1f5e272600
Infrastructure: - docker-compose.yml: replace bifrost container with LiteLLM proxy (host.docker.internal:4000); complex model → deepseek-r1:free via OpenRouter; add Matrix URL env var; mount logs volume - bifrost-config.json: add auth_config + postgres config_store (archived) Routing: - router.py: full semantic 3-tier classifier rewrite — nomic-embed-text centroids for light/medium/complex; regex pre-classifiers for all tiers; Russian utterance sets expanded - agent.py: wire LiteLLM URL; add dry_run support; add Matrix channel Channels: - channels.py: add Matrix adapter (_matrix_send via mx- session prefix) Rules / docs: - agent-pipeline.md: remove /think prefix requirement; document automatic complex tier classification - llm-inference.md: update BIFROST_URL → LITELLM_URL references; add remote model note for complex tier - ARCHITECTURE.md: deleted (superseded by README.md) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
23 lines
1.4 KiB
Markdown
23 lines
1.4 KiB
Markdown
# Agent Pipeline Rules
|
|
|
|
## Tiers
|
|
- Routing is fully automatic: router classifies into light/medium/complex via 3-way embedding similarity.
|
|
- Complex tier is reached automatically for deep research queries — no prefix required.
|
|
- Medium is the default tier. Light is only for trivial static-knowledge queries matched by regex or embedding.
|
|
- Light tier upgrade to medium is automatic when URL content is pre-fetched or a fast tool matches.
|
|
- `tier_override` API parameter still allows callers to force a specific tier (e.g. `adolf-deep` model → complex).
|
|
|
|
## Medium agent
|
|
- `_DirectModel` makes a single `ainvoke()` call with no tool schema. Do not add tools to the medium agent.
|
|
- `qwen3:4b` behaves unreliably when a tool array is present in the request — inject context via system prompt instead.
|
|
|
|
## Memory
|
|
- `add_memory` and `search_memory` are called directly in `run_agent_task()`, outside the agent loop.
|
|
- Never add memory tools to any agent's tool list.
|
|
- Memory storage (`_store_memory`) runs as an asyncio background task after the semaphore is released.
|
|
|
|
## Fast tools
|
|
- `FastToolRunner.run_matching()` runs in the pre-flight `asyncio.gather` alongside URL fetch and memory retrieval.
|
|
- Fast tool results are injected as a system prompt block, not returned to the user directly.
|
|
- When `any_matches()` is true, the router forces medium tier before LLM classification.
|