reminix-llamaindex 0.0.7

Reminix adapter for LlamaIndex - serve agents as REST APIs

reminix-llamaindex

Reminix Runtime adapter for LlamaIndex. Serve LlamaIndex chat engines as a REST API.

Ready to go live? Deploy to Reminix Cloud for zero-config hosting, or self-host on your own infrastructure.

Installation

pip install reminix-llamaindex

This will also install reminix-runtime as a dependency.

Quick Start

from llama_index.core.chat_engine import SimpleChatEngine
from llama_index.llms.openai import OpenAI
from reminix_llamaindex import serve_agent

llm = OpenAI(model="gpt-4o")
engine = SimpleChatEngine.from_defaults(llm=llm)
serve_agent(engine, name="my-chatbot", port=8080)

For more flexibility (e.g., serving multiple agents), use wrap_agent and serve separately:

from llama_index.core.chat_engine import SimpleChatEngine
from llama_index.llms.openai import OpenAI
from reminix_llamaindex import wrap_agent
from reminix_runtime import serve

llm = OpenAI(model="gpt-4o")
engine = SimpleChatEngine.from_defaults(llm=llm)
agent = wrap_agent(engine, name="my-chatbot")
serve(agents=[agent], port=8080)

Your agent is now available at:

• POST /agents/my-chatbot/invoke - Stateless invocation
• POST /agents/my-chatbot/chat - Conversational chat

API Reference

serve_agent(engine, name, port, host)

Wrap a LlamaIndex chat engine and serve it immediately. Combines wrap_agent and serve for single-agent setups.

Parameter	Type	Default	Description
engine	BaseChatEngine	required	A LlamaIndex chat engine
name	str	"llamaindex-agent"	Name for the agent (used in URL path)
port	int	8080	Port to serve on
host	str	"0.0.0.0"	Host to bind to

wrap_agent(engine, name)

Wrap a LlamaIndex chat engine for use with Reminix Runtime. Use this with serve from reminix_runtime for multi-agent setups.

Parameter	Type	Default	Description
engine	BaseChatEngine	required	A LlamaIndex chat engine
name	str	"llamaindex-agent"	Name for the agent (used in URL path)

Returns: LlamaIndexAgentAdapter - A Reminix adapter instance

Example with RAG

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from reminix_llamaindex import wrap_agent
from reminix_runtime import serve

# Load documents and create index
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)

# Create a chat engine with the index
engine = index.as_chat_engine(llm=OpenAI(model="gpt-4o"))

# Wrap and serve
agent = wrap_agent(engine, name="rag-chatbot")
serve(agents=[agent], port=8080)

Endpoint Input/Output Formats

POST /agents/{name}/invoke

Stateless invocation for task-oriented operations.

Request:

{
  "input": {
    "query": "What is the capital of France?"
  }
}

Or with prompt:

{
  "input": {
    "prompt": "Summarize this text: ..."
  }
}

Response:

{
  "output": "The capital of France is Paris."
}

POST /agents/{name}/chat

Conversational chat. The adapter extracts the last user message.

Request:

{
  "messages": [
    {"role": "user", "content": "What is the capital of France?"}
  ]
}

Response:

{
  "output": "The capital of France is Paris.",
  "messages": [
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."}
  ]
}

Runtime Documentation

For information about the server, endpoints, request/response formats, and more, see the reminix-runtime package.

Deployment

Ready to go live?

• Deploy to Reminix Cloud - Zero-config cloud hosting
• Self-host - Run on your own infrastructure

Links

• GitHub Repository
• LlamaIndex Documentation

License

Apache-2.0