llama-index-memory-infolang 0.1.0

InfoLang memory integration for LlamaIndex

LlamaIndex Memory Integration: InfoLang

InfoLang is a hosted semantic memory API: remember stores text into a namespace, recall/recall_hybrid retrieves the most relevant chunks for a query. This package adapts it to LlamaIndex's BaseMemory interface.

Installation

Once published to PyPI, installation will be:

pip install llama-index-memory-infolang

Note: PyPI publication of this package and of its infolang>=0.2.0 dependency is pending. Until then, install both from source:

pip install git+https://github.com/InfoLang-Inc/llama-index-memory-infolang.git

Usage

from llama_index.memory.infolang import InfoLangMemory

memory = InfoLangMemory.from_api_key(
    api_key="il_live_...",
    namespace="user-123",  # optional; falls back to the key's default namespace
    top_k=5,  # optional, default is 5
)

You can also pass an already-constructed infolang.InfoLang client, e.g. to point at a self-hosted runtime or reuse an existing client:

from infolang import InfoLang
from llama_index.memory.infolang import InfoLangMemory

client = InfoLang(dev_key="il_dev_...", base_url="http://127.0.0.1:8766")
memory = InfoLangMemory.from_client(client, namespace="user-123")

Each put/set call writes new user/assistant messages to InfoLang via remember. Each get call folds the current turn (or an explicit input string) into an InfoLang recall, and prepends the recalled chunks to the local chat history as a system message — local chat history itself is kept in-process and is not read back from InfoLang, so reset() only clears the local buffer; it does not delete anything server-side.

With a chat engine

import os
from llama_index.llms.openai import OpenAI
from llama_index.core import SimpleChatEngine

os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
llm = OpenAI(model="gpt-4o")

chat_engine = SimpleChatEngine.from_defaults(llm=llm, memory=memory)
response = chat_engine.chat("Hi, my name is Mayank")
print(response)

With an agent

from llama_index.core.agent.workflow import FunctionAgent

agent = FunctionAgent(tools=[...], llm=llm)
response = await agent.run("Hi, my name is Mayank", memory=memory)
print(response)

Note: For a full walkthrough see the example notebook.

References

• InfoLang
• InfoLang Python SDK