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Simple Chat

Basic example: 
import asyncio
from langchat import LangChat
from langchat.llm import OpenAI
from langchat.vector_db import Pinecone
from langchat.database import Supabase

async def main():
    llm = OpenAI(api_key="sk-...", model="gpt-4o-mini")
    vector_db = Pinecone(api_key="...", index_name="...")
    db = Supabase(url="https://...", key="...")
    
    ai = LangChat(llm=llm, vector_db=vector_db, db=db)
    
    result = await ai.chat(
        query="Hello! What can you help me with?",
        user_id="user123"
    )
    print(result["response"])

asyncio.run(main())

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Conversation

LangChat remembers previous messages: 
import asyncio
from langchat import LangChat
from langchat.llm import OpenAI
from langchat.vector_db import Pinecone
from langchat.database import Supabase

async def main():
    llm = OpenAI(api_key="sk-...", model="gpt-4o-mini")
    vector_db = Pinecone(api_key="...", index_name="...")
    db = Supabase(url="https://...", key="...")
    
    ai = LangChat(llm=llm, vector_db=vector_db, db=db)
    user_id = "user123"
    
    # First message
    result1 = await ai.chat(
        query="What universities offer computer science?",
        user_id=user_id
    )
    
    # Second message (remembers context)
    result2 = await ai.chat(
        query="What about in Europe?",
        user_id=user_id
    )
    
    # Third message (continues conversation)
    result3 = await ai.chat(
        query="Which accept IELTS 6.5?",
        user_id=user_id
    )

asyncio.run(main())
LangChat automatically maintains conversation history for each user. No manual memory management needed!

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Custom Prompts

Control how your chatbot responds: 
from langchat import LangChat
from langchat.llm import OpenAI
from langchat.vector_db import Pinecone
from langchat.database import Supabase

llm = OpenAI(api_key="sk-...", model="gpt-4o-mini")
vector_db = Pinecone(api_key="...", index_name="...")
db = Supabase(url="https://...", key="...")

# Custom system prompt
custom_prompt = """You are a helpful assistant. 
Answer questions clearly and concisely.
Always be friendly and professional."""

ai = LangChat(
    llm=llm,
    vector_db=vector_db,
    db=db,
    prompt_template=custom_prompt
)

result = await ai.chat(
    query="What is Python?",
    user_id="user123"
)

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Error Handling

Handle errors gracefully: 
import asyncio
from langchat import LangChat
from langchat.llm import OpenAI
from langchat.vector_db import Pinecone
from langchat.database import Supabase

async def main():
    try:
        llm = OpenAI(api_key="sk-...", model="gpt-4o-mini")
        vector_db = Pinecone(api_key="...", index_name="...")
        db = Supabase(url="https://...", key="...")
        
        ai = LangChat(llm=llm, vector_db=vector_db, db=db)
        
        result = await ai.chat(
            query="Hello!",
            user_id="user123"
        )
        
        if result["status"] == "success":
            print(result["response"])
        else:
            print(f"Error: {result.get('error')}")
            
    except Exception as e:
        print(f"Error: {e}")

asyncio.run(main())

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Next Steps

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