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>
55 lines
2.3 KiB
Bash
55 lines
2.3 KiB
Bash
# Copy to .env and adjust. All values below are defaults baked into
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# docker-compose.yml — only uncomment/change what you actually want to override.
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# --- llama.cpp / model ---
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LLAMA_MODEL_FILE=Qwen3.8-27B-UD-Q4_K_XL.gguf
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LLAMA_GPU_LAYERS=999
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# 65536 (64K) fits comfortably in 32GB VRAM alongside the model weights.
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# Raise toward 131072 if you need more context; see docs/research/qwen3.8-27b-quant.md
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# for the VRAM math at larger context sizes.
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LLAMA_CTX_SIZE=65536
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# --- Open WebUI ---
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WEBUI_PORT=8008
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# Required — create an "openwebui" virtual key in LiteLLM's Admin UI first
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# (see docs/proxy-key-onboarding.md), then paste it here.
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OPENWEBUI_LITELLM_KEY=
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# --- Lazytainer ---
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# Seconds of inactivity before llama-server is stopped. 900 = 15 min.
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LAZYTAINER_INACTIVE_TIMEOUT=900
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# --- Embedding model (knowledgebase, see docs/memory-knowledgebase.md) ---
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EMBEDDING_MODEL_FILE=nomic-embed-text-v1.5.Q8_0.gguf
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# --- SearXNG web search (see docs/research/litellm-searxng-search.md) ---
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# Required — the litellm container can't resolve search.home's LAN mDNS
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# name on its own; this becomes an extra_hosts entry. Use a static
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# DHCP-reserved IP so it doesn't drift.
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SEARXNG_LAN_IP=
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# --- LiteLLM proxy (see docs/proxy-key-onboarding.md, docs/network-access.md) ---
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LITELLM_PORT=4000
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# Required — generate real random values before first run, e.g. `openssl rand -hex 32`.
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# LITELLM_SALT_KEY encrypts stored data; do not change it after the first run
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# (existing encrypted data becomes unreadable if you do).
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# Required — generate a real random value before first run, e.g. `openssl rand -hex 32`.
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LITELLM_MASTER_KEY=
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LITELLM_SALT_KEY=
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LITELLM_DB_PASSWORD=
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# Admin UI login (https://<proxy>/ui). Without these, LiteLLM falls back to
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# username "admin" / password = LITELLM_MASTER_KEY — set these instead so the
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# master key never has to be typed into the browser.
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UI_USERNAME=admin
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UI_PASSWORD=
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# --- Knowledgebase (pgvector, see docs/memory-knowledgebase.md) ---
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# Required — random values, e.g. `openssl rand -hex 32`.
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PGVECTOR_DB_PASSWORD=
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# Auth key litellm-pgvector requires on its own API (its SERVER_API_KEY).
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LITELLM_PGVECTOR_API_KEY=
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# A virtual key litellm-pgvector uses to call back into litellm for
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# embeddings — create it in the Admin UI like any other workload key
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# (see docs/proxy-key-onboarding.md), name it "litellm-pgvector".
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LITELLM_PGVECTOR_EMBEDDING_KEY=
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