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https://gitee.com/wanwujie/deer-flow
synced 2026-05-01 17:50:44 +08:00
refactor: split backend into harness (deerflow.*) and app (app.*) (#1131)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
DanielWalnut
339
backend/packages/harness/deerflow/agents/memory/prompt.py
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backend/packages/harness/deerflow/agents/memory/prompt.py
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"""Prompt templates for memory update and injection."""
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import math
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import re
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from typing import Any
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try:
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import tiktoken
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TIKTOKEN_AVAILABLE = True
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except ImportError:
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TIKTOKEN_AVAILABLE = False
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# Prompt template for updating memory based on conversation
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MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
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Current Memory State:
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<current_memory>
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{current_memory}
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</current_memory>
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New Conversation to Process:
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<conversation>
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{conversation}
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</conversation>
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Instructions:
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1. Analyze the conversation for important information about the user
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2. Extract relevant facts, preferences, and context with specific details (numbers, names, technologies)
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3. Update the memory sections as needed following the detailed length guidelines below
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Memory Section Guidelines:
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**User Context** (Current state - concise summaries):
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- workContext: Professional role, company, key projects, main technologies (2-3 sentences)
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Example: Core contributor, project names with metrics (16k+ stars), technical stack
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- personalContext: Languages, communication preferences, key interests (1-2 sentences)
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Example: Bilingual capabilities, specific interest areas, expertise domains
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- topOfMind: Multiple ongoing focus areas and priorities (3-5 sentences, detailed paragraph)
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Example: Primary project work, parallel technical investigations, ongoing learning/tracking
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Include: Active implementation work, troubleshooting issues, market/research interests
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Note: This captures SEVERAL concurrent focus areas, not just one task
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**History** (Temporal context - rich paragraphs):
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- recentMonths: Detailed summary of recent activities (4-6 sentences or 1-2 paragraphs)
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Timeline: Last 1-3 months of interactions
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Include: Technologies explored, projects worked on, problems solved, interests demonstrated
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- earlierContext: Important historical patterns (3-5 sentences or 1 paragraph)
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Timeline: 3-12 months ago
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Include: Past projects, learning journeys, established patterns
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- longTermBackground: Persistent background and foundational context (2-4 sentences)
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Timeline: Overall/foundational information
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Include: Core expertise, longstanding interests, fundamental working style
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**Facts Extraction**:
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- Extract specific, quantifiable details (e.g., "16k+ GitHub stars", "200+ datasets")
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- Include proper nouns (company names, project names, technology names)
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- Preserve technical terminology and version numbers
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- Categories:
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* preference: Tools, styles, approaches user prefers/dislikes
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* knowledge: Specific expertise, technologies mastered, domain knowledge
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* context: Background facts (job title, projects, locations, languages)
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* behavior: Working patterns, communication habits, problem-solving approaches
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* goal: Stated objectives, learning targets, project ambitions
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- Confidence levels:
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* 0.9-1.0: Explicitly stated facts ("I work on X", "My role is Y")
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* 0.7-0.8: Strongly implied from actions/discussions
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* 0.5-0.6: Inferred patterns (use sparingly, only for clear patterns)
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**What Goes Where**:
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- workContext: Current job, active projects, primary tech stack
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- personalContext: Languages, personality, interests outside direct work tasks
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- topOfMind: Multiple ongoing priorities and focus areas user cares about recently (gets updated most frequently)
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Should capture 3-5 concurrent themes: main work, side explorations, learning/tracking interests
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- recentMonths: Detailed account of recent technical explorations and work
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- earlierContext: Patterns from slightly older interactions still relevant
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- longTermBackground: Unchanging foundational facts about the user
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**Multilingual Content**:
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- Preserve original language for proper nouns and company names
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- Keep technical terms in their original form (DeepSeek, LangGraph, etc.)
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- Note language capabilities in personalContext
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Output Format (JSON):
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{{
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"user": {{
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"workContext": {{ "summary": "...", "shouldUpdate": true/false }},
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"personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
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"topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
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}},
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"history": {{
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"recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
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"earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
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"longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
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}},
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"newFacts": [
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{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
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],
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"factsToRemove": ["fact_id_1", "fact_id_2"]
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}}
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Important Rules:
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- Only set shouldUpdate=true if there's meaningful new information
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- Follow length guidelines: workContext/personalContext are concise (1-3 sentences), topOfMind and history sections are detailed (paragraphs)
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- Include specific metrics, version numbers, and proper nouns in facts
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- Only add facts that are clearly stated (0.9+) or strongly implied (0.7+)
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- Remove facts that are contradicted by new information
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- When updating topOfMind, integrate new focus areas while removing completed/abandoned ones
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Keep 3-5 concurrent focus themes that are still active and relevant
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- For history sections, integrate new information chronologically into appropriate time period
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- Preserve technical accuracy - keep exact names of technologies, companies, projects
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- Focus on information useful for future interactions and personalization
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- IMPORTANT: Do NOT record file upload events in memory. Uploaded files are
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session-specific and ephemeral — they will not be accessible in future sessions.
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Recording upload events causes confusion in subsequent conversations.
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Return ONLY valid JSON, no explanation or markdown."""
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# Prompt template for extracting facts from a single message
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FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
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Message:
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{message}
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Extract facts in this JSON format:
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{{
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"facts": [
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{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
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]
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}}
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Categories:
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- preference: User preferences (likes/dislikes, styles, tools)
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- knowledge: User's expertise or knowledge areas
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- context: Background context (location, job, projects)
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- behavior: Behavioral patterns
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- goal: User's goals or objectives
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Rules:
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- Only extract clear, specific facts
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- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
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- Skip vague or temporary information
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Return ONLY valid JSON."""
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def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
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"""Count tokens in text using tiktoken.
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Args:
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text: The text to count tokens for.
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encoding_name: The encoding to use (default: cl100k_base for GPT-4/3.5).
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Returns:
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The number of tokens in the text.
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"""
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if not TIKTOKEN_AVAILABLE:
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# Fallback to character-based estimation if tiktoken is not available
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return len(text) // 4
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try:
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encoding = tiktoken.get_encoding(encoding_name)
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return len(encoding.encode(text))
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except Exception:
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# Fallback to character-based estimation on error
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return len(text) // 4
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def _coerce_confidence(value: Any, default: float = 0.0) -> float:
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"""Coerce a confidence-like value to a bounded float in [0, 1].
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Non-finite values (NaN, inf, -inf) are treated as invalid and fall back
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to the default before clamping, preventing them from dominating ranking.
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The ``default`` parameter is assumed to be a finite value.
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"""
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try:
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confidence = float(value)
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except (TypeError, ValueError):
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return max(0.0, min(1.0, default))
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if not math.isfinite(confidence):
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return max(0.0, min(1.0, default))
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return max(0.0, min(1.0, confidence))
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def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
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"""Format memory data for injection into system prompt.
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Args:
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memory_data: The memory data dictionary.
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max_tokens: Maximum tokens to use (counted via tiktoken for accuracy).
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Returns:
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Formatted memory string for system prompt injection.
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"""
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if not memory_data:
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return ""
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sections = []
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# Format user context
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user_data = memory_data.get("user", {})
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if user_data:
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user_sections = []
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work_ctx = user_data.get("workContext", {})
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if work_ctx.get("summary"):
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user_sections.append(f"Work: {work_ctx['summary']}")
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personal_ctx = user_data.get("personalContext", {})
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if personal_ctx.get("summary"):
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user_sections.append(f"Personal: {personal_ctx['summary']}")
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top_of_mind = user_data.get("topOfMind", {})
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if top_of_mind.get("summary"):
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user_sections.append(f"Current Focus: {top_of_mind['summary']}")
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if user_sections:
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sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
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# Format history
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history_data = memory_data.get("history", {})
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if history_data:
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history_sections = []
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recent = history_data.get("recentMonths", {})
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if recent.get("summary"):
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history_sections.append(f"Recent: {recent['summary']}")
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earlier = history_data.get("earlierContext", {})
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if earlier.get("summary"):
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history_sections.append(f"Earlier: {earlier['summary']}")
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if history_sections:
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sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
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# Format facts (sorted by confidence; include as many as token budget allows)
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facts_data = memory_data.get("facts", [])
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if isinstance(facts_data, list) and facts_data:
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ranked_facts = sorted(
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(
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f
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for f in facts_data
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if isinstance(f, dict)
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and isinstance(f.get("content"), str)
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and f.get("content").strip()
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),
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key=lambda fact: _coerce_confidence(fact.get("confidence"), default=0.0),
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reverse=True,
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)
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# Compute token count for existing sections once, then account
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# incrementally for each fact line to avoid full-string re-tokenization.
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base_text = "\n\n".join(sections)
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base_tokens = _count_tokens(base_text) if base_text else 0
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# Account for the separator between existing sections and the facts section.
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facts_header = "Facts:\n"
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separator_tokens = _count_tokens("\n\n" + facts_header) if base_text else _count_tokens(facts_header)
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running_tokens = base_tokens + separator_tokens
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fact_lines: list[str] = []
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for fact in ranked_facts:
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content_value = fact.get("content")
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if not isinstance(content_value, str):
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continue
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content = content_value.strip()
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if not content:
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continue
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category = str(fact.get("category", "context")).strip() or "context"
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confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
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line = f"- [{category} | {confidence:.2f}] {content}"
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# Each additional line is preceded by a newline (except the first).
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line_text = ("\n" + line) if fact_lines else line
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line_tokens = _count_tokens(line_text)
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if running_tokens + line_tokens <= max_tokens:
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fact_lines.append(line)
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running_tokens += line_tokens
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else:
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break
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if fact_lines:
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sections.append("Facts:\n" + "\n".join(fact_lines))
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if not sections:
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return ""
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result = "\n\n".join(sections)
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# Use accurate token counting with tiktoken
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token_count = _count_tokens(result)
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if token_count > max_tokens:
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# Truncate to fit within token limit
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# Estimate characters to remove based on token ratio
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char_per_token = len(result) / token_count
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target_chars = int(max_tokens * char_per_token * 0.95) # 95% to leave margin
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result = result[:target_chars] + "\n..."
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return result
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def format_conversation_for_update(messages: list[Any]) -> str:
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"""Format conversation messages for memory update prompt.
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Args:
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messages: List of conversation messages.
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Returns:
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Formatted conversation string.
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"""
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lines = []
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for msg in messages:
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role = getattr(msg, "type", "unknown")
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content = getattr(msg, "content", str(msg))
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# Handle content that might be a list (multimodal)
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if isinstance(content, list):
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text_parts = [p.get("text", "") for p in content if isinstance(p, dict) and "text" in p]
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content = " ".join(text_parts) if text_parts else str(content)
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# Strip uploaded_files tags from human messages to avoid persisting
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# ephemeral file path info into long-term memory. Skip the turn entirely
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# when nothing remains after stripping (upload-only message).
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if role == "human":
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content = re.sub(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", "", str(content)).strip()
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if not content:
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continue
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# Truncate very long messages
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if len(str(content)) > 1000:
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content = str(content)[:1000] + "..."
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if role == "human":
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lines.append(f"User: {content}")
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elif role == "ai":
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lines.append(f"Assistant: {content}")
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return "\n\n".join(lines)
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