This repository has been archived on 2026-07-19. You can view files and clone it. You cannot open issues or pull requests or push a commit.
Files
shira-hermes/api/routes/smart_assistant.py
T
chaim 4aad81e11e feat: add skills, memory, delegation and self-learning (Phase 3)
Add three Hermes-inspired capabilities to Shira:

- Skills system: firm-wide workflow templates (list/view/manage tools),
  4 pre-seeded skills (case summary, hearing prep, direct access report,
  deadline tracker), auto-learning via post-conversation meta-prompt
- Cross-conversation memory: per-lawyer profiles and per-case memory
  stored as bounded markdown files, injected into system prompts
- Sub-agent delegation: spawn up to 3 child AgentRunner instances for
  parallel document analysis and complex multi-step tasks

New files: skills.py, memory.py, delegate.py, learner.py, 4 SKILL.md
Updated: agent_runner (6 new tools, depth/blocked_tools support),
prompt_builder (skills/memory injection), route (memory loading,
background learning), main.py (data dirs), Dockerfile (pyyaml, skills)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-13 19:33:57 +00:00

147 lines
4.8 KiB
Python

"""SmartAssistant chat endpoint — same HTTP contract as ai-gateway."""
from __future__ import annotations
import asyncio
import os
import logging
from fastapi import APIRouter, Request, HTTPException
from pydantic import BaseModel
from api.services.prompt_builder import build_case_prompt, build_office_prompt
from api.services.agent_runner import AgentRunner
from api.services.skills import list_skills
from api.services.memory import load_user_profile, load_case_memory
from api.services.learner import extract_skills
logger = logging.getLogger("shira.api")
router = APIRouter()
def _get_api_key() -> str:
return os.environ.get("API_KEY", "")
def _verify_auth(request: Request):
"""Verify X-Api-Key header if API_KEY is configured."""
expected = _get_api_key()
if not expected:
return # No auth configured
provided = request.headers.get("X-Api-Key", "")
if provided != expected:
logger.warning(
"[auth] Key mismatch — expected=%s... provided=%s...",
expected[:8] if expected else "NONE",
provided[:8] if provided else "NONE",
)
raise HTTPException(status_code=401, detail="Unauthorized")
def _get_runner() -> AgentRunner:
return AgentRunner(
ai_gateway_url=os.environ.get("AI_GATEWAY_URL", "http://localhost:3000"),
ai_gateway_api_key=os.environ.get("AI_GATEWAY_API_KEY", ""),
model=os.environ.get("CLAUDE_MODEL", "sonnet"),
max_tokens=int(os.environ.get("MAX_OUTPUT_TOKENS", "4096")),
max_iterations=int(os.environ.get("MAX_AGENT_ITERATIONS", "10")),
)
@router.post("/api/smart-assistant/chat")
async def smart_assistant_chat(request: Request):
_verify_auth(request)
body = await request.json()
message = body.get("message", "")
context = body.get("context", {})
conversation_id = body.get("conversationId", "")
conversation_history = body.get("conversationHistory", [])
mode = body.get("mode", "case")
# EspoCRM credentials
espocrm = body.get("espocrm", {})
espocrm_url = espocrm.get("url") or os.environ.get("ESPOCRM_URL", "")
espocrm_api_key = espocrm.get("apiKey") or os.environ.get("ESPOCRM_API_KEY", "")
if not message and not conversation_history:
return {"text": "no message", "actions": [], "conversationId": conversation_id}
# Extract IDs for memory loading
case_id = (context.get("case") or {}).get("id") or (context.get("drillDown", {}).get("case", {}).get("id"))
user_id = (context.get("currentUser") or {}).get("id")
# Load cross-conversation memory and skills
user_profile = load_user_profile(user_id) if user_id else ""
case_memory_notes = load_case_memory(case_id) if case_id else ""
available_skills = list_skills()
# Build system prompt with memory and skills injected
if mode == "office":
system_prompt = build_office_prompt(
context,
user_profile=user_profile,
available_skills=available_skills,
)
else:
system_prompt = build_case_prompt(
context,
user_profile=user_profile,
case_memory_notes=case_memory_notes,
available_skills=available_skills,
)
# Build messages from conversation history
messages = []
for m in conversation_history:
if m.get("role") == "tool_result":
messages.append({
"role": "user",
"content": f'[תוצאת כלי {m.get("toolName", "")}]:\n{m.get("content", "")}',
})
else:
messages.append({
"role": "assistant" if m.get("role") == "assistant" else "user",
"content": m.get("content", ""),
})
if message:
messages.append({"role": "user", "content": message})
logger.info(
'[smart-assistant] mode=%s conv=%s espocrm=%s msg="%s..."',
mode, conversation_id, espocrm_url, message[:50],
)
runner = _get_runner()
text = await runner.run(
system_prompt=system_prompt,
messages=messages,
context=context,
espocrm_url=espocrm_url,
espocrm_api_key=espocrm_api_key,
)
logger.info("[smart-assistant] response: %s...", text[:80])
# Trigger self-learning in the background (non-blocking)
conversation_messages = runner.get_messages()
if conversation_messages:
asyncio.create_task(_learn_in_background(conversation_messages))
return {
"text": text,
"actions": [],
"executedActions": [],
"conversationId": conversation_id,
}
async def _learn_in_background(messages: list[dict]) -> None:
"""Run skill extraction in background — errors are swallowed and logged."""
try:
await extract_skills(messages)
except Exception as e:
logger.error("[learner] background learning failed: %s", e)