feat(batch-agent): extract Batch Agent Service (Step 3)
- agent_runner: local directory + cloud agent orchestration via Redis - 5 domain agents: filesystem, task, note, project, timeline - integrations: Gmail, MS Graph (Outlook + Teams) - journey: guided chatbot conversation to build prompt_template - routes: REST endpoints (catalog, can-create, trigger) - redis_consumer: subscribes to batch:request:* pattern - ws_context: Redis-based execute_on_client for tool round-trip - Dockerfile with 300s timeout for long-running batch jobs
This commit is contained in:
Roberto Musso
385
services/batch-agent/app/journey.py
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385
services/batch-agent/app/journey.py
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"""Chatbot Journey — guided conversation to build an agent prompt_template.
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Adapted for Batch Agent Service: imports from app.agents.filesystem_agent
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and app.llm instead of monolith paths. Session state is in-memory (could
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be moved to Redis for horizontal scaling in the future).
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Journey flow:
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1. Redis consumer dispatches ``journey_start`` with basic agent config.
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2. Server creates an in-memory session, runs the setup LLM with
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file-system tools to explore the directory, returns first question.
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3. ``journey_message`` frames drive the conversation.
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4. After 3-5 turns the LLM emits PROMPT_TEMPLATE_START / _END block.
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5. Server parses the block and returns ``journey_reply`` with ``done=True``.
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"""
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from __future__ import annotations
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import json
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import logging
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import time
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import uuid
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from dataclasses import dataclass, field
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from typing import Any
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
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from app.agents.filesystem_agent import FILESYSTEM_TOOLS
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from app.llm import get_llm
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logger = logging.getLogger(__name__)
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# ── Session TTL ───────────────────────────────────────────────────────────
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_SESSION_TTL_SECONDS: int = 1800 # 30 minutes
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# Sentinel strings used to delimit the LLM-produced prompt_template.
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_TEMPLATE_START = "PROMPT_TEMPLATE_START"
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_TEMPLATE_END = "PROMPT_TEMPLATE_END"
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_MIN_TURNS_BEFORE_NUDGE: int = 3
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_MAX_TURNS: int = 15
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_MAX_TOOL_STEPS: int = 6
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# ── In-memory session store ───────────────────────────────────────────────
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@dataclass
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class JourneySession:
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session_id: str
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user_id: str
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agent_type: str # "local" | "cloud"
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directory: str
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data_types: list[str]
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history: list[dict[str, Any]] = field(default_factory=list)
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system_prompt: str = ""
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created_at: float = field(default_factory=time.monotonic)
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def is_expired(self) -> bool:
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return (time.monotonic() - self.created_at) > _SESSION_TTL_SECONDS
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# session_id → session
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_sessions: dict[str, JourneySession] = {}
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def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
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"""Retrieve session; return None on missing, expired, or wrong owner."""
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s = _sessions.get(session_id)
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if s is None or s.is_expired():
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_sessions.pop(session_id, None)
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return None
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if s.user_id != user_id:
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return None
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return s
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# ── System prompt builder ─────────────────────────────────────────────────
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_SYSTEM_PROMPT_TEMPLATE = """\
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You are a friendly assistant helping a freelancer configure a data-extraction agent.
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Your job is to understand exactly what data the user wants to extract from their
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local directory and produce a detailed prompt_template that a separate AI will use
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as its instruction set.
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The extraction agent already has this base behaviour built in:
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- Reads each file using file-system tools.
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- Creates records (tasks, notes, timelines, projects) via CRUD tools.
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- Sets isAiSuggested=1 on every new record.
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- Only extracts data explicitly present in the files — it never invents information.
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The user's custom prompt is appended AFTER this base behaviour, so focus on
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what to look for and how to map it — not on the general extraction mechanics.
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You have access to file-system tools to explore the user's directory:
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- list_directory: to see folder structure
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- read_file_content: to peek at file contents
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- get_file_metadata: to check file info
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The user's configured directory is: {directory}
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Target data types: {data_types}
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IMPORTANT — project assignment is handled automatically by the main agent runner
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before the custom prompt is ever used. You MUST NOT ask the user about projects,
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projectId, or how to link records to projects. Never include projectId logic or
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project creation instructions in the generated prompt_template.
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Start by exploring the directory to understand its structure. Then ask concise,
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focused questions one at a time. Cover these topics (not necessarily in this order):
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1. The type and format of the source content (confirmed by your exploration).
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2. How fields should be mapped (e.g. filename → task title).
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3. Priority or status rules (e.g. "urgent" keyword → high priority).
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4. Any special handling, date extraction, or exclusions.
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Once you reach 90% confidence, output the final prompt_template between these exact
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markers on their own lines:
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{template_start}
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<the complete extraction prompt here>
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{template_end}
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The prompt_template must be a self-contained instruction for an AI that reads files
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and must perform CRUD operations using tools to create records. It should specify:
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- What entity types to create (tasks, notes, timelines) — never projects.
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- How to map file content to record fields (camelCase: title, status, priority,
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dueDate, content, etc.) — never include projectId.
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- That isAiSuggested must be set to 1 on every new record.
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- Concrete examples of mappings based on what you discovered in the directory.
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{existing_section}\
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Keep asking clarifying questions until you are at least 90% confident you have
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enough information to generate an accurate prompt_template. Once you reach that
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confidence level, stop asking and produce the final template immediately.
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Begin by exploring the directory, then ask your first question.\
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"""
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def _build_system_prompt(
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directory: str,
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data_types: list[str],
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existing_template: str | None = None,
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) -> str:
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existing_section = (
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f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
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f"---\n{existing_template}\n---\n"
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if existing_template
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else ""
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)
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return _SYSTEM_PROMPT_TEMPLATE.format(
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directory=directory,
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data_types=", ".join(data_types),
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template_start=_TEMPLATE_START,
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template_end=_TEMPLATE_END,
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existing_section=existing_section,
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)
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# ── Template extraction ───────────────────────────────────────────────────
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def _extract_template(text: str) -> str | None:
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"""Return the text between PROMPT_TEMPLATE_START and PROMPT_TEMPLATE_END, or None."""
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if _TEMPLATE_START not in text or _TEMPLATE_END not in text:
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return None
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start_idx = text.index(_TEMPLATE_START) + len(_TEMPLATE_START)
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end_idx = text.index(_TEMPLATE_END)
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return text[start_idx:end_idx].strip() or None
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# ── LLM call with tool support ───────────────────────────────────────────
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def _as_text(content: Any) -> str:
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts: list[str] = []
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for item in content:
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if isinstance(item, str):
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parts.append(item)
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elif isinstance(item, dict):
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text = item.get("text")
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if isinstance(text, str):
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parts.append(text)
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return "".join(parts)
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return str(content)
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async def _call_llm_with_tools(
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system_prompt: str,
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history: list[dict[str, Any]],
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tools: list[Any],
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) -> str:
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"""Build LangChain messages from history and invoke the LLM with tools.
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Handles tool-calling loops: if the LLM calls tools, execute them and
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continue until a final text response is produced.
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"""
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messages: list[Any] = [SystemMessage(content=system_prompt)]
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for turn in history:
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if turn["role"] == "user":
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messages.append(HumanMessage(content=turn["content"]))
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else:
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messages.append(AIMessage(content=turn["content"]))
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llm = get_llm(model=None, temperature=0.4)
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llm_with_tools = llm.bind_tools(tools)
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tool_map = {tool_def.name: tool_def for tool_def in tools}
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for _ in range(_MAX_TOOL_STEPS):
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response: AIMessage = await llm_with_tools.ainvoke(messages)
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messages.append(response)
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if not response.tool_calls:
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return _as_text(response.content)
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for call in response.tool_calls:
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call_name = str(call.get("name", ""))
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call_args = call.get("args", {})
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logger.info(
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"journey: tool_call name=%s args=%s",
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call_name,
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json.dumps(call_args, ensure_ascii=True)[:500],
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)
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tool_fn = tool_map.get(call_name)
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if tool_fn is None:
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tool_output = f"Unknown tool: {call_name}"
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else:
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tool_output = await tool_fn.ainvoke(call_args)
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logger.info(
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"journey: tool_result name=%s output=%s",
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call_name,
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str(tool_output)[:800],
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)
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messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
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# Fallback: exceeded max tool steps.
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final = await llm.ainvoke(messages)
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return _as_text(final.content)
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# ── Journey handlers (called from redis_consumer) ────────────────────────
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async def handle_journey_start(
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user_id: str,
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frame: dict[str, Any],
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) -> dict[str, Any]:
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"""Handle a ``journey_start`` request.
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Creates a session, runs the setup LLM with directory exploration,
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and returns the ``journey_reply`` payload.
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"""
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agent_type = frame.get("agent_type", "local")
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directory = frame.get("directory", "")
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data_types = frame.get("data_types", [])
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existing_template = frame.get("existing_template")
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session_id = frame.get("session_id") or str(uuid.uuid4())
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system_prompt = _build_system_prompt(directory, data_types, existing_template)
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session = JourneySession(
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session_id=session_id,
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user_id=user_id,
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agent_type=agent_type,
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directory=directory,
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data_types=data_types,
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system_prompt=system_prompt,
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)
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seed_history: list[dict[str, Any]] = [
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{"role": "user", "content": "Hi, I'm ready to set up my agent. Please explore my directory and ask me your first question."},
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]
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ai_reply = await _call_llm_with_tools(
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system_prompt=system_prompt,
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history=seed_history,
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tools=list(FILESYSTEM_TOOLS),
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)
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session.history.extend(seed_history)
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session.history.append({"role": "assistant", "content": ai_reply})
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_sessions[session_id] = session
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logger.info(
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"journey: session %s started for user %s (directory=%s)",
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session_id,
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user_id,
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directory,
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)
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prompt_template = _extract_template(ai_reply)
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done = prompt_template is not None
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display_message = ai_reply
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if done:
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display_message = (
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ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
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or "Here is your agent configuration. You can save it or continue refining."
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)
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_sessions.pop(session_id, None)
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return {
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"type": "journey_reply",
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"session_id": session_id,
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"message": display_message,
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"done": done,
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"prompt_template": prompt_template,
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}
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async def handle_journey_message(
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user_id: str,
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frame: dict[str, Any],
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) -> dict[str, Any]:
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"""Handle a ``journey_message`` request.
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Appends the user message, calls the LLM, and returns the
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``journey_reply`` payload.
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"""
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session_id = frame.get("session_id", "")
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message = frame.get("message", "")
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session = get_journey_session(session_id, user_id)
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if session is None:
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return {
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"type": "journey_reply",
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"session_id": session_id,
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"message": "Journey session not found or expired. Please start a new setup.",
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"done": True,
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"prompt_template": None,
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}
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session.history.append({"role": "user", "content": message})
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ai_reply = await _call_llm_with_tools(
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system_prompt=session.system_prompt,
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history=session.history,
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tools=list(FILESYSTEM_TOOLS),
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)
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session.history.append({"role": "assistant", "content": ai_reply})
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prompt_template = _extract_template(ai_reply)
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done = prompt_template is not None
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if not done:
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turns = sum(1 for t in session.history if t["role"] == "user")
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if turns >= _MAX_TURNS:
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nudge_content = (
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"[System: You have enough information. Please generate the final "
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f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
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)
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session.history.append({"role": "user", "content": nudge_content})
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nudge_reply = await _call_llm_with_tools(
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system_prompt=session.system_prompt,
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history=session.history,
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tools=list(FILESYSTEM_TOOLS),
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)
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session.history.append({"role": "assistant", "content": nudge_reply})
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prompt_template = _extract_template(nudge_reply)
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if prompt_template is not None:
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done = True
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ai_reply = nudge_reply
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display_message = ai_reply
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if done:
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display_message = (
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ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
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if _TEMPLATE_START in ai_reply
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else "Here is your agent configuration. You can save it or continue refining."
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)
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_sessions.pop(session_id, None)
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logger.info("journey: session %s completed for user %s", session_id, user_id)
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return {
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"type": "journey_reply",
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"session_id": session_id,
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"message": display_message,
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"done": done,
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"prompt_template": prompt_template,
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}
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Block a user