29 lines
4.5 KiB
YAML
29 lines
4.5 KiB
YAML
name: qdrant
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repo: https://github.com/kuehnberger/hermes-qdrant-memory
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sha: b2475f7ca020bd6f847a3e74a1e611b1b99a16a1
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version: "0.1.7"
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image: https://raw.githubusercontent.com/kuehnberger/hermes-qdrant-memory/b2475f7ca020bd6f847a3e74a1e611b1b99a16a1/docs/banner.jpg
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description: "Dense semantic memory for Hermes, stored in a Qdrant collection you host. Each turn becomes one point — user and assistant text verbatim, no LLM summarization step — embedded on-device at 384 dimensions with fastembed (ONNX; the default install is 206 MB of runtime dependencies with no torch, versus ~1.2 GB when sentence-transformers was a hard dependency) and recalled by cosine search with an optional session_id payload filter, so a result quotes what was actually said. Registers 7 tools: qdrant_search, qdrant_upsert, qdrant_recall, qdrant_collect, qdrant_prepare, qdrant_forget (point-targeted deletion, dry-run by default, no delete-all), and md_search (local markdown knowledge index). An optional progress display (off/minimal/verbose) reports stored and recalled counts in the CLI. sentence-transformers is selectable on GPU hosts; both backends produce identical vectors for all-MiniLM-L6-v2, so switching backends needs no re-embedding. A circuit breaker (5 consecutive I/O failures, 120 s cooldown) degrades to no new memories instead of crashing, and is_available()/check_backend()/unavailable_reason() distinguish a misconfiguration from a dead server. Hybrid dense+sparse RRF search and INT8 quantization exist in _backend.py but are not on the live path. Local embedded Qdrant mode is not supported: the provider is REST-only and needs a reachable server. Disclosure — all memory content (verbatim user and assistant turns) is stored in and read back from the Qdrant server at the configured url (default http://localhost:6333); QDRANT_API_KEY is only needed for Qdrant Cloud, is kept in .env and read through the Hermes secret scope, and is never written to config.yaml or qdrant.json. Disclosure — the only other outbound traffic is a one-time download of the embedding model weights from huggingface.co (fastembed falls back to storage.googleapis.com/qdrant-fastembed if that fails); embedding runs on-device and no memory content goes to any other party. Disclosure — settings are stored in <HERMES_HOME>/qdrant.json (created 0600, no credentials) and last-operation status in <HERMES_HOME>/qdrant-status.json. Disclosure — md_search returns snippets of local markdown to the model from ~/.hermes/skills, the Obsidian vault at ~/Documents/hermes_default, ~/.hermes/hermes-agent/website and any path in the md_docs_roots key of qdrant.json; its index is built only when you run scripts/md_ingest.py and lives in <base Hermes home>/state/md-search/ (shared by all profiles). Disclosure — the optional semantic tier (md_ingest.py --semantic, and md_search queries with few lexical hits) sends embedding vectors plus file-path labels and section headings, never chunk text, to a separate hermes_md_docs collection on the Qdrant server named by the QDRANT_URL env var (default http://localhost:6333, no API key), and downloads paraphrase-multilingual-MiniLM-L12-v2 from huggingface.co on first use. Disclosure — the plugin is not a server and registers no MCP server, but it is loaded in-process into every Hermes process that embeds, so each one pays its own model copy (measured ~194-211 MB per process)."
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maintainer: kuehnberger
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tier: community
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category: memory
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requires_hermes: ">=0.21.5"
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docs_url: https://github.com/kuehnberger/hermes-qdrant-memory#readme
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known_issues:
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- "Windows on ARM is unsupported: grpcio, pulled in by qdrant-client, has no win_arm64 wheel. Linux and macOS on x86_64/arm64 and Windows x86_64 are supported."
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- "Local embedded Qdrant mode is not supported — the provider is REST-only and requires a reachable server."
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- "md_search returns 'no markdown index yet' until you run the bundled ingest script once (python scripts/md_ingest.py, lexical only and fast; add --semantic for the embedding tier, which takes about 7 minutes for 5500 chunks on a 5-core CPU). No background process does this for you."
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- "Running the plugin's pytest suite inside an installed copy still changes the PM dependency stamp (the .pytest_cache directory is hashed as a build input); only an upstream core change can exclude gitignored state."
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capabilities:
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provides_tools:
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- qdrant_search
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- qdrant_upsert
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- qdrant_recall
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- qdrant_collect
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- qdrant_prepare
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- qdrant_forget
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- md_search
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provides_hooks: []
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provides_middleware: []
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requires_env: []
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