> ## 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.

# Why LangChat?

> The problem LangChat solves and the time it saves.

## The problem

Building a production-grade RAG chatbot requires integrating many moving parts:

* An LLM provider (and handling retries, key rotation, rate limits)
* A vector database for semantic search
* An embedding pipeline to index your documents
* A reranker to improve search precision
* A conversation memory system
* A database to persist chat history
* A REST API to expose it all
* Session management per user and per application

Done from scratch, this takes **weeks**. LangChat does it in minutes.

## What LangChat gives you

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

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

response = await lc.chat(query="What's our refund policy?", user_id="alice")
print(response)
```

That single `chat()` call:

1. Rephrases the question as a standalone query (handles "it", "that", follow-ups)
2. Embeds the question and searches Pinecone
3. Reranks the top results with Flashrank
4. Builds a prompt combining context + conversation history
5. Calls the LLM
6. Saves the exchange to Supabase (non-blocking, in the background)
7. Returns a typed `ChatResponse` with `.text`, `.status`, `.response_time`

## Compared to alternatives

| Feature                | LangChat          | Raw LangChain | LlamaIndex    | Building from scratch |
| ---------------------- | ----------------- | ------------- | ------------- | --------------------- |
| RAG pipeline           | Built-in          | Manual        | Manual        | Manual                |
| Session management     | Built-in          | Manual        | Manual        | Manual                |
| Multiple LLM providers | 6 built-in        | Via community | Via community | Manual                |
| API server             | One function call | Manual        | Manual        | Manual                |
| Chat history storage   | Built-in          | Manual        | Manual        | Manual                |
| Reranking              | Built-in          | Manual        | Manual        | Manual                |
| Document indexing      | `lc.index(path)`  | Manual        | Manual        | Manual                |
| Typed responses        | `ChatResponse`    | dict          | dict          | Manual                |
| Time to first chatbot  | **5 minutes**     | Days          | Days          | Weeks                 |

## Design principles

**Environment variables first.** Every provider reads credentials from the environment. No keys in code, no keys in config files.

**Sensible defaults.** Flashrank reranker, `text-embedding-3-large`, 20-message history window — everything works without any configuration.

**Hexagonal architecture.** Core logic is isolated from adapters. Swap out any provider without touching business logic.

**Async-first, sync available.** All chat methods are async for high-throughput APIs, with sync wrappers for scripts and notebooks.

**Typed responses.** `ChatResponse` is a dataclass — use `response.text`, `if response:`, `print(response)`. No more `result["response"]` dict access.

## Who uses LangChat

* **SaaS companies** — add AI chat to their product without a dedicated ML team
* **Enterprise teams** — chatbots over internal documents, wikis, and knowledge bases
* **Agencies** — spin up white-label chatbots for clients in hours
* **Solo developers** — ship RAG products without deep ML knowledge