Merge branch 'main' into main

.github/workflows/python_test.yml

@@ -73,7 +73,7 @@ jobs:
         with:
           timeout_minutes: 12
           max_attempts: 2
-          command: make unit_tests async=false args="-x --splits ${{ matrix.splitCount }} --group ${{ matrix.group }}"
+          command: make unit_tests async=false args="-x -vv --splits ${{ matrix.splitCount }} --group ${{ matrix.group }}"
       - name: Minimize uv cache
         run: uv cache prune --ci
   integration-tests:

.github/workflows/store_pytest_durations.yml

@@ -43,11 +43,9 @@ jobs:
       - name: Install the project
         run: uv sync --dev
       - name: Run unit tests
-        uses: nick-fields/retry@v3
-        with:
-          timeout_minutes: 12
-          max_attempts: 2
-          command: uv run pytest src/backend/tests --durations-path src/backend/tests/.test_durations --splitting-algorithm least_duration --store-durations
+        id: run_tests
+        continue-on-error: true
+        run: uv run pytest src/backend/tests/unit --durations-path src/backend/tests/.test_durations --splitting-algorithm least_duration --store-durations
       - name: Minimize uv cache
         run: uv cache prune --ci
 

CONTRIBUTING.md

@@ -35,7 +35,7 @@ so that more people can benefit from it.
 
 You can develop Langflow locally and contribute to the Project!
 
-See [DEVELOPMENT.md](DEVELOPMENT.md) for instructions on setting up and using a development envrionment.
+See [DEVELOPMENT.md](DEVELOPMENT.md) for instructions on setting up and using a development environment.
 
 ## Opening a pull request
 

DEVELOPMENT.md

@@ -13,7 +13,7 @@ You will push changes to a fork of the Langflow repository, and from there creat
 
 Fork the [Langflow GitHub repository](https://github.com/langflow-ai/langflow/fork), and follow the instructions to create a new fork.
 
-On your new fork, click the "<> Code" button to get a URL to [clone](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) using your preferred method, and clone the repostory; for example using `https`:
+On your new fork, click the "<> Code" button to get a URL to [clone](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) using your preferred method, and clone the repository; for example using `https`:
 
 ```bash
 git clone https://github.com/<your username>/langflow.git

README.md

@@ -75,3 +75,4 @@ We welcome contributions from developers of all levels. If you'd like to contrib
 ## ❤️ Contributors
 
 [![langflow contributors](https://contrib.rocks/image?repo=langflow-ai/langflow)](https://github.com/langflow-ai/langflow/graphs/contributors)
+

docs/docs/API-Reference/api-reference-api-examples.md

@@ -22,7 +22,7 @@ export LANGFLOW_URL="http://127.0.0.1:7860"
 ```
 
 * Export the `flow-id` in your terminal.
-The `flow-id` is found in the [API pane](/workspace-api) or in the flow's URL.
+The `flow-id` is found in the [API pane](/concepts-api) or in the flow's URL.
 ```plain
 export FLOW_ID="359cd752-07ea-46f2-9d3b-a4407ef618da"
 ```

docs/docs/Components/components-embedding-models.md

@@ -21,16 +21,14 @@ This embeddings component uses an OpenAI API key for authentication. Refer to yo
 
 This component generates embeddings using the [AI/ML API](https://docs.aimlapi.com/api-overview/embeddings).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
 | model_name | String | The name of the AI/ML embedding model to use |
 | aiml_api_key | SecretString | API key for authenticating with the AI/ML service |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -40,9 +38,7 @@ This component generates embeddings using the [AI/ML API](https://docs.aimlapi.c
 
 This component is used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -51,7 +47,7 @@ This component is used to load embedding models from [Amazon Bedrock](https://aw
 | endpoint_url | String | URL to set a specific service endpoint other than the default AWS endpoint |
 | region_name | String | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -64,9 +60,7 @@ Connect this component to the **Embeddings** port of the [Astra DB vector store
 This component requires that your Astra DB database has a collection that uses a vectorize embedding provider integration.
 For more information and instructions, see [Embedding Generation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -76,7 +70,7 @@ For more information and instructions, see [Embedding Generation](https://docs.d
 | provider_api_key | Provider API Key | As an alternative to `authentication`, directly provide your embedding provider credentials. |
 | model_parameters | Model Parameters | Additional model parameters |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -86,9 +80,7 @@ For more information and instructions, see [Embedding Generation](https://docs.d
 
 This component generates embeddings using Azure OpenAI models.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -98,7 +90,7 @@ This component generates embeddings using Azure OpenAI models.
 | API Version | String | The API version to use, options include various dates |
 | API Key | String | The API key to access the Azure OpenAI service |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -108,17 +100,15 @@ This component generates embeddings using Azure OpenAI models.
 
 This component is used to load embedding models from [Cohere](https://cohere.com/).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
 | cohere_api_key | String | API key required to authenticate with the Cohere service |
 | model | String | Language model used for embedding text documents and performing queries (default: `embed-english-v2.0`) |
 | truncate | Boolean | Whether to truncate the input text to fit within the model's constraints (default: `False`) |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -128,16 +118,14 @@ This component is used to load embedding models from [Cohere](https://cohere.com
 
 This component computes selected forms of similarity between two embedding vectors.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
 | embedding_vectors | Embedding Vectors | A list containing exactly two data objects with embedding vectors to compare. |
 | similarity_metric | Similarity Metric | Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance". |
 
-#### Outputs
+### Outputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -147,16 +135,14 @@ This component computes selected forms of similarity between two embedding vecto
 
 This component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the `langchain-google-genai` package.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
 | api_key | API Key | Secret API key for accessing Google's generative AI service (required) |
 | model_name | Model Name | Name of the embedding model to use (default: "models/text-embedding-004") |
 
-#### Outputs
+### Outputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -173,9 +159,7 @@ This component loads embedding models from HuggingFace.
 
 Use this component to generate embeddings using locally downloaded Hugging Face models. Ensure you have sufficient computational resources to run the models.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -191,9 +175,7 @@ This component generates embeddings using Hugging Face Inference API models.
 
 Use this component to create embeddings with Hugging Face's hosted models. Ensure you have a valid Hugging Face API key.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -209,9 +191,7 @@ Use this component to create embeddings with Hugging Face's hosted models. Ensur
 
 This component generates embeddings using MistralAI models.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -222,7 +202,7 @@ This component generates embeddings using MistralAI models.
 | timeout | Integer | Request timeout in seconds (default: 120) |
 | endpoint | String | Custom API endpoint URL (default: `https://api.mistral.ai/v1/`) |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -232,9 +212,7 @@ This component generates embeddings using MistralAI models.
 
 This component generates embeddings using NVIDIA models.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -243,7 +221,7 @@ This component generates embeddings using NVIDIA models.
 | nvidia_api_key | SecretString | API key for authenticating with NVIDIA's service |
 | temperature | Float | Model temperature for embedding generation (default: `0.1`) |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -253,17 +231,15 @@ This component generates embeddings using NVIDIA models.
 
 This component generates embeddings using Ollama models.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
 | Ollama Model | String | Name of the Ollama model to use (default: `llama2`) |
 | Ollama Base URL | String | Base URL of the Ollama API (default: `http://localhost:11434`) |
 | Model Temperature | Float | Temperature parameter for the model. Adjusts the randomness in the generated embeddings |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -273,9 +249,7 @@ This component generates embeddings using Ollama models.
 
 This component is used to load embedding models from [OpenAI](https://openai.com/).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -302,7 +276,7 @@ This component is used to load embedding models from [OpenAI](https://openai.com
 | TikToken Enable | Boolean | Whether to enable TikToken (default: `True`) |
 | TikToken Model Name | String | Name of the TikToken model |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -312,16 +286,14 @@ This component is used to load embedding models from [OpenAI](https://openai.com
 
 This component generates embeddings for a given message using a specified embedding model.
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
 | embedding_model | Embedding Model | The embedding model to use for generating embeddings. |
 | message | Message | The message for which to generate embeddings. |
 
-#### Outputs
+### Outputs
 
 | Name | Display Name | Info |
 |------|--------------|------|
@@ -331,9 +303,7 @@ This component generates embeddings for a given message using a specified embedd
 
 This component is a wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
 
-### Parameters
-
-#### Inputs
+### Inputs
 
 | Name | Type | Description |
 |------|------|-------------|
@@ -349,7 +319,7 @@ This component is a wrapper around [Google Vertex AI](https://cloud.google.com/v
 | tuned_model_name | String | The name of a tuned model. If provided, `model_name` is ignored |
 | verbose | Boolean | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug (default: `False`) |
 
-#### Outputs
+### Outputs
 
 | Name | Type | Description |
 |------|------|-------------|

docs/docs/Components/components-io.md

@@ -9,7 +9,7 @@ This category of components defines where data enters and exits your flow. They
 
 The difference between Chat Input and Text Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground.
 
-### Chat Input
+## Chat Input
 
 This component collects user input from the chat.
 
@@ -83,13 +83,13 @@ The Chat Output component creates a [Message](/configuration-objects) object tha
 
 The TextOutputComponent displays text output in the **Playground**. It takes a single input of text and returns a [Message](/configuration-objects) object containing that text. The component is simpler compared to the Chat Output, but focuses solely on displaying text without additional chat-specific features or customizations.
 
-## Inputs
+### Inputs
 
 | Name | Display Name | Info | Type |
 |------|--------------|------|------|
 |input_value|Text|Text to be passed as output.|MultilineInput|
 
-## Outputs
+### Outputs
 
 | Name | Display Name | Info |
 |------|--------------|------|