reminix-llamaindex 0.0.8

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/execute - Execute the agent

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}/execute

Execute the agent with a query or prompt.

Request with query:

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

Request with prompt:

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

Response:

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

Streaming

For streaming responses, set stream: true in the request:

{
  "query": "Tell me about Paris",
  "stream": true
}

The response will be sent as Server-Sent Events (SSE).

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