Release -> Output Inspection, Session Management, General Bug Fixing and UI Improvements (#2104)
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
aa94e42e0d
443 changed files with 20610 additions and 11379 deletions
1
.gitattributes
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1
.gitattributes
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@ -32,3 +32,4 @@ Dockerfile text
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*.mp4 binary
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*.svg binary
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*.csv binary
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8
.vscode/launch.json
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8
.vscode/launch.json
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@ -3,7 +3,7 @@
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"configurations": [
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{
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"name": "Debug Backend",
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"type": "python",
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"type": "debugpy",
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"request": "launch",
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"module": "uvicorn",
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"args": [
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@ -26,7 +26,7 @@
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},
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{
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"name": "Debug CLI",
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"type": "python",
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"type": "debugpy",
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"request": "launch",
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"module": "langflow",
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"args": [
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@ -43,7 +43,7 @@
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},
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{
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"name": "Python: Remote Attach",
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"type": "python",
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"type": "debugpy",
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"request": "attach",
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"justMyCode": true,
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"connect": {
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@ -65,7 +65,7 @@
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},
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{
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"name": "Python: Debug Tests",
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"type": "python",
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"type": "debugpy",
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"request": "launch",
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"program": "${file}",
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"purpose": ["debug-test"],
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3
Makefile
3
Makefile
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@ -44,7 +44,8 @@ coverage:
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poetry run pytest --cov \
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--cov-config=.coveragerc \
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--cov-report xml \
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--cov-report term-missing:skip-covered
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--cov-report term-missing:skip-covered \
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--cov-report lcov:coverage/lcov-pytest.info
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# allow passing arguments to pytest
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tests:
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@ -181,9 +181,8 @@ Use the widget API to customize your Chat Widget:
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format {<span>"key":"value"</span>}.
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</Admonition>
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| Prop | Type | Required | Description |
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|-----------------------|---------|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| bot_message_style | JSON | No | Applies custom formatting to bot messages. |
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| chat_input_field | String | Yes | Defines the type of the input field for chat messages. |
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| chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. |
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@ -207,4 +206,3 @@ Use the widget API to customize your Chat Widget:
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| user_message_style | JSON | No | Determines the formatting for user messages in the chat window. |
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| width | Number | No | Sets the width of the chat window in pixels. |
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| window_title | String | No | Sets the title displayed in the chat window's header or title bar. |
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@ -4,12 +4,12 @@ Langflow's Command Line Interface (CLI) is a powerful tool that allows you to in
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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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- [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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@ -23,21 +23,21 @@ langflow --help
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python -m langflow
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```
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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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| 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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### Options
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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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| 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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## langflow api-key
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@ -61,10 +61,10 @@ python -m langflow api-key
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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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| 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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@ -87,12 +87,12 @@ 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] |
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| `--help` | Show this message and exit. |
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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] |
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| `--help` | Show this message and exit. |
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## langflow run
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@ -106,26 +106,26 @@ python -m langflow run
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### Options
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| Option | Description |
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|-------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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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. 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. |
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| Option | Description |
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||||
| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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. |
|
||||
| `--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. |
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#### CLI environment variables
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@ -145,10 +145,9 @@ python -m langflow superuser
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### Options
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| Option | Type | Description |
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|----------------|-------|-------------------------------------------------------------|
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| `--username` | TEXT | Username for the superuser. [default: None] [required] |
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| `--password` | TEXT | Password for the superuser. [default: None] [required] |
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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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| Option | Type | Description |
|
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| ------------- | ---- | ------------------------------------------------------------- |
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| `--username` | TEXT | Username for the superuser. [default: None] [required] |
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| `--password` | TEXT | Password for the superuser. [default: None] [required] |
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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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@ -86,7 +86,7 @@ With _`LANGFLOW_AUTO_LOGIN`_ set to _`False`_, Langflow requires users to sign u
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light: useBaseUrl("img/sign-up.png"),
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dark: useBaseUrl("img/sign-up.png"),
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}}
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style={{ width: "40%", margin: "20px auto" }}
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style={{ width: "40%", margin: "20px auto" }}
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/>
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## Profile settings
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@ -40,14 +40,13 @@ The Playground's appearance changes depending on what components are in your can
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Adding or removing any of the below components modifies your Playground so you can monitor the inputs and outputs.
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* Chat Input
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* Text Input
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* Chat Output
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* Text Output
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* Records Output
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* Inspect Memory
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- Chat Input
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- Text Input
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- Chat Output
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- Text Output
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- Records Output
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- Inspect Memory
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You can also select **Options** > **Logs** to see your flow's logs.
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For more information, see [Inputs and Outputs](../components/inputs-and-outputs.mdx).
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@ -1,4 +1,4 @@
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import Admonition from '@theme/Admonition';
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import Admonition from "@theme/Admonition";
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# Agents
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@ -81,4 +81,4 @@ The `ZeroShotAgent` uses the ReAct framework to decide which tool to use based o
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**Parameters**:
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- **Allowed Tools:** The tools accessible to the agent.
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- **LLM Chain:** The LLM Chain used by the agent.
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- **LLM Chain:** The LLM Chain used by the agent.
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|
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@ -1,4 +1,4 @@
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import Admonition from '@theme/Admonition';
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import Admonition from "@theme/Admonition";
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# Data
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|
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@ -4,113 +4,113 @@
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Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
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| **Parameter** | **Type** | **Description** | **Default** |
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||||
|-----------------------------|-------------------|------------------------------------------------------------------------------------------------------------------------------------|-------------|
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| `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | |
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| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | |
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| `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | |
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||||
| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | |
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||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| -------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- |
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||||
| `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | |
|
||||
| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | |
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||||
| `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | |
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||||
| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | |
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## Cohere Embeddings
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||||
Used to load embedding models from [Cohere](https://cohere.com/).
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||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `cohere_api_key` | `str` | API key required to authenticate with the Cohere service. | |
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| `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` |
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| `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` |
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||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ---------------- | -------- | ------------------------------------------------------------------------- | -------------------- |
|
||||
| `cohere_api_key` | `str` | API key required to authenticate with the Cohere service. | |
|
||||
| `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` |
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||||
| `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` |
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||||
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||||
## Azure OpenAI Embeddings
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||||
|
||||
Generate embeddings using Azure OpenAI models.
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||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | |
|
||||
| `Deployment Name` | `str` | The name of the deployment. | |
|
||||
| `API Version` | `str` | The API version to use, options include various dates. | |
|
||||
| `API Key` | `str` | The API key to access the Azure OpenAI service. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ----------------- | -------- | -------------------------------------------------------------------------------------------------- | ----------- |
|
||||
| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | |
|
||||
| `Deployment Name` | `str` | The name of the deployment. | |
|
||||
| `API Version` | `str` | The API version to use, options include various dates. | |
|
||||
| `API Key` | `str` | The API key to access the Azure OpenAI service. | |
|
||||
|
||||
## Hugging Face API Embeddings
|
||||
|
||||
Generate embeddings using Hugging Face Inference API models.
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `API Key` | `str` | API key for accessing the Hugging Face Inference API. | |
|
||||
| `API URL` | `str` | URL of the Hugging Face Inference API. | `http://localhost:8080` |
|
||||
| `Model Name` | `str` | Name of the model to use for embeddings. | `BAAI/bge-large-en-v1.5` |
|
||||
| `Cache Folder` | `str` | Folder path to cache Hugging Face models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------- | -------- | ----------------------------------------------------- | ------------------------ |
|
||||
| `API Key` | `str` | API key for accessing the Hugging Face Inference API. | |
|
||||
| `API URL` | `str` | URL of the Hugging Face Inference API. | `http://localhost:8080` |
|
||||
| `Model Name` | `str` | Name of the model to use for embeddings. | `BAAI/bge-large-en-v1.5` |
|
||||
| `Cache Folder` | `str` | Folder path to cache Hugging Face models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
|
||||
## Hugging Face Embeddings
|
||||
|
||||
Used to load embedding models from [HuggingFace](https://huggingface.co).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `Cache Folder` | `str` | Folder path to cache HuggingFace models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------- | -------- | ---------------------------------------------- | ----------------------------------------- |
|
||||
| `Cache Folder` | `str` | Folder path to cache HuggingFace models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
|
||||
## OpenAI Embeddings
|
||||
|
||||
Used to load embedding models from [OpenAI](https://openai.com/).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
||||
| `OpenAI API Key` | `str` | The API key to use for accessing the OpenAI API. | |
|
||||
| `Default Headers` | `Dict[str, str]` | Default headers for the HTTP requests. | |
|
||||
| `Default Query` | `NestedDict` | Default query parameters for the HTTP requests. | |
|
||||
| `Allowed Special` | `List[str]` | Special tokens allowed for processing. | `[]` |
|
||||
| `Disallowed Special` | `List[str]` | Special tokens disallowed for processing. | `["all"]` |
|
||||
| `Chunk Size` | `int` | Chunk size for processing. | `1000` |
|
||||
| `Client` | `Any` | HTTP client for making requests. | |
|
||||
| `Deployment` | `str` | Deployment name for the model. | `text-embedding-3-small` |
|
||||
| `Embedding Context Length` | `int` | Length of embedding context. | `8191` |
|
||||
| `Max Retries` | `int` | Maximum number of retries for failed requests. | `6` |
|
||||
| `Model` | `str` | Name of the model to use. | `text-embedding-3-small` |
|
||||
| `Model Kwargs` | `NestedDict` | Additional keyword arguments for the model. | |
|
||||
| `OpenAI API Base` | `str` | Base URL of the OpenAI API. | |
|
||||
| `OpenAI API Type` | `str` | Type of the OpenAI API. | |
|
||||
| `OpenAI API Version` | `str` | Version of the OpenAI API. | |
|
||||
| `OpenAI Organization` | `str` | Organization associated with the API key. | |
|
||||
| `OpenAI Proxy` | `str` | Proxy server for the requests. | |
|
||||
| `Request Timeout` | `float` | Timeout for the HTTP requests. | |
|
||||
| `Show Progress Bar` | `bool` | Whether to show a progress bar for processing. | `False` |
|
||||
| `Skip Empty` | `bool` | Whether to skip empty inputs. | `False` |
|
||||
| `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` |
|
||||
| `TikToken Model Name` | `str` | Name of the TikToken model. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| -------------------------- | ---------------- | ------------------------------------------------ | ------------------------ |
|
||||
| `OpenAI API Key` | `str` | The API key to use for accessing the OpenAI API. | |
|
||||
| `Default Headers` | `Dict[str, str]` | Default headers for the HTTP requests. | |
|
||||
| `Default Query` | `NestedDict` | Default query parameters for the HTTP requests. | |
|
||||
| `Allowed Special` | `List[str]` | Special tokens allowed for processing. | `[]` |
|
||||
| `Disallowed Special` | `List[str]` | Special tokens disallowed for processing. | `["all"]` |
|
||||
| `Chunk Size` | `int` | Chunk size for processing. | `1000` |
|
||||
| `Client` | `Any` | HTTP client for making requests. | |
|
||||
| `Deployment` | `str` | Deployment name for the model. | `text-embedding-3-small` |
|
||||
| `Embedding Context Length` | `int` | Length of embedding context. | `8191` |
|
||||
| `Max Retries` | `int` | Maximum number of retries for failed requests. | `6` |
|
||||
| `Model` | `str` | Name of the model to use. | `text-embedding-3-small` |
|
||||
| `Model Kwargs` | `NestedDict` | Additional keyword arguments for the model. | |
|
||||
| `OpenAI API Base` | `str` | Base URL of the OpenAI API. | |
|
||||
| `OpenAI API Type` | `str` | Type of the OpenAI API. | |
|
||||
| `OpenAI API Version` | `str` | Version of the OpenAI API. | |
|
||||
| `OpenAI Organization` | `str` | Organization associated with the API key. | |
|
||||
| `OpenAI Proxy` | `str` | Proxy server for the requests. | |
|
||||
| `Request Timeout` | `float` | Timeout for the HTTP requests. | |
|
||||
| `Show Progress Bar` | `bool` | Whether to show a progress bar for processing. | `False` |
|
||||
| `Skip Empty` | `bool` | Whether to skip empty inputs. | `False` |
|
||||
| `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` |
|
||||
| `TikToken Model Name` | `str` | Name of the TikToken model. | |
|
||||
|
||||
## Ollama Embeddings
|
||||
|
||||
Generate embeddings using Ollama models.
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|--------------------------------------------------------------------------------------------------------------------|---------------------------|
|
||||
| `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` |
|
||||
| `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` |
|
||||
| `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ------------------- | -------- | ---------------------------------------------------------------------------------------- | ------------------------ |
|
||||
| `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` |
|
||||
| `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` |
|
||||
| `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | |
|
||||
|
||||
## VertexAI Embeddings
|
||||
|
||||
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
||||
| `credentials` | `Credentials` | The default custom credentials to use. | |
|
||||
| `location` | `str` | The default location to use when making API calls. | `us-central1`|
|
||||
| `max_output_tokens` | `int` | Token limit determines the maximum amount of text output from one prompt. | `128` |
|
||||
| `model_name` | `str` | The name of the Vertex AI large language model. | `text-bison`|
|
||||
| `project` | `str` | The default GCP project to use when making Vertex API calls. | |
|
||||
| `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` |
|
||||
| `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` |
|
||||
| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` |
|
||||
| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` |
|
||||
| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | |
|
||||
| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------- |
|
||||
| `credentials` | `Credentials` | The default custom credentials to use. | |
|
||||
| `location` | `str` | The default location to use when making API calls. | `us-central1` |
|
||||
| `max_output_tokens` | `int` | Token limit determines the maximum amount of text output from one prompt. | `128` |
|
||||
| `model_name` | `str` | The name of the Vertex AI large language model. | `text-bison` |
|
||||
| `project` | `str` | The default GCP project to use when making Vertex API calls. | |
|
||||
| `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` |
|
||||
| `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` |
|
||||
| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` |
|
||||
| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` |
|
||||
| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | |
|
||||
| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` |
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Experimental
|
||||
|
||||
|
|
@ -31,10 +31,12 @@ This component extracts specified keys from a record.
|
|||
**Parameters**
|
||||
|
||||
- **Record:**
|
||||
|
||||
- **Display Name:** Record
|
||||
- **Info:** The record from which to extract keys.
|
||||
|
||||
- **Keys:**
|
||||
|
||||
- **Display Name:** Keys
|
||||
- **Info:** The keys to be extracted.
|
||||
|
||||
|
|
@ -56,6 +58,7 @@ This component turns a function running a flow into a Tool.
|
|||
**Parameters**
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
@ -63,10 +66,12 @@ This component turns a function running a flow into a Tool.
|
|||
- **Refresh Button:** True
|
||||
|
||||
- **Name:**
|
||||
|
||||
- **Display Name:** Name
|
||||
- **Description:** The tool's name.
|
||||
|
||||
- **Description:**
|
||||
|
||||
- **Display Name:** Description
|
||||
- **Description:** Describes the tool.
|
||||
|
||||
|
|
@ -129,10 +134,12 @@ This component generates a notification.
|
|||
**Parameters**
|
||||
|
||||
- **Name:**
|
||||
|
||||
- **Display Name:** Name
|
||||
- **Info:** The notification's name.
|
||||
|
||||
- **Record:**
|
||||
|
||||
- **Display Name:** Record
|
||||
- **Info:** Optionally, a record to store in the notification.
|
||||
|
||||
|
|
@ -153,10 +160,12 @@ This component runs a specified flow.
|
|||
**Parameters**
|
||||
|
||||
- **Input Value:**
|
||||
|
||||
- **Display Name:** Input Value
|
||||
- **Multiline:** True
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
@ -179,14 +188,17 @@ This component executes a specified runnable.
|
|||
**Parameters**
|
||||
|
||||
- **Input Key:**
|
||||
|
||||
- **Display Name:** Input Key
|
||||
- **Info:** The input key.
|
||||
|
||||
- **Inputs:**
|
||||
|
||||
- **Display Name:** Inputs
|
||||
- **Info:** Inputs for the runnable.
|
||||
|
||||
- **Runnable:**
|
||||
|
||||
- **Display Name:** Runnable
|
||||
- **Info:** The runnable to execute.
|
||||
|
||||
|
|
@ -207,14 +219,17 @@ This component executes an SQL query.
|
|||
**Parameters**
|
||||
|
||||
- **Database URL:**
|
||||
|
||||
- **Display Name:** Database URL
|
||||
- **Info:** The database's URL.
|
||||
|
||||
- **Include Columns:**
|
||||
|
||||
- **Display Name:** Include Columns
|
||||
- **Info:** Whether to include columns in the result.
|
||||
|
||||
- **Passthrough:**
|
||||
|
||||
- **Display Name:** Passthrough
|
||||
- **Info:** Returns the query instead of raising an exception if an error occurs.
|
||||
|
||||
|
|
@ -235,10 +250,12 @@ This component dynamically generates a tool from a flow.
|
|||
**Parameters**
|
||||
|
||||
- **Input Value:**
|
||||
|
||||
- **Display Name:** Input Value
|
||||
- **Multiline:** True
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Helpers
|
||||
|
||||
|
|
@ -49,9 +49,10 @@ Use this component as a template to create your custom component.
|
|||
- **Parameter:** Describe the purpose of this parameter.
|
||||
|
||||
<Admonition type="info" title="Info">
|
||||
<p>
|
||||
Customize the <code>build_config</code> and <code>build</code> methods according to your requirements.
|
||||
</p>
|
||||
<p>
|
||||
Customize the <code>build_config</code> and <code>build</code> methods
|
||||
according to your requirements.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Learn more about creating custom components at [Custom Component](http://docs.langflow.org/components/custom).
|
||||
|
|
|
|||
|
|
@ -48,8 +48,8 @@ One significant capability of the Chat Input component is its ability to transfo
|
|||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: ("img/playground-chat.png"),
|
||||
dark: ("img/playground-chat.png"),
|
||||
light: "img/playground-chat.png",
|
||||
dark: "img/playground-chat.png",
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Memories
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
Thanks for your patience as we improve our documentation—it might have some rough edges. Share your feedback or report issues to help us enhance it! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
Thanks for your patience as we improve our documentation—it might have some
|
||||
rough edges. Share your feedback or report issues to help us enhance it!
|
||||
🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Memory is a concept in chat-based applications that allows the system to remember previous interactions. This capability helps maintain the context of the conversation and enables the system to understand new messages in light of past messages.
|
||||
|
|
@ -24,9 +26,13 @@ This component retrieves stored messages using various filters such as sender ty
|
|||
- **number_of_messages**: Specifies the number of messages to retrieve. Defaults to `5`. Determines the number of recent messages from the chat history to fetch.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
The component retrieves messages based on the provided criteria, including the specific file path for stored messages. If no specific criteria are provided, it returns the most recent messages up to the specified limit. This component can be used to review past interactions and analyze conversation flows.
|
||||
</p>
|
||||
<p>
|
||||
The component retrieves messages based on the provided criteria, including
|
||||
the specific file path for stored messages. If no specific criteria are
|
||||
provided, it returns the most recent messages up to the specified limit.
|
||||
This component can be used to review past interactions and analyze
|
||||
conversation flows.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
### ConversationBufferMemory
|
||||
|
|
@ -84,7 +90,8 @@ The `ConversationKGMemory` utilizes a knowledge graph to enhance memory capabili
|
|||
- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
|
||||
- **output_key**: Identifies the key under which the generated response
|
||||
|
||||
is stored, enabling retrieval using this key.
|
||||
is stored, enabling retrieval using this key.
|
||||
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
|
||||
---
|
||||
|
|
@ -124,4 +131,4 @@ The `VectorRetrieverMemory` retrieves vectors based on queries, facilitating vec
|
|||
- **Retriever**: The tool used to fetch documents.
|
||||
- **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation.
|
||||
- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Large Language Models (LLMs)
|
||||
|
||||
|
|
@ -31,7 +31,9 @@ This is a wrapper for Anthropic's large language model designed for chat-based i
|
|||
`CTransformers` provides access to Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library.
|
||||
|
||||
<Admonition type="info">
|
||||
Ensure the `ctransformers` Python package is installed. Discover more about installation, supported models, and usage [here](https://github.com/marella/ctransformers).
|
||||
Ensure the `ctransformers` Python package is installed. Discover more about
|
||||
installation, supported models, and usage
|
||||
[here](https://github.com/marella/ctransformers).
|
||||
</Admonition>
|
||||
|
||||
- **config:** This configuration is for the Transformer models. Check the default settings and possible configurations at [config](https://github.com/marella/ctransformers#config).
|
||||
|
|
@ -122,7 +124,8 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
|
|||
|
||||
- **credentials**: Custom
|
||||
|
||||
credentials used for API interactions.
|
||||
credentials used for API interactions.
|
||||
|
||||
- **location**: The default location for API calls, defaulting to `us-central1`.
|
||||
- **max_output_tokens**: Limits the output tokens per prompt, defaulting to `128`.
|
||||
- **model_name**: The name of the Vertex AI model in use, defaulting to `text-bison`.
|
||||
|
|
@ -134,4 +137,4 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
|
|||
- **tuned_model_name**: Specifies a tuned model name, which overrides the default model name if provided.
|
||||
- **verbose**: Controls the output verbosity to assist in debugging and understanding the operational details, defaulting to `False`.
|
||||
|
||||
---
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Retrievers
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,11 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Toolkits
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation - it may
|
||||
contain some rough edges. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Tools
|
||||
|
||||
|
|
|
|||
|
|
@ -80,7 +80,11 @@ Generates a unique identifier (UUID) for each instance it is invoked, providing
|
|||
- Returns a unique identifier (UUID) as a string. This UUID is generated using Python's `uuid` module, ensuring that each identifier is unique and can be used as a reliable reference in your application.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
The Unique ID Generator is crucial for scenarios requiring distinct identifiers, such as session management, transaction tracking, or any context where different instances or entities must be uniquely identified. The generated UUID is provided as a hexadecimal string, offering a high level of uniqueness and security for identification purposes.
|
||||
The Unique ID Generator is crucial for scenarios requiring distinct
|
||||
identifiers, such as session management, transaction tracking, or any context
|
||||
where different instances or entities must be uniquely identified. The
|
||||
generated UUID is provided as a hexadecimal string, offering a high level of
|
||||
uniqueness and security for identification purposes.
|
||||
</Admonition>
|
||||
|
||||
For additional information and examples, please consult the [Langflow Components Custom Documentation](http://docs.langflow.org/components/custom).
|
||||
|
|
|
|||
|
|
@ -1,24 +1,26 @@
|
|||
# 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`.
|
||||
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)
|
||||
- [Langflow installed](../getting-started/install-langflow.mdx)
|
||||
|
||||
* [OpenAI API key](https://platform.openai.com)
|
||||
- [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
* [A Langflow flow created](../starter-projects/basic-prompting.mdx)
|
||||
- [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:
|
||||
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" \
|
||||
|
|
@ -33,19 +35,22 @@ curl -X POST \
|
|||
"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.
|
||||
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:
|
||||
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!
|
||||
|
||||
|
|
@ -55,7 +60,8 @@ 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:
|
||||
The code will look something like this:
|
||||
|
||||
```python
|
||||
import requests
|
||||
from typing import Optional
|
||||
|
|
@ -99,15 +105,19 @@ 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.
|
||||
See [API](../administration/api.mdx) for more ways to interact with your headless Langflow server.
|
||||
|
|
|
|||
|
|
@ -38,7 +38,6 @@ For example, the [Basic prompting](../starter-projects/basic-prompting.mdx) flow
|
|||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
||||
|
||||
In this flow, the **OpenAI LLM component** receives input (left side) and produces output (right side) - in this case, receiving input from the **Chat Input** and **Prompt** components and producing output to the **Chat Output** component.
|
||||
|
||||
## Component
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@ You'll be presented with the following screen:
|
|||
light: "img/duplicate-space.png",
|
||||
dark: "img/duplicate-space.png",
|
||||
}}
|
||||
style={{ width: "80%", maxWidth: "800px", margin: "0 auto" }}
|
||||
style={{ width: "80%", maxWidth: "800px", margin: "0 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!
|
||||
|
|
|
|||
|
|
@ -32,8 +32,9 @@ There are two possible reasons for this error:
|
|||
Clear the cache by deleting the contents of the cache folder.
|
||||
|
||||
This folder can be found at:
|
||||
- **Linux or WSL2 on Windows**: `home/<username>/.cache/langflow/`
|
||||
- **MacOS**: `/Users/<username>/Library/Caches/langflow/`
|
||||
|
||||
- **Linux or WSL2 on Windows**: `home/<username>/.cache/langflow/`
|
||||
- **MacOS**: `/Users/<username>/Library/Caches/langflow/`
|
||||
|
||||
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.
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ Build a question-and-answer chatbot with a document loaded from local memory.
|
|||
light: "img/document-qa.png",
|
||||
dark: "img/document-qa.png",
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
||||
This flow creates a basic chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components.
|
||||
|
|
|
|||
|
|
@ -13,7 +13,8 @@ In this guide, we will use Astra DB as a vector store to store and retrieve the
|
|||
|
||||
<Admonition type="tip">
|
||||
This guide assumes that you have Langflow up and running. If you are new to
|
||||
Langflow, you can check out the [Getting Started](../getting-started/install-langflow.mdx) guide.
|
||||
Langflow, you can check out the [Getting
|
||||
Started](../getting-started/install-langflow.mdx) guide.
|
||||
</Admonition>
|
||||
|
||||
TLDR;
|
||||
|
|
|
|||
|
|
@ -114,7 +114,8 @@ module.exports = {
|
|||
type: "category",
|
||||
label: "Deployment",
|
||||
collapsed: true,
|
||||
items: ["deployment/docker",
|
||||
items: [
|
||||
"deployment/docker",
|
||||
"deployment/backend-only",
|
||||
"deployment/gcp-deployment",
|
||||
],
|
||||
|
|
|
|||
20
poetry.lock
generated
20
poetry.lock
generated
|
|
@ -1608,6 +1608,23 @@ files = [
|
|||
[package.dependencies]
|
||||
packaging = "*"
|
||||
|
||||
[[package]]
|
||||
name = "dictdiffer"
|
||||
version = "0.9.0"
|
||||
description = "Dictdiffer is a library that helps you to diff and patch dictionaries."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "dictdiffer-0.9.0-py2.py3-none-any.whl", hash = "sha256:442bfc693cfcadaf46674575d2eba1c53b42f5e404218ca2c2ff549f2df56595"},
|
||||
{file = "dictdiffer-0.9.0.tar.gz", hash = "sha256:17bacf5fbfe613ccf1b6d512bd766e6b21fb798822a133aa86098b8ac9997578"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
all = ["Sphinx (>=3)", "check-manifest (>=0.42)", "mock (>=1.3.0)", "numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "sphinx-rtd-theme (>=0.2)", "tox (>=3.7.0)"]
|
||||
docs = ["Sphinx (>=3)", "sphinx-rtd-theme (>=0.2)"]
|
||||
numpy = ["numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)"]
|
||||
tests = ["check-manifest (>=0.42)", "mock (>=1.3.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "tox (>=3.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "dill"
|
||||
version = "0.3.7"
|
||||
|
|
@ -10445,6 +10462,7 @@ test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
|
|||
testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
|
||||
|
||||
[extras]
|
||||
cassio = ["cassio"]
|
||||
couchbase = ["couchbase"]
|
||||
deploy = ["celery", "flower", "redis"]
|
||||
local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
||||
|
|
@ -10452,4 +10470,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "4e16ddf83311fa2c894623b76832a9dda98eec2b88975c087297364954dbdac6"
|
||||
content-hash = "6cfa9f164710bf283b50a8e12e9d0c91c58f5d0176bbb277b5f5d3630ec8b2cb"
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@ youtube-transcript-api = "^0.6.2"
|
|||
markdown = "^3.6"
|
||||
langchain-chroma = "^0.1.1"
|
||||
upstash-vector = "^0.4.0"
|
||||
cassio = "^0.1.7"
|
||||
cassio = { extras = ["cassio"], version = "^0.1.7", optional = true }
|
||||
unstructured = {extras = ["docx", "md", "pptx"], version = "^0.14.4"}
|
||||
|
||||
|
||||
|
|
@ -117,10 +117,12 @@ pytest-asyncio = "^0.23.0"
|
|||
pytest-profiling = "^1.7.0"
|
||||
pre-commit = "^3.7.0"
|
||||
vulture = "^2.11"
|
||||
dictdiffer = "^0.9.0"
|
||||
|
||||
[tool.poetry.extras]
|
||||
deploy = ["celery", "redis", "flower"]
|
||||
couchbase = ["couchbase"]
|
||||
cassio = ["cassio"]
|
||||
local = ["llama-cpp-python", "sentence-transformers", "ctransformers"]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -86,6 +86,10 @@ def update_frontend_node_with_template_values(frontend_node, raw_frontend_node):
|
|||
|
||||
update_template_values(frontend_node["template"], raw_frontend_node["template"])
|
||||
|
||||
old_code = raw_frontend_node["template"]["code"]["value"]
|
||||
new_code = frontend_node["template"]["code"]["value"]
|
||||
frontend_node["edited"] = old_code != new_code
|
||||
|
||||
return frontend_node
|
||||
|
||||
|
||||
|
|
@ -204,16 +208,18 @@ def format_elapsed_time(elapsed_time: float) -> str:
|
|||
return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
|
||||
|
||||
|
||||
async def build_and_cache_graph_from_db(
|
||||
flow_id: str,
|
||||
session: Session,
|
||||
chat_service: "ChatService",
|
||||
):
|
||||
async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
|
||||
"""Build and cache the graph."""
|
||||
flow: Optional[Flow] = session.get(Flow, flow_id)
|
||||
if not flow or not flow.data:
|
||||
raise ValueError("Invalid flow ID")
|
||||
graph = Graph.from_payload(flow.data, flow_id)
|
||||
for vertex_id in graph._has_session_id_vertices:
|
||||
vertex = graph.get_vertex(vertex_id)
|
||||
if vertex is None:
|
||||
raise ValueError(f"Vertex {vertex_id} not found")
|
||||
if not vertex._raw_params.get("session_id"):
|
||||
vertex.update_raw_params({"session_id": flow_id}, overwrite=True)
|
||||
await chat_service.set_cache(flow_id, graph)
|
||||
return graph
|
||||
|
||||
|
|
@ -317,3 +323,4 @@ def parse_exception(exc):
|
|||
if hasattr(exc, "body"):
|
||||
return exc.body["message"]
|
||||
return str(exc)
|
||||
return str(exc)
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ from langflow.api.v1.schemas import (
|
|||
VertexBuildResponse,
|
||||
VerticesOrderResponse,
|
||||
)
|
||||
from langflow.schema.schema import Log
|
||||
from langflow.services.auth.utils import get_current_active_user
|
||||
from langflow.services.chat.service import ChatService
|
||||
from langflow.services.deps import get_chat_service, get_session, get_session_service
|
||||
|
|
@ -123,6 +124,7 @@ async def build_vertex(
|
|||
vertex_id: str,
|
||||
background_tasks: BackgroundTasks,
|
||||
inputs: Annotated[Optional[InputValueRequest], Body(embed=True)] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
chat_service: "ChatService" = Depends(get_chat_service),
|
||||
current_user=Depends(get_current_active_user),
|
||||
):
|
||||
|
|
@ -159,6 +161,7 @@ async def build_vertex(
|
|||
else:
|
||||
graph = cache.get("result")
|
||||
vertex = graph.get_vertex(vertex_id)
|
||||
|
||||
try:
|
||||
lock = chat_service._cache_locks[flow_id_str]
|
||||
(
|
||||
|
|
@ -175,19 +178,25 @@ async def build_vertex(
|
|||
vertex_id=vertex_id,
|
||||
user_id=current_user.id,
|
||||
inputs_dict=inputs.model_dump() if inputs else {},
|
||||
files=files,
|
||||
)
|
||||
log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
|
||||
result_data_response = ResultDataResponse(**result_dict.model_dump())
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error building vertex: {exc}")
|
||||
params = format_exception_message(exc)
|
||||
valid = False
|
||||
log_obj = Log(message=params, type="error")
|
||||
result_data_response = ResultDataResponse(results={})
|
||||
artifacts = {}
|
||||
# If there's an error building the vertex
|
||||
# we need to clear the cache
|
||||
await chat_service.clear_cache(flow_id_str)
|
||||
|
||||
result_data_response.message = artifacts
|
||||
result_data_response.logs.append(log_obj)
|
||||
|
||||
# Log the vertex build
|
||||
if not vertex.will_stream:
|
||||
background_tasks.add_task(
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import hashlib
|
|||
from http import HTTPStatus
|
||||
from io import BytesIO
|
||||
from uuid import UUID
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
|
@ -99,6 +100,46 @@ async def download_image(file_name: str, flow_id: UUID, storage_service: Storage
|
|||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/profile_pictures/{folder_name}/{file_name}")
|
||||
async def download_profile_picture(
|
||||
folder_name: str,
|
||||
file_name: str,
|
||||
storage_service: StorageService = Depends(get_storage_service),
|
||||
):
|
||||
try:
|
||||
extension = file_name.split(".")[-1]
|
||||
config_dir = get_storage_service().settings_service.settings.config_dir
|
||||
config_path = Path(config_dir)
|
||||
folder_path = config_path / "profile_pictures" / folder_name
|
||||
content_type = build_content_type_from_extension(extension)
|
||||
file_content = await storage_service.get_file(flow_id=folder_path, file_name=file_name)
|
||||
return StreamingResponse(BytesIO(file_content), media_type=content_type)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/profile_pictures/list")
|
||||
async def list_profile_pictures(storage_service: StorageService = Depends(get_storage_service)):
|
||||
try:
|
||||
config_dir = get_storage_service().settings_service.settings.config_dir
|
||||
config_path = Path(config_dir)
|
||||
|
||||
people_path = config_path / "profile_pictures/People"
|
||||
space_path = config_path / "profile_pictures/Space"
|
||||
|
||||
people = await storage_service.list_files(flow_id=people_path)
|
||||
space = await storage_service.list_files(flow_id=space_path)
|
||||
|
||||
files = [Path("People") / i for i in people]
|
||||
files += [Path("Space") / i for i in space]
|
||||
|
||||
return {"files": files}
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/list/{flow_id}")
|
||||
async def list_files(
|
||||
flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
|
||||
|
|
|
|||
|
|
@ -31,25 +31,60 @@ def create_flow(
|
|||
flow: FlowCreate,
|
||||
current_user: User = Depends(get_current_active_user),
|
||||
):
|
||||
"""Create a new flow."""
|
||||
if flow.user_id is None:
|
||||
flow.user_id = current_user.id
|
||||
try:
|
||||
"""Create a new flow."""
|
||||
if flow.user_id is None:
|
||||
flow.user_id = current_user.id
|
||||
|
||||
db_flow = Flow.model_validate(flow, from_attributes=True)
|
||||
db_flow.updated_at = datetime.now(timezone.utc)
|
||||
# First check if the flow.name is unique
|
||||
# there might be flows with name like: "MyFlow", "MyFlow (1)", "MyFlow (2)"
|
||||
# so we need to check if the name is unique with `like` operator
|
||||
# if we find a flow with the same name, we add a number to the end of the name
|
||||
# based on the highest number found
|
||||
if session.exec(select(Flow).where(Flow.name == flow.name).where(Flow.user_id == current_user.id)).first():
|
||||
flows = session.exec(
|
||||
select(Flow).where(Flow.name.like(f"{flow.name} (%")).where(Flow.user_id == current_user.id)
|
||||
).all()
|
||||
if flows:
|
||||
numbers = [int(flow.name.split("(")[1].split(")")[0]) for flow in flows]
|
||||
flow.name = f"{flow.name} ({max(numbers) + 1})"
|
||||
else:
|
||||
flow.name = f"{flow.name} (1)"
|
||||
|
||||
if db_flow.folder_id is None:
|
||||
# Make sure flows always have a folder
|
||||
default_folder = session.exec(
|
||||
select(Folder).where(Folder.name == DEFAULT_FOLDER_NAME, Folder.user_id == current_user.id)
|
||||
).first()
|
||||
if default_folder:
|
||||
db_flow.folder_id = default_folder.id
|
||||
db_flow = Flow.model_validate(flow, from_attributes=True)
|
||||
db_flow.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
session.add(db_flow)
|
||||
session.commit()
|
||||
session.refresh(db_flow)
|
||||
return db_flow
|
||||
if db_flow.folder_id is None:
|
||||
# Make sure flows always have a folder
|
||||
default_folder = session.exec(
|
||||
select(Folder).where(Folder.name == DEFAULT_FOLDER_NAME, Folder.user_id == current_user.id)
|
||||
).first()
|
||||
if default_folder:
|
||||
db_flow.folder_id = default_folder.id
|
||||
|
||||
session.add(db_flow)
|
||||
session.commit()
|
||||
session.refresh(db_flow)
|
||||
return db_flow
|
||||
except Exception as e:
|
||||
# If it is a validation error, return the error message
|
||||
if hasattr(e, "errors"):
|
||||
raise HTTPException(status_code=400, detail=str(e)) from e
|
||||
elif "UNIQUE constraint failed" in str(e):
|
||||
# Get the name of the column that failed
|
||||
columns = str(e).split("UNIQUE constraint failed: ")[1].split(".")[1].split("\n")[0]
|
||||
# UNIQUE constraint failed: flow.user_id, flow.name
|
||||
# or UNIQUE constraint failed: flow.name
|
||||
# if the column has id in it, we want the other column
|
||||
column = columns.split(",")[1] if "id" in columns.split(",")[0] else columns.split(",")[0]
|
||||
|
||||
raise HTTPException(
|
||||
status_code=400, detail=f"{column.capitalize().replace('_', ' ')} must be unique"
|
||||
) from e
|
||||
elif isinstance(e, HTTPException):
|
||||
raise e
|
||||
else:
|
||||
raise HTTPException(status_code=500, detail=str(e)) from e
|
||||
|
||||
|
||||
@router.get("/", response_model=list[FlowRead], status_code=200)
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@ from sqlmodel import Session, select
|
|||
|
||||
from langflow.api.v1.flows import create_flows
|
||||
from langflow.api.v1.schemas import FlowListCreate, FlowListReadWithFolderName
|
||||
from langflow.helpers.flow import generate_unique_flow_name
|
||||
from langflow.helpers.folders import generate_unique_folder_name
|
||||
from langflow.services.auth.utils import get_current_active_user
|
||||
from langflow.services.database.models.flow.model import Flow, FlowCreate, FlowRead
|
||||
from langflow.services.database.models.folder.constants import DEFAULT_FOLDER_NAME
|
||||
|
|
@ -33,17 +35,27 @@ def create_folder(
|
|||
try:
|
||||
new_folder = Folder.model_validate(folder, from_attributes=True)
|
||||
new_folder.user_id = current_user.id
|
||||
|
||||
folder_results = session.exec(
|
||||
select(Folder).where(
|
||||
Folder.name.like(f"{new_folder.name}%"), # type: ignore
|
||||
Folder.user_id == current_user.id,
|
||||
# First check if the folder.name is unique
|
||||
# there might be flows with name like: "MyFlow", "MyFlow (1)", "MyFlow (2)"
|
||||
# so we need to check if the name is unique with `like` operator
|
||||
# if we find a flow with the same name, we add a number to the end of the name
|
||||
# based on the highest number found
|
||||
if session.exec(
|
||||
statement=select(Folder).where(Folder.name == new_folder.name).where(Folder.user_id == current_user.id)
|
||||
).first():
|
||||
folder_results = session.exec(
|
||||
select(Folder).where(
|
||||
Folder.name.like(f"{new_folder.name}%"), # type: ignore
|
||||
Folder.user_id == current_user.id,
|
||||
)
|
||||
)
|
||||
)
|
||||
existing_folder_names = [folder.name for folder in folder_results]
|
||||
|
||||
if existing_folder_names:
|
||||
new_folder.name = f"{new_folder.name} ({len(existing_folder_names) + 1})"
|
||||
if folder_results:
|
||||
folder_names = [folder.name for folder in folder_results]
|
||||
folder_numbers = [int(name.split("(")[-1].split(")")[0]) for name in folder_names if "(" in name]
|
||||
if folder_numbers:
|
||||
new_folder.name = f"{new_folder.name} ({max(folder_numbers) + 1})"
|
||||
else:
|
||||
new_folder.name = f"{new_folder.name} (1)"
|
||||
|
||||
session.add(new_folder)
|
||||
session.commit()
|
||||
|
|
@ -203,16 +215,9 @@ async def upload_file(
|
|||
if not data:
|
||||
raise HTTPException(status_code=400, detail="No flows found in the file")
|
||||
|
||||
folder_results = session.exec(
|
||||
select(Folder).where(
|
||||
Folder.name == data["folder_name"],
|
||||
Folder.user_id == current_user.id,
|
||||
)
|
||||
)
|
||||
existing_folder_names = [folder.name for folder in folder_results]
|
||||
folder_name = generate_unique_folder_name(data["folder_name"], current_user.id, session)
|
||||
|
||||
if existing_folder_names:
|
||||
data["folder_name"] = f"{data['folder_name']} ({len(existing_folder_names) + 1})"
|
||||
data["folder_name"] = folder_name
|
||||
|
||||
folder = FolderCreate(name=data["folder_name"], description=data["folder_description"])
|
||||
|
||||
|
|
@ -232,6 +237,8 @@ async def upload_file(
|
|||
raise HTTPException(status_code=400, detail="No flows found in the data")
|
||||
# Now we set the user_id for all flows
|
||||
for flow in flow_list.flows:
|
||||
flow_name = generate_unique_flow_name(flow.name, current_user.id, session)
|
||||
flow.name = flow_name
|
||||
flow.user_id = current_user.id
|
||||
flow.folder_id = new_folder.id
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
|
||||
from langflow.services.deps import get_monitor_service
|
||||
|
|
@ -79,7 +80,7 @@ async def delete_messages(
|
|||
|
||||
@router.post("/messages/{message_id}", response_model=MessageModelResponse)
|
||||
async def update_message(
|
||||
message_id: str,
|
||||
message_id: int,
|
||||
message: MessageModelRequest,
|
||||
monitor_service: MonitorService = Depends(get_monitor_service),
|
||||
):
|
||||
|
|
@ -117,6 +118,22 @@ async def get_transactions(
|
|||
dicts = monitor_service.get_transactions(
|
||||
source=source, target=target, status=status, order_by=order_by, flow_id=flow_id
|
||||
)
|
||||
return [TransactionModelResponse(**d) for d in dicts]
|
||||
result = []
|
||||
for d in dicts:
|
||||
d = TransactionModelResponse(
|
||||
index=d["index"],
|
||||
timestamp=d["timestamp"],
|
||||
vertex_id=d["vertex_id"],
|
||||
inputs=d["inputs"],
|
||||
outputs=d["outputs"],
|
||||
status=d["status"],
|
||||
error=d["error"],
|
||||
flow_id=d["flow_id"],
|
||||
source=d["vertex_id"],
|
||||
target=d["target_id"],
|
||||
)
|
||||
result.append(d)
|
||||
return result
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serial
|
|||
from langflow.graph.schema import RunOutputs
|
||||
from langflow.schema import dotdict
|
||||
from langflow.schema.graph import Tweaks
|
||||
from langflow.schema.schema import InputType, OutputType
|
||||
from langflow.schema.schema import InputType, Log, OutputType
|
||||
from langflow.services.database.models.api_key.model import ApiKeyRead
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
from langflow.services.database.models.flow import FlowCreate, FlowRead
|
||||
|
|
@ -245,6 +245,8 @@ class VerticesOrderResponse(BaseModel):
|
|||
|
||||
class ResultDataResponse(BaseModel):
|
||||
results: Optional[Any] = Field(default_factory=dict)
|
||||
logs: List[Log | None] = Field(default_factory=list)
|
||||
message: Optional[Any] = Field(default_factory=dict)
|
||||
artifacts: Optional[Any] = Field(default_factory=dict)
|
||||
timedelta: Optional[float] = None
|
||||
duration: Optional[str] = None
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from langchain_core.runnables import Runnable
|
|||
from langflow.base.agents.utils import get_agents_list, records_to_messages
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class LCAgentComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
|
|||
from langchain_core.tools import BaseTool
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
from .default_prompts import XML_AGENT_PROMPT
|
||||
|
||||
|
|
|
|||
|
|
@ -7,9 +7,11 @@ Constants:
|
|||
- FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields.
|
||||
"""
|
||||
|
||||
import orjson
|
||||
|
||||
STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
|
||||
|
||||
NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"]
|
||||
NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description", "output_types"]
|
||||
|
||||
|
||||
FIELD_FORMAT_ATTRIBUTES = [
|
||||
|
|
@ -27,3 +29,5 @@ FIELD_FORMAT_ATTRIBUTES = [
|
|||
"refresh_button_text",
|
||||
"options",
|
||||
]
|
||||
|
||||
ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
import json
|
||||
import unicodedata
|
||||
import xml.etree.ElementTree as ET
|
||||
from concurrent import futures
|
||||
from pathlib import Path
|
||||
from typing import Callable, List, Optional, Text
|
||||
|
||||
import chardet
|
||||
import orjson
|
||||
import yaml
|
||||
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
# Types of files that can be read simply by file.read()
|
||||
# and have 100% to be completely readable
|
||||
|
|
@ -32,6 +33,17 @@ TEXT_FILE_TYPES = [
|
|||
"tsx",
|
||||
]
|
||||
|
||||
IMG_FILE_TYPES = [
|
||||
"jpg",
|
||||
"jpeg",
|
||||
"png",
|
||||
"bmp",
|
||||
]
|
||||
|
||||
|
||||
def normalize_text(text):
|
||||
return unicodedata.normalize("NFKD", text)
|
||||
|
||||
|
||||
def is_hidden(path: Path) -> bool:
|
||||
return path.name.startswith(".")
|
||||
|
|
@ -125,9 +137,15 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
|
|||
text = read_docx_file(file_path)
|
||||
else:
|
||||
text = read_text_file(file_path)
|
||||
|
||||
# if file is json, yaml, or xml, we can parse it
|
||||
if file_path.endswith(".json"):
|
||||
text = json.loads(text)
|
||||
text = orjson.loads(text)
|
||||
if isinstance(text, dict):
|
||||
text = {k: normalize_text(v) if isinstance(v, str) else v for k, v in text.items()}
|
||||
elif isinstance(text, list):
|
||||
text = [normalize_text(item) if isinstance(item, str) else item for item in text]
|
||||
|
||||
elif file_path.endswith(".yaml") or file_path.endswith(".yml"):
|
||||
text = yaml.safe_load(text)
|
||||
elif file_path.endswith(".xml"):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import List
|
||||
|
||||
from langflow.graph.schema import ResultData, RunOutputs
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
def build_records_from_run_outputs(run_outputs: RunOutputs) -> List[Record]:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.memory import store_message
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatComponent(CustomComponent):
|
||||
|
|
@ -15,7 +15,7 @@ class ChatComponent(CustomComponent):
|
|||
return {
|
||||
"input_value": {
|
||||
"input_types": ["Text"],
|
||||
"display_name": "Message",
|
||||
"display_name": "Text",
|
||||
"multiline": True,
|
||||
},
|
||||
"sender": {
|
||||
|
|
@ -23,7 +23,7 @@ class ChatComponent(CustomComponent):
|
|||
"display_name": "Sender Type",
|
||||
"advanced": True,
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name"},
|
||||
"sender_name": {"display_name": "Sender Name", "advanced": True},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "If provided, the message will be stored in the memory.",
|
||||
|
|
@ -40,98 +40,45 @@ class ChatComponent(CustomComponent):
|
|||
"info": "In case of Message being a Record, this template will be used to convert it to text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"files": {
|
||||
"field_type": "file",
|
||||
"display_name": "Files",
|
||||
"file_types": TEXT_FILE_TYPES + IMG_FILE_TYPES,
|
||||
"info": "Files to be sent with the message.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def store_message(
|
||||
self,
|
||||
message: Union[str, Text, Record],
|
||||
session_id: Optional[str] = None,
|
||||
sender: Optional[str] = None,
|
||||
sender_name: Optional[str] = None,
|
||||
) -> list[Record]:
|
||||
records = store_message(
|
||||
message: Message,
|
||||
) -> list[Message]:
|
||||
messages = store_message(
|
||||
message,
|
||||
session_id=session_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
flow_id=self.graph.flow_id,
|
||||
)
|
||||
|
||||
self.status = records
|
||||
return records
|
||||
self.status = messages
|
||||
return messages
|
||||
|
||||
def build_with_record(
|
||||
self,
|
||||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[Union[str, Record]] = None,
|
||||
input_value: Optional[Union[str, Record, Message]] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
) -> Union[Text, Record]:
|
||||
input_value_record: Optional[Record] = None
|
||||
if return_record:
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
input_value.data["sender"] = sender
|
||||
input_value.data["sender_name"] = sender_name
|
||||
input_value.data["session_id"] = session_id
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
data={
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
input_value = records_to_text(template=record_template, records=input_value)
|
||||
if not input_value:
|
||||
input_value = ""
|
||||
if return_record and input_value_record:
|
||||
result: Union[Text, Record] = input_value_record
|
||||
else:
|
||||
result = input_value
|
||||
self.status = result
|
||||
if session_id and isinstance(result, (Record, str)):
|
||||
self.store_message(result, session_id, sender, sender_name)
|
||||
return result
|
||||
) -> Message:
|
||||
message: Message | None = None
|
||||
|
||||
def build_no_record(
|
||||
self,
|
||||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
) -> Union[Text, Record]:
|
||||
input_value_record: Optional[Record] = None
|
||||
if return_record:
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
input_value.data["sender"] = sender
|
||||
input_value.data["sender_name"] = sender_name
|
||||
input_value.data["session_id"] = session_id
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
data={
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
input_value = records_to_text(template=record_template, records=input_value)
|
||||
if not input_value:
|
||||
input_value = ""
|
||||
if return_record and input_value_record:
|
||||
result: Union[Text, Record] = input_value_record
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
message = Message.from_record(input_value)
|
||||
else:
|
||||
result = input_value
|
||||
self.status = result
|
||||
if session_id and isinstance(result, (Record, str)):
|
||||
self.store_message(result, session_id, sender, sender_name)
|
||||
return result
|
||||
message = Message(
|
||||
text=input_value, sender=sender, sender_name=sender_name, files=files, session_id=session_id
|
||||
)
|
||||
self.status = message
|
||||
if session_id and isinstance(message, Message) and isinstance(message.text, str):
|
||||
self.store_message(message)
|
||||
return message
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class TextComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class BaseMemoryComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import warnings
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
|
|
@ -5,6 +6,7 @@ from langchain_core.language_models.llms import LLM
|
|||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing.prompt import Prompt
|
||||
|
||||
|
||||
class LCModelComponent(CustomComponent):
|
||||
|
|
@ -53,19 +55,28 @@ class LCModelComponent(CustomComponent):
|
|||
key in response_metadata["token_usage"] for key in inner_openai_keys
|
||||
):
|
||||
token_usage = response_metadata["token_usage"]
|
||||
completion_tokens = token_usage["completion_tokens"]
|
||||
prompt_tokens = token_usage["prompt_tokens"]
|
||||
total_tokens = token_usage["total_tokens"]
|
||||
finish_reason = response_metadata["finish_reason"]
|
||||
status_message = f"Tokens:\nInput: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}"
|
||||
status_message = {
|
||||
"tokens": {
|
||||
"input": token_usage["prompt_tokens"],
|
||||
"output": token_usage["completion_tokens"],
|
||||
"total": token_usage["total_tokens"],
|
||||
"stop_reason": response_metadata["finish_reason"],
|
||||
"response": content,
|
||||
}
|
||||
}
|
||||
|
||||
elif all(key in response_metadata for key in anthropic_keys) and all(
|
||||
key in response_metadata["usage"] for key in inner_anthropic_keys
|
||||
):
|
||||
usage = response_metadata["usage"]
|
||||
input_tokens = usage["input_tokens"]
|
||||
output_tokens = usage["output_tokens"]
|
||||
stop_reason = response_metadata["stop_reason"]
|
||||
status_message = f"Tokens:\nInput: {input_tokens}\nOutput: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
|
||||
status_message = {
|
||||
"tokens": {
|
||||
"input": usage["input_tokens"],
|
||||
"output": usage["output_tokens"],
|
||||
"stop_reason": response_metadata["stop_reason"],
|
||||
"response": content,
|
||||
}
|
||||
}
|
||||
else:
|
||||
status_message = f"Response: {content}"
|
||||
else:
|
||||
|
|
@ -73,7 +84,7 @@ class LCModelComponent(CustomComponent):
|
|||
return status_message
|
||||
|
||||
def get_chat_result(
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str | Prompt, system_message: Optional[str] = None
|
||||
):
|
||||
messages: list[Union[HumanMessage, SystemMessage]] = []
|
||||
if not input_value and not system_message:
|
||||
|
|
@ -81,11 +92,21 @@ class LCModelComponent(CustomComponent):
|
|||
if system_message:
|
||||
messages.append(SystemMessage(content=system_message))
|
||||
if input_value:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
if isinstance(input_value, Prompt):
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
if "prompt" in input_value:
|
||||
prompt = input_value.load_lc_prompt()
|
||||
runnable = prompt | runnable
|
||||
else:
|
||||
messages.append(input_value.to_lc_message())
|
||||
else:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
inputs = messages or {}
|
||||
if stream:
|
||||
return runnable.stream(messages)
|
||||
return runnable.stream(inputs)
|
||||
else:
|
||||
message = runnable.invoke(messages)
|
||||
message = runnable.invoke(inputs)
|
||||
result = message.content
|
||||
if isinstance(message, AIMessage):
|
||||
status_message = self.build_status_message(message)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from copy import deepcopy
|
||||
|
||||
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
def record_to_string(record: Record) -> str:
|
||||
|
|
@ -35,10 +35,14 @@ def dict_values_to_string(d: dict) -> dict:
|
|||
# it could be a list of records or documents or strings
|
||||
if isinstance(value, list):
|
||||
for i, item in enumerate(value):
|
||||
if isinstance(item, Record):
|
||||
if isinstance(item, Message):
|
||||
d_copy[key][i] = item.text
|
||||
elif isinstance(item, Record):
|
||||
d_copy[key][i] = record_to_string(item)
|
||||
elif isinstance(item, Document):
|
||||
d_copy[key][i] = document_to_string(item)
|
||||
elif isinstance(value, Message):
|
||||
d_copy[key] = value.text
|
||||
elif isinstance(value, Record):
|
||||
d_copy[key] = record_to_string(value)
|
||||
elif isinstance(value, Document):
|
||||
|
|
|
|||
0
src/backend/base/langflow/base/vectorstores/__init__.py
Normal file
0
src/backend/base/langflow/base/vectorstores/__init__.py
Normal file
24
src/backend/base/langflow/base/vectorstores/utils.py
Normal file
24
src/backend/base/langflow/base/vectorstores/utils.py
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
from langflow.schema import Record
|
||||
|
||||
|
||||
def chroma_collection_to_records(collection_dict: dict):
|
||||
"""
|
||||
Converts a collection of chroma vectors into a list of records.
|
||||
|
||||
Args:
|
||||
collection_dict (dict): A dictionary containing the collection of chroma vectors.
|
||||
|
||||
Returns:
|
||||
list: A list of records, where each record represents a document in the collection.
|
||||
"""
|
||||
records = []
|
||||
for i, doc in enumerate(collection_dict["documents"]):
|
||||
record_dict = {
|
||||
"id": collection_dict["ids"][i],
|
||||
"text": doc,
|
||||
}
|
||||
if "metadatas" in collection_dict:
|
||||
for key, value in collection_dict["metadatas"][i].items():
|
||||
record_dict[key] = value
|
||||
records.append(Record(**record_dict))
|
||||
return records
|
||||
|
|
@ -5,7 +5,7 @@ from langchain_core.prompts import ChatPromptTemplate
|
|||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ToolCallingAgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
|
|
@ -3,10 +3,9 @@ from typing import List, Optional
|
|||
from langchain.agents import create_xml_agent
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class XMLAgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langchain_core.documents import Document
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class RetrievalQAComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ class URLComponent(CustomComponent):
|
|||
self,
|
||||
urls: list[str],
|
||||
) -> list[Record]:
|
||||
loader = WebBaseLoader(web_paths=urls)
|
||||
loader = WebBaseLoader(web_paths=[url for url in urls if url])
|
||||
docs = loader.load()
|
||||
records = self.to_records(docs)
|
||||
self.status = records
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ import uuid
|
|||
from typing import Any, Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class WebhookComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessag
|
|||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class AgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
15
src/backend/base/langflow/components/experimental/Embed.py
Normal file
15
src/backend/base/langflow/components/experimental/Embed.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
from langflow.custom import CustomComponent
|
||||
from langflow.schema import Record
|
||||
from langflow.field_typing import Embeddings
|
||||
|
||||
|
||||
class EmbedComponent(CustomComponent):
|
||||
display_name = "Embed Texts"
|
||||
|
||||
def build_config(self):
|
||||
return {"texts": {"display_name": "Texts"}, "embbedings": {"display_name": "Embeddings"}}
|
||||
|
||||
def build(self, texts: list[str], embbedings: Embeddings) -> Embeddings:
|
||||
vectors = Record(vector=embbedings.embed_documents(texts))
|
||||
self.status = vectors
|
||||
return vectors
|
||||
|
|
@ -7,8 +7,8 @@ from langflow.custom import CustomComponent
|
|||
from langflow.field_typing import Tool
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.helpers.flow import get_flow_inputs
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class FlowToolComponent(CustomComponent):
|
||||
|
|
|
|||
38
src/backend/base/langflow/components/experimental/Message.py
Normal file
38
src/backend/base/langflow/components/experimental/Message.py
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class MessageComponent(CustomComponent):
|
||||
display_name = "Message"
|
||||
description = "Creates a Message object given a Session ID."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User"],
|
||||
"display_name": "Sender Type",
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name"},
|
||||
"text": {"display_name": "Text"},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
sender: str = "User",
|
||||
sender_name: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
text: str = "",
|
||||
) -> Message:
|
||||
message = Message(
|
||||
text=text, sender=sender, sender_name=sender_name, flow_id=self.graph.flow_id, session_id=session_id
|
||||
)
|
||||
|
||||
self.status = message
|
||||
return message
|
||||
|
|
@ -1,43 +1,22 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.memory import get_messages, store_message
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class StoreMessageComponent(CustomComponent):
|
||||
display_name = "Store Message"
|
||||
description = "Stores a chat message given a Session ID."
|
||||
beta: bool = True
|
||||
description = "Stores a chat message."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User"],
|
||||
"display_name": "Sender Type",
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name"},
|
||||
"message": {"display_name": "Message"},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
sender: str = "User",
|
||||
sender_name: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
message: str = "",
|
||||
) -> List[Record]:
|
||||
store_message(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
session_id=session_id,
|
||||
message=message,
|
||||
)
|
||||
message: Message,
|
||||
) -> Message:
|
||||
store_message(message, flow_id=self.graph.flow_id)
|
||||
self.status = get_messages()
|
||||
|
||||
self.status = get_messages(session_id=session_id)
|
||||
return get_messages(session_id=session_id)
|
||||
return message
|
||||
|
|
|
|||
|
|
@ -2,9 +2,9 @@ from typing import Optional
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.helpers.record import messages_to_text
|
||||
from langflow.memory import get_messages
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class MemoryComponent(BaseMemoryComponent):
|
||||
|
|
@ -43,7 +43,7 @@ class MemoryComponent(BaseMemoryComponent):
|
|||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
def get_messages(self, **kwargs) -> list[Message]:
|
||||
# Validate kwargs by checking if it contains the correct keys
|
||||
if "sender" not in kwargs:
|
||||
kwargs["sender"] = None
|
||||
|
|
@ -77,6 +77,6 @@ class MemoryComponent(BaseMemoryComponent):
|
|||
limit=n_messages,
|
||||
order=order,
|
||||
)
|
||||
messages_str = records_to_text(template=record_template or "", records=messages)
|
||||
messages_str = messages_to_text(template=record_template or "", messages=messages)
|
||||
self.status = messages_str
|
||||
return messages_str
|
||||
|
|
|
|||
|
|
@ -6,25 +6,27 @@ from langflow.schema import Record
|
|||
|
||||
|
||||
class MessageHistoryComponent(CustomComponent):
|
||||
display_name = "Message History"
|
||||
description = "Retrieves stored chat messages given a specific Session ID."
|
||||
beta: bool = True
|
||||
display_name = "Memory"
|
||||
description = "Retrieves stored chat messages."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User", "Machine and User"],
|
||||
"display_name": "Sender Type",
|
||||
"advanced": True,
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name", "advanced": True},
|
||||
"n_messages": {
|
||||
"display_name": "Number of Messages",
|
||||
"info": "Number of messages to retrieve.",
|
||||
"advanced": True,
|
||||
},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
"advanced": True,
|
||||
},
|
||||
"order": {
|
||||
"options": ["Ascending", "Descending"],
|
||||
|
|
@ -39,7 +41,7 @@ class MessageHistoryComponent(CustomComponent):
|
|||
sender: Optional[str] = "Machine and User",
|
||||
sender_name: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
n_messages: int = 5,
|
||||
n_messages: int = 100,
|
||||
order: Optional[str] = "Descending",
|
||||
) -> List[Record]:
|
||||
order = "DESC" if order == "Descending" else "ASC"
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ class RecordsToTextComponent(CustomComponent):
|
|||
"template": {
|
||||
"display_name": "Template",
|
||||
"info": "The template to use for formatting the records. It can contain the keys {text}, {data} or any other key in the Record.",
|
||||
"multiline": True,
|
||||
},
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langflow.base.io.chat import ChatComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatInput(ChatComponent):
|
||||
|
|
@ -14,7 +13,7 @@ class ChatInput(ChatComponent):
|
|||
build_config = super().build_config()
|
||||
build_config["input_value"] = {
|
||||
"input_types": [],
|
||||
"display_name": "Message",
|
||||
"display_name": "Text",
|
||||
"multiline": True,
|
||||
}
|
||||
|
||||
|
|
@ -25,13 +24,13 @@ class ChatInput(ChatComponent):
|
|||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[str] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
) -> Union[Text, Record]:
|
||||
return super().build_no_record(
|
||||
) -> Message:
|
||||
return super().build_with_record(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
input_value=input_value,
|
||||
files=files,
|
||||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Prompt, TemplateField, Text
|
||||
from langflow.field_typing import TemplateField
|
||||
from langflow.field_typing.prompt import Prompt
|
||||
|
||||
|
||||
class PromptComponent(CustomComponent):
|
||||
|
|
@ -15,19 +14,11 @@ class PromptComponent(CustomComponent):
|
|||
"code": TemplateField(advanced=True),
|
||||
}
|
||||
|
||||
def build(
|
||||
async def build(
|
||||
self,
|
||||
template: Prompt,
|
||||
**kwargs,
|
||||
) -> Text:
|
||||
from langflow.base.prompts.utils import dict_values_to_string
|
||||
|
||||
prompt_template = PromptTemplate.from_template(Text(template))
|
||||
kwargs = dict_values_to_string(kwargs)
|
||||
kwargs = {k: "\n".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}
|
||||
try:
|
||||
formated_prompt = prompt_template.format(**kwargs)
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error formatting prompt: {exc}") from exc
|
||||
self.status = f'Prompt:\n"{formated_prompt}"'
|
||||
return formated_prompt
|
||||
) -> Prompt:
|
||||
prompt = await Prompt.from_template_and_variables(template, kwargs)
|
||||
self.status = prompt.format_text()
|
||||
return prompt
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ class TextInput(TextComponent):
|
|||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Value",
|
||||
"display_name": "Text",
|
||||
"input_types": ["Record", "Text"],
|
||||
"info": "Text or Record to be passed as input.",
|
||||
},
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional, cast
|
||||
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema.record import Record
|
||||
|
||||
|
||||
class AstraDBMessageReaderComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
from langchain_core.messages import BaseMessage
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.record import Record
|
||||
|
||||
|
||||
class AstraDBMessageWriterComponent(BaseMemoryComponent):
|
||||
display_name = "Astra DB Message Writer"
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional, cast
|
|||
from langchain_community.chat_message_histories import CassandraChatMessageHistory
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema.record import Record
|
||||
|
||||
|
||||
class CassandraMessageReaderComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_community.chat_message_histories import CassandraChatMessageHistory
|
||||
from langchain_core.messages import BaseMessage
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.schema.record import Record
|
||||
|
||||
|
||||
class CassandraMessageWriterComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_community.chat_message_histories.zep import SearchScope, SearchTy
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ZepMessageReaderComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Optional
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ class AmazonBedrockComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"cache": {"display_name": "Cache"},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ class AnthropicLLM(LCModelComponent):
|
|||
"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -78,7 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
|
|||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -81,7 +81,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
|
|||
"info": "Endpoint of the Qianfan LLM, required if custom model used.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
|
|||
"required": False,
|
||||
"default": False,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_cohere import ChatCohere
|
||||
from pydantic.v1 import SecretStr
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langchain_cohere import ChatCohere
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CohereComponent(LCModelComponent):
|
||||
|
|
@ -42,7 +43,7 @@ class CohereComponent(LCModelComponent):
|
|||
"type": "float",
|
||||
"show": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -69,3 +70,4 @@ class CohereComponent(LCModelComponent):
|
|||
temperature=temperature,
|
||||
)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,10 @@ from typing import Optional
|
|||
|
||||
from langchain_community.chat_models.huggingface import ChatHuggingFace
|
||||
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(LCModelComponent):
|
||||
|
|
@ -36,7 +37,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -72,3 +73,4 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
output = ChatHuggingFace(llm=llm)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class MistralAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -194,7 +194,7 @@ class ChatOllamaComponent(LCModelComponent):
|
|||
"info": "Template to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
@ -79,7 +79,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
input_value: Text,
|
||||
openai_api_key: str,
|
||||
temperature: float = 0.1,
|
||||
model_name: str = "gpt-4o",
|
||||
model_name: str = "gpt-3.5-turbo",
|
||||
max_tokens: Optional[int] = 256,
|
||||
model_kwargs: NestedDict = {},
|
||||
openai_api_base: Optional[str] = None,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import Optional
|
||||
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
|
@ -74,7 +73,7 @@ class ChatVertexAIComponent(LCModelComponent):
|
|||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langflow.base.io.chat import ChatComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatOutput(ChatComponent):
|
||||
|
|
@ -16,14 +15,12 @@ class ChatOutput(ChatComponent):
|
|||
sender_name: Optional[str] = "AI",
|
||||
input_value: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: Optional[str] = "{text}",
|
||||
) -> Union[Text, Record]:
|
||||
files: Optional[list[str]] = None,
|
||||
) -> Message:
|
||||
return super().build_with_record(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
input_value=input_value,
|
||||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
record_template=record_template or "",
|
||||
files=files,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,19 @@ from langflow.custom import CustomComponent
|
|||
from langflow.schema import Record
|
||||
|
||||
|
||||
class RecordsOutput(CustomComponent):
|
||||
class RecordOutput(CustomComponent):
|
||||
display_name = "Records Output"
|
||||
description = "Display Records as a Table"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Records",
|
||||
"input_types": ["Record"],
|
||||
"info": "Record or Record list to be passed as input.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(self, input_value: Record) -> Record:
|
||||
self.status = input_value
|
||||
return input_value
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ class TextOutput(TextComponent):
|
|||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Value",
|
||||
"display_name": "Text",
|
||||
"input_types": ["Record", "Text"],
|
||||
"info": "Text or Record to be passed as output.",
|
||||
},
|
||||
|
|
|
|||
24
src/backend/base/langflow/components/prompts/Prompt.py
Normal file
24
src/backend/base/langflow/components/prompts/Prompt.py
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import TemplateField
|
||||
from langflow.field_typing.prompt import Prompt
|
||||
|
||||
|
||||
class PromptComponent(CustomComponent):
|
||||
display_name: str = "Empty Prompt"
|
||||
description: str = "Create a prompt template with dynamic variables."
|
||||
icon = "prompts"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"template": TemplateField(display_name="Template"),
|
||||
"code": TemplateField(advanced=True),
|
||||
}
|
||||
|
||||
async def build(
|
||||
self,
|
||||
template: Prompt,
|
||||
**kwargs,
|
||||
) -> Prompt:
|
||||
prompt = await Prompt.from_template_and_variables(template, kwargs) # type: ignore
|
||||
self.status = prompt.format_text()
|
||||
return prompt
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
# from langflow.field_typing import Data
|
||||
from langchain.chains.query_constructor.base import AttributeInfo
|
||||
from langchain.retrievers.self_query.base import SelfQueryRetriever
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class SelfQueryRetrieverComponent(CustomComponent):
|
||||
display_name: str = "Self Query Retriever"
|
||||
description: str = "Retriever that uses a vector store and an LLM to generate the vector store queries."
|
||||
icon = "LangChain"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"query": {
|
||||
"display_name": "Query",
|
||||
"input_types": ["Message", "Text"],
|
||||
"info": "Query to be passed as input.",
|
||||
},
|
||||
"vectorstore": {
|
||||
"display_name": "Vector Store",
|
||||
"info": "Vector Store to be passed as input.",
|
||||
},
|
||||
"attribute_infos": {
|
||||
"display_name": "Metadata Field Info",
|
||||
"info": "Metadata Field Info to be passed as input.",
|
||||
},
|
||||
"document_content_description": {
|
||||
"display_name": "Document Content Description",
|
||||
"info": "Document Content Description to be passed as input.",
|
||||
},
|
||||
"llm": {
|
||||
"display_name": "LLM",
|
||||
"info": "LLM to be passed as input.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
query: Message,
|
||||
vectorstore: VectorStore,
|
||||
attribute_infos: list[Record],
|
||||
document_content_description: Text,
|
||||
llm: BaseLanguageModel,
|
||||
) -> Record:
|
||||
metadata_field_infos = [AttributeInfo(**record.data) for record in attribute_infos]
|
||||
self_query_retriever = SelfQueryRetriever.from_llm(
|
||||
llm=llm,
|
||||
vectorstore=vectorstore,
|
||||
document_contents=document_content_description,
|
||||
metadata_field_info=metadata_field_infos,
|
||||
enable_limit=True,
|
||||
)
|
||||
|
||||
if isinstance(query, Message):
|
||||
input_text = query.text
|
||||
elif isinstance(query, str):
|
||||
input_text = query
|
||||
else:
|
||||
raise ValueError(f"Query type {type(query)} not supported.")
|
||||
documents = self_query_retriever.invoke(input=input_text)
|
||||
records = [Record.from_document(document) for document in documents]
|
||||
self.status = records
|
||||
return records
|
||||
|
|
@ -3,7 +3,7 @@ from typing import List
|
|||
from langchain_text_splitters import CharacterTextSplitter
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.utils.util import unescape_string
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import List, Optional
|
|||
from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class LanguageRecursiveTextSplitterComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -91,4 +91,4 @@ class PythonCodeStructuredTool(CustomComponent):
|
|||
tool = StructuredTool.from_function(
|
||||
func=func, args_schema=_class, name=name, description=description, return_direct=return_direct
|
||||
)
|
||||
return tool
|
||||
return tool # type: ignore
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Redis import RedisComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.pgvector import PGVectorComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -163,3 +163,4 @@ class AstraDBVectorStoreComponent(CustomComponent):
|
|||
)
|
||||
|
||||
return vector_store
|
||||
return vector_store
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
from copy import deepcopy
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import chromadb
|
||||
|
|
@ -7,8 +8,9 @@ from langchain_core.embeddings import Embeddings
|
|||
from langchain_core.retrievers import BaseRetriever
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langflow.base.vectorstores.utils import chroma_collection_to_records
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ChromaComponent(CustomComponent):
|
||||
|
|
@ -48,6 +50,11 @@ class ChromaComponent(CustomComponent):
|
|||
"display_name": "Server SSL Enabled",
|
||||
"advanced": True,
|
||||
},
|
||||
"allow_duplicates": {
|
||||
"display_name": "Allow Duplicates",
|
||||
"advanced": True,
|
||||
"info": "If false, will not add documents that are already in the Vector Store.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
|
|
@ -61,6 +68,7 @@ class ChromaComponent(CustomComponent):
|
|||
chroma_server_host: Optional[str] = None,
|
||||
chroma_server_http_port: Optional[int] = None,
|
||||
chroma_server_grpc_port: Optional[int] = None,
|
||||
allow_duplicates: bool = False,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
"""
|
||||
Builds the Vector Store or BaseRetriever object.
|
||||
|
|
@ -75,6 +83,7 @@ class ChromaComponent(CustomComponent):
|
|||
- chroma_server_host (Optional[str]): The host for the Chroma server.
|
||||
- chroma_server_http_port (Optional[int]): The HTTP port for the Chroma server.
|
||||
- chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.
|
||||
- allow_duplicates (bool): Whether to allow duplicates in the Vector Store.
|
||||
|
||||
Returns:
|
||||
- Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.
|
||||
|
|
@ -93,32 +102,34 @@ class ChromaComponent(CustomComponent):
|
|||
)
|
||||
client = chromadb.HttpClient(settings=chroma_settings)
|
||||
|
||||
# If documents, then we need to create a Chroma instance using .from_documents
|
||||
|
||||
# Check index_directory and expand it if it is a relative path
|
||||
if index_directory is not None:
|
||||
index_directory = self.resolve_path(index_directory)
|
||||
|
||||
chroma = Chroma(
|
||||
persist_directory=index_directory,
|
||||
client=client,
|
||||
embedding_function=embedding,
|
||||
collection_name=collection_name,
|
||||
)
|
||||
if allow_duplicates:
|
||||
stored_records = []
|
||||
else:
|
||||
stored_records = chroma_collection_to_records(chroma.get())
|
||||
_stored_documents_without_id = []
|
||||
for record in deepcopy(stored_records):
|
||||
del record.id
|
||||
_stored_documents_without_id.append(record)
|
||||
documents = []
|
||||
for _input in inputs or []:
|
||||
if isinstance(_input, Record):
|
||||
documents.append(_input.to_lc_document())
|
||||
if _input not in _stored_documents_without_id:
|
||||
documents.append(_input.to_lc_document())
|
||||
else:
|
||||
documents.append(_input)
|
||||
if documents is not None and embedding is not None:
|
||||
if len(documents) == 0:
|
||||
raise ValueError("If documents are provided, there must be at least one document.")
|
||||
chroma = Chroma.from_documents(
|
||||
documents=documents, # type: ignore
|
||||
persist_directory=index_directory,
|
||||
collection_name=collection_name,
|
||||
embedding=embedding,
|
||||
client=client,
|
||||
)
|
||||
else:
|
||||
chroma = Chroma(
|
||||
persist_directory=index_directory,
|
||||
client=client,
|
||||
embedding_function=embedding,
|
||||
)
|
||||
raise ValueError("Inputs must be a Record objects.")
|
||||
|
||||
if documents and embedding is not None:
|
||||
chroma.add_documents(documents)
|
||||
|
||||
self.status = stored_records
|
||||
return chroma
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class FAISSComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSea
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class MongoDBAtlasComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from langchain_pinecone.vectorstores import PineconeVectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class PineconeComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class QdrantComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from langchain_core.retrievers import BaseRetriever
|
|||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class RedisComponent(CustomComponent):
|
||||
|
|
|
|||
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Reference in a new issue