Files
qihuaiflow/backend/python/workflow_templates/rag-search.yaml
T
QihuAI Design a2504bce34 fix(chat): resolve Auto-route failover event-loop freeze (#812)
Root cause: ensure_structured_column() executed a synchronous blocking
engine.connect().execute(SELECT information_schema.columns ...) inside the
async request path (_build_chat_history / _maybe_compress_history), freezing
the uvicorn event loop and hanging all endpoints (~200s, recovered only by
docker restart).

Fix:
- chat_context.py: remove per-request ensure_structured_column() calls; run
  once idempotently at app startup (main.py lifespan). Use CURRENT_DATABASE()
  for PostgreSQL compatibility.
- main.py: add startup-only ensure_structured_column() hook (try/except).
- llm_client.py: trust_env=False + bounded httpx.Timeout/wait_for so broken
  endpoints fail fast instead of hanging the event loop.
- chat_service.py: run media generation in a background daemon thread with its
  own event loop (_launch_media_task_bg) so SSE end does not cancel image/video
  tasks (#811); remove TEMP faulthandler diagnostic.

Safe-align: persist accumulated live container changes (admin/*, knowledge,
market, models, config, observability, mcp_client, platform_knowledge,
im_bot_platform, chat_media, capability_registry, requirements) and add
workflow_templates.

Verified: live failover test (model-006 -> conn-refused) returns in ~1.9s with
concurrent login 0.83s/200; event-loop responsiveness restored (sub-second).
2026-07-26 00:35:35 +08:00

26 lines
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YAML

version: "1.0"
name: "RAG Search Pipeline"
description: "Search knowledge base → augment with context → generate answer. Standard RAG pipeline."
category: productivity
nodes:
- id: search
type: llm
description: "Generate search queries and retrieve relevant context"
on_error: continue
config:
model: auto
system_prompt: "You are a search specialist. For the given question, identify 2-3 key search points. Then search the provided knowledge base for relevant information. Return the most relevant passages with source references."
- id: answer
type: llm
description: "Generate final answer with citations"
on_error: continue
config:
model: auto
system_prompt: "You are a knowledge assistant. Using the search results provided, generate a comprehensive answer. Always cite your sources. If information is insufficient, clearly state what is missing."
edges:
- from: search
to: answer