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Fix conversation history duplication and optimize file embedding

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This major refactoring addresses critical bugs in conversation history management
and significantly improves token efficiency through intelligent file embedding:

**Key Improvements:**
• Fixed conversation history duplication bug by centralizing reconstruction in server.py
• Added intelligent file filtering to prevent re-embedding files already in conversation history
• Centralized file processing logic in BaseTool._prepare_file_content_for_prompt()
• Enhanced log monitoring with better categorization and file embedding visibility
• Updated comprehensive test suite to verify new architecture and edge cases

**Architecture Changes:**
• Removed duplicate conversation history reconstruction from tools/base.py
• Conversation history now handled exclusively by server.py:reconstruct_thread_context
• All tools now use centralized file processing with automatic deduplication
• Improved token efficiency by embedding unique files only once per conversation

**Performance Benefits:**
• Reduced token usage through smart file filtering
• Eliminated redundant file embeddings in continued conversations
• Better observability with detailed debug logging for file operations

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Fahad
2025-06-11 11:40:12 +04:00
parent 4466d1d1fe
commit 5a94737516
11 changed files with 501 additions and 84 deletions
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9
tools/analyze.py
View File

@@ -9,7 +9,6 @@ from pydantic import Field
from config import TEMPERATURE_ANALYTICAL
from prompts import ANALYZE_PROMPT
from utils import read_files
from .base import BaseTool, ToolRequest
from .models import ToolOutput
@@ -132,11 +131,9 @@ class AnalyzeTool(BaseTool):
if updated_files is not None:
request.files = updated_files
# Read all files
file_content = read_files(request.files)
# Check token limits
self._validate_token_limit(file_content, "Files")
# Use centralized file processing logic
continuation_id = getattr(request, "continuation_id", None)
file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Files")
# Build analysis instructions
analysis_focus = []

134
tools/base.py
View File

@@ -30,14 +30,16 @@ from utils import check_token_limit
from utils.conversation_memory import (
MAX_CONVERSATION_TURNS,
add_turn,
build_conversation_history,
create_thread,
get_conversation_file_list,
get_thread,
)
from utils.file_utils import read_file_content, translate_path_for_environment
from utils.file_utils import read_file_content, read_files, translate_path_for_environment
from .models import ClarificationRequest, ContinuationOffer, FollowUpRequest, ToolOutput
logger = logging.getLogger(__name__)
class ToolRequest(BaseModel):
"""
@@ -163,6 +165,123 @@ class BaseTool(ABC):
"""
return "medium" # Default to medium thinking for better reasoning
def get_conversation_embedded_files(self, continuation_id: Optional[str]) -> list[str]:
"""
Get list of files already embedded in conversation history.
This method returns the list of files that have already been embedded
in the conversation history for a given continuation thread. Tools can
use this to avoid re-embedding files that are already available in the
conversation context.
Args:
continuation_id: Thread continuation ID, or None for new conversations
Returns:
list[str]: List of file paths already embedded in conversation history
"""
if not continuation_id:
# New conversation, no files embedded yet
return []
thread_context = get_thread(continuation_id)
if not thread_context:
# Thread not found, no files embedded
return []
return get_conversation_file_list(thread_context)
def filter_new_files(self, requested_files: list[str], continuation_id: Optional[str]) -> list[str]:
"""
Filter out files that are already embedded in conversation history.
This method takes a list of requested files and removes any that have
already been embedded in the conversation history, preventing duplicate
file embeddings and optimizing token usage.
Args:
requested_files: List of files requested for current tool execution
continuation_id: Thread continuation ID, or None for new conversations
Returns:
list[str]: List of files that need to be embedded (not already in history)
"""
if not continuation_id:
# New conversation, all files are new
return requested_files
embedded_files = set(self.get_conversation_embedded_files(continuation_id))
# Return only files that haven't been embedded yet
new_files = [f for f in requested_files if f not in embedded_files]
return new_files
def _prepare_file_content_for_prompt(
self, request_files: list[str], continuation_id: Optional[str], context_description: str = "New files"
) -> str:
"""
Centralized file processing for tool prompts.
This method handles the common pattern across all tools:
1. Filter out files already embedded in conversation history
2. Read content of only new files
3. Generate informative note about skipped files
Args:
request_files: List of files requested for current tool execution
continuation_id: Thread continuation ID, or None for new conversations
context_description: Description for token limit validation (e.g. "Code", "New files")
Returns:
str: Formatted file content string ready for prompt inclusion
"""
if not request_files:
return ""
files_to_embed = self.filter_new_files(request_files, continuation_id)
content_parts = []
# Read content of new files only
if files_to_embed:
logger.debug(f"📁 {self.name} tool embedding {len(files_to_embed)} new files: {', '.join(files_to_embed)}")
try:
file_content = read_files(files_to_embed)
self._validate_token_limit(file_content, context_description)
content_parts.append(file_content)
# Estimate tokens for debug logging
from utils.token_utils import estimate_tokens
content_tokens = estimate_tokens(file_content)
logger.debug(
f"📁 {self.name} tool successfully embedded {len(files_to_embed)} files ({content_tokens:,} tokens)"
)
except Exception as e:
logger.error(f"📁 {self.name} tool failed to embed files {files_to_embed}: {type(e).__name__}: {e}")
raise
# Generate note about files already in conversation history
if continuation_id and len(files_to_embed) < len(request_files):
embedded_files = self.get_conversation_embedded_files(continuation_id)
skipped_files = [f for f in request_files if f in embedded_files]
if skipped_files:
logger.debug(
f"📁 {self.name} tool skipping {len(skipped_files)} files already in conversation history: {', '.join(skipped_files)}"
)
if content_parts:
content_parts.append("\n\n")
note_lines = [
"--- NOTE: Additional files referenced in conversation history ---",
"The following files are already available in our conversation context:",
"\n".join(f" - {f}" for f in skipped_files),
"--- END NOTE ---",
]
content_parts.append("\n".join(note_lines))
return "".join(content_parts) if content_parts else ""
def get_websearch_instruction(self, use_websearch: bool, tool_specific: Optional[str] = None) -> str:
"""
Generate web search instruction based on the use_websearch parameter.
@@ -413,15 +532,8 @@ If any of these would strengthen your analysis, specify what Claude should searc
pass
else:
logger.debug(f"Continuing {self.name} conversation with thread {continuation_id}")
# Add conversation history when continuing a threaded conversation
thread_context = get_thread(continuation_id)
if thread_context:
conversation_history = build_conversation_history(thread_context)
prompt = f"{conversation_history}\n\n{prompt}"
logger.debug(f"Added conversation history to {self.name} prompt for thread {continuation_id}")
else:
logger.warning(f"Thread {continuation_id} not found for {self.name} - continuing without history")
# History reconstruction is handled by server.py:reconstruct_thread_context
# No need to rebuild it here - prompt already contains conversation history
# Extract model configuration from request or use defaults
model_name = getattr(request, "model", None) or GEMINI_MODEL

11
tools/codereview.py
View File

@@ -21,7 +21,6 @@ from pydantic import Field
from config import TEMPERATURE_ANALYTICAL
from prompts import CODEREVIEW_PROMPT
from utils import read_files
from .base import BaseTool, ToolRequest
from .models import ToolOutput
@@ -70,6 +69,8 @@ class CodeReviewTool(BaseTool):
"PROFESSIONAL CODE REVIEW - Comprehensive analysis for bugs, security, and quality. "
"Supports both individual files and entire directories/projects. "
"Use this when you need to review code, check for issues, find bugs, or perform security audits. "
"ALSO use this to validate claims about code, verify code flow and logic, confirm assertions, "
"cross-check functionality, or investigate how code actually behaves when you need to be certain. "
"I'll identify issues by severity (Critical→High→Medium→Low) with specific fixes. "
"Supports focused reviews: security, performance, or quick checks. "
"Choose thinking_mode based on review scope: 'low' for small code snippets, "
@@ -185,11 +186,9 @@ class CodeReviewTool(BaseTool):
if updated_files is not None:
request.files = updated_files
# Read all requested files, expanding directories as needed
file_content = read_files(request.files)
# Validate that the code fits within model context limits
self._validate_token_limit(file_content, "Code")
# Use centralized file processing logic
continuation_id = getattr(request, "continuation_id", None)
file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
# Build customized review instructions based on review type
review_focus = []

9
tools/debug.py
View File

@@ -9,7 +9,6 @@ from pydantic import Field
from config import TEMPERATURE_ANALYTICAL
from prompts import DEBUG_ISSUE_PROMPT
from utils import read_files
from .base import BaseTool, ToolRequest
from .models import ToolOutput
@@ -159,8 +158,12 @@ class DebugIssueTool(BaseTool):
# Add relevant files if provided
if request.files:
file_content = read_files(request.files)
context_parts.append(f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ===")
# Use centralized file processing logic
continuation_id = getattr(request, "continuation_id", None)
file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
if file_content:
context_parts.append(f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ===")
full_context = "\n".join(context_parts)

9
tools/thinkdeep.py
View File

@@ -9,7 +9,6 @@ from pydantic import Field
from config import TEMPERATURE_CREATIVE
from prompts import THINKDEEP_PROMPT
from utils import read_files
from .base import BaseTool, ToolRequest
from .models import ToolOutput
@@ -142,8 +141,12 @@ class ThinkDeepTool(BaseTool):
# Add reference files if provided
if request.files:
file_content = read_files(request.files)
context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")
# Use centralized file processing logic
continuation_id = getattr(request, "continuation_id", None)
file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Reference files")
if file_content:
context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")
full_context = "\n".join(context_parts)