Fix import errors and type annotations

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

@@ -1,7 +1,8 @@
-from langflow import CustomComponent
 from langchain.embeddings.base import Embeddings
 from langchain_community.embeddings import AzureOpenAIEmbeddings
 
+from langflow import CustomComponent
+
 
 class AzureOpenAIEmbeddingsComponent(CustomComponent):
     display_name: str = "AzureOpenAIEmbeddings"
@@ -53,9 +54,9 @@ class AzureOpenAIEmbeddingsComponent(CustomComponent):
         try:
             embeddings = AzureOpenAIEmbeddings(
                 azure_endpoint=azure_endpoint,
-                deployment=azure_deployment,
-                openai_api_version=api_version,
-                openai_api_key=api_key,
+                azure_deployment=azure_deployment,
+                api_version=api_version,
+                api_key=api_key,
             )
 
         except Exception as e:

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

@@ -1,7 +1,6 @@
 from typing import Optional
 
 from langchain_community.embeddings.cohere import CohereEmbeddings
-
 from langflow import CustomComponent
 
 
@@ -27,7 +26,7 @@ class CohereEmbeddingsComponent(CustomComponent):
         truncate: Optional[str] = None,
         user_agent: str = "langchain",
     ) -> CohereEmbeddings:
-        return CohereEmbeddings(
+        return CohereEmbeddings(  # type: ignore
             max_retries=max_retries,
             user_agent=user_agent,
             request_timeout=request_timeout,

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

@@ -1,7 +1,9 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+from langchain_openai.embeddings.base import OpenAIEmbeddings
+
 from langflow import CustomComponent
 from langflow.field_typing import NestedDict
-from typing import List, Optional, Dict, Any, Union, Callable
-from langchain_openai.embeddings.base import OpenAIEmbeddings
 
 
 class OpenAIEmbeddingsComponent(CustomComponent):
@@ -102,13 +104,13 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             max_retries=max_retries,
             model=model,
             model_kwargs=model_kwargs,
-            openai_api_base=openai_api_base,
-            openai_api_key=openai_api_key,
+            base_url=openai_api_base,
+            api_key=openai_api_key,
             openai_api_type=openai_api_type,
-            openai_api_version=openai_api_version,
-            openai_organization=openai_organization,
+            api_version=openai_api_version,
+            organization=openai_organization,
             openai_proxy=openai_proxy,
-            request_timeout=request_timeout,
+            timeout=request_timeout,
             show_progress_bar=show_progress_bar,
             skip_empty=skip_empty,
             tiktoken_model_name=tiktoken_model_name,

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

@@ -1,8 +1,10 @@
-from pydantic import SecretStr
-from langflow import CustomComponent
 from typing import Optional
-from langflow.field_typing import BaseLanguageModel, NestedDict
+
 from langchain_community.llms.anthropic import Anthropic
+from pydantic.v1 import SecretStr
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, NestedDict
 
 
 class AnthropicComponent(CustomComponent):

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

@@ -1,6 +1,8 @@
-from langflow import CustomComponent
+from typing import Dict, Optional
+
 from langchain_community.llms.ctransformers import CTransformers
-from typing import Optional, Dict
+
+from langflow import CustomComponent
 
 
 class CTransformersComponent(CustomComponent):
@@ -28,4 +30,4 @@ class CTransformersComponent(CustomComponent):
         }
 
     def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:
-        return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)
+        return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)  # type: ignore

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

@@ -2,7 +2,6 @@ from typing import Optional, Union
 
 from langchain.llms import BaseLLM
 from langchain_community.chat_models.openai import ChatOpenAI
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, NestedDict
 
@@ -78,8 +77,8 @@ class ChatOpenAIComponent(CustomComponent):
         return ChatOpenAI(
             max_tokens=max_tokens,
             model_kwargs=model_kwargs,
-            model_name=model_name,
-            openai_api_base=openai_api_base,
-            openai_api_key=openai_api_key,
+            model=model_name,
+            base_url=openai_api_base,
+            api_key=openai_api_key,
             temperature=temperature,
         )

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

@@ -1,6 +1,6 @@
-from langflow import CustomComponent
-from langchain_core.language_models.base import BaseLanguageModel
 from langchain_community.llms.cohere import Cohere
+from langchain_core.language_models.base import BaseLanguageModel
+from langflow import CustomComponent
 
 
 class CohereComponent(CustomComponent):
@@ -21,4 +21,4 @@ class CohereComponent(CustomComponent):
         max_tokens: int = 256,
         temperature: float = 0.75,
     ) -> BaseLanguageModel:
-        return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)
+        return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)  # type: ignore

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

@@ -1,60 +0,0 @@
-from typing import Dict, Optional
-
-from langchain_openai.llms.base import OpenAI
-
-from langflow import CustomComponent
-
-
-class OpenAIComponent(CustomComponent):
-    display_name = "OpenAI"
-    description = "OpenAI large language models."
-
-    def build_config(self):
-        return {
-            "max_tokens": {"display_name": "Max Tokens", "default": 256},
-            "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
-            "model_name": {
-                "display_name": "Model Name",
-                "value": "text-davinci-003",
-                "options": [
-                    "text-davinci-003",
-                    "text-davinci-002",
-                    "text-curie-001",
-                    "text-babbage-001",
-                    "text-ada-001",
-                ],
-            },
-            "openai_api_base": {
-                "display_name": "OpenAI API Base",
-                "info": (
-                    "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n"
-                    "You can change this to use other APIs like JinaChat, LocalAI and Prem."
-                ),
-            },
-            "openai_api_key": {
-                "display_name": "OpenAI API Key",
-                "value": "",
-                "password": True,
-            },
-            "temperature": {"display_name": "Temperature", "value": 0.7},
-        }
-
-    def build(
-        self,
-        max_tokens: Optional[int] = 256,
-        model_kwargs: Optional[Dict] = None,
-        model_name: str = "text-davinci-003",
-        openai_api_base: Optional[str] = "",
-        openai_api_key: str = "",
-        temperature: Optional[float] = 0.7,
-    ) -> OpenAI:
-        if not openai_api_base:
-            openai_api_base = "https://api.openai.com/v1"
-        return OpenAI(
-            max_tokens=max_tokens,
-            model_kwargs=model_kwargs or {},
-            model_name=model_name,
-            openai_api_base=openai_api_base,
-            openai_api_key=openai_api_key,
-            temperature=temperature,
-        )

src/backend/langflow/components/retrievers/MultiQueryRetriever.py

@@ -1,11 +1,8 @@
-from langflow import CustomComponent
+from typing import Callable, Optional, Union
+
 from langchain.retrievers import MultiQueryRetriever
-from typing import Optional, Union, Callable
-from langflow.field_typing import (
-    PromptTemplate,
-    BaseLLM,
-    BaseRetriever,
-)
+from langflow import CustomComponent
+from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate
 
 
 class MultiQueryRetrieverComponent(CustomComponent):
@@ -46,4 +43,7 @@ class MultiQueryRetrieverComponent(CustomComponent):
         prompt: Optional[PromptTemplate] = None,
         parser_key: str = "lines",
     ) -> Union[Callable, MultiQueryRetriever]:
-        return MultiQueryRetriever(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)
+        if not prompt:
+            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)
+        else:
+            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)

src/backend/langflow/components/utilities/SerpAPIWrapper.py

@@ -1,6 +1,7 @@
-from langflow import CustomComponent
 from typing import Callable, Union
+
 from langchain_community.utilities.serpapi import SerpAPIWrapper
+from langflow import CustomComponent
 
 
 class SerpAPIWrapperComponent(CustomComponent):
@@ -24,7 +25,7 @@ class SerpAPIWrapperComponent(CustomComponent):
         serpapi_api_key: str,
         params: dict,
     ) -> Union[SerpAPIWrapper, Callable]:  # Removed quotes around SerpAPIWrapper
-        return SerpAPIWrapper(
+        return SerpAPIWrapper(  # type: ignore
             serpapi_api_key=serpapi_api_key,
             params=params,
         )

src/backend/langflow/components/utilities/WikipediaAPIWrapper.py

@@ -1,6 +1,7 @@
-from langflow import CustomComponent
-from typing import Union, Callable
+from typing import Callable, Union
+
 from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
+from langflow import CustomComponent
 
 # Assuming WikipediaAPIWrapper is a class that needs to be imported.
 # The import statement is not included as it is not provided in the JSON
@@ -14,5 +15,16 @@ class WikipediaAPIWrapperComponent(CustomComponent):
     def build_config(self):
         return {}
 
-    def build(self) -> Union[WikipediaAPIWrapper, Callable]:
-        return WikipediaAPIWrapper()
+    def build(
+        self,
+        top_k_results: int = 3,
+        lang: str = "en",
+        load_all_available_meta: bool = False,
+        doc_content_chars_max: int = 4000,
+    ) -> Union[WikipediaAPIWrapper, Callable]:
+        return WikipediaAPIWrapper(  # type: ignore
+            top_k_results=top_k_results,
+            lang=lang,
+            load_all_available_meta=load_all_available_meta,
+            doc_content_chars_max=doc_content_chars_max,
+        )

src/backend/langflow/components/utilities/WolframAlphaAPIWrapper.py

@@ -1,6 +1,8 @@
-from langflow import CustomComponent
 from typing import Callable, Union
+
 from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
+from langflow import CustomComponent
+
 # Since all the fields in the JSON have show=False, we will only create a basic component
 # without any configurable fields.
 
@@ -13,4 +15,4 @@ class WolframAlphaAPIWrapperComponent(CustomComponent):
         return {"appid": {"display_name": "App ID", "type": "str", "password": True}}
 
     def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:
-        return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)
+        return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)  # type: ignore

src/backend/langflow/components/vectorstores/Pinecone.py

@@ -7,7 +7,7 @@ from langflow.field_typing import (
 )
 from langchain.schema import BaseRetriever
 from langchain.vectorstores.base import VectorStore
-import pinecone
+import pinecone  # type: ignore
 
 
 class PineconeComponent(CustomComponent):