fix: updating components to access secret key with new method get_secret_value() (#4243)

* updating components to access secret key with new method

* [autofix.ci] apply automated fixes

* 📝 (text_embedder.py): Add error messages as constants for better error handling and readability
📝 (text_embedder.py): Improve error handling and validation for embedding model and text content
📝 (text_embedder.py): Ensure proper protocol for the base URL in the embedding model client
📝 (text_embedder.py): Validate the output of embeddings and handle exceptions with logging
📝 (text_embedder.py): Refactor code to encapsulate results in a Data object and update status accordingly
📝 (similarity.spec.ts): Add a delay before clicking the button to run the text output test for better synchronization

* uv fix

* [autofix.ci] apply automated fixes

* 📝 (text_embedder.py): refactor error messages to use inline strings for better readability and maintainability
🐛 (text_embedder.py): fix issue with extracting the first element from embeddings list to ensure correct data handling

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Hare <ericrhare@gmail.com>

src/backend/base/langflow/components/embeddings/huggingface_inference_api.py

@@ -91,7 +91,7 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
             msg = "API Key is required for non-local inference endpoints"
             raise ValueError(msg)
         else:
-            api_key = SecretStr(self.api_key)
+            api_key = SecretStr(self.api_key).get_secret_value()
 
         try:
             return self.create_huggingface_embeddings(api_key, api_url, self.model_name)

src/backend/base/langflow/components/embeddings/mistral.py

@@ -46,7 +46,7 @@ class MistralAIEmbeddingsComponent(LCModelComponent):
             msg = "Mistral API Key is required"
             raise ValueError(msg)
 
-        api_key = SecretStr(self.mistral_api_key)
+        api_key = SecretStr(self.mistral_api_key).get_secret_value()
 
         return MistralAIEmbeddings(
             api_key=api_key,

src/backend/base/langflow/components/embeddings/text_embedder.py

@@ -1,3 +1,4 @@
+import logging
 from typing import TYPE_CHECKING
 
 from langflow.custom import Component
@@ -13,7 +14,6 @@ class TextEmbedderComponent(Component):
     display_name: str = "Text Embedder"
     description: str = "Generate embeddings for a given message using the specified embedding model."
     icon = "binary"
-
     inputs = [
         HandleInput(
             name="embedding_model",
@@ -27,26 +27,55 @@ class TextEmbedderComponent(Component):
             info="The message to generate embeddings for.",
         ),
     ]
-
     outputs = [
         Output(display_name="Embedding Data", name="embeddings", method="generate_embeddings"),
     ]
 
     def generate_embeddings(self) -> Data:
-        embedding_model: Embeddings = self.embedding_model
-        message: Message = self.message
+        try:
+            embedding_model: Embeddings = self.embedding_model
+            message: Message = self.message
 
-        # Extract the text content from the message
-        text_content = message.text
+            # Validate embedding model
+            if not embedding_model:
+                msg = "Embedding model not provided"
+                raise ValueError(msg)
 
-        # Generate embeddings using the provided embedding model
-        embeddings = embedding_model.embed_documents([text_content])
+            # Extract the text content from the message
+            text_content = message.text if message and message.text else ""
+            if not text_content:
+                msg = "No text content found in message"
+                raise ValueError(msg)
 
-        # Assuming the embedding model returns a list of embeddings, we take the first one
-        embedding_vector = embeddings[0] if embeddings else []
+            # Check if the embedding model has the required attributes
+            if not hasattr(embedding_model, "client") or not embedding_model.client:
+                msg = "Embedding model client not properly initialized"
+                raise ValueError(msg)
+
+            # Ensure the base URL has proper protocol
+            if hasattr(embedding_model.client, "base_url"):
+                base_url = embedding_model.client.base_url
+                if not base_url.startswith(("http://", "https://")):
+                    embedding_model.client.base_url = f"https://{base_url}"
+
+            # Generate embeddings using the provided embedding model
+            embeddings = embedding_model.embed_documents([text_content])
+
+            # Validate embeddings output
+            if not embeddings or not isinstance(embeddings, list):
+                msg = "Invalid embeddings generated"
+                raise ValueError(msg)
+
+            embedding_vector = embeddings[0]
+
+        except Exception as e:
+            logging.exception("Error generating embeddings")
+            # Return empty data with error status
+            error_data = Data(data={"text": "", "embeddings": [], "error": str(e)})
+            self.status = {"error": str(e)}
+            return error_data
 
         # Create a Data object to encapsulate the results
         result_data = Data(data={"text": text_content, "embeddings": embedding_vector})
-
         self.status = {"text": text_content, "embeddings": embedding_vector}
         return result_data

src/backend/base/langflow/components/models/anthropic.py

@@ -68,7 +68,7 @@ class AnthropicModelComponent(LCModelComponent):
         try:
             output = ChatAnthropic(
                 model=model,
-                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
+                anthropic_api_key=(SecretStr(anthropic_api_key).get_secret_value() if anthropic_api_key else None),
                 max_tokens_to_sample=max_tokens,
                 temperature=temperature,
                 anthropic_api_url=anthropic_api_url,

src/backend/base/langflow/components/models/baidu_qianfan_chat.py

@@ -88,8 +88,8 @@ class QianfanChatEndpointComponent(LCModelComponent):
         try:
             output = QianfanChatEndpoint(
                 model=model,
-                qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,
-                qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,
+                qianfan_ak=SecretStr(qianfan_ak).get_secret_value() if qianfan_ak else None,
+                qianfan_sk=SecretStr(qianfan_sk).get_secret_value() if qianfan_sk else None,
                 top_p=top_p,
                 temperature=temperature,
                 penalty_score=penalty_score,

src/backend/base/langflow/components/models/cohere.py

@@ -37,7 +37,7 @@ class CohereComponent(LCModelComponent):
         cohere_api_key = self.cohere_api_key
         temperature = self.temperature
 
-        api_key = SecretStr(cohere_api_key) if cohere_api_key else None
+        api_key = SecretStr(cohere_api_key).get_secret_value() if cohere_api_key else None
 
         return ChatCohere(
             temperature=temperature or 0.75,

src/backend/base/langflow/components/models/google_generative_ai.py

@@ -80,5 +80,5 @@ class GoogleGenerativeAIComponent(LCModelComponent):
             top_k=top_k or None,
             top_p=top_p or None,
             n=n or 1,
-            google_api_key=SecretStr(google_api_key),
+            google_api_key=SecretStr(google_api_key).get_secret_value(),
         )

src/backend/base/langflow/components/models/groq.py

@@ -98,6 +98,6 @@ class GroqModel(LCModelComponent):
             temperature=temperature,
             base_url=groq_api_base,
             n=n or 1,
-            api_key=SecretStr(groq_api_key),
+            api_key=SecretStr(groq_api_key).get_secret_value(),
             streaming=stream,
         )

src/backend/base/langflow/components/models/mistral.py

@@ -77,7 +77,7 @@ class MistralAIModelComponent(LCModelComponent):
         random_seed = self.random_seed
         safe_mode = self.safe_mode
 
-        api_key = SecretStr(mistral_api_key) if mistral_api_key else None
+        api_key = SecretStr(mistral_api_key).get_secret_value() if mistral_api_key else None
 
         return ChatMistralAI(
             max_tokens=max_tokens or None,

src/backend/base/langflow/components/models/openai.py

@@ -95,7 +95,7 @@ class OpenAIModelComponent(LCModelComponent):
         json_mode = bool(output_schema_dict) or self.json_mode
         seed = self.seed
 
-        api_key = SecretStr(openai_api_key) if openai_api_key else None
+        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None
         output = ChatOpenAI(
             max_tokens=max_tokens or None,
             model_kwargs=model_kwargs,

src/backend/base/langflow/services/settings/auth.py

@@ -110,4 +110,4 @@ class AuthSettings(BaseSettings):
                 write_secret_to_file(secret_key_path, value)
                 logger.debug("Saved secret key")
 
-        return value if isinstance(value, SecretStr) else SecretStr(value)
+        return value if isinstance(value, SecretStr) else SecretStr(value).get_secret_value()

src/frontend/package-lock.json

@@ -923,7 +923,6 @@
     },
     "node_modules/@clack/prompts/node_modules/is-unicode-supported": {
       "version": "1.3.0",
-      "extraneous": true,
       "inBundle": true,
       "license": "MIT",
       "engines": {

src/frontend/tests/core/integrations/similarity.spec.ts

@@ -319,6 +319,8 @@ test("user must be able to check similarity between embedding texts", async ({
   await textOutputInput.hover();
   await page.mouse.up();
 
+  await page.waitForTimeout(3000);
+
   await page.getByTestId("button_run_text output").click();
 
   await page.waitForSelector("text=built successfully", { timeout: 30000 });