Documentation Index Fetch the complete documentation index at: https://docs.rippletide.com/llms.txt
Use this file to discover all available pages before exploring further.
Prerequisites Before chatting, you need an agent with knowledge configured. Follow the Create your Hypergraph guide first to get your agent_id.
Chat via the SDK API Send a message and get a response:
import os import uuid import requests API_KEY = os.environ[ "RIPPLETIDE_API_KEY" ] BASE_URL = "https://agent.rippletide.com/api/sdk" headers = { "x-api-key" : API_KEY , "Content-Type" : "application/json" } agent_id = "your-agent-id" # from the agent creation step conversation_id = str (uuid.uuid4()) # one ID per conversation session response = requests.post( f " { BASE_URL } /chat/ { agent_id } " , headers = headers, json = { "user_message" : "What products can I order?" , "conversation_uuid" : conversation_id }) print (response.json()[ "answer" ])
Use the same conversation_uuid for follow-up messages in the same conversation. Generate a new UUID to start a fresh session.
API Reference : Chat API LangChain Integration This feature is experimental.
You can use your Rippletide agent directly as a LLM in LangChain and LangGraph. This lets you replace your current LLM (e.g. ChatGPT) with a hallucination-free Rippletide agent in a few lines.
Installation pip install langchain langchain-openai
Usage Rippletide exposes an Azure OpenAI-compatible endpoint, so you can use AzureChatOpenAI from LangChain:
import os import uuid from langchain_core.messages import SystemMessage, HumanMessage from langchain_openai import AzureChatOpenAI API_KEY = os.environ[ "RIPPLETIDE_API_KEY" ] agent_id = "your-agent-id" # from the agent creation step conversation_id = str (uuid.uuid4()) rippletide_llm = AzureChatOpenAI( model = "v1" , api_key = API_KEY , azure_endpoint = "https://agent.rippletide.com" , azure_deployment = "v1" , api_version = "2024-12-01-preview" , openai_api_type = "azure" , default_headers = { "x-rippletide-agent-id" : agent_id, "x-rippletide-conversation-id" : str (conversation_id), }, ) messages = [ SystemMessage( content = "You are a helpful assistant." ), HumanMessage( content = "What products can I order?" ) ] response = rippletide_llm.invoke(messages) print (response.content)
Use in a LangChain chain Once initialized, rippletide_llm works like any other LangChain LLM:
from langchain.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages([ ( "system" , "You are a helpful assistant for an electronics store." ), ( "human" , " {question} " ) ]) chain = prompt | rippletide_llm result = chain.invoke({ "question" : "How long does delivery take?" }) print (result.content)
Next Steps
Evaluate your agent Test for hallucinations before deploying to production
Full Developer Guide Complete API reference with advanced examples
