46cc17e5d5
- Changed `thinking_budget` to `thinking_level` in `config.yaml` for Gemini models, updating the logic to reflect the new parameter. - Updated LLM models in `config.yaml` to `gemini-3.8-flash` and `gemini-3.1-pro-preview`, adjusting their respective `max_tokens` and `thinking_level`. - Enhanced `docker-compose.yml` to include a new `reranker-transformers` service for improved product ranking. - Modified vector search logic in `vector.py` to incorporate reranking with the local transformers model, improving the accuracy of product recommendations. - Updated unit tests to validate the new reranking functionality and ensure correct behavior in product searches.
111 lines
5.7 KiB
YAML
111 lines
5.7 KiB
YAML
# ── LLM configuration ─────────────────────────────────────────────────────────
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# Two independent profiles, one per pipeline stage. Each accepts the same keys:
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# provider: openai | anthropic | gemini
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# openai_model / anthropic_model / gemini_model / max_tokens / temperature
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# thinking_level (optional, Gemini 3 only): low | medium | high.
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# Gemini counts thinking tokens inside max_tokens, so max_tokens is the
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# combined thinking-plus-output cap. Omit thinking_level to use the model's default.
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llm:
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# Part_One: vector-search query synthesis (small structured JSON in,
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# 1-2 short queries out). A lighter/cheaper model is sufficient here.
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query_synthesis:
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provider: gemini
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openai_model: gpt-4o-mini
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anthropic_model: claude-haiku-4-5
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gemini_model: gemini-3.8-flash
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max_tokens: 2048
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temperature: 0.0
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# Part_Two: the farmer-facing advisory generator. Receives the full
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# json_for_advice_generation payload (weather, phenology, model output,
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# applied treatments, product labels) and returns the structured advice
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# JSON stored in the advisories table. Needs a more capable model.
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advice_generation:
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provider: gemini
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openai_model: gpt-4o
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anthropic_model: claude-opus-4-5
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gemini_model: gemini-3.1-pro-preview
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max_tokens: 13312
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thinking_level: medium
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temperature: 0.0
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# ── Crop-first worklist ────────────────────────────────────────────────────────
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# `python -m pipeline batch` discovers jobs from this list: for every crop, it
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# finds all fields growing it (AI_agrosupport_cmp_layers ⨝ an_colture) that also
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# have a matching row in AI_agrosupport_agro_models for each listed disease's
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# model_name, and runs the full single-field pipeline for every resulting
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# (field, disease) pair. `disease` is the canonical English name used for the
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# product prefilter, the vector search, and the advice/prompt lookup; it must
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# exist in vocab/diseases.yaml. `model_name` is matched (case-insensitively)
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# against AI_agrosupport_agro_models.anmod_model.
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#
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# Each crop-disease pair needs a matching prompt directory under
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# prompts/advice/<crop>__<disease slug>/ (see prompts/README or the pipeline
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# README for the exact slug rules); pairs without one fall back to
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# prompts/advice/_default/ and are logged at startup.
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crops:
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- crop: grapevine
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diseases:
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- model_name: PERONOSPORA
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disease: downy mildew
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# ── Worklist filters ───────────────────────────────────────────────────────────
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worklist:
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# AI_agrosupport_agro_models.anmod_enabled is True for every model row in the
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# current database; keep this on so a disabled model never generates advice.
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require_enabled_model: true
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# Restrict the batch to specific field IDs while testing; empty = no filter.
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field_allowlist: []
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# ── Concurrency & rate limiting ────────────────────────────────────────────────
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# One "gemini" budget covers both direct LLM calls (query synthesis, advice
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# generation) and Weaviate near_text search, because the ProductProfile
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# collection is vectorised with text2vec-palm (gemini-embedding-001) using the
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# same GEMINI_API_KEY — they share one quota.
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concurrency:
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workers: 12
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limits:
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sql: 6
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gemini: 12
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gemini_requests_per_minute: 60
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# ── Retry policy for transient LLM / Weaviate / SQL failures ──────────────────
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retry:
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attempts: 3
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initial_backoff_seconds: 2
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max_backoff_seconds: 30
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# ── Daily SLA ───────────────────────────────────────────────────────────────────
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# The batch must not still be running after this local time; jobs not yet
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# started by then are skipped (status skipped_deadline) rather than risking a
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# late advisory. Schedule the run itself no earlier than ~07:00 so weather is
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# fresh (see README).
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schedule:
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deadline: "09:00"
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# ── Single-field debug mode (`python -m pipeline one`) ────────────────────────
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# Not used by `batch`; kept for ad-hoc single-field runs and debugging.
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field_id: 4012
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disease_name: "PERONOSPORA"
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# ── Vocabulary paths ──────────────────────────────────────────────────────────
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vocab:
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crops: vocab/crops.yaml
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diseases: vocab/diseases.yaml
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# ── Observability (optional; off by default) ──────────────────────────────────
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# Sends OpenTelemetry/OpenInference spans to the self-hosted Phoenix container
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# in docker-compose.yml (http://localhost:6006). Never required: if this is
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# off, or Phoenix is down/unreachable, `batch` and `one` run exactly as if
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# this block did not exist (see pipeline/observability.py).
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observability:
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enabled: false
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project_name: ai-agro-support
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endpoint: http://localhost:6006/v1/traces
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# Keep prompt/payload text out of Phoenix spans. json_for_advice_generation
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# carries the full product-label payload and is already persisted under
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# output/<date>/*.json; spans only need model/token/status metadata.
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hide_prompts: true
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# Hard cap on every exported string attribute (OTel span attribute limit).
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max_attribute_chars: 4096
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