# Example: Implementing a vector-based memory system with LangChain and Chroma

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain

# Initialize embedding model
embeddings = OpenAIEmbeddings()

# Create a vector store to hold memories
memory_db = Chroma(embedding_function=embeddings, collection_name="agent_memories")

# Function to add a new memory
def store_memory(text, metadata=None):
    # Split long texts into chunks
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    texts = text_splitter.split_text(text)
    
    # Store in vector database with optional metadata
    memory_db.add_texts(texts=texts, metadatas=[metadata] * len(texts))
    print(f"Stored new memory: {text[:50]}...")

# Function to retrieve relevant memories
def retrieve_memories(query, k=3):
    docs = memory_db.similarity_search(query, k=k)
    return [doc.page_content for doc in docs]

# Example usage in an agent
def agent_with_memory(user_input):
    # Retrieve relevant memories based on user input
    relevant_memories = retrieve_memories(user_input)
    
    # Use memories to enhance the context for the response
    context = "\n".join(["Relevant information:", *relevant_memories])
    
    # Generate response using the enriched context
    llm = OpenAI(temperature=0)
    response = llm(f"Context: {context}\nUser question: {user_input}\nResponse:")
    
    # Store this interaction as a new memory
    store_memory(f"User: {user_input}\nAgent: {response}")
    
    return response

# Example: Function calling with OpenAI

from openai import OpenAI
import json
import requests
from datetime import datetime

client = OpenAI()

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City and state, e.g., San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query"
                    }
                },
                "required": ["query"]
            }
        }
    }
]

# Implement the actual functions
def get_weather(location, unit="celsius"):
    # In a real implementation, this would call a weather API
    # This is a mock implementation
    weather_data = {
        "location": location,
        "temperature": "22" if unit == "celsius" else "72",
        "unit": unit,
        "condition": "Sunny",
        "humidity": "45%"
    }
    return json.dumps(weather_data)

def search_web(query):
    # In a real implementation, this would call a search API
    # This is a mock implementation
    return json.dumps({
        "results": [
            {"title": f"Result for {query}", "snippet": "This is a sample search result."}
        ]
    })

# Agent with tool-use capability
def agent_with_tools(user_input):
    messages = [{"role": "user", "content": user_input}]
    
    # First, let the model decide which tool to use (if any)
    response = client.chat.completions.create(
        model="gpt-4",
        messages=messages,
        tools=tools,
        tool_choice="auto"
    )
    
    response_message = response.choices[0].message
    tool_calls = response_message.tool_calls
    
    # If the model wants to use tools
    if tool_calls:
        # Add the model's response planning to use tools
        messages.append(response_message)
        
        # Process each tool call
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
            
            # Call the appropriate function
            if function_name == "get_weather":
                function_response = get_weather(**function_args)
            elif function_name == "search_web":
                function_response = search_web(**function_args)
            else:
                function_response = f"Error: Function {function_name} not found"
            
            # Append the function response to messages
            messages.append({
                "tool_call_id": tool_call.id,
                "role": "tool",
                "name": function_name,
                "content": function_response
            })
        
        # Get the final response after tool use
        second_response = client.chat.completions.create(
            model="gpt-4",
            messages=messages
        )
        
        return second_response.choices[0].message.content
    else:
        # Model chose not to use tools
        return response_message.content

# Example: Dynamic tool selection based on query analysis

class ToolRegistry:
    def __init__(self):
        self.tools = {}
        self.tool_descriptions = {}
    
    def register_tool(self, name, function, description):
        self.tools[name] = function
        self.tool_descriptions[name] = description
    
    def get_relevant_tools(self, query, max_tools=3):
        """Select the most relevant tools for a given query"""
        # In a real implementation, use embeddings or LLM to rank tools
        # This is a simplified implementation
        tool_scores = {}
        
        for name, description in self.tool_descriptions.items():
            # Simple keyword matching (use embeddings in a real system)
            score = sum(keyword in query.lower() for keyword in description.lower().split())
            tool_scores[name] = score
        
        # Get top N tools
        relevant_tools = sorted(tool_scores.items(), key=lambda x: x[1], reverse=True)[:max_tools]
        return [name for name, score in relevant_tools if score > 0]
    
    def execute_tool(self, name, **kwargs):
        if name in self.tools:
            return self.tools[name](**kwargs)
        else:
            return f"Error: Tool '{name}' not found"

# Usage example
registry = ToolRegistry()
registry.register_tool("get_weather", get_weather, "Get weather information for a location")
registry.register_tool("search_web", search_web, "Search the web for information")
# Register more tools...

def agent_with_dynamic_tools(user_input):
    # Select relevant tools for this query
    relevant_tool_names = registry.get_relevant_tools(user_input)
    relevant_tools = [t for t in tools if t["function"]["name"] in relevant_tool_names]
    
    # Only provide relevant tools to the model
    # Rest of the implementation follows the previous example...

# Example: RAG-powered agent

from langchain.agents import initialize_agent, Tool
from langchain.memory import ConversationBufferMemory

# Define tools including RAG
tools = [
    Tool(
        name="DocumentSearch",
        func=lambda q: "\n".join(doc.page_content for doc in enhanced_retrieval(q)),
        description="Useful for when you need to find specific information in documents. Input should be a search query."
    ),
    # Add other tools like web search, calculator, etc.
]

# Set up memory
memory = ConversationBufferMemory(memory_key="chat_history")

# Initialize the agent
agent = initialize_agent(
    tools=tools,
    llm=llm,
    memory=memory,
    agent="chat-conversational-react-description",
    verbose=True
)

# Agent handler function
def rag_agent_response(user_input):
    try:
        response = agent.run(input=user_input)
        return response
    except Exception as e:
        return f"I encountered an error: {str(e)}"

# Example: Hub and spoke multi-agent system

class AgentManager:
    def __init__(self, llm):
        self.llm = llm
        self.specialist_agents = {}
    
    def register_specialist(self, name, description, handler_function):
        """Register a specialist agent with the manager"""
        self.specialist_agents[name] = {
            "description": description,
            "handler": handler_function
        }
    
    def route_task(self, user_query):
        """Determine which specialist should handle this query"""
        agent_descriptions = "\n".join([
            f"- {name}: {details['description']}" 
            for name, details in self.specialist_agents.items()
        ])
        
        routing_prompt = f"""
Based on the user query, determine which specialist agent should handle this task.
Available specialists:
{agent_descriptions}

User query: "{user_query}"

Select the most appropriate specialist by name. If multiple specialists are needed, 
list them in order of priority. If no specialist is appropriate, respond with "DIRECT_RESPONSE".
"""
        
        response = self.llm(routing_prompt)
        # Extract agent name(s) - in a real system, use more robust parsing
        selected_agent = response.strip()
        
        return selected_agent
    
    def process_query(self, user_query):
        """Process user query by routing to appropriate specialist(s)"""
        selected_agent = self.route_task(user_query)
        
        if selected_agent == "DIRECT_RESPONSE":
            # No specialist needed, respond directly
            return self.generate_direct_response(user_query)
        
        # Check if the selected agent exists
        if selected_agent in self.specialist_agents:
            # Route to the specialist
            return self.specialist_agents[selected_agent]["handler"](user_query)
        else:
            # Fallback if routing returned an invalid agent
            return self.generate_direct_response(user_query)
    
    def generate_direct_response(self, user_query):
        """Generate a direct response when no specialist is needed"""
        response = self.llm(f"User query: {user_query}\nResponse:")
        return response

# Example usage
manager = AgentManager(llm)

# Register specialist agents
manager.register_specialist(
    name="ResearchAgent",
    description="Handles in-depth research queries requiring information synthesis",
    handler_function=lambda q: research_agent_handler(q)
)

manager.register_specialist(
    name="CodeAgent",
    description="Specializes in writing, explaining, and debugging code",
    handler_function=lambda q: code_agent_handler(q)
)

manager.register_specialist(
    name="DataAnalysisAgent",
    description="Processes and analyzes data, creates visualizations",
    handler_function=lambda q: data_analysis_handler(q)
)

# Process user query
response = manager.process_query("Can you help me analyze this CSV file of sales data?")

# Example: Debate framework for complex reasoning

def debate_framework(question, num_agents=3, rounds=2):
    """
    Use a debate framework where multiple agents discuss a question
    to arrive at a more accurate answer
    """
    # Initialize debate with the question
    debate_history = [f"Question: {question}\n\nThe agents will debate this question."]
    
    # Create agent personas with different perspectives
    agent_personas = [
        "You are a critical thinker who questions assumptions and looks for logical flaws.",
        "You are a creative thinker who considers unconventional approaches and possibilities.",
        "You are a detail-oriented analyst who focuses on facts and empirical evidence."
    ][:num_agents]
    
    # Conduct the debate for the specified number of rounds
    for round_num in range(1, rounds + 1):
        debate_history.append(f"\n\n--- Round {round_num} ---")
        
        # Each agent takes a turn
        for agent_idx, persona in enumerate(agent_personas):
            agent_prompt = f"""
{persona}

Below is the debate so far:
{''.join(debate_history)}

As Agent {agent_idx + 1}, provide your perspective on the question. 
If this is not the first round, respond to the points made by other agents.
Be concise but thorough in your reasoning.
"""
            # Get this agent's contribution
            agent_response = llm(agent_prompt, max_tokens=500)
            
            # Add to debate history
            debate_history.append(f"\n\nAgent {agent_idx + 1}:\n{agent_response}")
    
    # Final synthesis prompt
    synthesis_prompt = f"""
A debate was conducted on the following question:
{question}

The full debate transcript is below:
{''.join(debate_history)}

Synthesize the key insights from this debate into a comprehensive answer.
Highlight areas of agreement and disagreement, and provide a balanced conclusion.
"""
    
    final_answer = llm(synthesis_prompt, max_tokens=800)
    return final_answer