knowlytix-knowledge

Geometric expert system with LLM-augmented learning. Ingest documents, query a geometric memory back-end, verify LLM answers against provable graph traversals, and grow the store over time.

knowlytix-knowledge is one of four packages in the Geometric Memory Systems family. It pairs a structured geometric knowledge store (knowlytix-core) with LLM reasoning, and writes verified outputs back into the store so the expert system improves with use.

• Package: knowlytix-knowledge
• License: Apache-2.0
• Python: 3.12+
• Status: alpha (v0.x)

Install

pip install knowlytix-knowledge

knowlytix-knowledge depends on knowlytix-core and knowlytix-benchmark (pinned to the same version under lockstep versioning) and routes every LLM call through LiteLLM — configure the provider of your choice (Anthropic, OpenAI, Bedrock, Azure, Ollama, …) via environment variables.

Quickstart

Ingest a document, train the geometric store, and run a query end-to-end. The snippet below uses the smoke-test fixture shipped in the wheel, so no external data is required.

import torch
from importlib.resources import files

from knowlytix.knowledge import (
    DocGMSConfig, GMSExpertStore, QueryEngine,
    ingest_document,
)
from knowlytix.knowledge.llm_backend import create_backend

# 1. Config + LLM backend (reads GMS_LLM_MODEL + provider key from env).
config = DocGMSConfig(store_path="./my_store")
llm = create_backend(config.convert)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 2. Fresh store + ingest the bundled sample document.
store = GMSExpertStore(config, device=device)
sample = files("knowlytix.knowledge.fixtures.smoke") / "sample.md"
ingest_document(store, str(sample), llm, config, device)

# 3. Query the expert system.
engine = QueryEngine(store, llm, config)
result = engine.query("Which divisions report to the Chief Operations Officer?")
print(result.answer)
print(f"source={result.source} confidence={result.confidence:.2f}")

The first ingestion trains the geometric knowledge graph from the document's triples. Subsequent ingestions grow the store incrementally.

Configuration

All tuning knobs read from environment variables (12-factor style). Copy .env.example from the repo and override only what you need.

DOCGMS_* — ingestion and verification

GMS_LLM_* — LLM routing (from knowlytix-core)

Provider API keys

Set exactly one set, matching your GMS_LLM_MODEL:

• Anthropic: ANTHROPIC_API_KEY
• OpenAI: OPENAI_API_KEY
• Bedrock: AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY + AWS_REGION
• Azure: AZURE_API_KEY + AZURE_API_BASE + AZURE_API_VERSION
• Ollama: OLLAMA_BASE_URL (no key — runs locally)

Missing keys surface an actionable LLMConfigError naming the exact env vars required for your selected model.

Public API

Import from the top-level package (see __all__):

from knowlytix.knowledge import (
    ConvertConfig, DocGMSConfig, DocGMSSettings,
    GMSExpertStore, IngestResult, QueryEngine, QueryResult,
    ingest_document,
)

Anything outside __all__ is internal and may change without notice. The non-shipped knowlytix.knowledge.mcp_server, knowlytix.knowledge.web_agent, and knowlytix.knowledge.cli modules live in the source repo but do not land in the wheel.

Related packages

Links

• Source: knowlytix/gmsh
• Book: Geometric Memory Systems (forthcoming)
• Paper: DocGMS: Geometric Expert Systems with LLM-Augmented Learning