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expert API key fallback

This commit is contained in:
AI Christianson 2024-12-13 15:19:21 -05:00
parent e47b586c06
commit ea34435b3f
1 changed files with 116 additions and 92 deletions
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208
ra_aid/__main__.py
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@ -102,8 +102,8 @@ research_memory = MemorySaver()
planning_memory = MemorySaver()
implementation_memory = MemorySaver()
def get_research_tools(research_only: bool = False, expert_enabled: bool = True, llm_enabled: bool = True) -> list:
"""Get the list of research tools based on mode and availability."""
def get_research_tools(research_only: bool = False, expert_enabled: bool = True) -> list:
"""Get the list of research tools based on mode and whether expert is enabled."""
tools = [
list_directory_tree,
emit_research_subtask,
@ -119,16 +119,16 @@ def get_research_tools(research_only: bool = False, expert_enabled: bool = True,
ripgrep_search
]
if expert_enabled and llm_enabled:
if expert_enabled:
tools.append(emit_expert_context)
tools.append(ask_expert)
if not research_only and llm_enabled:
if not research_only:
tools.append(request_implementation)
return tools
def get_planning_tools(expert_enabled: bool = True, llm_enabled: bool = True) -> list:
def get_planning_tools(expert_enabled: bool = True) -> list:
tools = [
list_directory_tree,
emit_plan,
@ -142,12 +142,12 @@ def get_planning_tools(expert_enabled: bool = True, llm_enabled: bool = True) ->
fuzzy_find_project_files,
ripgrep_search
]
if expert_enabled and llm_enabled:
if expert_enabled:
tools.append(ask_expert)
tools.append(emit_expert_context)
return tools
def get_implementation_tools(expert_enabled: bool = True, llm_enabled: bool = True) -> list:
def get_implementation_tools(expert_enabled: bool = True) -> list:
tools = [
list_directory_tree,
run_shell_command,
@ -161,22 +161,25 @@ def get_implementation_tools(expert_enabled: bool = True, llm_enabled: bool = Tr
fuzzy_find_project_files,
ripgrep_search
]
if expert_enabled and llm_enabled:
if expert_enabled:
tools.append(ask_expert)
tools.append(emit_expert_context)
return tools
def is_informational_query() -> bool:
"""Determine if the current query is informational based on implementation_requested state."""
return _global_memory.get('config', {}).get('research_only', False) or not is_stage_requested('implementation')
def is_stage_requested(stage: str) -> bool:
"""Check if a stage has been requested to proceed."""
if stage == 'implementation':
return len(_global_memory.get('implementation_requested', [])) > 0
return False
def run_agent_with_retry(agent, prompt: str, config: dict):
"""Run an agent with retry logic for internal server errors."""
max_retries = 20
base_delay = 1
base_delay = 1 # Initial delay in seconds
for attempt in range(max_retries):
try:
@ -190,19 +193,21 @@ def run_agent_with_retry(agent, prompt: str, config: dict):
if attempt == max_retries - 1:
raise RuntimeError(f"Max retries ({max_retries}) exceeded. Last error: {str(e)}")
delay = base_delay * (2 ** attempt)
delay = base_delay * (2 ** attempt) # Exponential backoff
error_type = e.__class__.__name__
print_error(f"Encountered {error_type}: {str(e)}. Retrying in {delay} seconds... (Attempt {attempt + 1}/{max_retries})")
time.sleep(delay)
continue
def run_implementation_stage(base_task, tasks, plan, related_files, model, expert_enabled: bool, llm_enabled: bool):
def run_implementation_stage(base_task, tasks, plan, related_files, model, expert_enabled: bool):
"""Run implementation stage with a distinct agent for each task."""
if not is_stage_requested('implementation'):
print_stage_header("Implementation Stage Skipped")
return
print_stage_header("Implementation Stage")
# Get tasks directly from memory
task_list = _global_memory['tasks']
print_task_header(f"Found {len(task_list)} tasks to implement")
@ -210,9 +215,13 @@ def run_implementation_stage(base_task, tasks, plan, related_files, model, exper
for i, task in enumerate(task_list, 1):
print_task_header(task)
# Create a unique memory instance for this task
task_memory = MemorySaver()
task_agent = create_react_agent(model, get_implementation_tools(expert_enabled=expert_enabled, llm_enabled=llm_enabled), checkpointer=task_memory)
# Create a fresh agent for each task
task_agent = create_react_agent(model, get_implementation_tools(expert_enabled=expert_enabled), checkpointer=task_memory)
# Construct task-specific prompt
task_prompt = IMPLEMENTATION_PROMPT.format(
plan=plan,
key_facts=get_memory_value('key_facts'),
@ -222,27 +231,29 @@ def run_implementation_stage(base_task, tasks, plan, related_files, model, exper
base_task=base_task
)
if llm_enabled:
run_agent_with_retry(task_agent, task_prompt, {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100})
else:
console.print(Panel("[yellow]LLM is disabled, cannot implement tasks[/yellow]", title="No LLM Available"))
# Run agent for this task
run_agent_with_retry(task_agent, task_prompt, {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100})
def run_research_subtasks(base_task: str, config: dict, model, expert_enabled: bool, llm_enabled: bool):
def run_research_subtasks(base_task: str, config: dict, model, expert_enabled: bool):
"""Run research subtasks with separate agents."""
subtasks = _global_memory.get('research_subtasks', [])
if not subtasks:
return
print_stage_header("Research Subtasks")
# Get tools for subtask agents (excluding emit_research_subtask and implementation)
research_only = _global_memory.get('config', {}).get('research_only', False)
subtask_tools = [
t for t in get_research_tools(research_only=research_only, expert_enabled=expert_enabled, llm_enabled=llm_enabled)
t for t in get_research_tools(research_only=research_only, expert_enabled=expert_enabled)
if t.name not in ['emit_research_subtask']
]
for i, subtask in enumerate(subtasks, 1):
print_task_header(f"Research Subtask {i}/{len(subtasks)}")
# Create fresh memory and agent for each subtask
subtask_memory = MemorySaver()
subtask_agent = create_react_agent(
model,
@ -250,19 +261,25 @@ def run_research_subtasks(base_task: str, config: dict, model, expert_enabled: b
checkpointer=subtask_memory
)
# Run the subtask agent
subtask_prompt = f"Research Subtask: {subtask}\n\n{RESEARCH_PROMPT}"
if llm_enabled:
run_agent_with_retry(subtask_agent, subtask_prompt, config)
else:
console.print(Panel("[yellow]LLM is disabled, cannot perform LLM-based research[/yellow]", title="No LLM Available"))
run_agent_with_retry(subtask_agent, subtask_prompt, config)
def validate_environment(args):
"""Validate required environment variables and dependencies.
Args:
args: The parsed command line arguments
Returns:
(expert_enabled, missing_expert_info): Tuple of bool indicating if expert is enabled, and list of missing expert info
"""
missing = []
provider = args.provider
expert_provider = args.expert_provider
# Main provider keys
# Check API keys based on provider
if provider == "anthropic":
if not os.environ.get('ANTHROPIC_API_KEY'):
missing.append('ANTHROPIC_API_KEY environment variable is not set')
@ -278,63 +295,74 @@ def validate_environment(args):
if not os.environ.get('OPENAI_API_BASE'):
missing.append('OPENAI_API_BASE environment variable is not set')
# Expert keys
expert_missing = []
# Check expert provider keys with fallback
if expert_provider == "anthropic":
expert_key_missing = not os.environ.get('EXPERT_ANTHROPIC_API_KEY')
fallback_available = expert_provider == provider and os.environ.get('ANTHROPIC_API_KEY')
if expert_key_missing and not fallback_available:
if expert_key_missing and fallback_available:
os.environ['EXPERT_ANTHROPIC_API_KEY'] = os.environ['ANTHROPIC_API_KEY']
expert_key_missing = False
if expert_key_missing:
expert_missing.append('EXPERT_ANTHROPIC_API_KEY environment variable is not set')
elif expert_provider == "openai":
expert_key_missing = not os.environ.get('EXPERT_OPENAI_API_KEY')
fallback_available = expert_provider == provider and os.environ.get('OPENAI_API_KEY')
if expert_key_missing and not fallback_available:
if expert_key_missing and fallback_available:
os.environ['EXPERT_OPENAI_API_KEY'] = os.environ['OPENAI_API_KEY']
expert_key_missing = False
if expert_key_missing:
expert_missing.append('EXPERT_OPENAI_API_KEY environment variable is not set')
elif expert_provider == "openrouter":
expert_key_missing = not os.environ.get('EXPERT_OPENROUTER_API_KEY')
fallback_available = expert_provider == provider and os.environ.get('OPENROUTER_API_KEY')
if expert_key_missing and not fallback_available:
if expert_key_missing and fallback_available:
os.environ['EXPERT_OPENROUTER_API_KEY'] = os.environ['OPENROUTER_API_KEY']
expert_key_missing = False
if expert_key_missing:
expert_missing.append('EXPERT_OPENROUTER_API_KEY environment variable is not set')
elif expert_provider == "openai-compatible":
expert_key_missing = not os.environ.get('EXPERT_OPENAI_API_KEY')
fallback_available = expert_provider == provider and os.environ.get('OPENAI_API_KEY')
if expert_key_missing and not fallback_available:
if expert_key_missing and fallback_available:
os.environ['EXPERT_OPENAI_API_KEY'] = os.environ['OPENAI_API_KEY']
expert_key_missing = False
if expert_key_missing:
expert_missing.append('EXPERT_OPENAI_API_KEY environment variable is not set')
expert_base_missing = not os.environ.get('EXPERT_OPENAI_API_BASE')
base_fallback_available = expert_provider == provider and os.environ.get('OPENAI_API_BASE')
if expert_base_missing and not base_fallback_available:
if expert_base_missing and base_fallback_available:
os.environ['EXPERT_OPENAI_API_BASE'] = os.environ['OPENAI_API_BASE']
expert_base_missing = False
if expert_base_missing:
expert_missing.append('EXPERT_OPENAI_API_BASE environment variable is not set')
# If main keys missing, just disable LLM entirely
llm_enabled = True
if missing:
llm_enabled = False
# If expert keys missing, disable expert
# If main keys missing, we must exit immediately
if missing:
print_error("Missing required dependencies:")
for item in missing:
print_error(f"- {item}")
sys.exit(1)
# If expert keys missing, we disable expert tools instead of exiting
expert_enabled = True
if expert_missing:
expert_enabled = False
return llm_enabled, expert_enabled, missing, expert_missing
return expert_enabled, expert_missing
def main():
"""Main entry point for the ra-aid command line tool."""
try:
try:
args = parse_arguments()
llm_enabled, expert_enabled, missing, expert_missing = validate_environment(args)
expert_enabled, expert_missing = validate_environment(args) # Will exit if main env vars missing
# If main LLM keys missing
if missing:
# Disable LLM completely
console.print(Panel(
f"[yellow]LLM disabled due to missing main provider configuration:[/yellow]\n" +
"\n".join(f"- {m}" for m in missing) +
"\nSet the required environment variables to enable LLM features.",
title="LLM Disabled",
style="yellow"
))
# If expert keys missing
if expert_missing:
console.print(Panel(
f"[yellow]Expert tools disabled due to missing configuration:[/yellow]\n" +
@ -344,7 +372,10 @@ def main():
style="yellow"
))
# If no message, exit
# Create the base model after validation
model = initialize_llm(args.provider, args.model)
# Validate message is provided
if not args.message:
print_error("--message is required")
sys.exit(1)
@ -359,71 +390,64 @@ def main():
"cowboy_mode": args.cowboy_mode
}
# Store config in global memory for access by is_informational_query
_global_memory['config'] = config
# Store expert provider and model in config
_global_memory['config']['expert_provider'] = args.expert_provider
_global_memory['config']['expert_model'] = args.expert_model
# Only initialize the model if LLM is enabled
model = None
if llm_enabled:
model = initialize_llm(args.provider, args.model)
# Run research stage
print_stage_header("Research Stage")
research_tools = get_research_tools(
research_only=_global_memory.get('config', {}).get('research_only', False),
expert_enabled=expert_enabled,
llm_enabled=llm_enabled
# Create research agent
research_agent = create_react_agent(
model,
get_research_tools(research_only=_global_memory.get('config', {}).get('research_only', False), expert_enabled=expert_enabled),
checkpointer=research_memory
)
# If no LLM, we can't run the agent, just print a message
if llm_enabled:
research_agent = create_react_agent(
model,
research_tools,
checkpointer=research_memory
)
research_prompt = f"""User query: {base_task} --keep it simple
research_prompt = f"""User query: {base_task} --keep it simple
{RESEARCH_PROMPT}
Be very thorough in your research and emit lots of snippets, key facts. If you take more than a few steps, be eager to emit research subtasks.{'' if args.research_only else ' Only request implementation if the user explicitly asked for changes to be made.'}"""
try:
run_agent_with_retry(research_agent, research_prompt, config)
except TaskCompletedException as e:
print_stage_header("Task Completed")
raise
else:
console.print(Panel("[yellow]LLM is disabled, cannot perform LLM-based research[/yellow]", title="No LLM Available"))
run_research_subtasks(base_task, config, model, expert_enabled=expert_enabled, llm_enabled=llm_enabled)
try:
run_agent_with_retry(research_agent, research_prompt, config)
except TaskCompletedException as e:
print_stage_header("Task Completed")
raise # Re-raise to be caught by outer handler
# Run any research subtasks
run_research_subtasks(base_task, config, model, expert_enabled=expert_enabled)
# Proceed with planning and implementation if not an informational query
if not is_informational_query():
print_stage_header("Planning Stage")
planning_tools = get_planning_tools(expert_enabled=expert_enabled, llm_enabled=llm_enabled)
if llm_enabled:
planning_agent = create_react_agent(model, planning_tools, checkpointer=planning_memory)
planning_prompt = PLANNING_PROMPT.format(
research_notes=get_memory_value('research_notes'),
key_facts=get_memory_value('key_facts'),
key_snippets=get_memory_value('key_snippets'),
base_task=base_task,
related_files="\n".join(get_related_files())
)
run_agent_with_retry(planning_agent, planning_prompt, config)
else:
console.print(Panel("[yellow]LLM is disabled, cannot perform planning[/yellow]", title="No LLM Available"))
# Create planning agent
planning_agent = create_react_agent(model, get_planning_tools(expert_enabled=expert_enabled), checkpointer=planning_memory)
planning_prompt = PLANNING_PROMPT.format(
research_notes=get_memory_value('research_notes'),
key_facts=get_memory_value('key_facts'),
key_snippets=get_memory_value('key_snippets'),
base_task=base_task,
related_files="\n".join(get_related_files())
)
# Run planning agent
run_agent_with_retry(planning_agent, planning_prompt, config)
# Run implementation stage with task-specific agents
run_implementation_stage(
base_task,
get_memory_value('tasks'),
get_memory_value('plan'),
get_related_files(),
model,
expert_enabled=expert_enabled,
llm_enabled=llm_enabled
expert_enabled=expert_enabled
)
except TaskCompletedException:
sys.exit(0)