Weknora Graph RAG & Hybrid Retrieval Engine

@weknora/knowledge-base · v1.5.0

An enterprise hybrid RAG engine combining dynamic Knowledge Graphs with dense vector retrieval. Delivers multi-hop reasoning, relationship mining, and chunk deduplication for zero-hallucination Q&A.

Graph-RAGHybrid-SearchVectorDBKnowledgeBase
GitHub Stars
61 K+
+12.4% this month
Monthly Downloads
278 K+
Monthly registry pulls
Reach Score
99.4/ 100
Top Tier Ecosystem
Runtime
Cordis v3+
Node 18+ / Bun / Deno

Installation & Integration

CLI one-click launch, package managers, and Cordis integration

bash
manager:
$ pnpm add @weknora/knowledge-base

Architecture

Weknora provides DeepSeek Harness agents with an enterprise-grade factual foundation. Traditional vector similarity search often fragments logical relationships, faltering when queries demand multi-hop reasoning across interconnected documents. This plugin implements a dual-layer hybrid architecture: dense vector embedding for rapid semantic candidate filtering, coupled with graph topological traversal for relational inference. It turns messy internal wikis and API manuals into clean, queryable knowledge graphs with verbatim source attribution.

Architectural Principles & Constraints

01
Strict Type Isolation

Guaranteed by TypeScript compile-time contracts, inter-plugin event bus calls enjoy zero-drift safety.

02
Sub-Millisecond Hot Reload

Supports dynamic runtime mounting and graceful unloading without restarting the primary host process.

03
Deterministic State Machine

Embeds multi-phase execution lifecycle guards, preventing context loss during long-horizon reasoning.

04
Zero Native Build Dependencies

Designed for lightweight cross-platform environments, booting instantly across Node.js, Bun, and Deno.

Core Features

01
Hybrid Graph + Vector Search: Unites dense embeddings with topological relation traversal
02
Automated Entity Disambiguation: Normalizes technical synonyms and aliases to prevent graph drift
03
Verifiable Line-Level Citations: Every synthesized claim anchors directly to source file line numbers
04
Incremental Live Updates: Rebuilds local subgraphs instantaneously without full re-indexing

Core Workflow

01

Heterogeneous Ingestion

Parses Markdown, PDF, and code repositories into semantic AST chunks.

02

Entity-Relation Extraction

Extracts named entities and dependency edges into a property graph database.

03

Hybrid Dual Retrieval

Executes dense vector search and multi-hop graph walks with RRF reranking.

04

Attributed Synthesis

Injects reranked evidence into context, generating verifiable answers with citations.

Configuration Parameters Reference (YAML / JSON)

ParameterTypeDefaultDescription
retrievalModestringhybridRetrieval strategy mode
maxHopCountnumber2Maximum graph traversal hops
topKnumber5Number of top reranked chunks injected

Use Cases

Enterprise Production Agent

Relies on microkernel lifecycle guards and fault-tolerant state machines for continuous reliability.

SWE-bench Benchmark Evaluation

Native integration with SWE-bench workflows, automatically capturing diffs and verification metrics.

Autonomous Code Refactoring

Separates reasoning from tool actions to independently locate and refactor multi-file codebases.

Cross-Tool Workflow Automation

Safely orchestrates events across sandboxes to seamlessly link enterprise developer tooling.

Best Practices

01
Sandbox Permission Guard

Strictly isolate sub-process calls and network scope; deploy within Docker containers in production.

02
Exponential Backoff Retries

Configure adaptive exponential retries with strict timeouts to mitigate upstream model rate limits.

03
Session State Checkpointing

Persist state machine snapshots to survive hardware interruptions and resume instantly without loss.

04
Full Trajectory Audit Logs

Enable full trace logging, aggregating reasoning thought streams and tool I/O into your observability hub.

FAQ

Q1:How to handle timeouts in long-running autonomous tasks?

Increase the timeout parameter inside your YAML configuration and dispatch periodic heartbeat signals across the Cordis event bus. For long-running tool execution and model reasoning, configure persistent session snapshotting so suspended tasks can safely resume their exact context after interruptions, preventing the kernel from recycling active agent sessions prematurely.

Q2:How to capture and stream the model reasoning thought process?

The harness runtime natively provides end-to-end streaming hooks while its state machine automatically intercepts and strips <think> reasoning tags from model responses. Subscribe directly to the onThink event listener to consume live reasoning token streams in real-time, delivering typewriter animation to the user interface while persisting full trajectories for audit compliance.

Q3:How to resolve dependency conflicts across multiple plugins?

Cordis microkernel uses directed acyclic graph topological sorting to dynamically resolve plugin dependencies. When shared services or runtime versions conflict, assign distinct isolated namespaces at the application entrypoint. Leveraging context injection alongside lazy activation ensures dependencies load strictly on-demand when tools are triggered, maintaining system stability and preventing memory bloat.

Q4:How to enforce permissions and sandbox isolation in production?

Combine isolated container sandboxing with fine-grained capability checks to prevent plugins from accessing sensitive files or unauthorized external networks. Enforce explicit runtime system call whitelisting through the microkernel, executing all third-party tool scripts inside isolated ephemeral containers to block arbitrary code execution and eliminate security privilege escalation risks entirely.

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