feat: add progress indicators for ai operations
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feat: update assistant api to provide progress updates
maintenance: bump python slugify version
maintenance: add aiosqlite dependency
refactor: inject knowledge context in autonomous mode
refactor: update assistant class to use graph memory
refactor: process messages with enhanced context and progress indicator
docs: update changelog with version 1.44.0 details
maintenance: update pyproject.toml version to 1.45.0
maintenance: update requirements.txt with aiosqlite
feat: test: mock time and uuid for process_message test
feat: test: mock fact_extractor and knowledge_store for process_message test
This commit is contained in:
retoor 2025-11-08 08:21:40 +01:00
parent 686115e0f6
commit 83d0c59884
8 changed files with 69 additions and 18 deletions
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8
CHANGELOG.md
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@ -40,6 +40,14 @@
## Version 1.44.0 - 2025-11-08
AI operations now show progress indicators, giving you better feedback on long tasks. The Assistant API has been updated to provide progress updates during these operations.
**Changes:** 5 files, 32 lines
**Languages:** Markdown (8 lines), Python (22 lines), TOML (2 lines)
## Version 1.43.0 - 2025-11-08 ## Version 1.43.0 - 2025-11-08

2
pyproject.toml
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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "rp" name = "rp"
version = "1.43.0" version = "1.44.0"
description = "R python edition. The ultimate autonomous AI CLI." description = "R python edition. The ultimate autonomous AI CLI."
readme = "README.md" readme = "README.md"
requires-python = ">=3.10" requires-python = ">=3.10"

2
requirements.txt
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@ -6,4 +6,4 @@ gitpython==3.1.43
websockets==13.0.1 websockets==13.0.1
pytest==8.3.2 pytest==8.3.2
bcrypt==4.1.3 bcrypt==4.1.3
python-slugify==8.0.4 python-slugify==8.0.4aiosqlite==0.20.0

2
rp/autonomous/mode.py
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@ -19,8 +19,8 @@ def run_autonomous_mode(assistant, task):
logger.debug(f"Task: {task}") logger.debug(f"Task: {task}")
from rp.core.knowledge_context import inject_knowledge_context from rp.core.knowledge_context import inject_knowledge_context
inject_knowledge_context(assistant, task)
assistant.messages.append({"role": "user", "content": f"{task}"}) assistant.messages.append({"role": "user", "content": f"{task}"})
inject_knowledge_context(assistant, assistant.messages[-1]["content"])
try: try:
while True: while True:
assistant.autonomous_iterations += 1 assistant.autonomous_iterations += 1

33
rp/core/assistant.py
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@ -6,7 +6,9 @@ import readline
import signal import signal
import sqlite3 import sqlite3
import sys import sys
import time
import traceback import traceback
import uuid
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from rp.commands import handle_command from rp.commands import handle_command
@ -109,9 +111,10 @@ class Assistant:
self.background_tasks = set() self.background_tasks = set()
self.last_result = None self.last_result = None
self.init_database() self.init_database()
from rp.memory import KnowledgeStore, FactExtractor from rp.memory import KnowledgeStore, FactExtractor, GraphMemory
self.knowledge_store = KnowledgeStore(DB_PATH, db_conn=self.db_conn) self.knowledge_store = KnowledgeStore(DB_PATH, db_conn=self.db_conn)
self.fact_extractor = FactExtractor() self.fact_extractor = FactExtractor()
self.graph_memory = GraphMemory(DB_PATH, db_conn=self.db_conn)
self.messages.append(init_system_message(args)) self.messages.append(init_system_message(args))
try: try:
from rp.core.enhanced_assistant import EnhancedAssistant from rp.core.enhanced_assistant import EnhancedAssistant
@ -324,6 +327,8 @@ class Assistant:
) )
return self.process_response(follow_up) return self.process_response(follow_up)
content = message.get("content", "") content = message.get("content", "")
with ProgressIndicator("Updating memory..."):
self.graph_memory.populate_from_text(content)
return render_markdown(content, self.syntax_highlighting) return render_markdown(content, self.syntax_highlighting)
def signal_handler(self, signum, frame): def signal_handler(self, signum, frame):
@ -412,16 +417,18 @@ class Assistant:
cmd_result = handle_command(self, user_input) cmd_result = handle_command(self, user_input)
if cmd_result is False: if cmd_result is False:
break break
elif cmd_result is True: # If cmd_result is True, the command was handled (e.g., /auto),
continue # and the blocking operation will complete before the next prompt.
# Use enhanced processing if available, otherwise fall back to basic processing # If cmd_result is None, it's not a special command, process with LLM.
if hasattr(self, "enhanced") and self.enhanced: elif cmd_result is None:
result = self.enhanced.process_with_enhanced_context(user_input) # Use enhanced processing if available, otherwise fall back to basic processing
if result != self.last_result: if hasattr(self, "enhanced") and self.enhanced:
print(result) result = self.enhanced.process_with_enhanced_context(user_input)
self.last_result = result if result != self.last_result:
else: print(result)
process_message(self, user_input) self.last_result = result
else:
process_message(self, user_input)
except EOFError: except EOFError:
break break
except KeyboardInterrupt: except KeyboardInterrupt:
@ -487,7 +494,6 @@ class Assistant:
def process_message(assistant, message): def process_message(assistant, message):
from rp.core.knowledge_context import inject_knowledge_context from rp.core.knowledge_context import inject_knowledge_context
inject_knowledge_context(assistant, message)
# Save the user message as a fact # Save the user message as a fact
import time import time
import uuid import uuid
@ -509,6 +515,9 @@ def process_message(assistant, message):
) )
assistant.knowledge_store.add_entry(entry) assistant.knowledge_store.add_entry(entry)
assistant.messages.append({"role": "user", "content": str(entry)}) assistant.messages.append({"role": "user", "content": str(entry)})
inject_knowledge_context(assistant, assistant.messages[-1]["content"])
with ProgressIndicator("Updating memory..."):
assistant.graph_memory.populate_from_text(message)
logger.debug(f"Processing user message: {message[:100]}...") logger.debug(f"Processing user message: {message[:100]}...")
logger.debug(f"Current message count: {len(assistant.messages)}") logger.debug(f"Current message count: {len(assistant.messages)}")
with ProgressIndicator("Querying AI..."): with ProgressIndicator("Querying AI..."):

2
rp/memory/__init__.py
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@ -2,6 +2,7 @@ from .conversation_memory import ConversationMemory
from .fact_extractor import FactExtractor from .fact_extractor import FactExtractor
from .knowledge_store import KnowledgeEntry, KnowledgeStore from .knowledge_store import KnowledgeEntry, KnowledgeStore
from .semantic_index import SemanticIndex from .semantic_index import SemanticIndex
from .graph_memory import GraphMemory
__all__ = [ __all__ = [
"KnowledgeStore", "KnowledgeStore",
@ -9,4 +10,5 @@ __all__ = [
"SemanticIndex", "SemanticIndex",
"ConversationMemory", "ConversationMemory",
"FactExtractor", "FactExtractor",
"GraphMemory",
] ]

2
rp/memory/knowledge_store.py
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@ -20,7 +20,7 @@ class KnowledgeEntry:
importance_score: float = 1.0 importance_score: float = 1.0
def __str__(self): def __str__(self):
return json.dumps(self.to_dict()) return json.dumps(self.to_dict(), indent=4, sort_keys=True,default=str)
def to_dict(self) -> Dict[str, Any]: def to_dict(self) -> Dict[str, Any]:
return { return {

36
tests/test_assistant.py
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@ -88,22 +88,54 @@ class TestAssistant(unittest.TestCase):
@patch("rp.core.assistant.call_api") @patch("rp.core.assistant.call_api")
@patch("rp.core.assistant.get_tools_definition") @patch("rp.core.assistant.get_tools_definition")
def test_process_message(self, mock_tools, mock_call): @patch("time.time")
@patch("uuid.uuid4")
def test_process_message(self, mock_uuid, mock_time, mock_tools, mock_call):
assistant = MagicMock() assistant = MagicMock()
assistant.verbose = False assistant.verbose = False
assistant.use_tools = True assistant.use_tools = True
assistant.model = "model" assistant.model = "model"
assistant.api_url = "url" assistant.api_url = "url"
assistant.api_key = "key" assistant.api_key = "key"
# Mock fact_extractor and its categorize_content method
assistant.fact_extractor = MagicMock()
assistant.fact_extractor.categorize_content.return_value = ["user_message"]
# Mock knowledge_store and its add_entry method
assistant.knowledge_store = MagicMock()
assistant.knowledge_store.add_entry.return_value = None
mock_tools.return_value = [] mock_tools.return_value = []
mock_call.return_value = {"choices": [{"message": {"content": "response"}}]} mock_call.return_value = {"choices": [{"message": {"content": "response"}}]}
mock_time.return_value = 1234567890.123456
mock_uuid.return_value = MagicMock()
mock_uuid.return_value.__str__ = MagicMock(return_value="mock_uuid_value")
with patch("rp.core.assistant.render_markdown", return_value="rendered"): with patch("rp.core.assistant.render_markdown", return_value="rendered"):
with patch("builtins.print"): with patch("builtins.print"):
process_message(assistant, "test message") process_message(assistant, "test message")
assistant.messages.append.assert_called_with({"role": "user", "content": "test message"}) from rp.memory import KnowledgeEntry
import json
import time
import uuid
from unittest.mock import ANY
# Mock time.time() and uuid.uuid4() to return consistent values
expected_entry = KnowledgeEntry(
entry_id="mock_uuid_value"[:16],
category="user_message",
content="test message",
metadata={
"type": "user_message",
"confidence": 1.0,
"source": "user_input",
},
created_at=1234567890.123456,
updated_at=1234567890.123456,
)
expected_content = str(expected_entry)
assistant.knowledge_store.add_entry.assert_called_once_with(expected_entry)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()