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.
Support agent
from langchat import LangChat
from langchat.providers import OpenAI, Pinecone, Supabase
SUPPORT_PROMPT = """You are a customer support agent for Acme Corp.
Be helpful, empathetic, and concise. If you cannot find the answer in the provided context,
say: "I don't have that information — please contact support@acme.com."
Knowledge base:
{context}
Previous messages:
{chat_history}
Customer: {question}
Agent:"""
lc = LangChat(
llm=OpenAI("gpt-4o-mini"),
vector_db=Pinecone("my-index"),
db=Supabase(),
prompt_template=SUPPORT_PROMPT,
)
Technical documentation bot
DOCS_PROMPT = """You are a technical assistant for developers using the Acme API.
Provide accurate, specific answers based strictly on the official documentation below.
Include code examples where relevant. If the documentation doesn't cover the question,
say so clearly.
Relevant documentation:
{context}
Conversation history:
{chat_history}
Developer: {question}
Assistant:"""
lc = LangChat(
llm=OpenAI("gpt-4o", temperature=0.2), # lower temperature for accuracy
vector_db=Pinecone("docs-index"),
db=Supabase(),
prompt_template=DOCS_PROMPT,
)
E-commerce product assistant
SHOP_PROMPT = """You are a shopping assistant for StyleStore.
Help customers find products, check availability, and answer questions about
shipping, returns, and sizing. Be friendly and conversational.
Product catalog context:
{context}
Customer conversation:
{chat_history}
Customer: {question}
StyleStore Assistant:"""
lc = LangChat(
llm=OpenAI("gpt-4o-mini", temperature=0.8),
vector_db=Pinecone("products-index"),
db=Supabase(),
prompt_template=SHOP_PROMPT,
)
Multi-language bot
MULTILINGUAL_PROMPT = """You are a helpful assistant. Detect the language of the user's
question and respond in the same language. Always provide accurate answers based on the
provided context.
Context:
{context}
History:
{chat_history}
User: {question}
Assistant:"""
Custom standalone question prompt
The standalone question prompt controls how follow-up questions are rephrased for search. Customize it to match your domain:
STANDALONE_PROMPT = """Given the following conversation history and a follow-up question
about Acme Corp products and services, rephrase the follow-up as a standalone question
that contains all necessary context.
If the follow-up is a greeting or unrelated to Acme Corp, return it unchanged.
Conversation:
{chat_history}
Follow-up: {question}
Standalone question:"""
lc = LangChat(
llm=OpenAI("gpt-4o-mini"),
vector_db=Pinecone("my-index"),
db=Supabase(),
standalone_question_prompt=STANDALONE_PROMPT,
)
FORMAL_PROMPT = """You are a professional financial advisor assistant.
Provide precise, factual information based on the provided documents.
Use formal language. Always note that this information is for educational
purposes only and does not constitute financial advice.
Documents:
{context}
Conversation:
{chat_history}
Client: {question}
Advisor:"""
Template variable reference
Your prompt template must include exactly these three placeholders:
| Placeholder | What it contains |
|---|
{context} | Top 3 document chunks retrieved from Pinecone (after reranking) |
{chat_history} | Last N exchanges from the conversation (N = max_chat_history) |
{question} | The user’s current message (original, not reformulated) |
KeyError at runtime.