aeee0d02e2
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Viktor wanted real semantic search over his ~300 Paperless documents and preferred a ready-made solution over building one. paperless-ai provides local-embedding RAG (ChromaDB + sentence-transformers, GPU-free) plus LLM-driven auto-analysis/tagging. Wiring: - LLM (chat answers + tagging) -> in-cluster llama-swap qwen3-8b (OpenAI-compatible); embeddings + vector store are local on the PVC. - Reads Paperless over the internal service via a dedicated `paperless-ai` superuser token (Vault secret/paperless-ai); app-admin creds also in Vault. - Encrypted PVC for /app/data (SQLite + ChromaDB + model cache). - Ingress paperless-ai.viktorbarzin.me behind Authentik (auth=required). - Third-party image pinned (docker.io/clusterzx/paperless-ai:3.0.9), no Keel. Runtime config persists to the PVC .env via the app's one-time setup; the deployment env vars are pre-fill/documentation only. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
26 lines
581 B
HCL
26 lines
581 B
HCL
include "root" {
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path = find_in_parent_folders()
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}
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dependency "platform" {
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config_path = "../platform"
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skip_outputs = true
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}
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dependency "vault" {
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config_path = "../vault"
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skip_outputs = true
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}
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# Reads the Paperless API over the in-cluster service.
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dependency "paperless-ngx" {
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config_path = "../paperless-ngx"
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skip_outputs = true
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}
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# LLM (chat/answer generation + auto-tagging) is served by llama-swap's
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# OpenAI-compatible endpoint; embeddings/semantic search are local in-pod.
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dependency "llama-cpp" {
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config_path = "../llama-cpp"
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skip_outputs = true
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}
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