feat(llms): Add support for using SecretStr from pydantic to store sensitive API keys securely

feat(llms): Add support for using process.env.PORT environment variable to configure server port

fix(llms): Fix incorrect default value for model_kwargs parameter in AnthropicComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in ChatAnthropicComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in ChatOpenAIComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in ChatVertexAIComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in CohereComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in LlamaCppComponent

fix(llms): Fix incorrect default value for model_kwargs parameter in VertexAIComponent

fix(utilities): Fix incorrect default value for k parameter in BingSearchAPIWrapperComponent

fix(vectorstores): Fix missing required documents parameter in FAISSComponent

fix(vectorstores): Fix missing required documents parameter in PineconeComponent

fix(vectorstores): Fix missing required documents parameter in QdrantComponent

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

@@ -1,3 +1,4 @@
+from pydantic import SecretStr
 from langflow import CustomComponent
 from typing import Optional
 from langflow.field_typing import BaseLanguageModel, NestedDict
@@ -34,11 +35,11 @@ class AnthropicComponent(CustomComponent):
         self,
         anthropic_api_key: str,
         anthropic_api_url: str,
-        model_kwargs: Optional[NestedDict],
+        model_kwargs: NestedDict = {},
         temperature: Optional[float] = None,
     ) -> BaseLanguageModel:
         return Anthropic(
-            anthropic_api_key=anthropic_api_key,
+            anthropic_api_key=SecretStr(anthropic_api_key),
             anthropic_api_url=anthropic_api_url,
             model_kwargs=model_kwargs,
             temperature=temperature,

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

@@ -1,3 +1,4 @@
+from pydantic import SecretStr
 from langflow import CustomComponent
 from typing import Optional, Union, Callable
 from langflow.field_typing import BaseLanguageModel
@@ -33,13 +34,13 @@ class ChatAnthropicComponent(CustomComponent):
 
     def build(
         self,
-        anthropic_api_key: Optional[str] = None,
+        anthropic_api_key: str,
         anthropic_api_url: Optional[str] = None,
         model_kwargs: dict = {},
         temperature: Optional[float] = None,
     ) -> Union[BaseLanguageModel, Callable]:
         return ChatAnthropic(
-            anthropic_api_key=anthropic_api_key,
+            anthropic_api_key=SecretStr(anthropic_api_key),
             anthropic_api_url=anthropic_api_url,
             model_kwargs=model_kwargs,
             temperature=temperature,

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

@@ -67,7 +67,7 @@ class ChatOpenAIComponent(CustomComponent):
     def build(
         self,
         max_tokens: Optional[int] = 256,
-        model_kwargs: Optional[NestedDict] = {},
+        model_kwargs: NestedDict = {},
         model_name: str = "gpt-4-1106-preview",
         openai_api_base: Optional[str] = None,
         openai_api_key: Optional[str] = None,

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

@@ -66,12 +66,12 @@ class ChatVertexAIComponent(CustomComponent):
         project: str,
         examples: Optional[List[BaseMessage]] = [],
         location: str = "us-central1",
-        max_output_tokens: Optional[int] = 128,
+        max_output_tokens: int = 128,
         model_name: str = "chat-bison",
-        temperature: Optional[float] = 0.0,
-        top_k: Optional[int] = 40,
-        top_p: Optional[float] = 0.95,
-        verbose: Optional[bool] = False,
+        temperature: float = 0.0,
+        top_k: int = 40,
+        top_p: float = 0.95,
+        verbose: bool = False,
     ) -> Union[BaseLanguageModel, BaseLLM]:
         return ChatVertexAI(
             credentials=credentials,

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

@@ -1,6 +1,5 @@
 from langflow import CustomComponent
 from langchain_core.language_models.base import BaseLanguageModel
-from typing import Optional
 from langchain_community.llms.cohere import Cohere
 
 
@@ -19,7 +18,7 @@ class CohereComponent(CustomComponent):
     def build(
         self,
         cohere_api_key: str,
-        max_tokens: Optional[int] = 256,
-        temperature: Optional[float] = 0.75,
+        max_tokens: int = 256,
+        temperature: float = 0.75,
     ) -> BaseLanguageModel:
         return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)

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

@@ -59,36 +59,36 @@ class LlamaCppComponent(CustomComponent):
         cache: Optional[bool] = None,
         client: Optional[Any] = None,
         echo: Optional[bool] = False,
-        f16_kv: Optional[bool] = True,
+        f16_kv: bool = True,
         grammar_path: Optional[str] = None,
         last_n_tokens_size: Optional[int] = 64,
-        logits_all: Optional[bool] = False,
+        logits_all: bool = False,
         logprobs: Optional[int] = None,
         lora_base: Optional[str] = None,
         lora_path: Optional[str] = None,
         max_tokens: Optional[int] = 256,
         metadata: Optional[Dict] = None,
-        model_kwargs: Optional[Dict] = {},
+        model_kwargs: Dict = {},
         n_batch: Optional[int] = 8,
-        n_ctx: Optional[int] = 512,
+        n_ctx: int = 512,
         n_gpu_layers: Optional[int] = 1,
-        n_parts: Optional[int] = -1,
+        n_parts: int = -1,
         n_threads: Optional[int] = 1,
         repeat_penalty: Optional[float] = 1.1,
-        rope_freq_base: Optional[float] = 10000.0,
-        rope_freq_scale: Optional[float] = 1.0,
-        seed: Optional[int] = -1,
+        rope_freq_base: float = 10000.0,
+        rope_freq_scale: float = 1.0,
+        seed: int = -1,
         stop: Optional[List[str]] = [],
-        streaming: Optional[bool] = True,
+        streaming: bool = True,
         suffix: Optional[str] = "",
         tags: Optional[List[str]] = [],
         temperature: Optional[float] = 0.8,
         top_k: Optional[int] = 40,
         top_p: Optional[float] = 0.95,
-        use_mlock: Optional[bool] = False,
+        use_mlock: bool = False,
         use_mmap: Optional[bool] = True,
-        verbose: Optional[bool] = True,
-        vocab_only: Optional[bool] = False,
+        verbose: bool = True,
+        vocab_only: bool = False,
     ) -> LlamaCpp:
         return LlamaCpp(
             model_path=model_path,

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

@@ -114,7 +114,7 @@ class VertexAIComponent(CustomComponent):
         location: str = "us-central1",
         max_output_tokens: int = 128,
         max_retries: int = 6,
-        metadata: Dict = None,
+        metadata: Dict = {},
         model_name: str = "text-bison",
         n: int = 1,
         name: Optional[str] = None,
@@ -127,8 +127,6 @@ class VertexAIComponent(CustomComponent):
         tuned_model_name: Optional[str] = None,
         verbose: bool = False,
     ) -> Union[BaseLLM, Callable]:
-        if metadata is None:
-            metadata = {}
         return VertexAI(
             credentials=credentials,
             location=location,

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

@@ -1,4 +1,3 @@
-from typing import Optional
 from langflow import CustomComponent
 
 # Assuming `BingSearchAPIWrapper` is a class that exists in the context
@@ -26,7 +25,7 @@ class BingSearchAPIWrapperComponent(CustomComponent):
         self,
         bing_search_url: str,
         bing_subscription_key: str,
-        k: Optional[int] = 10,
+        k: int = 10,
     ) -> BingSearchAPIWrapper:
         # 'k' has a default value and is not shown (show=False), so it is hardcoded here
         return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)

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

@@ -23,6 +23,6 @@ class FAISSComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
-        documents: List[Document] = None,
+        documents: List[Document],
     ) -> Union[VectorStore, FAISS, BaseRetriever]:
         return FAISS.from_documents(documents=documents, embedding=embedding)

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

@@ -28,7 +28,7 @@ class PineconeComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
-        documents: List[Document] = None,
+        documents: List[Document],
         index_name: Optional[str] = None,
         pinecone_api_key: Optional[str] = None,
         pinecone_env: Optional[str] = None,

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

@@ -35,7 +35,7 @@ class QdrantComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
-        documents: List[Document] = None,
+        documents: List[Document],
         api_key: Optional[str] = None,
         collection_name: Optional[str] = None,
         content_payload_key: str = "page_content",