(Refactor) Code Quality improvement - Use Common base handler for `an…

…thropic_text/` (BerriAI#7143)

* add anthropic text provider

* add ANTHROPIC_TEXT to LlmProviders

* fix anthropic text implementation

* working anthropic text claude-2

* test_acompletion_claude2_stream

* add param mapping for anthropic text

* fix unused imports

* fix anthropic completion handler.py

litellm/__init__.py

@@ -846,6 +846,7 @@ class LlmProviders(str, Enum):
     COHERE_CHAT = "cohere_chat"
     CLARIFAI = "clarifai"
     ANTHROPIC = "anthropic"
+    ANTHROPIC_TEXT = "anthropic_text"
     REPLICATE = "replicate"
     HUGGINGFACE = "huggingface"
     TOGETHER_AI = "together_ai"
@@ -1060,7 +1061,7 @@ class LlmProviders(str, Enum):
     AnthropicExperimentalPassThroughConfig,
 )
 from .llms.groq.stt.transformation import GroqSTTConfig
-from .llms.anthropic.completion import AnthropicTextConfig
+from .llms.anthropic.completion.transformation import AnthropicTextConfig
 from .llms.databricks.chat.transformation import DatabricksConfig
 from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig
 from .llms.predibase import PredibaseConfig

litellm/constants.py

@@ -12,6 +12,7 @@
     "cohere_chat",
     "clarifai",
     "anthropic",
+    "anthropic_text",
     "replicate",
     "huggingface",
     "together_ai",

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -52,6 +52,39 @@ def handle_cohere_chat_model_custom_llm_provider(
     return model, custom_llm_provider
 
 
+def handle_anthropic_text_model_custom_llm_provider(
+    model: str, custom_llm_provider: Optional[str] = None
+) -> Tuple[str, Optional[str]]:
+    """
+    if user sets model = "anthropic/claude-2" -> use custom_llm_provider = "anthropic_text"
+
+    Args:
+        model:
+        custom_llm_provider:
+
+    Returns:
+        model, custom_llm_provider
+    """
+
+    if custom_llm_provider:
+        if (
+            custom_llm_provider == "anthropic"
+            and litellm.AnthropicTextConfig._is_anthropic_text_model(model)
+        ):
+            return model, "anthropic_text"
+
+    if "/" in model:
+        _custom_llm_provider, _model = model.split("/", 1)
+        if (
+            _custom_llm_provider
+            and _custom_llm_provider == "anthropic"
+            and litellm.AnthropicTextConfig._is_anthropic_text_model(_model)
+        ):
+            return _model, "anthropic_text"
+
+    return model, custom_llm_provider
+
+
 def get_llm_provider(  # noqa: PLR0915
     model: str,
     custom_llm_provider: Optional[str] = None,
@@ -92,6 +125,10 @@ def get_llm_provider(  # noqa: PLR0915
             model, custom_llm_provider
         )
 
+        model, custom_llm_provider = handle_anthropic_text_model_custom_llm_provider(
+            model, custom_llm_provider
+        )
+
         if custom_llm_provider:
             if (
                 model.split("/")[0] == custom_llm_provider
@@ -210,7 +247,10 @@ def get_llm_provider(  # noqa: PLR0915
             custom_llm_provider = "text-completion-openai"
         ## anthropic
         elif model in litellm.anthropic_models:
-            custom_llm_provider = "anthropic"
+            if litellm.AnthropicTextConfig._is_anthropic_text_model(model):
+                custom_llm_provider = "anthropic_text"
+            else:
+                custom_llm_provider = "anthropic"
         ## cohere
         elif model in litellm.cohere_models or model in litellm.cohere_embedding_models:
             custom_llm_provider = "cohere"
@@ -531,7 +571,9 @@ def _get_openai_compatible_provider_info(  # noqa: PLR0915
         )
     elif custom_llm_provider == "galadriel":
         api_base = (
-            api_base or get_secret("GALADRIEL_API_BASE") or "https://api.galadriel.com/v1"
+            api_base
+            or get_secret("GALADRIEL_API_BASE")
+            or "https://api.galadriel.com/v1"
         )  # type: ignore
         dynamic_api_key = api_key or get_secret_str("GALADRIEL_API_KEY")
     if api_base is not None and not isinstance(api_base, str):

litellm/litellm_core_utils/streaming_handler.py

@@ -223,40 +223,6 @@ def check_special_tokens(self, chunk: str, finish_reason: Optional[str]):
             self.holding_chunk = ""
         return hold, curr_chunk
 
-    def handle_anthropic_text_chunk(self, chunk):
-        """
-        For old anthropic models - claude-1, claude-2.
-
-        Claude-3 is handled from within Anthropic.py VIA ModelResponseIterator()
-        """
-        str_line = chunk
-        if isinstance(chunk, bytes):  # Handle binary data
-            str_line = chunk.decode("utf-8")  # Convert bytes to string
-        text = ""
-        is_finished = False
-        finish_reason = None
-        if str_line.startswith("data:"):
-            data_json = json.loads(str_line[5:])
-            type_chunk = data_json.get("type", None)
-            if type_chunk == "completion":
-                text = data_json.get("completion")
-                finish_reason = data_json.get("stop_reason")
-                if finish_reason is not None:
-                    is_finished = True
-            return {
-                "text": text,
-                "is_finished": is_finished,
-                "finish_reason": finish_reason,
-            }
-        elif "error" in str_line:
-            raise ValueError(f"Unable to parse response. Original response: {str_line}")
-        else:
-            return {
-                "text": text,
-                "is_finished": is_finished,
-                "finish_reason": finish_reason,
-            }
-
     def handle_predibase_chunk(self, chunk):
         try:
             if not isinstance(chunk, str):
@@ -1005,14 +971,6 @@ def chunk_creator(self, chunk):  # type: ignore  # noqa: PLR0915
                         setattr(model_response, key, value)
 
                 response_obj = anthropic_response_obj
-            elif (
-                self.custom_llm_provider
-                and self.custom_llm_provider == "anthropic_text"
-            ):
-                response_obj = self.handle_anthropic_text_chunk(chunk)
-                completion_obj["content"] = response_obj["text"]
-                if response_obj["is_finished"]:
-                    self.received_finish_reason = response_obj["finish_reason"]
             elif self.model == "replicate" or self.custom_llm_provider == "replicate":
                 response_obj = self.handle_replicate_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]