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Phase 3: AI integration with Ollama via Python sidecar

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- sidecar: FastAPI app with /embed, /generate, /rerank hitting Ollama
- sidecar: Dockerfile, env var config (EMBED_MODEL, GEN_MODEL, RERANK_MODEL)
- aibridge: reqwest HTTP client with typed request/response structs
- aibridge: Axum proxy endpoints (POST /ai/embed, /ai/generate, /ai/rerank)
- gateway: wires AiClient with SIDECAR_URL env var
- e2e verified: nomic-embed-text returns 768d vectors, qwen2.5 generates text

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-03-27 05:53:56 -05:00
co-authored by Claude Opus 4.6
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**Gate: PASSED** — SELECT *, WHERE/ORDER BY, COUNT/AVG all return correct results via catalog.
## Phase 3: AI Integration
- [ ] 3.1 — Python sidecar: FastAPI + Ollama (embed/generate/rerank)
- [ ] 3.2 — Dockerfile for sidecar
- [ ] 3.3 — aibridge/client.rs: HTTP client to sidecar
- [ ] 3.4 — aibridge service: Axum proxy endpoints
- [ ] 3.5 — Model config via env vars
- [x] 3.1 — Python sidecar: FastAPI + Ollama (embed/generate/rerank) — real models, no mocks
- [x] 3.2 — Dockerfile for sidecar
- [x] 3.3 — aibridge/client.rs: reqwest HTTP client with 120s timeout
- [x] 3.4 — aibridge service: Axum proxy endpoints (POST /ai/embed, /ai/generate, /ai/rerank)
- [x] 3.5 — Model config via env vars (EMBED_MODEL, GEN_MODEL, RERANK_MODEL, SIDECAR_URL)
**Gate:** Rust → Python → Ollama → real embeddings return.
**Gate: PASSED** — Gateway → aibridge → sidecar → Ollama → real 768d embeddings + generation.
## Phase 4: Frontend
- [ ] 4.1 — Dioxus scaffold, WASM build