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>
65 lines
3.6 KiB
Markdown
65 lines
3.6 KiB
Markdown
# Knowledgebase, memory, and web search
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Three gateway-level capabilities added on top of the [AI gateway/proxy](https://git.arthurerlich.de/haylan/LLM-Server/issues/9), so every client behind LiteLLM gets them — not just Open WebUI. See [issue #21](https://git.arthurerlich.de/haylan/LLM-Server/issues/21) for the rationale.
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**Not yet verified on real hardware** — see [issue #24](https://git.arthurerlich.de/haylan/LLM-Server/issues/24). In particular: `litellm-pgvector`'s Prisma migrations on first boot, and the exact `vector_store_registry` field names for the `pg_vector` provider.
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## Web search (SearXNG)
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`litellm-config.yaml`'s `search_tools` block wires the LAN's SearXNG instance in as a **standalone REST endpoint**, not a model-callable tool — call it directly:
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```bash
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curl http://<proxy>:4000/v1/search/searxng-search \
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-H "Authorization: Bearer <a virtual key>" \
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-H "Content-Type: application/json" \
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-d '{"query": "...", "max_results": 5}'
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```
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Because this doesn't ask the model to emit a tool call, it sidesteps Qwen3.8-27B's known-flaky tool-calling (`docs/research/qwen3.8-27b-tool-calling.md`) entirely. Open WebUI's own web-search setting can point at this endpoint the same way.
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Requires `SEARXNG_LAN_IP` set in `.env` (SearXNG's stable LAN IP — use a static DHCP reservation) so the `litellm` container can resolve `search.home` via `extra_hosts`. Full research: `docs/research/litellm-searxng-search.md`.
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## Knowledgebase (vector store / RAG)
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LiteLLM's native knowledgebase feature has **no Qdrant backend** — the `qdrant` service in this stack only serves Open WebUI's own separate RAG/Memory feature and is unrelated to this. The only self-hosted path is [litellm-pgvector](https://github.com/BerriAI/litellm-pgvector), a companion service backed by its own Postgres+pgvector database (`pgvector-db`), which this stack now runs alongside `litellm`. Full research: `docs/research/litellm-knowledgebase.md`.
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New pieces:
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- **`embedding-server`** — a second llama.cpp instance (small footprint, `nomic-embed-text-v1.5`) serving `/v1/embeddings`. The chat model isn't embedding-trained and llama.cpp serves one model per process, so this can't just be a flag on `llama-server`.
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- **`pgvector-db`** — Postgres with the pgvector extension, separate from `litellm-db`.
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- **`litellm-pgvector`** — the connector service; built straight from its upstream repo (no published image exists).
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- `litellm-config.yaml`'s `local-embedding` model entry and `vector_store_registry` block, tying it together.
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### First-time setup
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```bash
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docker compose --profile tools run --rm downloader-embedding # fetch the embedding model
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docker compose up -d embedding-server pgvector-db litellm-pgvector
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```
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Create a `litellm-pgvector` virtual key in LiteLLM's Admin UI (per `docs/proxy-key-onboarding.md`) and set it as `LITELLM_PGVECTOR_EMBEDDING_KEY` in `.env` — the connector calls back into `litellm` for embeddings, same as any other workload.
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### Loading memory into it
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`data/memory.md` and `data/claude-legacy-memory.md` — Claude-memory-style fact files — get loaded via:
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```bash
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./scripts/ingest-memory.sh
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```
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One chunk per fact/paragraph line, tagged with `source`/`section` metadata. Re-run after editing either file (see the script's header comment for the no-dedup caveat).
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### Querying it
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Via the OpenAI Assistants-style `file_search` tool on a chat completion:
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```json
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{
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"model": "qwen3.8-27b-local",
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"messages": [...],
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"tools": [{"type": "file_search", "vector_store_ids": ["memory-and-notes"]}]
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}
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```
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or directly: `POST /v1/vector_stores/memory-and-notes/search` with `{"query": "..."}`.
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