> ## Documentation Index
> Fetch the complete documentation index at: https://langchat.neurobrains.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Getting Started

> Build a complete RAG chatbot step by step.

This guide walks through building a real chatbot from scratch — a support bot that answers questions from your documents.

## Step 1 — Install and configure

Install LangChat and create your `.env` file:

```bash theme={null}
pip install langchat
```

```bash .env theme={null}
OPENAI_API_KEY=sk-...
PINECONE_API_KEY=pcsk-...
SUPABASE_URL=https://xxxx.supabase.co
SUPABASE_KEY=eyJhbGc...
```

## Step 2 — Index your documents

Before the chatbot can answer questions, it needs to read your content. Put your documents (PDFs, text files, CSVs) in a folder and index them:

```python theme={null}
# index_docs.py
from langchat import LangChat
from langchat.providers import OpenAI, Pinecone, Supabase

LangChat.load_env()

lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
)

# Index a single file
result = lc.index("docs/faq.pdf")
print(f"Indexed {result['chunks_indexed']} chunks")

# Or index an entire folder
result = lc.index("docs/")
print(f"Indexed {result['chunks_indexed']} chunks, skipped {result['chunks_skipped']} duplicates")
```

Run this once (or whenever your documents change):

```bash theme={null}
python index_docs.py
```

<Note>
  LangChat automatically detects duplicate chunks using a content hash, so re-running `index()` on the same files is safe.
</Note>

## Step 3 — Build the chatbot

```python theme={null}
# chatbot.py
import asyncio
from langchat import LangChat, ChatResponse
from langchat.providers import OpenAI, Pinecone, Supabase

LangChat.load_env()

lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
)

async def chat_with_user(user_id: str):
    print("Support Bot ready. Type 'quit' to exit.\n")

    while True:
        query = input("You: ").strip()
        if query.lower() == "quit":
            break

        response: ChatResponse = await lc.chat(
            query=query,
            user_id=user_id,
        )

        if response:
            print(f"Bot: {response.text}")
            print(f"     ({response.response_time:.2f}s)\n")
        else:
            print(f"Bot: Sorry, something went wrong. ({response.error})\n")

asyncio.run(chat_with_user("alice"))
```

## Step 4 — Handle multiple users

Each `user_id` gets its own conversation history. Use `platform` to separate different applications sharing the same backend:

```python theme={null}
# User in mobile app
response = await lc.chat(
    query="What's my order status?",
    user_id="user_123",
    platform="mobile-app",
)

# Same user in web app — separate conversation
response = await lc.chat(
    query="What's my order status?",
    user_id="user_123",
    platform="web-app",
)
```

## Step 5 — Add a custom persona

Make the bot speak in your brand voice by customizing the prompt:

```python theme={null}
lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
    prompt_template="""You are Aria, a friendly support agent for Acme Corp.
Always be polite and professional. If you don't know the answer, say so clearly.

Context from our knowledge base:
{context}

Conversation so far:
{chat_history}

Customer question: {question}

Aria's response:""",
)
```

The three template variables `{context}`, `{chat_history}`, and `{question}` are filled in automatically.

## Step 6 — Deploy as an API

Turn your chatbot into a production REST API in one step:

```python theme={null}
# server.py
from langchat.api import create_app
from langchat.providers import OpenAI, Pinecone, Supabase
import uvicorn

app = create_app(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
)

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)
```

```bash theme={null}
python server.py
```

Your API exposes:

| Endpoint        | Description      |
| --------------- | ---------------- |
| `POST /chat`    | Send a message   |
| `GET /health`   | Health check     |
| `GET /frontend` | Built-in chat UI |

## What's next

<CardGroup cols={2}>
  <Card title="Configuration" icon="gear" href="/guides/configuration">
    Switch LLM providers, configure Pinecone namespaces, tune history length
  </Card>

  <Card title="Custom Prompts" icon="pen" href="/guides/prompts">
    Full guide to prompt templating and standalone question customization
  </Card>

  <Card title="Document Indexing" icon="database" href="/guides/document-indexing">
    Supported file formats, chunking strategy, namespace organization
  </Card>

  <Card title="API Server" icon="server" href="/examples/api-server">
    Production server setup, CORS, Docker, environment configuration
  </Card>
</CardGroup>