Support fallback deployments to accompany fallback models

This is useful for example in Azure OpenAI deployments where you have a different deployment per model, so the current fallback implementation doesn't work (still uses the same deployment for each fallback attempt)

pr_agent/algo/ai_handler.py

@@ -27,7 +27,6 @@ class AiHandler:
             self.azure = False
             if get_settings().get("OPENAI.ORG", None):
                 litellm.organization = get_settings().openai.org
-            self.deployment_id = get_settings().get("OPENAI.DEPLOYMENT_ID", None)
             if get_settings().get("OPENAI.API_TYPE", None):
                 if get_settings().openai.api_type == "azure":
                     self.azure = True
@@ -45,6 +44,13 @@ class AiHandler:
         except AttributeError as e:
             raise ValueError("OpenAI key is required") from e
 
+    @property
+    def deployment_id(self):
+        """
+        Returns the deployment ID for the OpenAI API.
+        """
+        return get_settings().get("OPENAI.DEPLOYMENT_ID", None)
+
     @retry(exceptions=(APIError, Timeout, TryAgain, AttributeError, RateLimitError),
            tries=OPENAI_RETRIES, delay=2, backoff=2, jitter=(1, 3))
     async def chat_completion(self, model: str, temperature: float, system: str, user: str):

pr_agent/algo/pr_processing.py

@@ -208,13 +208,26 @@ def pr_generate_compressed_diff(top_langs: list, token_handler: TokenHandler, mo
 
 
 async def retry_with_fallback_models(f: Callable):
+    # getting all models
     model = get_settings().config.model
     fallback_models = get_settings().config.fallback_models
     if not isinstance(fallback_models, list):
-        fallback_models = [fallback_models]
+        fallback_models = [m.strip() for m in fallback_models.split(",")]
     all_models = [model] + fallback_models
-    for i, model in enumerate(all_models):
+
+    # getting all deployments
+    deployment_id = get_settings().get("openai.deployment_id", None)
+    fallback_deployments = get_settings().get("openai.fallback_deployments", [])
+    if not isinstance(fallback_deployments, list) and fallback_deployments:
+        fallback_deployments = [d.strip() for d in fallback_deployments.split(",")]
+    if fallback_deployments:
+        all_deployments = [deployment_id] + fallback_deployments
+    else:
+        all_deployments = [deployment_id] * len(all_models)
+    # try each (model, deployment_id) pair until one is successful, otherwise raise exception
+    for i, (model, deployment_id) in enumerate(zip(all_models, all_deployments)):
         try:
+            get_settings().set("openai.deployment_id", deployment_id)
             return await f(model)
         except Exception as e:
             logging.warning(f"Failed to generate prediction with {model}: {traceback.format_exc()}")

pr_agent/settings/.secrets_template.toml

@@ -14,6 +14,7 @@ key = ""  # Acquire through https://platform.openai.com
 #api_version = '2023-05-15'  # Check Azure documentation for the current API version
 #api_base = ""  # The base URL for your Azure OpenAI resource. e.g. "https://<your resource name>.openai.azure.com"
 #deployment_id = ""  # The deployment name you chose when you deployed the engine
+#fallback_deployments = []  # Match your fallback models from configuration.toml with the appropriate deployment_id
 
 [anthropic]
 key = "" # Optional, uncomment if you want to use Anthropic. Acquire through https://www.anthropic.com/