feat(config.py): add RETRY_FALLBACK_COUNT and RETRY_FALLBACK_DELAY
to configure retry behavior for fallback models refactor(fallback_handler.py): enhance fallback handling logic to support both prompt-based and function-calling fallbacks with retries fix(fallback_handler.py): update fallback model selection to return dictionaries for better structure and access to model properties
This commit is contained in:
Ariel Frischer
parent
55abf6e5dd
commit
0521b3ff9a
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@ -4,6 +4,8 @@ DEFAULT_RECURSION_LIMIT = 100
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DEFAULT_MAX_TEST_CMD_RETRIES = 3
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DEFAULT_MAX_TOOL_FAILURES = 3
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FALLBACK_TOOL_MODEL_LIMIT = 5
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RETRY_FALLBACK_COUNT = 3
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RETRY_FALLBACK_DELAY = 2
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VALID_PROVIDERS = [
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"anthropic",
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@ -1,4 +1,4 @@
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from ra_aid.config import DEFAULT_MAX_TOOL_FAILURES, FALLBACK_TOOL_MODEL_LIMIT
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from ra_aid.config import DEFAULT_MAX_TOOL_FAILURES, FALLBACK_TOOL_MODEL_LIMIT, RETRY_FALLBACK_COUNT, RETRY_FALLBACK_DELAY
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from ra_aid.tool_leaderboard import supported_top_tool_models
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from rich.console import Console
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from rich.markdown import Markdown
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@ -17,9 +17,11 @@ class FallbackHandler:
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def _load_fallback_tool_models(self, config):
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fallback_tool_models_config = config.get("fallback_tool_models")
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if fallback_tool_models_config:
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return [
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m.strip() for m in fallback_tool_models_config.split(",") if m.strip()
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]
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# Assume comma-separated model names; wrap each in a dict with default type "prompt"
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models = []
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for m in [x.strip() for x in fallback_tool_models_config.split(",") if x.strip()]:
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models.append({"model": m, "type": "prompt"})
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return models
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else:
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console = Console()
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supported = []
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@ -28,13 +30,13 @@ class FallbackHandler:
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provider = item.get("provider")
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model_name = item.get("model")
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if validate_provider_env(provider):
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supported.append(model_name)
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supported.append(item)
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if len(supported) == FALLBACK_TOOL_MODEL_LIMIT:
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break
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else:
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skipped.append(model_name)
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final_models = supported[:FALLBACK_TOOL_MODEL_LIMIT]
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message = "Fallback models selected: " + ", ".join(final_models)
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final_models = supported # list of dicts
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message = "Fallback models selected: " + ", ".join([m["model"] for m in final_models])
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if skipped:
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message += (
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"\nSkipped top tool calling models due to missing provider ENV API keys: "
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@ -64,36 +66,51 @@ class FallbackHandler:
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def attempt_fallback(self, code: str, logger, agent):
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logger.debug(f"_attempt_fallback: initiating fallback for code: {code}")
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new_model = self.fallback_tool_models[0]
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fallback_model = self.fallback_tool_models[0]
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failed_tool_call_name = code.split("(")[0].strip()
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logger.error(
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f"Tool call failed {self.tool_failure_consecutive_failures} times. Attempting fallback to model: {new_model} for tool: {failed_tool_call_name}"
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f"Tool call failed {self.tool_failure_consecutive_failures} times. Attempting fallback to model: {fallback_model['model']} for tool: {failed_tool_call_name}"
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)
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try:
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logger.debug(f"_attempt_fallback: validating provider {agent.provider}")
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if not validate_provider_env(agent.provider):
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logger.error(
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f"Missing environment configuration for provider {agent.provider}. Cannot fallback."
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)
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else:
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logger.debug(
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f"_attempt_fallback: initializing fallback model {new_model}"
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)
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agent.model = initialize_llm(agent.provider, new_model)
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logger.debug(
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f"_attempt_fallback: binding tools to new model using tool: {failed_tool_call_name}"
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)
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agent.model.bind_tools(agent.tools, tool_choice=failed_tool_call_name)
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self.tool_failure_used_fallbacks.add(new_model)
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logger.debug("_attempt_fallback: merging chat history for fallback")
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merge_chat_history()
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self.tool_failure_consecutive_failures = 0
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logger.debug(
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"_attempt_fallback: fallback successful and tool failure counter reset"
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)
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except Exception as switch_e:
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logger.error(f"Fallback model switching failed: {switch_e}")
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if fallback_model.get("type", "prompt").lower() == "fc":
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self.attempt_fallback_function(code, logger, agent)
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else:
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self.attempt_fallback_prompt(code, logger, agent)
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def reset_fallback_handler(self):
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self.tool_failure_consecutive_failures = 0
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self.tool_failure_used_fallbacks.clear()
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def attempt_fallback_prompt(self, code: str, logger, agent):
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logger.debug("Attempting prompt-based fallback using fallback models")
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failed_tool_call_name = code.split("(")[0].strip()
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for fallback_model in self.fallback_tool_models:
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try:
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logger.debug(f"Trying fallback model: {fallback_model['model']}")
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model = initialize_llm(agent.provider, fallback_model['model']).with_retry(retries=RETRY_FALLBACK_COUNT, delay=RETRY_FALLBACK_DELAY)
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model.bind_tools(agent.tools, tool_choice=failed_tool_call_name)
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response = model.invoke(code)
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self.tool_failure_used_fallbacks.add(fallback_model['model'])
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agent.model = model
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self.reset_fallback_handler()
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logger.debug("Prompt-based fallback executed successfully with model: " + fallback_model['model'])
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return response
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except Exception as e:
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logger.error(f"Prompt-based fallback with model {fallback_model['model']} failed: {e}")
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raise Exception("All prompt-based fallback models failed")
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def attempt_fallback_function(self, code: str, logger, agent):
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logger.debug("Attempting function-calling fallback using fallback models")
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failed_tool_call_name = code.split("(")[0].strip()
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for fallback_model in self.fallback_tool_models:
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try:
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logger.debug(f"Trying fallback model: {fallback_model['model']}")
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model = initialize_llm(agent.provider, fallback_model['model']).with_retry(retries=RETRY_FALLBACK_COUNT, delay=RETRY_FALLBACK_DELAY)
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model.bind_tools(agent.tools, tool_choice=failed_tool_call_name)
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response = model.invoke(code)
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self.tool_failure_used_fallbacks.add(fallback_model['model'])
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agent.model = model
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self.reset_fallback_handler()
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logger.debug("Function-calling fallback executed successfully with model: " + fallback_model['model'])
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return response
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except Exception as e:
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logger.error(f"Function-calling fallback with model {fallback_model['model']} failed: {e}")
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raise Exception("All function-calling fallback models failed")
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