feat(kb/ask): SSE streaming endpoint with live agent progress

Adds POST /kb/ask/stream that returns text/event-stream. The agent
runs in a fire-and-forget task; an on_event callback wired into
AgentRunner.run pushes {type, label, ...} events into an asyncio.Queue,
and the response generator consumes them as SSE data: lines.

Event types and Hebrew labels:
- thinking — before each LLM call ("קוראת את השאלה" / "ממשיכה לחקור")
- tool_start — before each tool. For search_insurance_kb the label
  includes the actual query string Shira chose, so the user can see
  WHAT she's searching for ("מחפשת בבסיס הידע: '...'"). Other tools
  fall back to a generic Hebrew label keyed off the function name.
- tool_done / tool_error — after each tool. For search_insurance_kb
  the label echoes the first non-empty line of the tool result (which
  the legal_kb tool formats as "## נמצאו N קטעים").
- writing — when the model returns text without tool calls.
- answer — final event with {text, sources, conversationId}, mirrors
  the non-streaming /kb/ask response shape.
- error — runner exception; client should close the EventSource.

Heartbeats: when the queue is idle for >15s the generator yields a
": keep-alive" comment line (ignored by EventSource) so any proxy in
the path doesn't drop the connection during long agent thinks.

The original POST /kb/ask is unchanged — the streaming endpoint is
additive. Both share _build_ask_runner_context + _build_ask_messages
helpers extracted from the original handler.

Refs Task Master #1

api/routes/kb_public.py

@@ -233,16 +233,8 @@ class AskRequest(BaseModel):
     conversation_history: list[dict] | None = None
 
 
-@router.post("/ask")
-async def ask(body: AskRequest, request: Request):
-    """Full agent loop: the user's question goes to shira, she calls
-    search_insurance_kb as needed and returns a summarized answer.
-
-    Thin wrapper around the existing smart-assistant chat endpoint so the
-    KnowledgeBase UI can expose an ask mode without rebuilding prompt
-    plumbing.
-    """
-    _verify_auth(request)
+def _build_ask_runner_context() -> tuple:
+    """Common construction shared by /kb/ask and /kb/ask/stream."""
     from api.services.agent_runner import AgentRunner
     from api.services.prompt_builder import build_office_prompt
     from api.services.skills import list_skills
@@ -254,15 +246,12 @@ async def ask(body: AskRequest, request: Request):
         max_tokens=int(os.environ.get("MAX_OUTPUT_TOKENS", "4096")),
         max_iterations=int(os.environ.get("MAX_AGENT_ITERATIONS", "10")),
     )
-
     context = {"currentUser": {"id": "kb-ui", "name": "KB UI"}}
     system_prompt = build_office_prompt(
         context,
         user_profile="",
         available_skills=list_skills(),
     )
-    # Pin the question to the insurance KB context so generic-law answers
-    # (e.g. "תקנה 37 לתקנות סדר הדין האזרחי") don't hijack the response.
     system_prompt += (
         "\n\nContext: The user is asking from the Knowledge Base UI of the "
         "Israeli National Insurance firm. Every question should be answered "
@@ -272,7 +261,10 @@ async def ask(body: AskRequest, request: Request):
         "Never answer from generic legal training if the KB has a matching "
         "section — if no KB section matches, say so explicitly."
     )
+    return runner, context, system_prompt
 
+
+def _build_ask_messages(body: AskRequest) -> list[dict]:
     messages: list[dict] = []
     for m in (body.conversation_history or []):
         role = m.get("role", "user")
@@ -281,12 +273,29 @@ async def ask(body: AskRequest, request: Request):
             "content": m.get("content", ""),
         })
     messages.append({"role": "user", "content": body.message})
+    return messages
 
-    # Collect the sources Shira actually searched through during this turn.
-    # The legal_kb tool appends to this list on every search_insurance_kb
-    # call; the UI uses it to render a side PDF viewer next to the answer.
+
+def _filter_pdf_sources(sources_used: list[dict]) -> list[dict]:
+    return [
+        s for s in sources_used
+        if (s.get("original_path") or "").lower().endswith(".pdf")
+    ]
+
+
+@router.post("/ask")
+async def ask(body: AskRequest, request: Request):
+    """Full agent loop: the user's question goes to shira, she calls
+    search_insurance_kb as needed and returns a summarized answer.
+
+    Thin wrapper around the existing smart-assistant chat endpoint so the
+    KnowledgeBase UI can expose an ask mode without rebuilding prompt
+    plumbing.
+    """
+    _verify_auth(request)
+    runner, context, system_prompt = _build_ask_runner_context()
+    messages = _build_ask_messages(body)
     sources_used: list[dict] = []
-
     try:
         text = await runner.run(
             system_prompt=system_prompt,
@@ -300,15 +309,86 @@ async def ask(body: AskRequest, request: Request):
     except Exception as e:
         logger.exception("[kb.ask] error")
         raise HTTPException(status_code=500, detail=str(e))
-
-    # Keep only the ones that actually have a PDF attached.
-    pdf_sources = [
-        s for s in sources_used
-        if (s.get("original_path") or "").lower().endswith(".pdf")
-    ]
-
     return {
         "text": text,
         "conversationId": body.conversation_id,
-        "sources": pdf_sources,
+        "sources": _filter_pdf_sources(sources_used),
     }
+
+
+@router.post("/ask/stream")
+async def ask_stream(body: AskRequest, request: Request):
+    """Streaming variant of /kb/ask. Returns text/event-stream with one
+    SSE event per agent step (thinking, tool_start, tool_done) and a
+    final {type:"answer",text,sources} event. The KnowledgeBase UI
+    consumes this via EventSource (proxied through an EspoCRM EntryPoint
+    so the auth token stays on the server).
+    """
+    import asyncio
+    import json as _json
+    from fastapi.responses import StreamingResponse
+
+    _verify_auth(request)
+    runner, context, system_prompt = _build_ask_runner_context()
+    messages = _build_ask_messages(body)
+    sources_used: list[dict] = []
+
+    queue: asyncio.Queue = asyncio.Queue()
+
+    def _on_event(ev: dict) -> None:
+        # AgentRunner calls this synchronously between awaits; an unbounded
+        # queue lets us put_nowait without blocking the loop.
+        try:
+            queue.put_nowait(ev)
+        except Exception as e:
+            logger.warning("[kb.ask/stream] queue put failed: %s", e)
+
+    async def _runner_task():
+        try:
+            text = await runner.run(
+                system_prompt=system_prompt,
+                messages=messages,
+                context=context,
+                espocrm_url=os.environ.get("ESPOCRM_URL", ""),
+                espocrm_api_key=os.environ.get("ESPOCRM_API_KEY", ""),
+                allowed_toolsets=["legal"],
+                kb_sources_used=sources_used,
+                on_event=_on_event,
+            )
+            await queue.put({
+                "type": "answer",
+                "text": text,
+                "sources": _filter_pdf_sources(sources_used),
+                "conversationId": body.conversation_id,
+            })
+        except Exception as e:
+            logger.exception("[kb.ask/stream] runner error")
+            await queue.put({"type": "error", "message": str(e)})
+        finally:
+            await queue.put(None)  # sentinel
+
+    asyncio.create_task(_runner_task())
+
+    async def _gen():
+        # Heartbeat every 15s keeps the connection open through any
+        # timeout-happy proxies; comment lines (`: ...`) are ignored by
+        # EventSource but keep TCP/HTTP alive.
+        while True:
+            try:
+                ev = await asyncio.wait_for(queue.get(), timeout=15.0)
+            except asyncio.TimeoutError:
+                yield ": keep-alive\n\n"
+                continue
+            if ev is None:
+                break
+            yield "data: " + _json.dumps(ev, ensure_ascii=False) + "\n\n"
+
+    return StreamingResponse(
+        _gen(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache, no-transform",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )

api/services/agent_runner.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import json
 import logging
-from typing import Any
+from typing import Any, Callable
 
 from openai import AsyncOpenAI
 
@@ -21,6 +21,49 @@ from api.services.delegate import register_delegate_tool
 logger = logging.getLogger("shira.agent")
 
 
+# Hebrew labels for the on_event callback in run(). These are surfaced
+# verbatim in the KnowledgeBase ask UI to give users live progress
+# (instead of just an elapsed-time counter). Keep them short — they
+# render in a single line of the progress log.
+
+def _thinking_label(iteration: int) -> str:
+    if iteration == 0:
+        return "קוראת את השאלה"
+    return "ממשיכה לחקור"
+
+
+def _tool_label(name: str, args: dict) -> str:
+    if name == "search_insurance_kb":
+        q = (args.get("query") or "").strip()
+        if q:
+            qs = q if len(q) <= 60 else q[:60] + "…"
+            return f'מחפשת בבסיס הידע: "{qs}"'
+        return "מחפשת בבסיס הידע"
+    if name == "list_skills" or name == "view_skill":
+        return "בודקת מיומנויות זמינות"
+    if name == "save_memory" or name == "view_memory":
+        return "מסתכלת בזיכרון"
+    if name.startswith("delegate"):
+        return "מאצילה משימת משנה לסוכן עזר"
+    if name.startswith("create_") or name.startswith("update_") or name.startswith("save_"):
+        return f"כותבת ב-CRM: {name}"
+    return f"מפעילה כלי: {name}"
+
+
+def _tool_done_label(name: str, result) -> str:
+    if name == "search_insurance_kb":
+        # legal_kb_tools formats the result as a Hebrew string starting
+        # with a "## נמצאו N קטעים" line. Surface that count.
+        s = str(result or "")
+        for line in s.splitlines():
+            line = line.strip().lstrip("#").strip()
+            if not line:
+                continue
+            return line[:80] if len(line) > 80 else line
+        return "סיימה לחפש"
+    return "הושלם"
+
+
 class AgentRunner:
     """
     Runs an agentic conversation loop using the OpenAI SDK pointed at ai-gateway.
@@ -59,6 +102,7 @@ class AgentRunner:
         blocked_tools: set[str] | None = None,
         allowed_toolsets: list[str] | None = None,
         kb_sources_used: list[dict] | None = None,
+        on_event: Callable[[dict], None] | None = None,
     ) -> str:
         """Run a conversation with tool-calling loop. Returns the final text response.
 
@@ -66,7 +110,20 @@ class AgentRunner:
             depth: Current delegation depth. 0 = main conversation, 1 = child agent.
             blocked_tools: Tool names to exclude (used by delegation to prevent recursion).
             allowed_toolsets: If set, only register tools from these categories (crm, documents, legal).
+            on_event: Optional sync callback fired between iterations and tool calls.
+                Receives a dict {type, label, ...} where type ∈ {thinking,
+                tool_start, tool_done, tool_error}. Used by /kb/ask/stream
+                to surface live progress to the UI. Exceptions in the
+                callback are logged and ignored — the agent never fails
+                because the caller's display logic threw.
         """
+        def emit(event: dict) -> None:
+            if on_event is None:
+                return
+            try:
+                on_event(event)
+            except Exception as e:
+                logger.warning("[agent] on_event callback failed: %s", e)
 
         # Build tools from context
         crm = EspoCrmClient(espocrm_url, espocrm_api_key)
@@ -119,6 +176,12 @@ class AgentRunner:
         for iteration in range(self.max_iterations):
             logger.info("[agent] depth=%d iteration %d, messages=%d", depth, iteration + 1, len(openai_messages))
 
+            emit({
+                "type": "thinking",
+                "iteration": iteration + 1,
+                "label": _thinking_label(iteration),
+            })
+
             try:
                 response = await self.client.chat.completions.create(
                     model=self.model,
@@ -135,6 +198,7 @@ class AgentRunner:
 
             # If no tool calls, return the text
             if not message.tool_calls:
+                emit({"type": "writing", "label": "מנסחת תשובה"})
                 return message.content or "לא הצלחתי להבין את הבקשה. נסי שוב."
 
             # Process tool calls
@@ -148,17 +212,29 @@ class AgentRunner:
                     fn_args = {}
 
                 logger.info("[agent] tool call: %s(%s)", fn_name, json.dumps(fn_args, ensure_ascii=False)[:100])
+                emit({
+                    "type": "tool_start",
+                    "name": fn_name,
+                    "label": _tool_label(fn_name, fn_args),
+                })
 
                 tool_def = tools_registry.get(fn_name)
                 if not tool_def:
                     result = f"❌ כלי לא ידוע: {fn_name}"
+                    emit({"type": "tool_error", "name": fn_name, "label": f"כלי לא מוכר: {fn_name}"})
                 else:
                     try:
                         handler = tool_def["handler"]
                         result = await handler(**fn_args)
+                        emit({
+                            "type": "tool_done",
+                            "name": fn_name,
+                            "label": _tool_done_label(fn_name, result),
+                        })
                     except Exception as e:
                         logger.error("[agent] tool error %s: %s", fn_name, e)
                         result = f"❌ שגיאה בהפעלת {fn_name}: {e}"
+                        emit({"type": "tool_error", "name": fn_name, "label": f"שגיאה ב-{fn_name}"})
 
                 openai_messages.append({
                     "role": "tool",