Refactor embeddings and chat components

src/backend/langflow/components/embeddings/CohereEmbeddings.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
 from langchain_community.embeddings.cohere import CohereEmbeddings
+
 from langflow import CustomComponent
 
 
@@ -21,7 +22,7 @@ class CohereEmbeddingsComponent(CustomComponent):
         self,
         request_timeout: Optional[float] = None,
         cohere_api_key: str = "",
-        max_retries: Optional[int] = None,
+        max_retries: int = 3,
         model: str = "embed-english-v2.0",
         truncate: Optional[str] = None,
         user_agent: str = "langchain",

src/backend/langflow/components/llms/ChatAnthropic.py

@@ -1,10 +1,10 @@
-from pydantic.v1.types import SecretStr
-from langflow import CustomComponent
-from typing import Optional, Union, Callable
-from langflow.field_typing import BaseLanguageModel
+from typing import Callable, Optional, Union
 
-# from langchain_community.chat_models.anthropic import ChatAnthropic
 from langchain_anthropic import ChatAnthropic
+from pydantic.v1.types import SecretStr
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel
 
 
 class ChatAnthropicComponent(CustomComponent):
@@ -60,7 +60,7 @@ class ChatAnthropicComponent(CustomComponent):
             model_kwargs=model_kwargs,
             model_name=model_name,
             temperature=temperature,
-            max_tokens=max_tokens,
+            max_tokens=max_tokens,  # type: ignore
             top_k=top_k,
             top_p=top_p,
         )

src/backend/langflow/processing/process.py

@@ -7,13 +7,13 @@ from langchain.schema import AgentAction, Document
 from langchain_community.vectorstores import VectorStore
 from langchain_core.messages import AIMessage
 from langchain_core.runnables.base import Runnable
+from loguru import logger
+from pydantic import BaseModel
+
 from langflow.graph.graph.base import Graph
-from langflow.interface.custom.custom_component import CustomComponent
 from langflow.interface.run import build_sorted_vertices, get_memory_key, update_memory_keys
 from langflow.services.deps import get_session_service
 from langflow.services.session.service import SessionService
-from loguru import logger
-from pydantic import BaseModel
 
 
 def fix_memory_inputs(langchain_object):
@@ -172,8 +172,6 @@ async def process_inputs_dict(built_object: Union[Chain, VectorStore, Runnable],
             result = result.content
         else:
             result = result
-    elif hasattr(built_object, "run") and isinstance(built_object, CustomComponent):
-        result = built_object.run(inputs)
     else:
         result = None