🐛 fix(flows.py): change Flow.from_orm() to Flow.model_validate() to ensure data integrity and validation 🐛 fix(users.py): remove unused import statements to improve code cleanliness and maintainability 🐛 fix(users.py): change User.from_orm() to User.model_validate() to ensure data integrity and validation 🐛 fix(LLMChain.py): remove unused import statements to improve code cleanliness and maintainability 🐛 fix(LLMChain.py): remove unnecessary line breaks to improve code readability 🐛 fix(base.py): remove unused import statements to improve code cleanliness and maintainability 🐛 fix(base.py): remove unnecessary line breaks to improve code readability 🐛 fix(base.py): fix condition to append vertex_id to top_level_vertices to avoid appending non-string values 🐛 fix(vertex/base.py): add parent_node_id attribute to Vertex class to support hierarchical graph structures 🐛 fix(base.py): remove unused import statements to improve code cleanliness and maintainability 🚀 feat(GroupTest): add a new node for a simple chat with a custom prompt template and conversational memory buffer ℹ️ This commit adds a new node to the GroupTest project. The node is a genericNode with the following properties: - Width: 384 - Height: 621 - ID: ChatOpenAI-rUJ1b - Type: genericNode - Position: x: 170.87326389541306, y: 465.8628482073749 - Data: - Type: ChatOpenAI - Node: - Template: - Callbacks: - Required: false - Placeholder: "" - Show: false - Multiline: false - Password: false - Name: callbacks - Advanced: false - Dynamic: false - Info: "" - Type: langchain.callbacks.base.BaseCallbackHandler - List: true - Cache: - Required: false - Placeholder: "" - Show: false - Multiline: false - Password: false - Name: cache - Advanced: false - Dynamic: false - Info: "" - Type: bool - List: false - Client: - Required: false - Placeholder: "" - Show: false - Multiline: false - Password: false - Name: client - Advanced: false - Dynamic: false - Info: "" - Type: Any - List: false - Max Retries: - Required: false - Placeholder: "" - Show: false - Multiline: false - Value: 6 - Password: false - Name: max_retries - Advanced: false - Dynamic: false - Info: "" - Type: int - List: false - Max Tokens: - Required: false - Placeholder: "" - Show: true - Multiline: false - Password: true - Name: max_tokens - Advanced: false - Dynamic: false - Info: "" - Type: int - List: false 🔧 chore: fix formatting issue in code 📝 docs: update documentation link for `OpenAI` Chat large language models API 🔧 chore: update prompt template configuration in LLMChain node 📝 docs: add documentation link for PromptTemplate in the description 📝 chore(grouped_chat.json): add grouped_chat.json test data file This commit adds the `grouped_chat.json` file to the `tests/data` directory. The file contains a JSON object representing grouped chat data. This file is necessary for testing and will be used in the test suite. 📝 chore(one_group_chat.json): add one_group_chat.json test data file This commit adds the one_group_chat.json file, which contains a simple chat with a custom prompt template and conversational memory buffer. This file is used for testing purposes. 🔧 chore: update node configuration for ConversationBufferMemory, ChatOpenAI, and LLMChain 📝 docs: update documentation links for ConversationBufferMemory and LLMChain 🔧 fix: update prompt template in LLMChain to include conversation history and text input variables 🔧 fix: update ConversationBufferMemory node to include description and documentation link 🎨 style: format and organize code for better readability and maintainability 🆕 feat(Vector Store): add Vector Store agent and Vector Store Info node The Vector Store agent allows querying a Vector Store. It can be used to construct an agent from a Vector Store. The Vector Store Info node provides information about a Vector Store. The Vector Store agent and Vector Store Info node are added to support the functionality of querying a Vector Store. 🔧 chore: update configuration options in the OpenAI API client The configuration options in the OpenAI API client have been updated. This commit includes changes to the following options: - `max_tokens`: Removed the `required` flag and set `show` to `true` - `metadata`: Set `show` to `false` - `model_kwargs`: Set `show` to `true` and `advanced` to `true` - `model_name`: Added options `gpt-3.5-turbo-0613`, `gpt-3.5-turbo`, `gpt-3.5-turbo-16k-0613`, `gpt-3.5-turbo-16k`, `gpt-4-0613`, `gpt-4-32k-0613`, `gpt-4`, `gpt-4-32k` - `n`: Removed the `show` flag - `openai_api_base`: Added `display_name` as "OpenAI API Base" and updated `info` with additional details - `openai_api_key`: Removed the `required` flag and set `show` to `true` - `openai_organization`: Removed the `show` flag - `openai_proxy`: Removed the `show` flag - `request_timeout`: Removed the `show` flag - `streaming`: Removed the `show` flag - `tags`: Removed the `show` flag - `temperature`: Removed the `show` flag - `tiktoken_model_name`: Removed the `show` flag - `verbose`: Removed the `show` flag 🔧 chore: update configuration for ChatOpenAI and Chroma nodes The configuration for the ChatOpenAI and Chroma nodes has been updated. This includes changes to the allowed_special, disallowed_special, chunk_size, client, deployment, embedding_ctx_length, and max_retries properties. These changes were made to improve the functionality and performance of the nodes. 🔧 chore(config): update OpenAIEmbeddings-YwSvx configuration options The OpenAIEmbeddings-YwSvx configuration options have been updated to include new fields and values. This commit updates the configuration file to reflect these changes. 🔧 chore(config): update configuration options for OpenAIEmbeddings and Chroma 🔧 chore(config): update configuration options for OpenAIEmbeddings and Chroma to improve flexibility and customization 🔧 chore: update configuration options for RecursiveCharacterTextSplitter and WebBaseLoader in flow The configuration options for RecursiveCharacterTextSplitter and WebBaseLoader in the flow have been updated. The changes include: - Persist Directory - Chroma: The persist directory option for Chroma has been modified. - Search Kwargs - Chroma: The search kwargs option for Chroma has been modified. - Chunk Overlap - RecursiveCharacterTextSplitter: The chunk overlap option for RecursiveCharacterTextSplitter has been modified. - Chunk Size - RecursiveCharacterTextSplitter: The chunk size option for RecursiveCharacterTextSplitter has been modified. - Separator Type - RecursiveCharacterTextSplitter: The separator type option for RecursiveCharacterTextSplitter has been modified. - Separator - RecursiveCharacterTextSplitter: The separator option for RecursiveCharacterTextSplitter has been modified. - Metadata - WebBaseLoader: The metadata option for WebBaseLoader has been modified. - Web Page - WebBaseLoader: The web page option for WebBaseLoader has been modified. 🔧 chore(OpenAIEmbeddings): update OpenAIEmbeddings configuration options The OpenAIEmbeddings node configuration options have been updated to include the following changes: - `allowed_special` and `disallowed_special` now accept a list of values instead of a single value - `chunk_size` now accepts an integer value - `deployment` now accepts a string value - `embedding_ctx_length` now accepts an integer value - `headers` now supports multiline values - `max_retries` now accepts an integer value - `model` now accepts a string value - `model_kwargs` now accepts code input - `openai_api_base` now accepts a password input - `openai_api_key` now accepts a password input - `openai_api_type` now accepts a password input - `openai_api_version` now accepts a password input - `openai_organization` has been removed from the configuration options 🔧 chore: update OpenAIEmbeddings configuration options in the UI The OpenAIEmbeddings configuration options in the UI have been updated to include the following changes: - Added the `openai_organization` option to specify the OpenAI organization. - Added the `openai_proxy` option to configure the OpenAI proxy. - Added the `request_timeout` option to set the request timeout. - Added the `show_progress_bar` option to control the visibility of the progress bar. - Changed the `tiktoken_model_name` option to be a password field. - Updated the documentation link for OpenAIEmbeddings. This commit updates the configuration options to improve the usability and functionality of the OpenAIEmbeddings module in the UI. 🔧 chore: clean up unused code and remove unnecessary fields in the configuration file 📝 docs: update documentation link for the Chroma vectorstore module 🔧 chore: update configuration options for RecursiveCharacterTextSplitter in flow The configuration options for the RecursiveCharacterTextSplitter node in the flow have been updated. The following changes were made: - `chunk_size` option: The default value has been changed to 1000. - `separator_type` option: The available options have been updated to include "Text", "cpp", "go", "html", "java", "js", "latex", "markdown", "php", "proto", "python", "rst", "ruby", "rust", "scala", "sol", and "swift". - `separators` option: The default value has been changed to ".". These changes were made to improve the usability and flexibility of the RecursiveCharacterTextSplitter node in the flow. 📝 chore(vector_store_grouped.json): add vector_store_grouped.json test data file 🔀 chore(vector_store_grouped.json): add vector_store_grouped.json test data file 🔨 refactor(test_graph.py): reformat import statements and improve code readability 🔨 refactor(test_prompts_template.py): change dynamic attribute to True for input variables, output parser, partial variables, template, and validate template 🔨 refactor(test_template.py): reformat import statements and remove duplicate import of BaseModel 🔨 refactor(test_template.py): update value for options in format_dict test |
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⛓️ Langflow
~ An effortless way to experiment and prototype LangChain pipelines ~
Table of Contents
- ⛓️ Langflow
- Table of Contents
- 📦 Installation
- 🖥️ Command Line Interface (CLI)
- Deployment
- 🎨 Creating Flows
- 👋 Contributing
- 📄 License
📦 Installation
Locally
You can install Langflow from pip:
# This installs the package without dependencies for local models
pip install langflow
To use local models (e.g llama-cpp-python) run:
pip install langflow[local]
This will install the following dependencies:
You can still use models from projects like LocalAI
Next, run:
python -m langflow
or
langflow run # or langflow --help
HuggingFace Spaces
You can also check it out on HuggingFace Spaces and run it in your browser! You can even clone it and have your own copy of Langflow to play with.
🖥️ Command Line Interface (CLI)
Langflow provides a command-line interface (CLI) for easy management and configuration.
Usage
You can run the Langflow using the following command:
langflow run [OPTIONS]
Each option is detailed below:
--help: Displays all available options.--host: Defines the host to bind the server to. Can be set using theLANGFLOW_HOSTenvironment variable. The default is127.0.0.1.--workers: Sets the number of worker processes. Can be set using theLANGFLOW_WORKERSenvironment variable. The default is1.--timeout: Sets the worker timeout in seconds. The default is60.--port: Sets the port to listen on. Can be set using theLANGFLOW_PORTenvironment variable. The default is7860.--config: Defines the path to the configuration file. The default isconfig.yaml.--env-file: Specifies the path to the .env file containing environment variables. The default is.env.--log-level: Defines the logging level. Can be set using theLANGFLOW_LOG_LEVELenvironment variable. The default iscritical.--components-path: Specifies the path to the directory containing custom components. Can be set using theLANGFLOW_COMPONENTS_PATHenvironment variable. The default islangflow/components.--log-file: Specifies the path to the log file. Can be set using theLANGFLOW_LOG_FILEenvironment variable. The default islogs/langflow.log.--cache: Selects the type of cache to use. Options areInMemoryCacheandSQLiteCache. Can be set using theLANGFLOW_LANGCHAIN_CACHEenvironment variable. The default isSQLiteCache.--dev/--no-dev: Toggles the development mode. The default isno-dev.--path: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using theLANGFLOW_FRONTEND_PATHenvironment variable.--open-browser/--no-open-browser: Toggles the option to open the browser after starting the server. Can be set using theLANGFLOW_OPEN_BROWSERenvironment variable. The default isopen-browser.--remove-api-keys/--no-remove-api-keys: Toggles the option to remove API keys from the projects saved in the database. Can be set using theLANGFLOW_REMOVE_API_KEYSenvironment variable. The default isno-remove-api-keys.--install-completion [bash|zsh|fish|powershell|pwsh]: Installs completion for the specified shell.--show-completion [bash|zsh|fish|powershell|pwsh]: Shows completion for the specified shell, allowing you to copy it or customize the installation.--backend-only: This parameter, with a default value ofFalse, allows running only the backend server without the frontend. It can also be set using theLANGFLOW_BACKEND_ONLYenvironment variable.store: This parameter, with a default value ofTrue, enables the store features, use--no-storeto deactivate it. It can be configured using theLANGFLOW_STOREenvironment variable.
These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios. You may want to update the documentation to include these parameters for completeness and clarity.
Environment Variables
You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a .env file and loaded using the --env-file option.
A sample .env file named .env.example is included with the project. Copy this file to a new file named .env and replace the example values with your actual settings. If you're setting values in both your OS and the .env file, the .env settings will take precedence.
Deployment
Deploy Langflow on Google Cloud Platform
Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the Langflow in Google Cloud Platform document.
Alternatively, click the "Open in Cloud Shell" button below to launch Google Cloud Shell, clone the Langflow repository, and start an interactive tutorial that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.
Deploy on Railway
Deploy on Render
🎨 Creating Flows
Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of LangChain components to choose from, including LLMs, prompt serializers, agents, and chains.
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
Once you're done, you can export your flow as a JSON file to use with LangChain. To do so, click the "Export" button in the top right corner of the canvas, then in Python, you can load the flow with:
from langflow import load_flow_from_json
flow = load_flow_from_json("path/to/flow.json")
# Now you can use it like any chain
flow("Hey, have you heard of Langflow?")
👋 Contributing
We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
Join our Discord server to ask questions, make suggestions and showcase your projects! 🦾
📄 License
Langflow is released under the MIT License. See the LICENSE file for details.