qortex 0.2.0

Knowledge graph ingestion engine for automated rule generation

qortex

Graph-enhanced retrieval engine. Transforms unstructured content into a knowledge graph with typed edges, then uses Personalized PageRank for structurally-aware search with feedback-driven learning.

Features

• Graph-enhanced retrieval: Vector similarity + PPR over typed edges (REQUIRES, REFINES, USES...)
• Explore and navigate: Traverse typed edges, discover neighbors and linked rules from any search result
• Rules auto-surfaced: Query results include linked rules with relevance scores, zero consumer effort
• Feedback-driven learning: Consumer outcomes adjust PPR teleportation factors; results improve over time
• Framework adapters: Drop-in for LangChain, Mastra, and any MCP client
• Flexible ingestion: PDF, Markdown, and text sources into a unified knowledge graph
• Projection pipeline: Source, Enricher, Target architecture for rule generation
• Multiple backends: InMemory (testing), Memgraph (production with MAGE algorithms)

Quick start

pip install qortex

# With optional dependencies
pip install qortex[vec]       # numpy + sentence-transformers
pip install qortex[mcp]       # MCP server
pip install qortex[memgraph]  # Memgraph backend
pip install qortex[all]       # Everything

Search, explore, learn

from qortex.client import LocalQortexClient

client = LocalQortexClient(vector_index, backend, embedding, mode="graph")

# Search: vec + graph combined scoring, rules auto-surfaced
result = client.query("OAuth2 authorization", domains=["security"], top_k=5)

# Explore: traverse typed edges from any result
explore = client.explore(result.items[0].node_id)
for edge in explore.edges:
    print(f"{edge.source_id} --{edge.relation_type}--> {edge.target_id}")

# Feedback: close the learning loop
client.feedback(result.query_id, {result.items[0].id: "accepted"})

LangChain VectorStore

from langchain_qortex import QortexVectorStore

vs = QortexVectorStore.from_texts(texts, embedding, domain="security")
docs = vs.similarity_search("authentication", k=5)
retriever = vs.as_retriever()

See langchain-qortex for the standalone package.

Mastra MastraVector

import { QortexVector } from "@peleke/mastra-qortex";

const qortex = new QortexVector({ id: "qortex" });
await qortex.createIndex({ indexName: "docs", dimension: 384 });
await qortex.upsert({ indexName: "docs", vectors: embeddings, metadata });
const results = await qortex.query({ indexName: "docs", queryVector: q, topK: 10 });

See @peleke/mastra-qortex for the standalone package.

MCP server

qortex mcp-serve  # stdio transport, works with Claude Desktop / any MCP client

Tools: qortex_query, qortex_explore, qortex_rules, qortex_feedback, qortex_ingest, qortex_domains, qortex_status, plus 9 vector-level tools (qortex_vector_*).

Project rules

from qortex.projectors.projection import Projection
from qortex.projectors.sources.flat import FlatRuleSource
from qortex.projectors.targets.buildlog_seed import BuildlogSeedTarget

projection = Projection(
    source=FlatRuleSource(backend=backend),
    target=BuildlogSeedTarget(persona_name="my_rules"),
)
result = projection.project(domains=["my_domain"])

Framework adapters

Framework	Package	Language	Interface
LangChain	langchain-qortex	Python	VectorStore ABC
Mastra	@peleke/mastra-qortex	TypeScript	MastraVector abstract class
Any MCP client	Built-in MCP server	Any	MCP tools (JSON-RPC)

Architecture

Sources (PDF/MD/Text)
  -> Ingestion (LLM extraction -> concepts + typed edges + rules)
    -> Knowledge Graph (InMemoryBackend | Memgraph)
      -> VectorIndex (NumpyVectorIndex | SqliteVecIndex)
        -> Retrieval (PPR + cosine -> combined scoring)
          -> Consumers (LangChain, Mastra, MCP, buildlog, agents)

Documentation

• Full docs
• LangChain integration
• Mastra integration

License

MIT