Why LangChat?

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The problem

• 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

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What LangChat gives you

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)

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

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Compared to alternatives

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Design principles

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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