expert file context

ra_aid/__main__.py

@@ -19,9 +19,8 @@ from ra_aid.tools import (
     fuzzy_find_project_files, ripgrep_search, list_directory_tree
 )
 from ra_aid.tools.note_tech_debt import BORDER_STYLE, TECH_DEBT_NOTE_EMOJI
-from ra_aid.tools.memory import _global_memory
+from ra_aid.tools.memory import _global_memory, get_related_files
 from ra_aid import print_agent_output, print_stage_header, print_task_header
-from ra_aid.tools.programmer import related_files
 from ra_aid.prompts import (
     RESEARCH_PROMPT,
     PLANNING_PROMPT,
@@ -424,7 +423,7 @@ Be very thorough in your research and emit lots of snippets, key facts. If you t
                     key_facts=get_memory_value('key_facts'),
                     key_snippets=get_memory_value('key_snippets'),
                     base_task=base_task,
-                    related_files="\n".join(related_files)
+                    related_files="\n".join(get_related_files())
                 )
 
                 # Run planning agent
@@ -445,7 +444,7 @@ Be very thorough in your research and emit lots of snippets, key facts. If you t
                     base_task,
                     get_memory_value('tasks'),
                     get_memory_value('plan'),
-                    related_files
+                    get_related_files()
                 )
         except TaskCompletedException:
             sys.exit(0)

ra_aid/prompts.py

@@ -26,6 +26,7 @@ You must:
     Describe what exists in these files (file names, directory structures, documentation found, code patterns, dependencies).
     Do so by incrementally and systematically exploring the filesystem with careful directory listing tool calls.
     You can use fuzzy file search to quickly find relevant files matching a search pattern.
+    Use ripgrep_search extensively to do *exhaustive* searches for all references to anything that might be changed as part of the base level task.
 
 You must not:
 

ra_aid/tools/expert.py

@@ -1,9 +1,11 @@
+from typing import List
+import os
 from langchain_core.tools import tool
 from rich.console import Console
 from rich.panel import Panel
 from rich.markdown import Markdown
 from langchain_openai import ChatOpenAI
-from .memory import get_memory_value
+from .memory import get_memory_value, get_related_files
 
 console = Console()
 model = ChatOpenAI(model_name="o1-preview")
@@ -39,6 +41,61 @@ def emit_expert_context(context: str) -> str:
     
     return f"Context added."
 
+def read_files_with_limit(file_paths: List[str], max_lines: int = 10000) -> str:
+    """Read multiple files and concatenate contents, stopping at line limit.
+    
+    Args:
+        file_paths: List of file paths to read
+        max_lines: Maximum total lines to read (default: 10000)
+        
+    Returns:
+        String containing concatenated file contents with headers
+        
+    Note:
+        - Each file's contents will be prefaced with its path as a header
+        - Stops reading files when max_lines limit is reached
+        - Files that would exceed the line limit are truncated
+    """
+    total_lines = 0
+    contents = []
+    
+    for path in file_paths:
+        try:
+            if not os.path.exists(path):
+                console.print(f"Warning: File not found: {path}", style="yellow")
+                continue
+                
+            with open(path, 'r', encoding='utf-8') as f:
+                file_content = []
+                for i, line in enumerate(f):
+                    if total_lines + i >= max_lines:
+                        file_content.append(f"\n... truncated after {max_lines} lines ...")
+                        break
+                    file_content.append(line)
+                
+                if file_content:
+                    contents.append(f'\n## File: {path}\n')
+                    contents.append(''.join(file_content))
+                    total_lines += len(file_content)
+            
+        except Exception as e:
+            console.print(f"Error reading file {path}: {str(e)}", style="red")
+            continue
+            
+    return ''.join(contents)
+
+def read_related_files() -> str:
+    """Read related files from memory.
+    
+    Returns:
+        String containing concatenated file contents with headers
+    """
+    related_files = get_related_files()
+    if not related_files:
+        return ''
+    
+    return read_files_with_limit(list(related_files), max_lines=10000)
+
 @tool("ask_expert")
 def ask_expert(question: str) -> str:
     """Ask a question to an expert AI model.
@@ -66,6 +123,11 @@ def ask_expert(question: str) -> str:
     # Build query with context and key facts
     query_parts = []
     
+    # Add related file contents if any exist first
+    related_contents = read_related_files()
+    if related_contents:
+        query_parts.extend(['# Related Files', related_contents])
+    
     # Add key facts if they exist
     key_facts = get_memory_value('key_facts')
     if key_facts and len(key_facts) > 0:
@@ -98,7 +160,7 @@ def ask_expert(question: str) -> str:
         border_style="yellow"
     ))
     
-    # Clear context after use
+    # Clear context after use (only after successful panel display)
     expert_context.clear()
     
     # Get response