
            raise ValueError(f"Model not found: {model_id}")
    
    def cleanup(self, args: Dict[str, Any]) -> Dict[str, Any]:
        """Cleanup all resources"""
        models_count = len(self.models)
        datasets_count = len(self.datasets)
        predictions_count = len(self.predictions)
        
        # Clear all data
        self.models.clear()
        self.datasets.clear()
        self.predictions.clear()
        
        # Cleanup framework resources
        # your_framework.cleanup()
        
        return {
            'status': 'cleaned',
            'models_cleared': models_count,
            'datasets_cleared': datasets_count,
            'predictions_cleared': predictions_count
        }


if __name__ == "__main__":
    import sys
    from datetime import datetime
    
    mode = "standalone"
    worker_id = None
    
    if len(sys.argv) > 1 and sys.argv[1] == "--pool-worker":
        mode = "pool_worker"
        if len(sys.argv) > 2:
            worker_id = sys.argv[2]
    
    bridge = CustomMLBridge(mode=mode, worker_id=worker_id)
    bridge.run()

defmodule DSPex.Adapters.CustomMLAdapter do
  @moduledoc """
  Template adapter for custom ML frameworks.
  
  This shows how to create an adapter for your own ML framework
  while leveraging DSPex infrastructure.
  """
  
  use DSPex.Adapters.BaseMLAdapter
  
  # Define your framework-specific types
  @type model_type :: :linear_regression | :random_forest | :neural_network | :custom
  @type dataset_format :: :csv | :json | :parquet | :numpy
  @type preprocessing_op :: :normalize | :standardize | :encode | :impute
  
  @impl true
  def get_framework_info do
    call_bridge("get_info", %{})
  end
  
  @impl true
  def validate_environment do
    # Add your framework-specific validation
    # For example, check for required files, libraries, etc.
    :ok
  end
  
  @impl true
  def initialize(options) do
    # Initialize your framework
    # This is called when the adapter starts
    
    # Example: Set default configuration
    config = %{
      cache_models: Keyword.get(options, :cache_models, true),
      auto_preprocessing: Keyword.get(options, :auto_preprocessing, false),
      default_model_type: Keyword.get(options, :default_model_type, :random_forest)
    }
    
    {:ok, config}
  end
  
  # Model Management Functions
  
  @doc """
  Load a pre-trained model from disk
  """
  def load_model(path, type \\ :auto, options \\ []) do
    args = %{
      path: path,
      type: type
    }
    
    call_bridge("load_model", args, options)
  end
  
  @doc """
  Train a new model
  """
  def train_model(type, dataset_id, hyperparameters \\ %{}, options \\ []) do
    args = %{
      type: type,
      dataset_id: dataset_id,
      hyperparameters: hyperparameters
    }
    
    call_bridge("train_model", args, options)
  end
  
  @doc """
  Make predictions with a model
  """
  def predict(model_id, inputs, options \\ []) do
    args = %{
      model_id: model_id,
      inputs: inputs,
      options: options
    }
    
    with {:ok, result} <- call_bridge("predict", args, options) do
      {:ok, %{
        predictions: result["predictions"],
        confidence: result["confidence"],
        prediction_id: result["prediction_id"]
      }}
    end
  end
  
  @doc """
  Evaluate model performance
  """
  def evaluate(model_id, dataset_id, metrics \\ nil, options \\ []) do
    args = %{
      model_id: model_id,
      dataset_id: dataset_id
    }
    
    if metrics do
      args = Map.put(args, :metrics, metrics)
    end
    
    call_bridge("evaluate", args, options)
  end
  
  @doc """
  Save a model to disk
  """
  def save_model(model_id, path, format \\ :native, options \\ []) do
    args = %{
      model_id: model_id,
      path: path,
      format: format
    }
    
    call_bridge("save_model", args, options)
  end
  
  # Data Management Functions
  
  @doc """
  Load a dataset
  """
  def load_dataset(source, format \\ :csv, options \\ []) do
    args = %{
      source: source,
      format: format,
      options: options
    }
    
    call_bridge("load_dataset", args, options)
  end
  
  @doc """
  Preprocess a dataset
  """
  def preprocess(dataset_id, operations, options \\ []) do
    args = %{
      dataset_id: dataset_id,
      operations: operations
    }
    
    call_bridge("preprocess", args, options)
  end
  
  # Query Functions
  
  @doc """
  List all loaded models
  """
  def list_models(options \\ []) do
    with {:ok, result} <- call_bridge("list_models", %{}, options) do
      {:ok, result["models"]}
    end
  end
  
  @doc """
  Delete a model
  """
  def delete_model(model_id, options \\ []) do
    call_bridge("delete_model", %{model_id: model_id}, options)
  end
  
  # High-Level Convenience Functions
  
  @doc """
  Train and evaluate a model in one call
  """
  def train_and_evaluate(type, train_dataset_id, test_dataset_id, hyperparameters \\ %{}, options \\ []) do
    with {:ok, %{"model_id" => model_id}} <- train_model(type, train_dataset_id, hyperparameters, options),
         {:ok, metrics} <- evaluate(model_id, test_dataset_id, nil, options) do
      {:ok, %{
        model_id: model_id,
        metrics: metrics["metrics"]
      }}
    end
  end
  
  @doc """
  Quick prediction - load model, predict, and cleanup
  """
  def quick_predict(model_path, inputs, options \\ []) do
    with {:ok, %{"model_id" => model_id}} <- load_model(model_path, :auto, options),
         {:ok, result} <- predict(model_id, inputs, options),
         {:ok, _} <- delete_model(model_id, options) do
      {:ok, result}
    end
  end
  
  @doc """
  Pipeline: load data, preprocess, train, evaluate
  """
  def run_pipeline(data_source, model_type, preprocessing_ops, options \\ []) do
    with {:ok, %{"dataset_id" => raw_dataset_id}} <- load_dataset(data_source, :csv, options),
         {:ok, %{"dataset_id" => processed_dataset_id}} <- preprocess(raw_dataset_id, preprocessing_ops, options),
         
         # Split dataset (this would be implemented in the bridge)
         {:ok, %{"train_id" => train_id, "test_id" => test_id}} <- 
           call_bridge("split_dataset", %{dataset_id: processed_dataset_id, ratio: 0.8}, options),
         
         # Train and evaluate
         {:ok, result} <- train_and_evaluate(model_type, train_id, test_id, %{}, options) do
      
      {:ok, result}
    end
  end
  
  # Framework-Specific Extensions
  
  @doc """
  Example of framework-specific functionality
  """
  def explain_prediction(model_id, input, options \\ []) do
    # If your framework supports model explainability
    args = %{
      model_id: model_id,
      input: input,
      method: Keyword.get(options, :method, :shap)
    }
    
    call_bridge("explain_prediction", args, options)
  end
  
  @doc """
  Hyperparameter tuning
  """
  def tune_hyperparameters(model_type, dataset_id, param_grid, options \\ []) do
    args = %{
      model_type: model_type,
      dataset_id: dataset_id,
      param_grid: param_grid,
      cv_folds: Keyword.get(options, :cv_folds, 5)
    }
    
    call_bridge("tune_hyperparameters", args, options)
  end
end

# Direct adapter usage
alias DSPex.Adapters.DSPyAdapter

# Configure language model
DSPyAdapter.configure_lm(%{
  type: "gemini",
  model: "gemini-1.5-flash",
  api_key: System.get_env("GEMINI_API_KEY")
})

# Create and use a signature
signature = %{
  name: "QuestionAnswer",
  inputs: %{
    question: %{description: "A question to answer"}
  },
  outputs: %{
    answer: %{description: "The answer to the question"}
  }
}

{:ok, result} = DSPyAdapter.predict(
  signature,
  %{question: "What is the capital of France?"}
)

IO.puts("Answer: #{result["answer"]}")

# Using the unified interface
alias DSPex.MLBridge

# Get LangChain adapter
{:ok, langchain} = MLBridge.get_adapter(:langchain)

# Simple Q&A
{:ok, answer} = langchain.ask("What is machine learning?")

# Conversational chat with memory
{:ok, response1} = langchain.chat("session_123", "Hello, my name is Alice")
{:ok, response2} = langchain.chat("session_123", "What's my name?")
# response2 will remember the name from the conversation

# Research agent
{:ok, research} = langchain.research("Recent advances in quantum computing")

# Custom ML workflow
alias DSPex.Adapters.CustomMLAdapter

# Load and use a model
{:ok, model_info} = CustomMLAdapter.load_model("/models/sentiment_classifier.pkl")
model_id = model_info["model_id"]

# Make predictions
{:ok, result} = CustomMLAdapter.predict(
  model_id,
  ["This movie was fantastic!", "Terrible experience"]
)

# Run a complete pipeline
{:ok, pipeline_result} = CustomMLAdapter.run_pipeline(
  "/data/training_data.csv",
  :random_forest,
  [:normalize, :encode]
)

# Use different frameworks for different tasks
defmodule MyApp.MLPipeline do
  alias DSPex.MLBridge
  
  def analyze_and_explain(text) do
    # Use LangChain for analysis
    {:ok, langchain} = MLBridge.get_adapter(:langchain)
    {:ok, analysis} = langchain.ask("Analyze the sentiment of: #{text}")
    
    # Use DSPy for structured extraction
    {:ok, dspy} = MLBridge.get_adapter(:dspy)
    {:ok, entities} = dspy.predict(
      %{
        name: "EntityExtraction",
        inputs: %{text: %{description: "Text to extract entities from"}},
        outputs: %{
          entities: %{description: "List of entities"},
          sentiment: %{description: "Overall sentiment"}
        }
      },
      %{text: text}
    )
    
    # Combine results
    %{
      analysis: analysis,
      entities: entities["entities"],
      sentiment: entities["sentiment"]
    }
  end
end