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@ -10,8 +10,7 @@ Langflow provides an API key functionality that allows users to access their ind
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The default user and password are set using the LANGFLOW_SUPERUSER and
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LANGFLOW_SUPERUSER_PASSWORD environment variables.
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The default values are
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langflow and langflow, respectively.
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The default values are `langflow` and `langflow`, respectively.
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</Admonition>
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@ -1,62 +1,51 @@
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# Command Line Interface (CLI)
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## Overview
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Langflow's Command Line Interface (CLI) is a powerful tool that allows you to interact with the Langflow server from the command line. The CLI provides a wide range of commands to help you shape Langflow to your needs.
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Running the CLI without any arguments will display a list of available commands and options.
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The available commands are below. Navigate to their individual sections of this page to see the parameters.
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* [langflow](#overview)
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* [langflow api-key](#langflow-api-key)
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* [langflow copy-db](#langflow-copy-db)
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* [langflow migration](#langflow-migration)
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* [langflow run](#langflow-run)
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* [langflow superuser](#langflow-superuser)
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## Overview
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Running the CLI without any arguments displays a list of available options and commands.
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```bash
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python -m langflow run --help
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langflow
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# or
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python -m langflow run
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langflow --help
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# or
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python -m langflow
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```
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Each option for `run` command are detailed below:
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| Command | Description |
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| ------- | ----------- |
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| `api-key` | Creates an API key for the default superuser if AUTO_LOGIN is enabled. |
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| `copy-db` | Copy the database files to the current directory (`which langflow`). |
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| `migration` | Run or test migrations. |
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| `run` | Run the Langflow. |
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| `superuser` | Create a superuser. |
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- `--help`: Displays all available options.
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- `--host`: Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`.
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- `--workers`: Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.
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- `--timeout`: Sets the worker timeout in seconds. The default is `60`.
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- `--port`: Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.
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- `--env-file`: Specifies the path to the .env file containing environment variables. The default is `.env`.
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- `--log-level`: Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.
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- `--components-path`: Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.
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- `--log-file`: Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.
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- `--cache`: Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.
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- `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`.
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- `--path`: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.
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- `--open-browser/--no-open-browser`: Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.
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- `--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 the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`.
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- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell.
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- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation.
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- `--backend-only`: This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable.
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- `--store`: This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable.
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### Options
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These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios.
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| Option | Description |
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||||
| ------ | ----------- |
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| `--install-completion` | Install completion for the current shell. |
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| `--show-completion` | Show completion for the current shell, to copy it or customize the installation. |
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| `--help` | Show this message and exit. |
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### API Key Command
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## langflow api-key
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The `api-key` command allows you to create an API key for accessing Langflow's API when `LANGFLOW_AUTO_LOGIN` is set to `True`.
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```bash
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python -m langflow api-key --help
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Usage: langflow api-key [OPTIONS]
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Creates an API key for the default superuser if AUTO_LOGIN is enabled.
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Args: log_level (str, optional): Logging level. Defaults to "error".
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Returns: None
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╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
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│ --log-level TEXT Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] │
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│ --help Show this message and exit. │
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╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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```
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Once you run the `api-key` command, it will create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`.
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Run the `api-key` command to create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`.
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```bash
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langflow api-key
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# or
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python -m langflow api-key
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╭─────────────────────────────────────────────────────────────────────╮
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│ API Key Created Successfully: │
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@ -67,11 +56,99 @@ python -m langflow api-key
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│ Make sure to store it in a secure location. │
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│ │
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│ The API key has been copied to your clipboard. Cmd + V to paste it. │
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╰─────────────────────────────────────────────────────────────────────╯
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╰──────────────────────────────
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```
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### Environment Variables
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### Options
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| Option | Type | Description |
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||||
|------------------|------|-------------------------------------------------------------|
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| --log-level | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] |
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||||
| --help | | Show this message and exit. |
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||||
|
||||
## langflow copy-db
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Run the `copy-db` command to copy the cached `langflow.db` and `langflow-pre.db` database files to the current directory.
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|
||||
If the files exist in the cache directory, they will be copied to the same directory as `__main__.py`, which can be found with `which langflow`.
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|
||||
### Options
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None.
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## langflow migration
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Run or test migrations with the [Alembic](https://pypi.org/project/alembic/) database tool.
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|
||||
```bash
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langflow migration
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# or
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python -m langflow migration
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```
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||||
|
||||
### Options
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||||
| Option | Description |
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||||
|-----------------|-------------------------------------------------------------|
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||||
| `--test, --no-test` | Run migrations in test mode. [default: test] |
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| `--fix, --no-fix` | Fix migrations. This is a destructive operation, and should only be used if you know what you are doing. [default: no-fix] |
|
||||
| `--help` | Show this message and exit. |
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||||
|
||||
|
||||
## langflow run
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||||
|
||||
Run Langflow.
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||||
|
||||
```bash
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langflow run
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||||
# or
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python -m langflow run
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||||
```
|
||||
|
||||
### Options
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||||
|
||||
| Option | Description |
|
||||
|-------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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||||
| `--help` | Displays all available options. |
|
||||
| `--host` | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. |
|
||||
| `--workers` | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. |
|
||||
| `--timeout` | Sets the worker timeout in seconds. The default is `60`. |
|
||||
| `--port` | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. |
|
||||
| `--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 the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. |
|
||||
| `--components-path` | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. |
|
||||
| `--log-file` | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. |
|
||||
| `--cache` | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. |
|
||||
| `--dev`/`--no-dev` | Toggles the development mode. The default is `no-dev`. |
|
||||
| `--path` | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. |
|
||||
| `--open-browser`/`--no-open-browser`| Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-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 the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-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 of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only.md).|
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| `--store` | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. |
|
||||
|
||||
#### Environment Variables
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||||
|
||||
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.
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||||
|
||||
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.
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||||
## langflow superuser
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||||
|
||||
Create a superuser for Langflow.
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||||
|
||||
```bash
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langflow superuser
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||||
# or
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python -m langflow superuser
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```
|
||||
|
||||
### Options
|
||||
|
||||
| Option | Type | Description |
|
||||
|----------------|-------|-------------------------------------------------------------|
|
||||
| `--username` | TEXT | Username for the superuser. [default: None] [required] |
|
||||
| `--password` | TEXT | Password for the superuser. [default: None] [required] |
|
||||
| `--log-level` | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] |
|
||||
| `--help` | | Show this message and exit. |
|
||||
|
||||
|
|
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|
|
@ -1,31 +1,39 @@
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import ThemedImage from "@theme/ThemedImage";
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||||
import useBaseUrl from "@docusaurus/useBaseUrl";
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||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
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||||
import Admonition from "@theme/Admonition";
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||||
import ReactPlayer from "react-player";
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||||
import Admonition from "@theme/Admonition";
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||||
|
||||
# Global Environment Variables
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||||
# Global Variables
|
||||
|
||||
Langflow 1.0 alpha includes the option to add **Global Environment Variables** for your application.
|
||||
Global Variables are a useful feature of Langflow, allowing you to define reusable variables accessed from any Text field in your project.
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||||
|
||||
## Add a global variable to a project
|
||||
## TL;DR
|
||||
|
||||
In this example, you'll add the `openai_api_key` credential as a global environment variable to the **Basic Prompting** starter project.
|
||||
- Global Variables are reusable variables accessible from any Text field in your project.
|
||||
- To create one, click the 🌐 button in a Text field and then **+ Add New Variable**.
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||||
- Define the **Name**, **Type**, and **Value** of the variable.
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||||
- Click **Save Variable** to create it.
|
||||
- All Credential Global Variables are encrypted and accessible only by you.
|
||||
- Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables.
|
||||
|
||||
For more information on the starter flow, see [Basic prompting](../starter-projects/basic-prompting.mdx).
|
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## Creating and Adding a Global Variable
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
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2. Select **Basic Prompting**.
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To create and add a global variable, click the 🌐 button in a Text field, and then click **+ Add New Variable**.
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||||
|
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The **Basic Prompting** flow is created.
|
||||
Text fields are where you write text without opening a Text area, and are identified with the 🌐 icon.
|
||||
|
||||
3. To create an environment variable for the **OpenAI** component:
|
||||
1. In the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
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2. In the **Variable Name** field, enter `openai_api_key`.
|
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3. In the **Value** field, paste your OpenAI API Key (`sk-...`).
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4. For the variable **Type**, select **Credential**.
|
||||
5. In the **Apply to Fields** field, select **OpenAI API Key** to apply this variable to all fields named **OpenAI API Key**.
|
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6. Click **Save Variable**.
|
||||
For example, to create an environment variable for the **OpenAI** component:
|
||||
|
||||
1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**.
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||||
2. Enter `openai_api_key` in the **Variable Name** field.
|
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3. Paste your OpenAI API Key (`sk-...`) in the **Value** field.
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||||
4. Select **Credential** for the **Type**.
|
||||
5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**.
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6. Click **Save Variable**.
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||||
|
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You now have a `openai_api_key` global environment variable for your Langflow project.
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Subsequently, clicking the 🌐 button in a Text field will display the new variable in the dropdown.
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|
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<Admonition type="tip">
|
||||
You can also create global variables in **Settings** > **Variables and
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|
|
@ -41,10 +49,55 @@ You now have a `openai_api_key` global environment variable for your Langflow pr
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style={{ width: "40%", margin: "20px auto" }}
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/>
|
||||
|
||||
4. To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**.
|
||||
To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**.
|
||||
|
||||
For more on variables in HuggingFace Spaces, see [Managing Secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets).
|
||||
|
||||
{/* All variables are encrypted */}
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||||
|
||||
<Admonition type="warning">
|
||||
All Credential Global Variables are encrypted and accessible only by you.
|
||||
</Admonition>
|
||||
|
||||
## Configuring Environment Variables in your .env file
|
||||
|
||||
Setting `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `true` in your `.env` file (default) adds all variables in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` to your user's Global Variables.
|
||||
|
||||
These variables are accessible like any other Global Variable.
|
||||
|
||||
<Admonition type="tip">
|
||||
To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to
|
||||
`false` in your `.env` file.
|
||||
</Admonition>
|
||||
|
||||
You can specify variables to get from the environment by listing them in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`.
|
||||
|
||||
Specify variables as a comma-separated list (e.g., _`"VARIABLE1, VARIABLE2"`_) or a JSON-encoded string (e.g., _`'["VARIABLE1", "VARIABLE2"]'`_).
|
||||
|
||||
The default list of variables includes:
|
||||
|
||||
- ANTHROPIC_API_KEY
|
||||
- ASTRA_DB_API_ENDPOINT
|
||||
- ASTRA_DB_APPLICATION_TOKEN
|
||||
- AZURE_OPENAI_API_KEY
|
||||
- AZURE_OPENAI_API_DEPLOYMENT_NAME
|
||||
- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME
|
||||
- AZURE_OPENAI_API_INSTANCE_NAME
|
||||
- AZURE_OPENAI_API_VERSION
|
||||
- COHERE_API_KEY
|
||||
- GOOGLE_API_KEY
|
||||
- GROQ_API_KEY
|
||||
- HUGGINGFACEHUB_API_TOKEN
|
||||
- OPENAI_API_KEY
|
||||
- PINECONE_API_KEY
|
||||
- SEARCHAPI_API_KEY
|
||||
- SERPAPI_API_KEY
|
||||
- UPSTASH_VECTOR_REST_URL
|
||||
- UPSTASH_VECTOR_REST_TOKEN
|
||||
- VECTARA_CUSTOMER_ID
|
||||
- VECTARA_CORPUS_ID
|
||||
- VECTARA_API_KEY
|
||||
|
||||
## Video
|
||||
|
||||
<div
|
||||
|
|
|
|||
|
|
@ -3,7 +3,8 @@ import Admonition from "@theme/Admonition";
|
|||
# Custom Components
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
Read the [Custom Component Guidelines](../administration/custom-component) for detailed information on custom components.
|
||||
Read the [Custom Component Guidelines](../administration/custom-component) for
|
||||
detailed information on custom components.
|
||||
</Admonition>
|
||||
|
||||
Custom components let you extend Langflow by creating reusable and configurable components from a Python script.
|
||||
|
|
@ -31,57 +32,60 @@ This class is the foundation for creating custom components. It allows users to
|
|||
|
||||
The following types are supported in the build method:
|
||||
|
||||
| Supported Types |
|
||||
| --------------------------------------------------------- |
|
||||
| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
|
||||
| _`langflow.field_typing.NestedDict`_ |
|
||||
| _`langflow.field_typing.Prompt`_ |
|
||||
| _`langchain.chains.base.Chain`_ |
|
||||
| _`langchain.PromptTemplate`_ |
|
||||
| Supported Types |
|
||||
| ----------------------------------------------------------------- |
|
||||
| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
|
||||
| _`langflow.field_typing.NestedDict`_ |
|
||||
| _`langflow.field_typing.Prompt`_ |
|
||||
| _`langchain.chains.base.Chain`_ |
|
||||
| _`langchain.PromptTemplate`_ |
|
||||
| _`from langchain.schema.language_model import BaseLanguageModel`_ |
|
||||
| _`langchain.Tool`_ |
|
||||
| _`langchain.document_loaders.base.BaseLoader`_ |
|
||||
| _`langchain.schema.Document`_ |
|
||||
| _`langchain.text_splitters.TextSplitter`_ |
|
||||
| _`langchain.vectorstores.base.VectorStore`_ |
|
||||
| _`langchain.embeddings.base.Embeddings`_ |
|
||||
| _`langchain.schema.BaseRetriever`_ |
|
||||
| _`langchain.Tool`_ |
|
||||
| _`langchain.document_loaders.base.BaseLoader`_ |
|
||||
| _`langchain.schema.Document`_ |
|
||||
| _`langchain.text_splitters.TextSplitter`_ |
|
||||
| _`langchain.vectorstores.base.VectorStore`_ |
|
||||
| _`langchain.embeddings.base.Embeddings`_ |
|
||||
| _`langchain.schema.BaseRetriever`_ |
|
||||
|
||||
The difference between _`dict`_ and _`langflow.field_typing.NestedDict`_ is that one adds a simple key-value pair field, while the other opens a more robust dictionary editor.
|
||||
|
||||
<Admonition type="info">
|
||||
Use the `Prompt` type by adding **kwargs to the build method.
|
||||
If you want to add the values of the variables to the template you defined, format the `PromptTemplate` inside the `CustomComponent` class.
|
||||
Use the `Prompt` type by adding **kwargs to the build method. If you want to
|
||||
add the values of the variables to the template you defined, format the
|
||||
`PromptTemplate` inside the `CustomComponent` class.
|
||||
</Admonition>
|
||||
|
||||
<Admonition type="info">
|
||||
Use base Python types without a handle by default. To add handles, use the `input_types` key in the `build_config` method.
|
||||
Use base Python types without a handle by default. To add handles, use the
|
||||
`input_types` key in the `build_config` method.
|
||||
</Admonition>
|
||||
|
||||
**build_config:** Defines the configuration fields of the component. This method returns a dictionary where each key represents a field name and each value defines the field's behavior.
|
||||
|
||||
Supported keys for configuring fields:
|
||||
|
||||
| Key | Description |
|
||||
| --------------------- | --------------------------------------------------- |
|
||||
| `is_list` | Boolean indicating if the field can hold multiple values. |
|
||||
| `options` | Dropdown menu options. |
|
||||
| `multiline` | Boolean indicating if a field allows multiline input. |
|
||||
| `input_types` | Allows connection handles for string fields. |
|
||||
| `display_name` | Field name displayed in the UI. |
|
||||
| `advanced` | Hides the field in the default UI view. |
|
||||
| `password` | Masks input, useful for sensitive data. |
|
||||
| `required` | Overrides the default behavior to make a field mandatory. |
|
||||
| `info` | Tooltip for the field. |
|
||||
| `file_types` | Accepted file types, useful for file fields. |
|
||||
| `range_spec` | Defines valid ranges for float fields. |
|
||||
| `title_case` | Boolean that controls field name capitalization. |
|
||||
| `refresh_button` | Adds a refresh button that updates field values. |
|
||||
| `real_time_refresh` | Updates the configuration as field values change. |
|
||||
| `field_type` | Automatically set based on the build method's type hint. |
|
||||
| Key | Description |
|
||||
| ------------------- | --------------------------------------------------------- |
|
||||
| `is_list` | Boolean indicating if the field can hold multiple values. |
|
||||
| `options` | Dropdown menu options. |
|
||||
| `multiline` | Boolean indicating if a field allows multiline input. |
|
||||
| `input_types` | Allows connection handles for string fields. |
|
||||
| `display_name` | Field name displayed in the UI. |
|
||||
| `advanced` | Hides the field in the default UI view. |
|
||||
| `password` | Masks input, useful for sensitive data. |
|
||||
| `required` | Overrides the default behavior to make a field mandatory. |
|
||||
| `info` | Tooltip for the field. |
|
||||
| `file_types` | Accepted file types, useful for file fields. |
|
||||
| `range_spec` | Defines valid ranges for float fields. |
|
||||
| `title_case` | Boolean that controls field name capitalization. |
|
||||
| `refresh_button` | Adds a refresh button that updates field values. |
|
||||
| `real_time_refresh` | Updates the configuration as field values change. |
|
||||
| `field_type` | Automatically set based on the build method's type hint. |
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
Use the `update_build_config` method to dynamically update configurations based on field values.
|
||||
Use the `update_build_config` method to dynamically update configurations
|
||||
based on field values.
|
||||
</Admonition>
|
||||
|
||||
## Additional methods and attributes
|
||||
|
|
@ -99,4 +103,3 @@ The `CustomComponent` class also provides helpful methods for specific tasks (e.
|
|||
- `status`: Shows values from the `build` method, useful for debugging.
|
||||
- `field_order`: Controls the display order of fields.
|
||||
- `icon`: Sets the canvas display icon.
|
||||
|
||||
|
|
|
|||
161
docs/docs/components/inputs-and-outputs.mdx
Normal file
161
docs/docs/components/inputs-and-outputs.mdx
Normal file
|
|
@ -0,0 +1,161 @@
|
|||
import Admonition from "@theme/Admonition";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
# Inputs and Outputs
|
||||
|
||||
TL;DR: Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow.
|
||||
They also dynamically change the Playground and can be renamed to facilitate building and maintaining your flows.
|
||||
|
||||
## Inputs
|
||||
|
||||
Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Record.
|
||||
|
||||
The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground.
|
||||
|
||||
Chat Input components can output `Text` or `Record`. When you want to pass the sender name or sender to the next component, use the `Record` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep.
|
||||
|
||||
You can find out more about Chat Input and other Inputs [here](#chat-input).
|
||||
|
||||
### Chat Input
|
||||
|
||||
This component collects user input from the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`.
|
||||
- **Sender Name:** Specifies the name of the sender. Defaults to `User`.
|
||||
- **Message:** Specifies the message text. It is a multiline text input.
|
||||
- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data of the
|
||||
`Record` will be updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/chat-input-expanded.png",
|
||||
dark: "img/chat-input-expanded.png",
|
||||
}}
|
||||
style={{ width: "40%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/interaction-panel-with-chat-input.png",
|
||||
dark: "img/interaction-panel-with-chat-input.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
### Text Input
|
||||
|
||||
The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string.
|
||||
- **Record Template:** Specifies how a `Record` should be converted into `Text`.
|
||||
|
||||
The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence.
|
||||
|
||||
For example, if you have a `Record` with the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "John Doe",
|
||||
"age": 30,
|
||||
"email": "johndoe@email.com"
|
||||
}
|
||||
```
|
||||
|
||||
A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`.
|
||||
|
||||
If you pass more than one `Record`, the text will be concatenated with a new line separator.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/text-input-expanded.png",
|
||||
dark: "img/text-input-expanded.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
## Outputs
|
||||
|
||||
Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground.
|
||||
|
||||
The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user.
|
||||
|
||||
You can find out more about it and the other Outputs [here](#chat-output).
|
||||
|
||||
### Chat Output
|
||||
|
||||
This component sends a message to the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`.
|
||||
|
||||
- **Sender Name:** Specifies the sender's name. Default is `"AI"`.
|
||||
|
||||
- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History.
|
||||
|
||||
- **Message:** Specifies the text of the message.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data in the
|
||||
`Record` is updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
### Text Output
|
||||
|
||||
This component displays text data to the user. It is useful when you want to show text without sending it to the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Value:** Specifies the text data to be displayed. Defaults to an empty string.
|
||||
|
||||
The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow.
|
||||
|
||||
## Prompts
|
||||
|
||||
A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables.
|
||||
|
||||
### Prompt
|
||||
|
||||
This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/prompt-with-template.png",
|
||||
dark: "img/prompt-with-template.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
### PromptTemplate
|
||||
|
||||
The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts.
|
||||
|
||||
<Admonition type="info">
|
||||
After defining a variable in the prompt template, it acts as its own component
|
||||
input. See [Prompt Customization](../administration/prompt-customization) for
|
||||
more details.
|
||||
</Admonition>
|
||||
|
||||
- **template:** The template used to format an individual request.
|
||||
|
|
@ -1,99 +0,0 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
# Inputs
|
||||
|
||||
## Chat Input
|
||||
|
||||
This component obtains user input from the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`.
|
||||
- **Sender Name:** Specifies the name of the sender. Defaults to `User`.
|
||||
- **Message:** Specifies the message text. It is a multiline text input.
|
||||
- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data
|
||||
of the `Record` will be updated with the `Sender`, `Sender Name`, and
|
||||
`Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/chat-input-expanded.png",
|
||||
dark: "img/chat-input-expanded.png",
|
||||
}}
|
||||
style={{ width: "40%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/interaction-panel-with-chat-input.png",
|
||||
dark: "img/interaction-panel-with-chat-input.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
---
|
||||
|
||||
## Prompt
|
||||
|
||||
This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/prompt-with-template.png",
|
||||
dark: "img/prompt-with-template.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
---
|
||||
|
||||
## Text Input
|
||||
|
||||
The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string.
|
||||
- **Record Template:** Specifies how a `Record` should be converted into `Text`.
|
||||
|
||||
The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence.
|
||||
|
||||
For example, if you have a `Record` with the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "John Doe",
|
||||
"age": 30,
|
||||
"email": "johndoe@email.com"
|
||||
}
|
||||
```
|
||||
|
||||
A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`.
|
||||
|
||||
If you pass more than one `Record`, the text will be concatenated with a new line separator.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/text-input-expanded.png",
|
||||
dark: "img/text-input-expanded.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
|
|
@ -1,34 +0,0 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
|
||||
# Outputs
|
||||
|
||||
## Chat Output
|
||||
|
||||
This component sends a message to the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`.
|
||||
|
||||
- **Sender Name:** Specifies the sender's name. Default is `"AI"`.
|
||||
|
||||
- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History.
|
||||
|
||||
- **Message:** Specifies the text of the message.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data in the `Record` is updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
## Text Output
|
||||
|
||||
This component displays text data to the user. It is useful when you want to show text without sending it to the chat.
|
||||
|
||||
**Parameters**
|
||||
|
||||
- **Value:** Specifies the text data to be displayed. Defaults to an empty string.
|
||||
|
||||
|
||||
The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow.
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Prompts
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="Zone Under Construction">
|
||||
<p>
|
||||
Thank you for your patience as we refine our documentation. It may
|
||||
still have some areas under development. Please share your feedback or report any issues to help us improve!
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables.
|
||||
|
||||
---
|
||||
|
||||
### PromptTemplate
|
||||
|
||||
The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts.
|
||||
|
||||
<Admonition type="info">
|
||||
After defining a variable in the prompt template, it acts as its own component
|
||||
input. See [Prompt Customization](../administration/prompt-customization) for more details.
|
||||
</Admonition>
|
||||
|
||||
- **template:** The template used to format an individual request.
|
||||
49
docs/docs/components/text-and-record.mdx
Normal file
49
docs/docs/components/text-and-record.mdx
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
# Text and Record
|
||||
|
||||
In Langflow 1.0, we added two main input and output types: `Text` and `Record`.
|
||||
|
||||
`Text` is a simple string input and output type, while `Record` is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
|
||||
|
||||
We've created a few components to help you work with these types. Let's see how a few of them work.
|
||||
|
||||
## Records To Text
|
||||
|
||||
This is a component that takes in Records and outputs a `Text`. It does this using a template string and concatenating the values of the `Record`, one per line.
|
||||
|
||||
If we have the following Records:
|
||||
|
||||
```json
|
||||
{
|
||||
"sender_name": "Alice",
|
||||
"message": "Hello!"
|
||||
}
|
||||
{
|
||||
"sender_name": "John",
|
||||
"message": "Hi!"
|
||||
}
|
||||
```
|
||||
|
||||
And the template string is: _`{sender_name}: {message}`_
|
||||
|
||||
The output is:
|
||||
|
||||
```
|
||||
Alice: Hello!
|
||||
John: Hi!
|
||||
```
|
||||
|
||||
## Create Record
|
||||
|
||||
This component allows you to create a `Record` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it.
|
||||
|
||||
## Documents To Records
|
||||
|
||||
This component takes in a LangChain `Document` and outputs a `Record`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Record` as text and data respectively.
|
||||
|
||||
## Why is this useful?
|
||||
|
||||
The idea was to create a unified way to work with complex data in Langflow and to make it easier to work with data that is not just a simple string. This way you can create more complex workflows and use the data in more ways.
|
||||
|
||||
## What's next?
|
||||
|
||||
We are planning to integrate an array of modalities to Langflow, such as images, audio, and video. This will allow you to create even more complex workflows and use cases. Stay tuned for more updates! 🚀
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Vector Stores Documentation
|
||||
# Vector Stores
|
||||
|
||||
### Astra DB
|
||||
|
||||
|
|
|
|||
113
docs/docs/deployment/backend-only.md
Normal file
113
docs/docs/deployment/backend-only.md
Normal file
|
|
@ -0,0 +1,113 @@
|
|||
# Backend-only
|
||||
You can run Langflow in `--backend-only` mode to expose your Langflow app as an API, without running the frontend UI.
|
||||
|
||||
Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
|
||||
|
||||
The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`.
|
||||
Langflow will now serve requests to its API without the frontend running.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
* [A Langflow flow created](../starter-projects/basic-prompting.mdx)
|
||||
|
||||
## Download your flow's curl call
|
||||
|
||||
1. Click API.
|
||||
2. Click **curl** > **Copy code** and save the code to your local machine.
|
||||
It will look something like this:
|
||||
```curl
|
||||
curl -X POST \
|
||||
"http://127.0.0.1:7864/api/v1/run/ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef?stream=false" \
|
||||
-H 'Content-Type: application/json'\
|
||||
-d '{"input_value": "message",
|
||||
"output_type": "chat",
|
||||
"input_type": "chat",
|
||||
"tweaks": {
|
||||
"Prompt-kvo86": {},
|
||||
"OpenAIModel-MilkD": {},
|
||||
"ChatOutput-ktwdw": {},
|
||||
"ChatInput-xXC4F": {}
|
||||
}}'
|
||||
```
|
||||
Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID in the UI as well to ensure you're querying the right flow.
|
||||
|
||||
## Start Langflow in backend-only mode
|
||||
|
||||
1. Stop Langflow with Ctrl+C.
|
||||
2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
|
||||
The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`.
|
||||
Langflow will now serve requests to its API.
|
||||
3. Run the curl code you copied from the UI.
|
||||
You should get a result like this:
|
||||
```bash
|
||||
{"session_id":"ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880","outputs":[{"inputs":{"input_value":"hi, are you there?"},"outputs":[{"results":{"result":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?"},"artifacts":{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-ktwdw"}],"component_display_name":"Chat Output","component_id":"ChatOutput-ktwdw","used_frozen_result":false}]}]}%
|
||||
```
|
||||
Again, note that the flow ID matches.
|
||||
Langflow is receiving your POST request, running the flow, and returning the result, all without running the frontend. Cool!
|
||||
|
||||
## Download your flow's Python API call
|
||||
|
||||
Instead of using curl, you can download your flow as a Python API call instead.
|
||||
|
||||
1. Click API.
|
||||
2. Click **Python API** > **Copy code** and save the code to your local machine.
|
||||
The code will look something like this:
|
||||
```python
|
||||
import requests
|
||||
from typing import Optional
|
||||
|
||||
BASE_API_URL = "http://127.0.0.1:7864/api/v1/run"
|
||||
FLOW_ID = "ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef"
|
||||
# You can tweak the flow by adding a tweaks dictionary
|
||||
# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
|
||||
|
||||
def run_flow(message: str,
|
||||
flow_id: str,
|
||||
output_type: str = "chat",
|
||||
input_type: str = "chat",
|
||||
tweaks: Optional[dict] = None,
|
||||
api_key: Optional[str] = None) -> dict:
|
||||
"""
|
||||
Run a flow with a given message and optional tweaks.
|
||||
|
||||
:param message: The message to send to the flow
|
||||
:param flow_id: The ID of the flow to run
|
||||
:param tweaks: Optional tweaks to customize the flow
|
||||
:return: The JSON response from the flow
|
||||
"""
|
||||
api_url = f"{BASE_API_URL}/{flow_id}"
|
||||
|
||||
payload = {
|
||||
"input_value": message,
|
||||
"output_type": output_type,
|
||||
"input_type": input_type,
|
||||
}
|
||||
headers = None
|
||||
if tweaks:
|
||||
payload["tweaks"] = tweaks
|
||||
if api_key:
|
||||
headers = {"x-api-key": api_key}
|
||||
response = requests.post(api_url, json=payload, headers=headers)
|
||||
return response.json()
|
||||
|
||||
# Setup any tweaks you want to apply to the flow
|
||||
message = "message"
|
||||
|
||||
print(run_flow(message=message, flow_id=FLOW_ID))
|
||||
```
|
||||
3. Run your Python app:
|
||||
```python
|
||||
python3 app.py
|
||||
```
|
||||
|
||||
The result is similar to the curl call:
|
||||
```bash
|
||||
{'session_id': 'ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880', 'outputs': [{'inputs': {'input_value': 'message'}, 'outputs': [{'results': {'result': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!"}, 'artifacts': {'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI'}, 'messages': [{'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI', 'component_id': 'ChatOutput-ktwdw'}], 'component_display_name': 'Chat Output', 'component_id': 'ChatOutput-ktwdw', 'used_frozen_result': False}]}]}
|
||||
```
|
||||
Your Python app POSTs to your Langflow server, and the server runs the flow and returns the result.
|
||||
|
||||
See [API](../administration/api.mdx) for more ways to interact with your headless Langflow server.
|
||||
65
docs/docs/deployment/docker.md
Normal file
65
docs/docs/deployment/docker.md
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
# Docker
|
||||
|
||||
This guide will help you get LangFlow up and running using Docker and Docker Compose.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Docker
|
||||
- Docker Compose
|
||||
|
||||
## Steps
|
||||
|
||||
1. Clone the LangFlow repository:
|
||||
|
||||
```sh
|
||||
git clone https://github.com/langflow-ai/langflow.git
|
||||
```
|
||||
|
||||
2. Navigate to the `docker_example` directory:
|
||||
|
||||
```sh
|
||||
cd langflow/docker_example
|
||||
```
|
||||
|
||||
3. Run the Docker Compose file:
|
||||
|
||||
```sh
|
||||
docker compose up
|
||||
```
|
||||
|
||||
LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/).
|
||||
|
||||
## Docker Compose Configuration
|
||||
|
||||
The Docker Compose configuration spins up two services: `langflow` and `postgres`.
|
||||
|
||||
### LangFlow Service
|
||||
|
||||
The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
|
||||
Environment variables:
|
||||
|
||||
- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database.
|
||||
- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys.
|
||||
|
||||
Volumes:
|
||||
|
||||
- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container.
|
||||
|
||||
### PostgreSQL Service
|
||||
|
||||
The `postgres` service uses the `postgres:16` Docker image and exposes port 5432.
|
||||
|
||||
Environment variables:
|
||||
|
||||
- `POSTGRES_USER`: The username for the PostgreSQL database.
|
||||
- `POSTGRES_PASSWORD`: The password for the PostgreSQL database.
|
||||
- `POSTGRES_DB`: The name of the PostgreSQL database.
|
||||
|
||||
Volumes:
|
||||
|
||||
- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container.
|
||||
|
||||
## Switching to a Specific LangFlow Version
|
||||
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`.
|
||||
|
|
@ -14,4 +14,4 @@ This component is available under the **Helpers** tab of the Langflow preview.
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/chat_memory.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -18,4 +18,4 @@ This component is available under the **Helpers** tab of the Langflow preview.
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/combine_text.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -14,4 +14,4 @@ The **Create Record** component allows you to dynamically create a `Record` from
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/create_record.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -14,4 +14,4 @@ The **Pass** component enables you to ignore one input and move forward with ano
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/pass.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -14,4 +14,4 @@ The **Message History** component can then be used to retrieve stored messages.
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/store_message.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -12,4 +12,4 @@ The **Sub Flow** component enables a user to select a previously built flow and
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/sub_flow.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -12,4 +12,4 @@ The **Text Operator** component simplifies logic. It evaluates the results from
|
|||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/text_operator.mp4" />
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -280,9 +280,3 @@ To see options for your project, in the upper left corner of the canvas, select
|
|||
**Export** - Download your current project to your local machine as a `.json` file.
|
||||
|
||||
**Undo** or **Redo** - Undo or redo your last action.
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import ThemedImage from '@theme/ThemedImage';
|
||||
import useBaseUrl from '@docusaurus/useBaseUrl';
|
||||
import ZoomableImage from '/src/theme/ZoomableImage.js';
|
||||
import ReactPlayer from 'react-player';
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# 🖥️ Flows, components, collections, and projects
|
||||
|
||||
|
|
@ -17,10 +17,4 @@ A [project](#project) can be a component or a flow. Projects are saved as part o
|
|||
|
||||
For example, the **OpenAI LLM** is a **component** of the **Basic prompting** flow, and the **flow** is stored in a **collection**.
|
||||
|
||||
|
||||
|
||||
## Component
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -6,33 +6,40 @@ import Admonition from "@theme/Admonition";
|
|||
# 📦 Install Langflow
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true),
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
Langflow requires [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your system.
|
||||
|
||||
Install Langflow with pip:
|
||||
|
||||
```bash
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
Install Langflow with pipx:
|
||||
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
## Install Langflow pre-release
|
||||
|
||||
To install a pre-release version of Langflow:
|
||||
|
||||
pip:
|
||||
|
||||
```bash
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
```
|
||||
|
||||
pipx:
|
||||
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
|
||||
```
|
||||
|
|
@ -52,11 +59,13 @@ python -m langflow --help
|
|||
## ⛓️ Run Langflow
|
||||
|
||||
1. To run Langflow, enter the following command.
|
||||
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in a Chromium-based browser.
|
||||
|
||||
```bash
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
|
|
@ -83,4 +92,4 @@ You'll be presented with the following screen:
|
|||
style={{ width: "100%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!
|
||||
Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!
|
||||
|
|
|
|||
|
|
@ -10,12 +10,15 @@ This guide demonstrates how to build a basic prompt flow and modify that prompt
|
|||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed and running](./install-langflow.mdx)
|
||||
- [Langflow installed and running](./install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key](https://platform.openai.com)
|
||||
- [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Hello World - Basic Prompting
|
||||
|
|
@ -44,25 +47,25 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A
|
|||
This should be interesting...
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can chat with your bot.
|
||||
The **Interaction Panel** opens, where you can chat with your bot.
|
||||
2. Type a message and press Enter.
|
||||
And... Ahoy! 🏴☠️
|
||||
The bot responds in a piratical manner!
|
||||
And... Ahoy! 🏴☠️
|
||||
The bot responds in a piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
The response will be markedly different.
|
||||
|
||||
## Next steps
|
||||
|
||||
|
|
@ -72,8 +75,6 @@ By adding Langflow components to your flow, you can create all sorts of interest
|
|||
|
||||
Here are a couple of examples:
|
||||
|
||||
* [Memory chatbot](/starter-projects/memory-chatbot.mdx)
|
||||
* [Blog writer](/starter-projects/blog-writer.mdx)
|
||||
* [Document QA](/starter-projects/document-qa.mdx)
|
||||
|
||||
|
||||
- [Memory chatbot](/starter-projects/memory-chatbot.mdx)
|
||||
- [Blog writer](/starter-projects/blog-writer.mdx)
|
||||
- [Document QA](/starter-projects/document-qa.mdx)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,10 @@ Its intuitive interface allows for easy manipulation of AI building blocks, enab
|
|||
- [Langflow Canvas](/getting-started/canvas) - Learn more about the Langflow canvas.
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Learn more about Langflow 1.0
|
||||
|
|
|
|||
|
|
@ -9,14 +9,11 @@ The `AddContentToPage` component converts markdown text to Notion blocks and app
|
|||
|
||||
[Notion Reference](https://developers.notion.com/reference/patch-block-children)
|
||||
|
||||
<Admonition type="tip" title="Component Functionality">
|
||||
|
||||
The `AddContentToPage` component enables you to:
|
||||
|
||||
- Convert markdown text to Notion blocks.
|
||||
- Append the converted blocks to a specified Notion page.
|
||||
- Seamlessly integrate Notion content creation into Langflow workflows.
|
||||
</Admonition>
|
||||
|
||||
## Component Usage
|
||||
|
||||
|
|
@ -100,23 +97,19 @@ class NotionPageCreator(CustomComponent):
|
|||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
|
||||
Example of using the `AddContentToPage` component in a Langflow flow using Markdown as input:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionDatabaseProperties Flow Example"
|
||||
sources={{
|
||||
alt="NotionDatabaseProperties Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/AddContentToPage_flow_example.png",
|
||||
dark: "img/notion/AddContentToPage_flow_example.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
In this example, the `AddContentToPage` component connects to a `MarkdownLoader` component to provide the markdown text input. The converted Notion blocks are appended to the specified Notion page using the provided `block_id` and `notion_secret`.
|
||||
|
||||
</Admonition>
|
||||
|
||||
## Best Practices
|
||||
|
||||
When using the `AddContentToPage` component:
|
||||
|
|
@ -131,8 +124,8 @@ The `AddContentToPage` component is a powerful tool for integrating Notion conte
|
|||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `AddContentToPage` component, consider the following:
|
||||
|
||||
- Verify the Notion integration token’s validity and permissions.
|
||||
- Check the Notion API documentation for updates.
|
||||
- Ensure markdown text is properly formatted.
|
||||
- Double-check the `block_id` for correctness.
|
||||
|
||||
|
|
|
|||
|
|
@ -8,12 +8,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
The Notion integration in Langflow enables seamless connectivity with Notion databases, pages, and users, facilitating automation and improving productivity.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Notion Components in Langflow"
|
||||
sources={{
|
||||
alt="Notion Components in Langflow"
|
||||
sources={{
|
||||
light: "img/notion/notion_bundle.jpg",
|
||||
dark: "img/notion/notion_bundle.jpg",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/Notion_Components_bundle.json" download>Download Notion Components Bundle</a>
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ class NotionDatabaseProperties(CustomComponent):
|
|||
description = "Retrieve properties of a Notion database."
|
||||
documentation: str = "https://docs.langflow.org/integrations/notion/list-database-properties"
|
||||
icon = "NotionDirectoryLoader"
|
||||
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"database_id": {
|
||||
|
|
@ -80,6 +80,7 @@ class NotionDatabaseProperties(CustomComponent):
|
|||
```
|
||||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
Here's an example of how you can use the `NotionDatabaseProperties` component in a Langflow flow:
|
||||
|
||||
|
|
@ -110,6 +111,7 @@ Feel free to explore the capabilities of the `NotionDatabaseProperties` componen
|
|||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `NotionDatabaseProperties` component, consider the following:
|
||||
|
||||
- Verify that the Notion integration token is valid and has the required permissions.
|
||||
- Check the database ID to ensure it matches the intended Notion database.
|
||||
- Inspect the response from the Notion API for any error messages or status codes that may indicate the cause of the issue.
|
||||
- Inspect the response from the Notion API for any error messages or status codes that may indicate the cause of the issue.
|
||||
|
|
|
|||
|
|
@ -140,16 +140,17 @@ class NotionListPages(CustomComponent):
|
|||
<Admonition type="info" title="Example Usage">
|
||||
|
||||
## Example Usage
|
||||
|
||||
Here's an example of how you can use the `NotionListPages` component in a Langflow flow and passing to the Prompt component:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionListPages
|
||||
Flow Example"
|
||||
sources={{
|
||||
alt="NotionListPages
|
||||
Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/NotionListPages_flow_example.png",
|
||||
dark: "img/notion/NotionListPages_flow_example_dark.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
In this example, the `NotionListPages` component is used to retrieve specific pages from a Notion database based on the provided filters and sorting options. The retrieved data can then be processed further in the subsequent components of the flow.
|
||||
|
|
@ -157,7 +158,7 @@ In this example, the `NotionListPages` component is used to retrieve specific pa
|
|||
|
||||
## Best Practices
|
||||
|
||||
When using the `NotionListPages
|
||||
When using the `NotionListPages
|
||||
` component, consider the following best practices:
|
||||
|
||||
- Ensure that you have a valid Notion integration token with the necessary permissions to query the desired database.
|
||||
|
|
@ -171,7 +172,7 @@ We encourage you to explore the capabilities of the `NotionListPages
|
|||
|
||||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `NotionListPages` component, consider the following:
|
||||
If you encounter any issues while using the `NotionListPages` component, consider the following:
|
||||
|
||||
- Double-check that the `notion_secret` and `database_id` are correct and valid.
|
||||
- Verify that the `query_payload` JSON string is properly formatted and contains valid filtering and sorting options.
|
||||
|
|
|
|||
|
|
@ -9,13 +9,11 @@ The `NotionUserList` component retrieves users from Notion. It provides a conven
|
|||
|
||||
[Notion Reference](https://developers.notion.com/reference/get-users)
|
||||
|
||||
<Admonition type="tip" title="Component Functionality">
|
||||
The `NotionUserList` component enables you to:
|
||||
The `NotionUserList` component enables you to:
|
||||
|
||||
- Retrieve user data from Notion
|
||||
- Access user information such as ID, type, name, and avatar URL
|
||||
- Integrate Notion user data seamlessly into your Langflow workflows
|
||||
</Admonition>
|
||||
|
||||
## Component Usage
|
||||
|
||||
|
|
@ -94,34 +92,31 @@ class NotionUserList(CustomComponent):
|
|||
```
|
||||
|
||||
## Example Usage
|
||||
<Admonition type="info" title="Example Usage">
|
||||
|
||||
Here's an example of how you can use the `NotionUserList` component in a Langflow flow and passing the outputs to the Prompt component:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionUserList Flow Example"
|
||||
sources={{
|
||||
alt="NotionUserList Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/NotionUserList_flow_example.png",
|
||||
dark: "img/notion/NotionUserList_flow_example_dark.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
</Admonition>
|
||||
|
||||
## Best Practices
|
||||
|
||||
When using the `NotionUserList` component, consider the following best practices:
|
||||
When using the `NotionUserList` component, consider the following best practices:
|
||||
|
||||
- Ensure that you have a valid Notion integration token with the necessary permissions to retrieve user data.
|
||||
- Handle the retrieved user data securely and in compliance with Notion's API usage guidelines.
|
||||
|
||||
The `NotionUserList` component provides a seamless way to integrate Notion user data into your Langflow workflows. By leveraging this component, you can easily retrieve and utilize user information from Notion, enhancing the capabilities of your Langflow applications. Feel free to explore and experiment with the `NotionUserList` component to unlock new possibilities in your Langflow projects!
|
||||
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `NotionUserList` component, consider the following:
|
||||
If you encounter any issues while using the `NotionUserList` component, consider the following:
|
||||
|
||||
- Double-check that your Notion integration token is valid and has the required permissions.
|
||||
- Verify that you have installed the necessary dependencies (`requests`) for the component to function properly.
|
||||
- Check the Notion API documentation for any updates or changes that may affect the component's functionality.
|
||||
- Check the Notion API documentation for any updates or changes that may affect the component's functionality.
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ The `NotionPageContent` component retrieves the content of a Notion page as plai
|
|||
|
||||
<Admonition type="tip" title="Component Functionality">
|
||||
|
||||
The `NotionPageContent` component enables you to:
|
||||
The `NotionPageContent` component enables you to:
|
||||
|
||||
- Retrieve the content of a Notion page as plain text
|
||||
- Extract text from various block types, including paragraphs, headings, lists, and more
|
||||
|
|
@ -114,18 +114,18 @@ class NotionPageContent(CustomComponent):
|
|||
Here's an example of how you can use the `NotionPageContent` component in a Langflow flow:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionPageContent Flow Example"
|
||||
sources={{
|
||||
alt="NotionPageContent Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/NotionPageContent_flow_example.png",
|
||||
dark: "img/notion/NotionPageContent_flow_example_dark.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
</Admonition>
|
||||
|
||||
## Best Practices
|
||||
|
||||
When using the `NotionPageContent` component, consider the following best practices:
|
||||
When using the `NotionPageContent` component, consider the following best practices:
|
||||
|
||||
- Ensure that you have the necessary permissions to access the Notion page you want to retrieve.
|
||||
- Keep your Notion integration token secure and avoid sharing it publicly.
|
||||
|
|
@ -135,7 +135,7 @@ The `NotionPageContent` component provides a seamless way to integrate Notion pa
|
|||
|
||||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `NotionPageContent` component, consider the following:
|
||||
If you encounter any issues while using the `NotionPageContent` component, consider the following:
|
||||
|
||||
- Double-check that you have provided the correct Notion page ID.
|
||||
- Verify that your Notion integration token is valid and has the necessary permissions.
|
||||
|
|
|
|||
|
|
@ -97,16 +97,17 @@ class NotionPageCreator(CustomComponent):
|
|||
```
|
||||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
Here's an example of how to use the `NotionPageCreator` component in a Langflow flow:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionPageCreator Flow Example"
|
||||
sources={{
|
||||
alt="NotionPageCreator Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/NotionPageCreator_flow_example.png",
|
||||
dark: "img/notion/NotionPageCreator_flow_example_dark.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
</Admonition>
|
||||
|
||||
|
|
@ -124,6 +125,7 @@ The `NotionPageCreator` component simplifies the process of creating pages in a
|
|||
## Troubleshooting
|
||||
|
||||
If you encounter any issues while using the `NotionPageCreator` component, consider the following:
|
||||
|
||||
- Double-check that the `database_id` and `notion_secret` inputs are correct and valid.
|
||||
- Verify that the `properties` input is properly formatted as a JSON string and matches the structure of your Notion database.
|
||||
- Check the Notion API documentation for any updates or changes that may affect the component's functionality.
|
||||
- Check the Notion API documentation for any updates or changes that may affect the component's functionality.
|
||||
|
|
|
|||
|
|
@ -146,16 +146,17 @@ class NotionSearch(CustomComponent):
|
|||
```
|
||||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
Here's an example of how you can use the `NotionSearch` component in a Langflow flow:
|
||||
|
||||
<ZoomableImage
|
||||
alt="NotionSearch Flow Example"
|
||||
sources={{
|
||||
alt="NotionSearch Flow Example"
|
||||
sources={{
|
||||
light: "img/notion/NotionSearch_flow_example.png",
|
||||
dark: "img/notion/NotionSearch_flow_example_dark.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
In this example, the `NotionSearch` component is used to search for pages and databases in Notion based on the provided query and filter criteria. The retrieved data can then be processed further in the subsequent components of the flow.
|
||||
|
|
|
|||
|
|
@ -76,4 +76,3 @@ Refer to the individual component documentation for more details on how to use e
|
|||
- [Notion Integration Capabilities](https://developers.notion.com/reference/capabilities)
|
||||
|
||||
If you encounter any issues or have questions, please reach out to our support team or consult the Langflow community forums.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,118 +0,0 @@
|
|||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Global Variables
|
||||
|
||||
## TLDR;
|
||||
|
||||
- Global Variables are reusable variables that can be accessed from any Text field in your project.
|
||||
- To create a Global Variable, click on the 🌐 button in a Text field and then **+ Add New Variable**.
|
||||
- Define the **Name**, **Type**, and **Value** of the variable.
|
||||
- Click on **Save Variable** to create the variable.
|
||||
- All Credential Global Variables are encrypted and cannot be accessed by anyone but you.
|
||||
- Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables.
|
||||
|
||||
Global Variables are a really useful feature of Langflow.
|
||||
They allow you to define reusable variables that can be accessed from any Text field in your project.
|
||||
|
||||
The first thing you need to do is find a **Text field** in a Component, so let's talk about what a Text field is.
|
||||
|
||||
## Text Fields
|
||||
|
||||
Text fields are the fields in a Component where you can write text but that does not allow you to open a Text Area.
|
||||
|
||||
The easiest way to find fields that are Text fields, though, is to look for fields that have a 🌐 button.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/ollama-gv.png",
|
||||
dark: "img/ollama-gv.png",
|
||||
}}
|
||||
style={{ width: "50%" }}
|
||||
/>
|
||||
|
||||
## Creating a Global Variable
|
||||
|
||||
To create a Global Variable, you need to click on the 🌐 button in a Text field and that will open a dropdown showing your currently available variables and at the end of it **+ Add New Variable**.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/add-new-variable.png",
|
||||
dark: "img/add-new-variable.png",
|
||||
}}
|
||||
style={{ width: "60%" }}
|
||||
/>
|
||||
|
||||
Click on **+ Add New Variable** and a window will open where you can define your new Global Variable.
|
||||
|
||||
In it, you can define the **Name** of the variable, the optional **Type** of the variable, and the **Value** of the variable.
|
||||
|
||||
The **Name** is the name that you will use to refer to the variable in your Text fields.
|
||||
|
||||
The **Type** is optional for now but will be used in the future to allow for more advanced features.
|
||||
|
||||
The **Value** is the value that the variable will have.
|
||||
{/* say that all variables are encrypted */}
|
||||
|
||||
<Admonition type="warning">
|
||||
All Credential Global Variables are encrypted and cannot be accessed by anyone
|
||||
but you.
|
||||
</Admonition>
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/create-variable-window.png",
|
||||
dark: "img/create-variable-window.png",
|
||||
}}
|
||||
style={{ width: "60%" }}
|
||||
/>
|
||||
|
||||
After you have defined your variable, click on **Save Variable** and your variable will be created.
|
||||
|
||||
After that, once you click on the 🌐 button in a Text field, you will see your new variable in the dropdown.
|
||||
|
||||
## Environment Variables
|
||||
|
||||
If you set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ (which is the default value) in your `.env` file, all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ will be added to your user's Global Variables.
|
||||
|
||||
All of these variables can be used in your project as any other Global Variable.
|
||||
|
||||
<Admonition type="tip">
|
||||
You can set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`false`_ in your
|
||||
`.env` file to prevent this behavior.
|
||||
</Admonition>
|
||||
|
||||
You can also set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to a list of variables that you want to get from the environment.
|
||||
|
||||
The default list at the moment is:
|
||||
|
||||
- ANTHROPIC_API_KEY
|
||||
- ASTRA_DB_API_ENDPOINT
|
||||
- ASTRA_DB_APPLICATION_TOKEN
|
||||
- AZURE_OPENAI_API_KEY
|
||||
- AZURE_OPENAI_API_DEPLOYMENT_NAME
|
||||
- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME
|
||||
- AZURE_OPENAI_API_INSTANCE_NAME
|
||||
- AZURE_OPENAI_API_VERSION
|
||||
- COHERE_API_KEY
|
||||
- GOOGLE_API_KEY
|
||||
- GROQ_API_KEY
|
||||
- HUGGINGFACEHUB_API_TOKEN
|
||||
- OPENAI_API_KEY
|
||||
- PINECONE_API_KEY
|
||||
- SEARCHAPI_API_KEY
|
||||
- SERPAPI_API_KEY
|
||||
- UPSTASH_VECTOR_REST_URL
|
||||
- UPSTASH_VECTOR_REST_TOKEN
|
||||
- VECTARA_CUSTOMER_ID
|
||||
- VECTARA_CORPUS_ID
|
||||
- VECTARA_API_KEY
|
||||
|
||||
<Admonition type="tip">
|
||||
Set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ as a comma-separated list
|
||||
of variables (e.g. _`"VARIABLE1, VARIABLE2"`_) or as a JSON-encoded string
|
||||
(e.g. _`'["VARIABLE1", "VARIABLE2"]'`_).
|
||||
</Admonition>
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
# Inputs and Outputs
|
||||
|
||||
TL;DR: Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. They also
|
||||
dynamically change the Playground and can be renamed to make it easier to build and maintain your flows.
|
||||
|
||||
## Introduction
|
||||
|
||||
Langflow 1.0 introduces new categories of components called Inputs and Outputs. They are used to make it easier to understand and interact with your flows.
|
||||
|
||||
Let's start with what they have in common:
|
||||
|
||||
- Components in these categories connect to components that have Text or Record inputs or outputs. Some can connect to both but you have to pick what type of data you want to output or input.
|
||||
- They can be renamed to help you identify them more easily in the Playground and while using the API.
|
||||
- They dynamically change the Playground to make it easier to understand and interact with your flows.
|
||||
|
||||
Native Langflow Components were created to be powerful tools that work around Langflow's features. They are designed to be easy to use and understand, and to help you build your flows faster.
|
||||
|
||||
Let's dive into Inputs and Outputs.
|
||||
|
||||
## Inputs
|
||||
|
||||
Inputs are components that are used to define where data comes into your flow. They can be used to receive data from the user, from a database, or from any other source that can be converted to Text or Record.
|
||||
|
||||
The difference between Chat Input and other Input components is the format of the output, the number of configurable fields, and the way they are displayed in the Playground.
|
||||
|
||||
Chat Input components can output Text or Record. When you want to pass the sender name, or sender to the next component, you can use the Record output, and when you want to pass the message only you can use the Text output. This is useful when saving the message to a database or a memory system like Zep.
|
||||
|
||||
You can find out more about it and the other Inputs [here](../components/inputs).
|
||||
|
||||
## Outputs
|
||||
|
||||
Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground.
|
||||
|
||||
The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user.
|
||||
|
||||
You can find out more about it and the other Outputs [here](../components/outputs).
|
||||
|
|
@ -41,7 +41,7 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat
|
|||
|
||||
Langflow 1.0 introduces adds the concept of Inputs and Outputs to flows, allowing a clear definition of the data flow between components. Discover how to use Inputs and Outputs to pass data between components and create more dynamic flows.
|
||||
|
||||
[Learn more about Inputs and Outputs of Components](../migration/inputs-and-outputs)
|
||||
[Learn more about Inputs and Outputs of Components](../components/inputs-and-outputs)
|
||||
|
||||
## To Compose or Not to Compose: the choice is yours
|
||||
|
||||
|
|
@ -71,7 +71,7 @@ Langflow 1.0 introduces many new native categories, including Inputs, Outputs, H
|
|||
|
||||
With the introduction of Text and Record types connections between Components are more intuitive and easier to understand. This is the first step in a series of improvements to the way you interact with Langflow. Learn how to use Text, and Record and how they help you build better flows.
|
||||
|
||||
[Learn more about Text and Record](../migration/text-and-record)
|
||||
[Learn more about Text and Record](../components/text-and-record)
|
||||
|
||||
## CustomComponent for All Components
|
||||
|
||||
|
|
@ -119,7 +119,7 @@ Things got a whole lot easier. You can now pass tweaks and inputs in the API by
|
|||
|
||||
Global Variables can be used in any Text Field across your projects. Learn how to define and utilize Global Variables to streamline your workflow.
|
||||
|
||||
[Learn more about Global Variables](../migration/global-variables)
|
||||
[Learn more about Global Variables](../administration/global-env.mdx)
|
||||
|
||||
## Experimental Components
|
||||
|
||||
|
|
|
|||
|
|
@ -25,11 +25,11 @@ ModuleNotFoundError: No module named 'langflow.__main__'
|
|||
There are two possible reasons for this error:
|
||||
|
||||
1. You've installed Langflow using _`pip install langflow`_ but you already had a previous version of Langflow installed in your system.
|
||||
In this case, you might be running the wrong executable.
|
||||
To solve this issue, run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_.
|
||||
If that doesn't work, try uninstalling and reinstalling Langflow with _`python -m pip install langflow --pre -U`_.
|
||||
In this case, you might be running the wrong executable.
|
||||
To solve this issue, run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_.
|
||||
If that doesn't work, try uninstalling and reinstalling Langflow with _`python -m pip install langflow --pre -U`_.
|
||||
2. Some version conflicts might have occurred during the installation process.
|
||||
Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall Langflow and its dependencies.
|
||||
Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall Langflow and its dependencies.
|
||||
|
||||
## _`Something went wrong running migrations. Please, run 'langflow migration --fix'`_
|
||||
|
||||
|
|
@ -45,4 +45,3 @@ There are two possible reasons for this error:
|
|||
This error can occur during Langflow upgrades when the new version can't override `langflow-pre.db` in `.cache/langflow/`. Clearing the cache removes this file but will also erase your settings.
|
||||
|
||||
If you wish to retain your files, back them up before clearing the folder.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,45 +0,0 @@
|
|||
# Text and Record
|
||||
|
||||
In Langflow 1.0 we added two main input and output types: Text and Record. Text is a simple string input and output type, while Record is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
|
||||
|
||||
We've created a few components to help you work with these types. Let's see how a few of them work.
|
||||
|
||||
### Records To Text
|
||||
|
||||
This is a Component that takes in Records and outputs a Text. It does this using a template string and concatenating the values of the Record, one per line.
|
||||
|
||||
If we have the following Records:
|
||||
|
||||
```json
|
||||
{
|
||||
"sender_name": "Alice",
|
||||
"message": "Hello!"
|
||||
}
|
||||
{
|
||||
"sender_name": "John",
|
||||
"message": "Hi!"
|
||||
}
|
||||
```
|
||||
|
||||
And the template string is: _`{sender_name}: {message}`_
|
||||
|
||||
```
|
||||
Alice: Hello!
|
||||
John: Hi!
|
||||
```
|
||||
|
||||
### Create Record
|
||||
|
||||
This Component allows you to create a Record from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15 😅). Once you've picked that number you'll need to write the name of the Key and can pass Text values from other components to it.
|
||||
|
||||
### Documents To Records
|
||||
|
||||
This Component takes in a [LangChain](https://langchain.com) Document and outputs a Record. It does this by extracting the _`page_content`_ and the _`metadata`_ from the Document and adding them to the Record as _`text`_ and _`data`_ respectively.
|
||||
|
||||
## Why is this useful?
|
||||
|
||||
The idea was to create a unified way to work with complex data in Langflow, and to make it easier to work with data that is not just a simple string. This way you can create more complex workflows and use the data in more ways.
|
||||
|
||||
## What's next?
|
||||
|
||||
We are planning to integrate an array of modalities to Langflow, such as images, audio, and video. This will allow you to create even more complex workflows and use cases. Stay tuned for more updates! 🚀
|
||||
|
|
@ -14,12 +14,15 @@ This article demonstrates how to use Langflow's prompt tools to issue basic prom
|
|||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
- [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key created](https://platform.openai.com)
|
||||
- [OpenAI API key created](https://platform.openai.com)
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Create the basic prompting project
|
||||
|
|
@ -42,25 +45,21 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A
|
|||
This should be interesting...
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot responds in a markedly piratical manner!
|
||||
The bot responds in a markedly piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
|
||||
|
||||
|
||||
|
||||
The response will be markedly different.
|
||||
|
|
|
|||
|
|
@ -10,12 +10,15 @@ Build a blog writer with OpenAI that uses URLs for reference content.
|
|||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
- [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key created](https://platform.openai.com)
|
||||
- [OpenAI API key created](https://platform.openai.com)
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Create the Blog Writer project
|
||||
|
|
@ -36,6 +39,7 @@ Build a blog writer with OpenAI that uses URLs for reference content.
|
|||
This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components.
|
||||
|
||||
The **Prompt** component's default **Template** field looks like this:
|
||||
|
||||
```bash
|
||||
Reference 1:
|
||||
|
||||
|
|
@ -59,16 +63,16 @@ The `{instructions}` value is received from the **Value** field of the **Instruc
|
|||
The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the Blog Writer flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can run your one-shot flow.
|
||||
The **Interaction Panel** opens, where you can run your one-shot flow.
|
||||
2. Click the **Lighting Bolt** icon to run your flow.
|
||||
3. The **OpenAI** component constructs a blog post with the **URL** items as context.
|
||||
The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs.
|
||||
The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs.
|
||||
|
||||
To write about something different, change the values in the **URL** components, and see what the LLM constructs.
|
||||
To write about something different, change the values in the **URL** components, and see what the LLM constructs.
|
||||
|
|
|
|||
|
|
@ -10,12 +10,15 @@ Build a question-and-answer chatbot with a document loaded from local memory.
|
|||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
- [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key created](https://platform.openai.com)
|
||||
- [OpenAI API key created](https://platform.openai.com)
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Create the Document QA project
|
||||
|
|
@ -39,24 +42,27 @@ The **Prompt** component is instructed to answer questions based on the contents
|
|||
Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
5. To select a document to load, in the **Files** component, click within the **Path** field.
|
||||
1. Select a local file, and then click **Open**.
|
||||
2. The file name appears in the field.
|
||||
<Admonition type="tip">
|
||||
The file must be of an extension type listed [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13).
|
||||
</Admonition>
|
||||
1. Select a local file, and then click **Open**.
|
||||
2. The file name appears in the field.
|
||||
<Admonition type="tip">
|
||||
The file must be of an extension type listed
|
||||
[here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13).
|
||||
</Admonition>
|
||||
|
||||
## Run the Document QA flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
For this example, we loaded an error log `.txt` file and asked, "What went wrong?"
|
||||
The bot responded:
|
||||
For this example, we loaded an error log `.txt` file and asked, "What went wrong?"
|
||||
The bot responded:
|
||||
|
||||
```
|
||||
The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties.
|
||||
```
|
||||
|
|
|
|||
|
|
@ -10,12 +10,15 @@ This flow extends the [basic prompting flow](./basic-prompting.mdx) to include c
|
|||
|
||||
## Prerequisites
|
||||
|
||||
* [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
- [Langflow installed and running](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key created](https://platform.openai.com)
|
||||
- [OpenAI API key created](https://platform.openai.com)
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
## Create the memory chatbot project
|
||||
|
|
@ -43,16 +46,16 @@ This chatbot is augmented with the **Chat Memory** component, which stores messa
|
|||
The **Chat History** component gives the **OpenAI** component a memory of previous questions.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the memory chatbot flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot will respond according to the template in the **Prompt** component.
|
||||
The bot will respond according to the template in the **Prompt** component.
|
||||
3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears.
|
||||
|
||||
## Modify the Session ID field to have multiple conversations
|
||||
|
|
@ -65,11 +68,11 @@ You can demonstrate this by modifying the **Session ID** value to switch between
|
|||
|
||||
1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`.
|
||||
2. Click the **Run** button to run your flow.
|
||||
In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button).
|
||||
In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button).
|
||||
3. Type a few questions to your bot.
|
||||
4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`.
|
||||
5. Run your flow.
|
||||
The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`.
|
||||
The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`.
|
||||
|
||||
## Store Session ID as a Langflow variable
|
||||
|
||||
|
|
@ -79,4 +82,3 @@ To store **Session ID** as a Langflow variable, in the **Session ID** field, cli
|
|||
2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`.
|
||||
3. Click **Save Variable**.
|
||||
4. Apply this variable to **Chat Input**.
|
||||
|
||||
|
|
|
|||
|
|
@ -17,16 +17,19 @@ We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pr
|
|||
## Prerequisites
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
|
||||
Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
|
||||
using this
|
||||
link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
to create your own Langflow workspace in minutes.
|
||||
</Admonition>
|
||||
|
||||
* [Langflow installed and running](../getting-started/install-langflow.mdx)
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- [Langflow installed and running](../getting-started/install-langflow.mdx)
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* [OpenAI API key](https://platform.openai.com)
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- [OpenAI API key](https://platform.openai.com)
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* [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with:
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* Application token (`AstraCS:WSnyFUhRxsrg…`)
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* API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`)
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- [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with:
|
||||
- Application token (`AstraCS:WSnyFUhRxsrg…`)
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- API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`)
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## Create the vector store RAG project
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|
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|
|
@ -49,38 +52,40 @@ The **ingestion** flow (bottom of the screen) populates the vector store with da
|
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It ingests data from a file (**File**), splits it into chunks (**Recursive Character Text Splitter**), indexes it in Astra DB (**Astra DB**), and computes embeddings for the chunks (**OpenAI Embeddings**).
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This forms a "brain" for the query flow.
|
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|
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The **query** flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex:
|
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The **query** flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex:
|
||||
|
||||
* **Chat Input** component defines where to put the user input coming from the Playground.
|
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* **OpenAI Embeddings** component generates embeddings from the user input.
|
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* **Astra DB Search** component retrieves the most relevant Records from the Astra DB database.
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* **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
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* **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model.
|
||||
* **OpenAI** component generates a response to the prompt.
|
||||
* **Chat Output** component displays the response in the Playground.
|
||||
- **Chat Input** component defines where to put the user input coming from the Playground.
|
||||
- **OpenAI Embeddings** component generates embeddings from the user input.
|
||||
- **Astra DB Search** component retrieves the most relevant Records from the Astra DB database.
|
||||
- **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
|
||||
- **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model.
|
||||
- **OpenAI** component generates a response to the prompt.
|
||||
- **Chat Output** component displays the response in the Playground.
|
||||
|
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4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
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||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
4. To create environment variables for the **Astra DB** and **Astra DB Search** components:
|
||||
1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
2. In the **Variable Name** field, enter `astra_token`.
|
||||
3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg…`).
|
||||
4. Click **Save Variable**.
|
||||
5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`).
|
||||
6. Add the global variable to both the **Astra DB** and **Astra DB Search** components.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
5. To create environment variables for the **Astra DB** and **Astra DB Search** components:
|
||||
1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
2. In the **Variable Name** field, enter `astra_token`.
|
||||
3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg…`).
|
||||
4. Click **Save Variable**.
|
||||
5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`).
|
||||
6. Add the global variable to both the **Astra DB** and **Astra DB Search** components.
|
||||
|
||||
## Run the vector store RAG flow
|
||||
|
||||
1. Click the **Playground** button.
|
||||
The **Playground** opens, where you can chat with your data.
|
||||
The **Playground** opens, where you can chat with your data.
|
||||
2. Type a message and press Enter. (Try something like "What topics do you know about?")
|
||||
3. The bot will respond with a summary of the data you've embedded.
|
||||
|
||||
For example, we embedded a PDF of an engine maintenance manual and asked, "How do I change the oil?"
|
||||
The bot responds:
|
||||
|
||||
```
|
||||
To change the oil in the engine, follow these steps:
|
||||
|
||||
|
|
@ -102,7 +107,3 @@ You should use a 3/8 inch wrench to remove the oil drain cap.
|
|||
```
|
||||
|
||||
This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store.
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ By having a clear definition of Inputs and Outputs, we could build the experienc
|
|||
When building a project testing and debugging is crucial. The Playground is a tool that changes dynamically based on the Inputs and Outputs you defined in your project.
|
||||
|
||||
For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt and the answer.
|
||||
Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Playground.
|
||||
Now, you could plug the output of your Prompt into a [Text Output](../components/inputs-and-outputs), rename that to "Prompt Result" and see the output of your Prompt in the Playground.
|
||||
|
||||
{/* Add image here of the described above */}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue