haylanandClaude-Bot
f508f5670a
- search_tools block in litellm-config.yaml (SearXNG as a first-class search_provider, standalone /v1/search endpoint, not a model tool) plus extra_hosts on the litellm service so it can resolve search.home. - New embedding-server (nomic-embed-text-v1.5 on a second llama.cpp instance), pgvector-db, and litellm-pgvector services — LiteLLM's native knowledgebase feature has no Qdrant backend, so this is the only self-hosted path (docs/research/litellm-knowledgebase.md). - vector_store_registry + local-embedding model entry in litellm-config.yaml, wiring it together. - scripts/ingest-memory.sh to load data/memory.md and data/claude-legacy-memory.md into the knowledgebase. - docs/memory-knowledgebase.md documenting the whole setup; data/ gitignored (personal memory content, not meant to be committed). - New .env vars (SEARXNG_LAN_IP, PGVECTOR_DB_PASSWORD, LITELLM_PGVECTOR_API_KEY, LITELLM_PGVECTOR_EMBEDDING_KEY, EMBEDDING_MODEL_FILE) and generate-secrets.sh support for the auto-generatable ones. Resolves #22 and #23 (wayfinder map #21). Not yet verified on real hardware — see #24. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
43 lines
3.2 KiB
Markdown
43 lines
3.2 KiB
Markdown
# LLM-Server
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Local AI inference stack: llama.cpp (ROCm) serving Qwen3.8-27B on an AMD Radeon AI PRO R9700, fronted by Open WebUI (RAG + Memory via Qdrant), with Lazytainer auto-suspending the inference container when idle.
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See the wayfinder map ([issue #1](https://git.arthurerlich.de/haylan/LLM-Server/issues/1)) for the full architecture rationale and open questions.
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## Quickstart
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```bash
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cp .env.example .env
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# set LITELLM_MASTER_KEY / LITELLM_SALT_KEY (openssl rand -hex 32), see .env.example
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./scripts/download-model.sh
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docker compose up -d litellm litellm-db llama-server qdrant # bring the proxy up first
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```
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Log into LiteLLM's Admin UI (`http://<this-machine>:4000/ui`), create an `openwebui` virtual key (see [`docs/proxy-key-onboarding.md`](docs/proxy-key-onboarding.md)), set `OPENWEBUI_LITELLM_KEY` in `.env` to it, then:
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```bash
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docker compose up -d
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```
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- Open WebUI: `http://<this-machine>:3000` locally, or `ai.home` / `ai.haylan.ch` once routed through Nginx Proxy Manager — see [`docs/network-access.md`](docs/network-access.md). First signup becomes the admin account (`WEBUI_AUTH` is on).
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- llama.cpp's own API is internal-only now — everything routes through the AI proxy below.
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Pointing Claude Code CLI, Kimi CLI, or OpenCode CLI at the local endpoint: see [`docs/coding-cli-setup.md`](docs/coding-cli-setup.md).
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**Known risk**: Qwen3.8-27B's tool-calling reliability against llama.cpp's Anthropic shim is not yet verified (open upstream parser bugs against its model lineage) — see `docs/research/qwen3.8-27b-tool-calling.md`.
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## AI proxy (LiteLLM)
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An [AI gateway/proxy](https://git.arthurerlich.de/haylan/LLM-Server/issues/9) fronts llama.cpp: per-workload virtual keys, usage tracking, and a shadow cost estimate ("what this would have cost on Claude Sonnet 5"). Before `docker compose up -d`, set `LITELLM_MASTER_KEY` and `LITELLM_SALT_KEY` in `.env` (see `.env.example`).
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- Proxy API: `http://<this-machine>:4000/v1` locally, or `proxy.ai.home` / `proxy.ai.haylan.ch` once routed through NPM — see [`docs/network-access.md`](docs/network-access.md).
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- Admin UI (`/ui`, key/budget management): LAN-only — see `docs/network-access.md`.
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- Issuing a key for a new workload: [`docs/proxy-key-onboarding.md`](docs/proxy-key-onboarding.md).
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- Request priority across workloads: [`docs/proxy-request-priority.md`](docs/proxy-request-priority.md).
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Open WebUI and the coding CLIs (see [`docs/coding-cli-setup.md`](docs/coding-cli-setup.md)) route through the proxy now — llama-server has no published host port anymore. **Not yet verified**: none of this has been smoke-tested on real hardware (LiteLLM's priority scheduler in particular is beta — see `docs/proxy-request-priority.md`) — see [issue #17](https://git.arthurerlich.de/haylan/LLM-Server/issues/17).
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### Web search, knowledgebase, and memory
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The gateway also fronts SearXNG-backed web search and a pgvector-backed knowledgebase (loaded with `data/memory.md` / `data/claude-legacy-memory.md`), wired at the LiteLLM layer so every client gets them, not just Open WebUI — see [`docs/memory-knowledgebase.md`](docs/memory-knowledgebase.md). **Not yet verified on real hardware** — see [issue #24](https://git.arthurerlich.de/haylan/LLM-Server/issues/24).
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