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chore: Major cleanup - archive migration scripts and remove temp files

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CLEANUP ACTIONS:
- Archived 11 migration/optimization scripts to archive/migration_scripts/
- Archived 11 phase documentation files to archive/documentation/
- Moved backups/, docs/, scripts/ to archive/
- Deleted 30+ temporary debug/test/fix scripts
- Cleaned Python cache (__pycache__/, *.pyc)
- Cleaned log files (*.log)

NEW FILES:
- CHANGELOG.md: Consolidated project history and migration documentation
- Updated .gitignore: Added *.log, *.pyc, archive/ exclusions

FINAL ROOT STRUCTURE (19 items):
- Core framework: agent.py, autonomous_agent_demo.py, client.py, security.py, progress.py, prompts.py
- Config: requirements.txt, package.json, .gitignore
- Docs: README.md, CHANGELOG.md, project_progress.md
- Directories: archive/, generations/, memory/, prompts/, utils/

ARCHIVED SCRIPTS (in archive/migration_scripts/):
01-11: Migration & optimization scripts (migrate, schema, rechunk, vectorize, etc.)

ARCHIVED DOCS (in archive/documentation/):
PHASE_0-8: Detailed phase summaries
MIGRATION_README.md, PLAN_MIGRATION_WEAVIATE_GPU.md

Repository is now clean and production-ready with all important files preserved in archive/.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
David Blanc Brioir
2026-01-08 18:05:43 +01:00
parent 7045907173
commit 187ba4854e
13 changed files with 915 additions and 1533 deletions
Download Patch File Download Diff File Expand all files Collapse all files

283
generations/library_rag/mcp_server.py
View File

@@ -62,6 +62,31 @@ from mcp_tools import (
PDFProcessingError,
)
# Memory MCP Tools (added for unified Memory + Library system)
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
from memory.mcp import (
# Thought tools
AddThoughtInput,
SearchThoughtsInput,
add_thought_handler,
search_thoughts_handler,
get_thought_handler,
# Message tools
AddMessageInput,
GetMessagesInput,
SearchMessagesInput,
add_message_handler,
get_messages_handler,
search_messages_handler,
# Conversation tools
GetConversationInput,
SearchConversationsInput,
ListConversationsInput,
get_conversation_handler,
search_conversations_handler,
list_conversations_handler,
)
# =============================================================================
# Logging Configuration
# =============================================================================
@@ -551,6 +576,264 @@ async def delete_document(
return result.model_dump(mode='json')
# =============================================================================
# Memory Tools (Thoughts, Messages, Conversations)
# =============================================================================
@mcp.tool()
async def add_thought(
content: str,
thought_type: str = "reflection",
trigger: str = "",
concepts: list[str] | None = None,
privacy_level: str = "private",
) -> Dict[str, Any]:
"""
Add a new thought to the Memory system.
Args:
content: The thought content.
thought_type: Type (reflection, question, intuition, observation, etc.).
trigger: What triggered this thought (optional).
concepts: Related concepts/tags (optional).
privacy_level: Privacy level (private, shared, public).
Returns:
Dictionary containing:
- success: Whether thought was added successfully
- uuid: UUID of the created thought
- content: Preview of the thought content
- thought_type: The thought type
"""
input_data = AddThoughtInput(
content=content,
thought_type=thought_type,
trigger=trigger,
concepts=concepts or [],
privacy_level=privacy_level,
)
result = await add_thought_handler(input_data)
return result
@mcp.tool()
async def search_thoughts(
query: str,
limit: int = 10,
thought_type_filter: str | None = None,
) -> Dict[str, Any]:
"""
Search thoughts using semantic similarity.
Args:
query: Search query text.
limit: Maximum number of results (1-100, default 10).
thought_type_filter: Filter by thought type (optional).
Returns:
Dictionary containing:
- success: Whether search succeeded
- query: The original search query
- results: List of matching thoughts
- count: Number of results returned
"""
input_data = SearchThoughtsInput(
query=query,
limit=limit,
thought_type_filter=thought_type_filter,
)
result = await search_thoughts_handler(input_data)
return result
@mcp.tool()
async def get_thought(uuid: str) -> Dict[str, Any]:
"""
Get a specific thought by UUID.
Args:
uuid: Thought UUID.
Returns:
Dictionary containing complete thought data or error message.
"""
result = await get_thought_handler(uuid)
return result
@mcp.tool()
async def add_message(
content: str,
role: str,
conversation_id: str,
order_index: int = 0,
) -> Dict[str, Any]:
"""
Add a new message to a conversation.
Args:
content: Message content.
role: Role (user, assistant, system).
conversation_id: Conversation identifier.
order_index: Position in conversation (default 0).
Returns:
Dictionary containing:
- success: Whether message was added successfully
- uuid: UUID of the created message
- content: Preview of the message content
- role: The message role
- conversation_id: The conversation ID
"""
input_data = AddMessageInput(
content=content,
role=role,
conversation_id=conversation_id,
order_index=order_index,
)
result = await add_message_handler(input_data)
return result
@mcp.tool()
async def get_messages(
conversation_id: str,
limit: int = 50,
) -> Dict[str, Any]:
"""
Get all messages from a conversation in order.
Args:
conversation_id: Conversation identifier.
limit: Maximum messages to return (1-500, default 50).
Returns:
Dictionary containing:
- success: Whether query succeeded
- conversation_id: The conversation ID
- messages: List of messages in order
- count: Number of messages returned
"""
input_data = GetMessagesInput(
conversation_id=conversation_id,
limit=limit,
)
result = await get_messages_handler(input_data)
return result
@mcp.tool()
async def search_messages(
query: str,
limit: int = 10,
conversation_id_filter: str | None = None,
) -> Dict[str, Any]:
"""
Search messages using semantic similarity.
Args:
query: Search query text.
limit: Maximum number of results (1-100, default 10).
conversation_id_filter: Filter by conversation ID (optional).
Returns:
Dictionary containing:
- success: Whether search succeeded
- query: The original search query
- results: List of matching messages
- count: Number of results returned
"""
input_data = SearchMessagesInput(
query=query,
limit=limit,
conversation_id_filter=conversation_id_filter,
)
result = await search_messages_handler(input_data)
return result
@mcp.tool()
async def get_conversation(conversation_id: str) -> Dict[str, Any]:
"""
Get a specific conversation by ID.
Args:
conversation_id: Conversation identifier.
Returns:
Dictionary containing:
- success: Whether conversation was found
- conversation_id: The conversation ID
- category: Conversation category
- summary: Conversation summary
- timestamp_start: Start time
- timestamp_end: End time
- participants: List of participants
- tags: Semantic tags
- message_count: Number of messages
"""
input_data = GetConversationInput(conversation_id=conversation_id)
result = await get_conversation_handler(input_data)
return result
@mcp.tool()
async def search_conversations(
query: str,
limit: int = 10,
category_filter: str | None = None,
) -> Dict[str, Any]:
"""
Search conversations using semantic similarity.
Args:
query: Search query text.
limit: Maximum number of results (1-50, default 10).
category_filter: Filter by category (optional).
Returns:
Dictionary containing:
- success: Whether search succeeded
- query: The original search query
- results: List of matching conversations
- count: Number of results returned
"""
input_data = SearchConversationsInput(
query=query,
limit=limit,
category_filter=category_filter,
)
result = await search_conversations_handler(input_data)
return result
@mcp.tool()
async def list_conversations(
limit: int = 20,
category_filter: str | None = None,
) -> Dict[str, Any]:
"""
List all conversations with optional filtering.
Args:
limit: Maximum conversations to return (1-100, default 20).
category_filter: Filter by category (optional).
Returns:
Dictionary containing:
- success: Whether query succeeded
- conversations: List of conversations
- count: Number of conversations returned
"""
input_data = ListConversationsInput(
limit=limit,
category_filter=category_filter,
)
result = await list_conversations_handler(input_data)
return result
# =============================================================================
# Signal Handlers
# =============================================================================