QRAS Semantic Memory

Name: QRAS Semantic Memory
Author: anvie

Checked

anvie·v1.0.0·ai-ml·MIT

Semantic memory search using Qdrant + Ollama embeddings (QRAS). Use as primary method for recalling workspace files, notes, decisions, and prior work before falling back to built-in search.

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Run this in your terminal
terminal
```
$ openclaw skills install qras
```

What we know about this skill

Publisher

anvie

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Pinned to a1b6e5a0

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Automated safety checks

scanned 5 Oct 2026

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

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About this skill

What it does

The Qdrant RAG System is a comprehensive Retrieval-Augmented Generation (RAG) system that combines semantic search with LLM generation. It provides both command-line interface (CLI) tools and a web interface for indexing documents, performing searches, and engaging in context-aware chat powered by Large Language Models (LLMs). The system supports multiple data sources and is designed to produce token-efficient output optimized for AI agents.

When to use it

Knowledge Base Search: Quickly find relevant information within a collection of documents when needing answers or insights. For example, searching internal documentation for specific procedures.
Chatbot Development: Build chatbots that can answer questions based on your own data, providing more accurate and contextually relevant responses than relying solely on the LLM's pre-existing knowledge.
Research & Analysis: Efficiently explore large datasets of text documents to identify patterns, trends, or specific information.
Internal Tooling: Create a searchable interface for internal teams to access company policies, training materials, or other critical resources.

Key capabilities

CLI Interface for indexing and searching
Semantic Vector Search
RAG Chat System with LLM integration
Support for JSON files and markdown directories as data sources
Hybrid Search (vector similarity + keyword matching)
Incremental Indexing of documents
LLM-Optimized Output format
Interactive search and chat interfaces
Streaming responses from the LLM
Web Interface with FastAPI backend and Svelte frontend

Example prompts

./qras query "machine learning algorithms" - Performs a semantic search for information on machine learning algorithms.
./qras chat "What is neural network?" --chat-model llama3 – Initiates an interactive chat session using the Llama3 model to answer questions about neural networks.
./qras index --input-path ./documents --collection docs - Indexes all files within the 'documents' directory into a collection named 'docs'.

Tips & gotchas

Prerequisites: Requires Python 3.9+, Ollama, and Qdrant to be installed and running before use. Ensure these dependencies are properly configured prior to attempting any indexing or searching operations.

Requirements

Required binaries

python3