# Knowledgebase, memory, and web search

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.

**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.

## Web search (SearXNG)

`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:

```bash
curl http://<proxy>:4000/v1/search/searxng-search \
  -H "Authorization: Bearer <a virtual key>" \
  -H "Content-Type: application/json" \
  -d '{"query": "...", "max_results": 5}'
```

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.

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`.

## Knowledgebase (vector store / RAG)

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`.

New pieces:

- **`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`.
- **`pgvector-db`** — Postgres with the pgvector extension, separate from `litellm-db`.
- **`litellm-pgvector`** — the connector service; no published image exists, so it's built from a vendored copy of the upstream repo at `vendor/litellm-pgvector/` (see that dir's `VENDORED.md`) — a remote git build context failed on the server's Docker/BuildKit setup.
- `litellm-config.yaml`'s `local-embedding` model entry and `vector_store_registry` block, tying it together.

### First-time setup

```bash
docker compose --profile tools run --rm downloader-embedding   # fetch the embedding model
docker compose up -d embedding-server pgvector-db litellm-pgvector
```

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.

### Loading memory into it

`data/memory.md` and `data/claude-legacy-memory.md` — Claude-memory-style fact files — get loaded via:

```bash
./scripts/ingest-memory.sh
```

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).

### Querying it

Via the OpenAI Assistants-style `file_search` tool on a chat completion:

```json
{
  "model": "qwen3.8-27b-local",
  "messages": [...],
  "tools": [{"type": "file_search", "vector_store_ids": ["memory-and-notes"]}]
}
```

or directly: `POST /v1/vector_stores/memory-and-notes/search` with `{"query": "..."}`.