Neural Nexus

from langchain.agents import initialize_agent, Tool
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

# Initialize retrieval system
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_existing_index(
    index_name="agricultural-data", 
    embedding=embeddings
)
retriever = vectorstore.as_retriever()

# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(temperature=0),
    chain_type="stuff",
    retriever=retriever
)

# Define tools
tools = [
    Tool(
        name="Agricultural Knowledge Base",
        func=qa_chain.run,
        description="Useful for answering questions about plant diseases, treatments, and farming practices."
    )
]

# Initialize agent
agent = initialize_agent(
    tools, 
    ChatOpenAI(temperature=0.2), 
    agent="chat-conversational-react-description",
    verbose=True
)

from langchain.agents import AgentExecutor, create_react_agent
from langchain.prompts import PromptTemplate
from langchain.tools import Tool
from langchain.chat_models import ChatOpenAI

# Define tools
market_research_tool = Tool(
    name="MarketResearch",
    func=lambda query: "Market size: $2.5B, Growth: 12% YoY, Competitors: 3 major players",
    description="Researches market conditions for a business idea"
)

financial_projection_tool = Tool(
    name="FinancialProjection",
    func=lambda query: "Year 1: $250K revenue, Year 2: $750K revenue, Year 3: $1.5M revenue",
    description="Creates financial projections for a business plan"
)

# Create prompt template
prompt = PromptTemplate.from_template(
    """You are an expert business consultant.
    
    Project Idea: {idea}
    Team Skills: {skills}
    
    Create a comprehensive business plan including:
    1. Executive Summary
    2. Market Analysis
    3. Organization Structure
    4. Financial Projections
    
    Use the tools available to research and provide accurate information.
    """
)

# Create agent
llm = ChatOpenAI(temperature=0.7)
agent = create_react_agent(llm, [market_research_tool, financial_projection_tool], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[market_research_tool, financial_projection_tool], verbose=True)