Merge cz/mergeAll into shortcuts_settings
This commit is contained in:
igorrCarvalho
commit
e531e36ad1
256 changed files with 19933 additions and 2731 deletions
7
.github/workflows/docker-build.yml
vendored
7
.github/workflows/docker-build.yml
vendored
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@ -80,7 +80,10 @@ jobs:
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|||
langflowai/langflow-frontend:1.0-alpha
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||||
|
||||
restart-space:
|
||||
name: Restart HuggingFace Spaces
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||||
if: ${{ inputs.release_type == 'main' }}
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||||
runs-on: ubuntu-latest
|
||||
needs: docker_build
|
||||
strategy:
|
||||
matrix:
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||||
python-version:
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||||
|
|
@ -100,6 +103,4 @@ jobs:
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|||
|
||||
- name: Restart HuggingFace Spaces Build
|
||||
run: |
|
||||
poetry run python ./scripts/factory_restart_space.py
|
||||
env:
|
||||
HUGGINGFACE_API_TOKEN: ${{ secrets.HUGGINGFACE_API_TOKEN }}
|
||||
poetry run python ./scripts/factory_restart_space.py --space "Langflow/Langflow-Preview" --token ${{ secrets.HUGGINGFACE_API_TOKEN }}
|
||||
|
|
|
|||
4
.github/workflows/pre-release.yml
vendored
4
.github/workflows/pre-release.yml
vendored
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@ -35,6 +35,10 @@ jobs:
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|||
with:
|
||||
python-version: "3.10"
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||||
cache: "poetry"
|
||||
- name: Set up Nodejs 20
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: "20"
|
||||
- name: Check Version
|
||||
id: check-version
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||||
run: |
|
||||
|
|
|
|||
1
Makefile
1
Makefile
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|
@ -168,6 +168,7 @@ build_and_install:
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|||
|
||||
build_frontend:
|
||||
cd src/frontend && CI='' npm run build
|
||||
rm -rf src/backend/base/langflow/frontend
|
||||
cp -r src/frontend/build src/backend/base/langflow/frontend
|
||||
|
||||
build:
|
||||
|
|
|
|||
171
README.PT.md
Normal file
171
README.PT.md
Normal file
|
|
@ -0,0 +1,171 @@
|
|||
<!-- markdownlint-disable MD030 -->
|
||||
|
||||
# [](https://www.langflow.org)
|
||||
|
||||
<p align="center"><strong>
|
||||
Um framework visual para criar apps de agentes autônomos e RAG
|
||||
</strong></p>
|
||||
<p align="center" style="font-size: 12px;">
|
||||
Open-source, construído em Python, totalmente personalizável, agnóstico em relação a modelos e databases
|
||||
</p>
|
||||
|
||||
<p align="center" style="font-size: 12px;">
|
||||
<a href="https://docs.langflow.org" style="text-decoration: underline;">Docs</a> -
|
||||
<a href="https://discord.com/invite/EqksyE2EX9" style="text-decoration: underline;">Junte-se ao nosso Discord</a> -
|
||||
<a href="https://twitter.com/langflow_ai" style="text-decoration: underline;">Siga-nos no X</a> -
|
||||
<a href="https://huggingface.co/spaces/Langflow/Langflow-Preview" style="text-decoration: underline;">Demonstração</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/langflow-ai/langflow">
|
||||
<img src="https://img.shields.io/github/stars/langflow-ai/langflow">
|
||||
</a>
|
||||
<a href="https://discord.com/invite/EqksyE2EX9">
|
||||
<img src="https://img.shields.io/discord/1116803230643527710?label=Discord">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
<div align="center">
|
||||
<a href="./README.md"><img alt="README em Inglês" src="https://img.shields.io/badge/English-d9d9d9"></a>
|
||||
<a href="./README.zh_CN.md"><img alt="README em Chinês Simplificado" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
|
||||
</div>
|
||||
|
||||
<p align="center">
|
||||
<img src="./docs/static/img/langflow_basic_howto.gif" alt="Seu GIF" style="border: 3px solid #211C43;">
|
||||
</p>
|
||||
|
||||
# 📝 Conteúdo
|
||||
|
||||
- [📝 Conteúdo](#-conteúdo)
|
||||
- [📦 Introdução](#-introdução)
|
||||
- [🎨 Criar Fluxos](#-criar-fluxos)
|
||||
- [Deploy](#deploy)
|
||||
- [Deploy usando Google Cloud Platform](#deploy-usando-google-cloud-platform)
|
||||
- [Deploy on Railway](#deploy-on-railway)
|
||||
- [Deploy on Render](#deploy-on-render)
|
||||
- [🖥️ Interface de Linha de Comando (CLI)](#️-interface-de-linha-de-comando-cli)
|
||||
- [Uso](#uso)
|
||||
- [Variáveis de Ambiente](#variáveis-de-ambiente)
|
||||
- [👋 Contribuir](#-contribuir)
|
||||
- [🌟 Contribuidores](#-contribuidores)
|
||||
- [📄 Licença](#-licença)
|
||||
|
||||
# 📦 Introdução
|
||||
|
||||
Você pode instalar o Langflow com pip:
|
||||
|
||||
```shell
|
||||
# Certifique-se de ter >=Python 3.10 instalado no seu sistema.
|
||||
# Instale a versão pré-lançamento (recomendada para as atualizações mais recentes)
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
|
||||
# ou versão estável
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
Então, execute o Langflow com:
|
||||
|
||||
```shell
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Você também pode visualizar o Langflow no [HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview). [Clone o Space usando este link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) para criar seu próprio workspace do Langflow em minutos.
|
||||
|
||||
# 🎨 Criar Fluxos
|
||||
|
||||
Criar fluxos com Langflow é fácil. Basta arrastar componentes da barra lateral para o canvas e conectá-los para começar a construir sua aplicação.
|
||||
|
||||
Explore editando os parâmetros do prompt, agrupando componentes e construindo seus próprios componentes personalizados (Custom Components).
|
||||
|
||||
Quando terminar, você pode exportar seu fluxo como um arquivo JSON.
|
||||
|
||||
Carregue o fluxo com:
|
||||
|
||||
```python
|
||||
from langflow.load import run_flow_from_json
|
||||
|
||||
results = run_flow_from_json("path/to/flow.json", input_value="Hello, World!")
|
||||
```
|
||||
|
||||
# Deploy
|
||||
|
||||
## Deploy usando Google Cloud Platform
|
||||
|
||||
Siga nosso passo a passo para fazer deploy do Langflow no Google Cloud Platform (GCP) usando o Google Cloud Shell. O guia está disponível no documento [**Langflow on Google Cloud Platform**](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/deployment/gcp-deployment.md).
|
||||
|
||||
Alternativamente, clique no botão **"Open in Cloud Shell"** abaixo para iniciar o Google Cloud Shell, clonar o repositório do Langflow e começar um **tutorial interativo** que o guiará pelo processo de configuração dos recursos necessários e deploy do Langflow no seu projeto GCP.
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
## Deploy on Railway
|
||||
|
||||
Use este template para implantar o Langflow 1.0 Preview no Railway:
|
||||
|
||||
[](https://railway.app/template/UsJ1uB?referralCode=MnPSdg)
|
||||
|
||||
Ou este para implantar o Langflow 0.6.x:
|
||||
|
||||
[](https://railway.app/template/JMXEWp?referralCode=MnPSdg)
|
||||
|
||||
## Deploy on Render
|
||||
|
||||
<a href="https://render.com/deploy?repo=https://github.com/langflow-ai/langflow/tree/dev">
|
||||
<img src="https://render.com/images/deploy-to-render-button.svg" alt="Deploy to Render" />
|
||||
</a>
|
||||
|
||||
# 🖥️ Interface de Linha de Comando (CLI)
|
||||
|
||||
O Langflow fornece uma interface de linha de comando (CLI) para fácil gerenciamento e configuração.
|
||||
|
||||
## Uso
|
||||
|
||||
Você pode executar o Langflow usando o seguinte comando:
|
||||
|
||||
```shell
|
||||
langflow run [OPTIONS]
|
||||
```
|
||||
|
||||
Cada opção é detalhada abaixo:
|
||||
|
||||
- `--help`: Exibe todas as opções disponíveis.
|
||||
- `--host`: Define o host para vincular o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_HOST`. O padrão é `127.0.0.1`.
|
||||
- `--workers`: Define o número de processos. Pode ser configurado usando a variável de ambiente `LANGFLOW_WORKERS`. O padrão é `1`.
|
||||
- `--timeout`: Define o tempo limite do worker em segundos. O padrão é `60`.
|
||||
- `--port`: Define a porta para escutar. Pode ser configurado usando a variável de ambiente `LANGFLOW_PORT`. O padrão é `7860`.
|
||||
- `--env-file`: Especifica o caminho para o arquivo .env contendo variáveis de ambiente. O padrão é `.env`.
|
||||
- `--log-level`: Define o nível de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_LEVEL`. O padrão é `critical`.
|
||||
- `--components-path`: Especifica o caminho para o diretório contendo componentes personalizados. Pode ser configurado usando a variável de ambiente `LANGFLOW_COMPONENTS_PATH`. O padrão é `langflow/components`.
|
||||
- `--log-file`: Especifica o caminho para o arquivo de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_FILE`. O padrão é `logs/langflow.log`.
|
||||
- `--cache`: Seleciona o tipo de cache a ser usado. As opções são `InMemoryCache` e `SQLiteCache`. Pode ser configurado usando a variável de ambiente `LANGFLOW_LANGCHAIN_CACHE`. O padrão é `SQLiteCache`.
|
||||
- `--dev/--no-dev`: Alterna o modo de desenvolvimento. O padrão é `no-dev`.
|
||||
- `--path`: Especifica o caminho para o diretório frontend contendo os arquivos de build. Esta opção é apenas para fins de desenvolvimento. Pode ser configurado usando a variável de ambiente `LANGFLOW_FRONTEND_PATH`.
|
||||
- `--open-browser/--no-open-browser`: Alterna a opção de abrir o navegador após iniciar o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_OPEN_BROWSER`. O padrão é `open-browser`.
|
||||
- `--remove-api-keys/--no-remove-api-keys`: Alterna a opção de remover as chaves de API dos projetos salvos no banco de dados. Pode ser configurado usando a variável de ambiente `LANGFLOW_REMOVE_API_KEYS`. O padrão é `no-remove-api-keys`.
|
||||
- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Instala a conclusão para o shell especificado.
|
||||
- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Exibe a conclusão para o shell especificado, permitindo que você copie ou personalize a instalação.
|
||||
- `--backend-only`: Este parâmetro, com valor padrão `False`, permite executar apenas o servidor backend sem o frontend. Também pode ser configurado usando a variável de ambiente `LANGFLOW_BACKEND_ONLY`.
|
||||
- `--store`: Este parâmetro, com valor padrão `True`, ativa os recursos da loja, use `--no-store` para desativá-los. Pode ser configurado usando a variável de ambiente `LANGFLOW_STORE`.
|
||||
|
||||
Esses parâmetros são importantes para usuários que precisam personalizar o comportamento do Langflow, especialmente em cenários de desenvolvimento ou deploy especializado.
|
||||
|
||||
### Variáveis de Ambiente
|
||||
|
||||
Você pode configurar muitas das opções de CLI usando variáveis de ambiente. Estas podem ser exportadas no seu sistema operacional ou adicionadas a um arquivo `.env` e carregadas usando a opção `--env-file`.
|
||||
|
||||
Um arquivo de exemplo `.env` chamado `.env.example` está incluído no projeto. Copie este arquivo para um novo arquivo chamado `.env` e substitua os valores de exemplo pelas suas configurações reais. Se você estiver definindo valores tanto no seu sistema operacional quanto no arquivo `.env`, as configurações do `.env` terão precedência.
|
||||
|
||||
# 👋 Contribuir
|
||||
|
||||
Aceitamos contribuições de desenvolvedores de todos os níveis para nosso projeto open-source no GitHub. Se você deseja contribuir, por favor, confira nossas [diretrizes de contribuição](./CONTRIBUTING.md) e ajude a tornar o Langflow mais acessível.
|
||||
|
||||
---
|
||||
|
||||
[](https://star-history.com/#langflow-ai/langflow&Date)
|
||||
|
||||
# 🌟 Contribuidores
|
||||
|
||||
[](https://github.com/langflow-ai/langflow/graphs/contributors)
|
||||
|
||||
# 📄 Licença
|
||||
|
||||
O Langflow é lançado sob a licença MIT. Veja o arquivo [LICENSE](LICENSE) para detalhes.
|
||||
|
|
@ -27,6 +27,7 @@
|
|||
|
||||
<div align="center">
|
||||
<a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
|
||||
<a href="./README.PT.md"><img alt="README in Portuguese" src="https://img.shields.io/badge/Portuguese-d9d9d9"></a>
|
||||
<a href="./README.zh_CN.md"><img alt="README in Simplified Chinese" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
|
||||
</div>
|
||||
|
||||
|
|
@ -36,7 +37,6 @@
|
|||
|
||||
# 📝 Content
|
||||
|
||||
- [](#)
|
||||
- [📝 Content](#-content)
|
||||
- [📦 Get Started](#-get-started)
|
||||
- [🎨 Create Flows](#-create-flows)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,14 @@
|
|||
# syntax=docker/dockerfile:1
|
||||
# Keep this syntax directive! It's used to enable Docker BuildKit
|
||||
|
||||
FROM node:20-bookworm-slim as builder-node
|
||||
WORKDIR /app
|
||||
COPY src/frontend/package.json src/frontend/package-lock.json ./
|
||||
RUN npm install
|
||||
COPY src/frontend/ ./
|
||||
RUN npm run build
|
||||
|
||||
|
||||
################################
|
||||
# BUILDER-BASE
|
||||
# Used to build deps + create our virtual environment
|
||||
|
|
@ -47,12 +55,11 @@ WORKDIR /app
|
|||
COPY pyproject.toml poetry.lock README.md ./
|
||||
COPY src/ ./src
|
||||
COPY scripts/ ./scripts
|
||||
|
||||
RUN python -m pip install requests --user && cd ./scripts && python update_dependencies.py
|
||||
COPY --from=builder-node /app/build ./src/backend/base/langflow/frontend
|
||||
RUN $POETRY_HOME/bin/poetry lock --no-update \
|
||||
&& $POETRY_HOME/bin/poetry install --no-interaction --no-ansi -E deploy \
|
||||
&& $POETRY_HOME/bin/poetry build -f wheel \
|
||||
&& $POETRY_HOME/bin/poetry run pip install dist/*.whl
|
||||
&& $POETRY_HOME/bin/poetry run pip install dist/*.whl --force-reinstall
|
||||
|
||||
################################
|
||||
# RUNTIME
|
||||
|
|
|
|||
|
|
@ -10,8 +10,7 @@ Langflow provides an API key functionality that allows users to access their ind
|
|||
The default user and password are set using the LANGFLOW_SUPERUSER and
|
||||
LANGFLOW_SUPERUSER_PASSWORD environment variables.
|
||||
|
||||
The default values are
|
||||
langflow and langflow, respectively.
|
||||
The default values are `langflow` and `langflow`, respectively.
|
||||
|
||||
</Admonition>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,62 +1,51 @@
|
|||
# Command Line Interface (CLI)
|
||||
|
||||
## Overview
|
||||
|
||||
Langflow's Command Line Interface (CLI) is a powerful tool that allows you to interact with the Langflow server from the command line. The CLI provides a wide range of commands to help you shape Langflow to your needs.
|
||||
|
||||
Running the CLI without any arguments will display a list of available commands and options.
|
||||
The available commands are below. Navigate to their individual sections of this page to see the parameters.
|
||||
|
||||
- [langflow](#overview)
|
||||
- [langflow api-key](#langflow-api-key)
|
||||
- [langflow copy-db](#langflow-copy-db)
|
||||
- [langflow migration](#langflow-migration)
|
||||
- [langflow run](#langflow-run)
|
||||
- [langflow superuser](#langflow-superuser)
|
||||
|
||||
## Overview
|
||||
|
||||
Running the CLI without any arguments displays a list of available options and commands.
|
||||
|
||||
```bash
|
||||
python -m langflow run --help
|
||||
langflow
|
||||
# or
|
||||
python -m langflow run
|
||||
langflow --help
|
||||
# or
|
||||
python -m langflow
|
||||
```
|
||||
|
||||
Each option for `run` command are detailed below:
|
||||
| Command | Description |
|
||||
| ----------- | ---------------------------------------------------------------------- |
|
||||
| `api-key` | Creates an API key for the default superuser if AUTO_LOGIN is enabled. |
|
||||
| `copy-db` | Copy the database files to the current directory (`which langflow`). |
|
||||
| `migration` | Run or test migrations. |
|
||||
| `run` | Run the Langflow. |
|
||||
| `superuser` | Create a superuser. |
|
||||
|
||||
- `--help`: Displays all available options.
|
||||
- `--host`: Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`.
|
||||
- `--workers`: Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.
|
||||
- `--timeout`: Sets the worker timeout in seconds. The default is `60`.
|
||||
- `--port`: Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.
|
||||
- `--env-file`: Specifies the path to the .env file containing environment variables. The default is `.env`.
|
||||
- `--log-level`: Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.
|
||||
- `--components-path`: Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.
|
||||
- `--log-file`: Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.
|
||||
- `--cache`: Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.
|
||||
- `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`.
|
||||
- `--path`: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.
|
||||
- `--open-browser/--no-open-browser`: Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.
|
||||
- `--remove-api-keys/--no-remove-api-keys`: Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`.
|
||||
- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell.
|
||||
- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation.
|
||||
- `--backend-only`: This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable.
|
||||
- `--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.
|
||||
### Options
|
||||
|
||||
These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios.
|
||||
| Option | Description |
|
||||
| ---------------------- | -------------------------------------------------------------------------------- |
|
||||
| `--install-completion` | Install completion for the current shell. |
|
||||
| `--show-completion` | Show completion for the current shell, to copy it or customize the installation. |
|
||||
| `--help` | Show this message and exit. |
|
||||
|
||||
### API Key Command
|
||||
## langflow api-key
|
||||
|
||||
The `api-key` command allows you to create an API key for accessing Langflow's API when `LANGFLOW_AUTO_LOGIN` is set to `True`.
|
||||
|
||||
```bash
|
||||
python -m langflow api-key --help
|
||||
|
||||
Usage: langflow api-key [OPTIONS]
|
||||
|
||||
Creates an API key for the default superuser if AUTO_LOGIN is enabled.
|
||||
Args: log_level (str, optional): Logging level. Defaults to "error".
|
||||
Returns: None
|
||||
|
||||
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
|
||||
│ --log-level TEXT Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] │
|
||||
│ --help Show this message and exit. │
|
||||
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
|
||||
```
|
||||
|
||||
Once you run the `api-key` command, it will create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`.
|
||||
Run the `api-key` command to create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`.
|
||||
|
||||
```bash
|
||||
langflow api-key
|
||||
# or
|
||||
python -m langflow api-key
|
||||
╭─────────────────────────────────────────────────────────────────────╮
|
||||
│ API Key Created Successfully: │
|
||||
|
|
@ -67,11 +56,98 @@ python -m langflow api-key
|
|||
│ Make sure to store it in a secure location. │
|
||||
│ │
|
||||
│ The API key has been copied to your clipboard. Cmd + V to paste it. │
|
||||
╰─────────────────────────────────────────────────────────────────────╯
|
||||
╰──────────────────────────────
|
||||
```
|
||||
|
||||
### Environment Variables
|
||||
### Options
|
||||
|
||||
| Option | Type | Description |
|
||||
| ----------- | ---- | ------------------------------------------------------------- |
|
||||
| --log-level | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] |
|
||||
| --help | | Show this message and exit. |
|
||||
|
||||
## langflow copy-db
|
||||
|
||||
Run the `copy-db` command to copy the cached `langflow.db` and `langflow-pre.db` database files to the current directory.
|
||||
|
||||
If the files exist in the cache directory, they will be copied to the same directory as `__main__.py`, which can be found with `which langflow`.
|
||||
|
||||
### Options
|
||||
|
||||
None.
|
||||
|
||||
## langflow migration
|
||||
|
||||
Run or test migrations with the [Alembic](https://pypi.org/project/alembic/) database tool.
|
||||
|
||||
```bash
|
||||
langflow migration
|
||||
# or
|
||||
python -m langflow migration
|
||||
```
|
||||
|
||||
### Options
|
||||
|
||||
| Option | Description |
|
||||
| ------------------- | -------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `--test, --no-test` | Run migrations in test mode. [default: test] |
|
||||
| `--fix, --no-fix` | Fix migrations. This is a destructive operation, and should only be used if you know what you are doing. [default: no-fix] |
|
||||
| `--help` | Show this message and exit. |
|
||||
|
||||
## langflow run
|
||||
|
||||
Run Langflow.
|
||||
|
||||
```bash
|
||||
langflow run
|
||||
# or
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
### Options
|
||||
|
||||
| Option | Description |
|
||||
| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `--help` | Displays all available options. |
|
||||
| `--host` | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. |
|
||||
| `--workers` | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. |
|
||||
| `--timeout` | Sets the worker timeout in seconds. The default is `60`. |
|
||||
| `--port` | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. |
|
||||
| `--env-file` | Specifies the path to the .env file containing environment variables. The default is `.env`. |
|
||||
| `--log-level` | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. |
|
||||
| `--components-path` | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. |
|
||||
| `--log-file` | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. |
|
||||
| `--cache` | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. |
|
||||
| `--dev`/`--no-dev` | Toggles the development mode. The default is `no-dev`. |
|
||||
| `--path` | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. |
|
||||
| `--open-browser`/`--no-open-browser` | Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`. |
|
||||
| `--remove-api-keys`/`--no-remove-api-keys` | Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. |
|
||||
| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Installs completion for the specified shell. |
|
||||
| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Shows completion for the specified shell, allowing you to copy it or customize the installation. |
|
||||
| `--backend-only` | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only.md). |
|
||||
| `--store` | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. |
|
||||
|
||||
#### Environment Variables
|
||||
|
||||
You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a `.env` file and loaded using the `--env-file` option.
|
||||
|
||||
A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence.
|
||||
|
||||
## langflow superuser
|
||||
|
||||
Create a superuser for Langflow.
|
||||
|
||||
```bash
|
||||
langflow superuser
|
||||
# or
|
||||
python -m langflow superuser
|
||||
```
|
||||
|
||||
### Options
|
||||
|
||||
| Option | Type | Description |
|
||||
| ------------- | ---- | ------------------------------------------------------------- |
|
||||
| `--username` | TEXT | Username for the superuser. [default: None] [required] |
|
||||
| `--password` | TEXT | Password for the superuser. [default: None] [required] |
|
||||
| `--log-level` | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] |
|
||||
| `--help` | | Show this message and exit. |
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
|
|||
|
||||
---
|
||||
|
||||
## 🐦 Stay tunned for **Langflow** on Twitter
|
||||
## 🐦 Stay tuned for **Langflow** on Twitter
|
||||
|
||||
Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**.
|
||||
|
||||
|
|
|
|||
123
docs/docs/deployment/backend-only.md
Normal file
123
docs/docs/deployment/backend-only.md
Normal file
|
|
@ -0,0 +1,123 @@
|
|||
# Backend-only
|
||||
|
||||
You can run Langflow in `--backend-only` mode to expose your Langflow app as an API, without running the frontend UI.
|
||||
|
||||
Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
|
||||
|
||||
The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
|
||||
Langflow will now serve requests to its API without the frontend running.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- [Langflow installed](../getting-started/install-langflow.mdx)
|
||||
|
||||
- [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
- [A Langflow flow created](../starter-projects/basic-prompting.mdx)
|
||||
|
||||
## Download your flow's curl call
|
||||
|
||||
1. Click API.
|
||||
2. Click **curl** > **Copy code** and save the code to your local machine.
|
||||
It will look something like this:
|
||||
|
||||
```curl
|
||||
curl -X POST \
|
||||
"http://127.0.0.1:7864/api/v1/run/ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef?stream=false" \
|
||||
-H 'Content-Type: application/json'\
|
||||
-d '{"input_value": "message",
|
||||
"output_type": "chat",
|
||||
"input_type": "chat",
|
||||
"tweaks": {
|
||||
"Prompt-kvo86": {},
|
||||
"OpenAIModel-MilkD": {},
|
||||
"ChatOutput-ktwdw": {},
|
||||
"ChatInput-xXC4F": {}
|
||||
}}'
|
||||
```
|
||||
|
||||
Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID in the UI as well to ensure you're querying the right flow.
|
||||
|
||||
## Start Langflow in backend-only mode
|
||||
|
||||
1. Stop Langflow with Ctrl+C.
|
||||
2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
|
||||
The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
|
||||
Langflow will now serve requests to its API.
|
||||
3. Run the curl code you copied from the UI.
|
||||
You should get a result like this:
|
||||
|
||||
```bash
|
||||
{"session_id":"ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880","outputs":[{"inputs":{"input_value":"hi, are you there?"},"outputs":[{"results":{"result":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?"},"artifacts":{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-ktwdw"}],"component_display_name":"Chat Output","component_id":"ChatOutput-ktwdw","used_frozen_result":false}]}]}%
|
||||
```
|
||||
|
||||
Again, note that the flow ID matches.
|
||||
Langflow is receiving your POST request, running the flow, and returning the result, all without running the frontend. Cool!
|
||||
|
||||
## Download your flow's Python API call
|
||||
|
||||
Instead of using curl, you can download your flow as a Python API call instead.
|
||||
|
||||
1. Click API.
|
||||
2. Click **Python API** > **Copy code** and save the code to your local machine.
|
||||
The code will look something like this:
|
||||
|
||||
```python
|
||||
import requests
|
||||
from typing import Optional
|
||||
|
||||
BASE_API_URL = "http://127.0.0.1:7864/api/v1/run"
|
||||
FLOW_ID = "ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef"
|
||||
# You can tweak the flow by adding a tweaks dictionary
|
||||
# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
|
||||
|
||||
def run_flow(message: str,
|
||||
flow_id: str,
|
||||
output_type: str = "chat",
|
||||
input_type: str = "chat",
|
||||
tweaks: Optional[dict] = None,
|
||||
api_key: Optional[str] = None) -> dict:
|
||||
"""
|
||||
Run a flow with a given message and optional tweaks.
|
||||
|
||||
:param message: The message to send to the flow
|
||||
:param flow_id: The ID of the flow to run
|
||||
:param tweaks: Optional tweaks to customize the flow
|
||||
:return: The JSON response from the flow
|
||||
"""
|
||||
api_url = f"{BASE_API_URL}/{flow_id}"
|
||||
|
||||
payload = {
|
||||
"input_value": message,
|
||||
"output_type": output_type,
|
||||
"input_type": input_type,
|
||||
}
|
||||
headers = None
|
||||
if tweaks:
|
||||
payload["tweaks"] = tweaks
|
||||
if api_key:
|
||||
headers = {"x-api-key": api_key}
|
||||
response = requests.post(api_url, json=payload, headers=headers)
|
||||
return response.json()
|
||||
|
||||
# Setup any tweaks you want to apply to the flow
|
||||
message = "message"
|
||||
|
||||
print(run_flow(message=message, flow_id=FLOW_ID))
|
||||
```
|
||||
|
||||
3. Run your Python app:
|
||||
|
||||
```python
|
||||
python3 app.py
|
||||
```
|
||||
|
||||
The result is similar to the curl call:
|
||||
|
||||
```bash
|
||||
{'session_id': 'ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880', 'outputs': [{'inputs': {'input_value': 'message'}, 'outputs': [{'results': {'result': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!"}, 'artifacts': {'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI'}, 'messages': [{'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI', 'component_id': 'ChatOutput-ktwdw'}], 'component_display_name': 'Chat Output', 'component_id': 'ChatOutput-ktwdw', 'used_frozen_result': False}]}]}
|
||||
```
|
||||
|
||||
Your Python app POSTs to your Langflow server, and the server runs the flow and returns the result.
|
||||
|
||||
See [API](../administration/api.mdx) for more ways to interact with your headless Langflow server.
|
||||
65
docs/docs/deployment/docker.md
Normal file
65
docs/docs/deployment/docker.md
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
# Docker
|
||||
|
||||
This guide will help you get LangFlow up and running using Docker and Docker Compose.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Docker
|
||||
- Docker Compose
|
||||
|
||||
## Steps
|
||||
|
||||
1. Clone the LangFlow repository:
|
||||
|
||||
```sh
|
||||
git clone https://github.com/langflow-ai/langflow.git
|
||||
```
|
||||
|
||||
2. Navigate to the `docker_example` directory:
|
||||
|
||||
```sh
|
||||
cd langflow/docker_example
|
||||
```
|
||||
|
||||
3. Run the Docker Compose file:
|
||||
|
||||
```sh
|
||||
docker compose up
|
||||
```
|
||||
|
||||
LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/).
|
||||
|
||||
## Docker Compose Configuration
|
||||
|
||||
The Docker Compose configuration spins up two services: `langflow` and `postgres`.
|
||||
|
||||
### LangFlow Service
|
||||
|
||||
The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
|
||||
Environment variables:
|
||||
|
||||
- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database.
|
||||
- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys.
|
||||
|
||||
Volumes:
|
||||
|
||||
- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container.
|
||||
|
||||
### PostgreSQL Service
|
||||
|
||||
The `postgres` service uses the `postgres:16` Docker image and exposes port 5432.
|
||||
|
||||
Environment variables:
|
||||
|
||||
- `POSTGRES_USER`: The username for the PostgreSQL database.
|
||||
- `POSTGRES_PASSWORD`: The password for the PostgreSQL database.
|
||||
- `POSTGRES_DB`: The name of the PostgreSQL database.
|
||||
|
||||
Volumes:
|
||||
|
||||
- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container.
|
||||
|
||||
## Switching to a Specific LangFlow Version
|
||||
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`.
|
||||
|
|
@ -9,14 +9,11 @@ The `AddContentToPage` component converts markdown text to Notion blocks and app
|
|||
|
||||
[Notion Reference](https://developers.notion.com/reference/patch-block-children)
|
||||
|
||||
<Admonition type="tip" title="Component Functionality">
|
||||
|
||||
The `AddContentToPage` component enables you to:
|
||||
|
||||
- Convert markdown text to Notion blocks.
|
||||
- Append the converted blocks to a specified Notion page.
|
||||
- Seamlessly integrate Notion content creation into Langflow workflows.
|
||||
</Admonition>
|
||||
|
||||
## Component Usage
|
||||
|
||||
|
|
@ -100,8 +97,6 @@ class NotionPageCreator(CustomComponent):
|
|||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
|
||||
Example of using the `AddContentToPage` component in a Langflow flow using Markdown as input:
|
||||
|
||||
<ZoomableImage
|
||||
|
|
@ -115,8 +110,6 @@ style={{ width: "100%", margin: "20px 0" }}
|
|||
|
||||
In this example, the `AddContentToPage` component connects to a `MarkdownLoader` component to provide the markdown text input. The converted Notion blocks are appended to the specified Notion page using the provided `block_id` and `notion_secret`.
|
||||
|
||||
</Admonition>
|
||||
|
||||
## Best Practices
|
||||
|
||||
When using the `AddContentToPage` component:
|
||||
|
|
|
|||
|
|
@ -9,13 +9,11 @@ The `NotionUserList` component retrieves users from Notion. It provides a conven
|
|||
|
||||
[Notion Reference](https://developers.notion.com/reference/get-users)
|
||||
|
||||
<Admonition type="tip" title="Component Functionality">
|
||||
The `NotionUserList` component enables you to:
|
||||
The `NotionUserList` component enables you to:
|
||||
|
||||
- Retrieve user data from Notion
|
||||
- Access user information such as ID, type, name, and avatar URL
|
||||
- Integrate Notion user data seamlessly into your Langflow workflows
|
||||
</Admonition>
|
||||
|
||||
## Component Usage
|
||||
|
||||
|
|
@ -95,7 +93,6 @@ class NotionUserList(CustomComponent):
|
|||
|
||||
## Example Usage
|
||||
|
||||
<Admonition type="info" title="Example Usage">
|
||||
Here's an example of how you can use the `NotionUserList` component in a Langflow flow and passing the outputs to the Prompt component:
|
||||
|
||||
<ZoomableImage
|
||||
|
|
@ -107,8 +104,6 @@ sources={{
|
|||
style={{ width: "100%", margin: "20px 0" }}
|
||||
/>
|
||||
|
||||
</Admonition>
|
||||
|
||||
## Best Practices
|
||||
|
||||
When using the `NotionUserList` component, consider the following best practices:
|
||||
|
|
|
|||
|
|
@ -113,7 +113,11 @@ module.exports = {
|
|||
type: "category",
|
||||
label: "Deployment",
|
||||
collapsed: true,
|
||||
items: ["deployment/gcp-deployment"],
|
||||
items: [
|
||||
"deployment/docker",
|
||||
"deployment/backend-only",
|
||||
"deployment/gcp-deployment",
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
|
|
|
|||
4
docs/static/logos/twitter.svg
vendored
4
docs/static/logos/twitter.svg
vendored
|
|
@ -1,3 +1,3 @@
|
|||
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311
poetry.lock
generated
311
poetry.lock
generated
|
|
@ -261,13 +261,13 @@ extras = ["pyaudio (>=0.2.13)"]
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|||
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||||
[[package]]
|
||||
name = "astrapy"
|
||||
version = "1.2.0"
|
||||
version = "1.2.1"
|
||||
description = "AstraPy is a Pythonic SDK for DataStax Astra and its Data API"
|
||||
optional = false
|
||||
python-versions = "<4.0.0,>=3.8.0"
|
||||
files = [
|
||||
{file = "astrapy-1.2.0-py3-none-any.whl", hash = "sha256:5d65242771934c38ebe16f330e9e517968c1437846dabdbe7e48470f7b1782e8"},
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||||
{file = "astrapy-1.2.0.tar.gz", hash = "sha256:6ce1b421d1ae21fe73373fa36048d8d56c775367886525504f01c48cbb742842"},
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||||
{file = "astrapy-1.2.1-py3-none-any.whl", hash = "sha256:0d7ca1e6f18a6a4e9a41ffaf2aa4cc585d36de3e983b5c5ce0bbb30a1595e30b"},
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{file = "astrapy-1.2.1.tar.gz", hash = "sha256:c4ba88ef16ac1e990ccba322d376b6ea256513a3004a0894c14bfa2403f1d646"},
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]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -471,17 +471,17 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.34.119"
|
||||
version = "1.34.121"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
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python-versions = ">=3.8"
|
||||
files = [
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{file = "boto3-1.34.119-py3-none-any.whl", hash = "sha256:8f9c43c54b3dfaa36c4a0d7b42c417227a515bc7a2e163e62802780000a5a3e2"},
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{file = "boto3-1.34.121-py3-none-any.whl", hash = "sha256:4e79e400d6d44b4eee5deda6ac0ecd08a3f5a30c45a0d30712795cdc4459fd79"},
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{file = "boto3-1.34.121.tar.gz", hash = "sha256:ec89f3e0b0dc959c418df29e14d3748c0b05ab7acf7c0b90c839e9f340a659fa"},
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]
|
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||||
[package.dependencies]
|
||||
botocore = ">=1.34.119,<1.35.0"
|
||||
botocore = ">=1.34.121,<1.35.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.10.0,<0.11.0"
|
||||
|
||||
|
|
@ -490,13 +490,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
|||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.34.119"
|
||||
version = "1.34.121"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
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files = [
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{file = "botocore-1.34.119-py3-none-any.whl", hash = "sha256:4bdf7926a1290b2650d62899ceba65073dd2693e61c35f5cdeb3a286a0aaa27b"},
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[package.dependencies]
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|
|
@ -505,7 +505,7 @@ python-dateutil = ">=2.1,<3.0.0"
|
|||
urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
|
||||
|
||||
[package.extras]
|
||||
crt = ["awscrt (==0.20.9)"]
|
||||
crt = ["awscrt (==0.20.11)"]
|
||||
|
||||
[[package]]
|
||||
name = "brotli"
|
||||
|
|
@ -698,13 +698,13 @@ graph = ["gremlinpython (==3.4.6)"]
|
|||
|
||||
[[package]]
|
||||
name = "cassio"
|
||||
version = "0.1.7"
|
||||
version = "0.1.8"
|
||||
description = "A framework-agnostic Python library to seamlessly integrate Apache Cassandra(R) with ML/LLM/genAI workloads."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
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||||
[package.dependencies]
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|
|
@ -1824,13 +1824,13 @@ develop = ["aiohttp", "furo", "httpx", "mock", "opentelemetry-api", "opentelemet
|
|||
|
||||
[[package]]
|
||||
name = "elasticsearch"
|
||||
version = "8.13.2"
|
||||
version = "8.14.0"
|
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description = "Python client for Elasticsearch"
|
||||
optional = false
|
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python-versions = ">=3.7"
|
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files = [
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[package.dependencies]
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|
@ -2897,13 +2897,13 @@ pydantic = ">=1.10,<3"
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[[package]]
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||||
name = "gprof2dot"
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||||
version = "2024.6.5"
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||||
description = "Generate a dot graph from the output of several profilers."
|
||||
optional = false
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python-versions = ">=2.7"
|
||||
python-versions = ">=3.8"
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files = [
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{file = "gprof2dot-2024.6.5-py2.py3-none-any.whl", hash = "sha256:0be69ac4f5e0d6f57e0c627fa8f6053bdca6a7a226ea6fd8a74b69c845c7d2df"},
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[[package]]
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|
@ -3446,100 +3446,105 @@ files = [
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[[package]]
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
{file = "ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a716e05547a39b788deaf22725490855337fc36613288aa8ae1601dc8c525553"},
|
||||
{file = "ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:473f5d921fadc135d1ad698e2697025045cd8ed7e5e842258295012d8a3bc702"},
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||||
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||||
{file = "ijson-3.3.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d0b6b637d05dbdb29d0bfac2ed8425bb369e7af5271b0cc7cf8b801cb7360c2"},
|
||||
{file = "ijson-3.3.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5378d0baa59ae422905c5f182ea0fd74fe7e52a23e3821067a7d58c8306b2191"},
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||||
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|
||||
{file = "ijson-3.3.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:45ff05de889f3dc3d37a59d02096948ce470699f2368b32113954818b21aa74a"},
|
||||
{file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1efb521090dd6cefa7aafd120581947b29af1713c902ff54336b7c7130f04c47"},
|
||||
{file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87c727691858fd3a1c085d9980d12395517fcbbf02c69fbb22dede8ee03422da"},
|
||||
{file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0420c24e50389bc251b43c8ed379ab3e3ba065ac8262d98beb6735ab14844460"},
|
||||
{file = "ijson-3.3.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:8fdf3721a2aa7d96577970f5604bd81f426969c1822d467f07b3d844fa2fecc7"},
|
||||
{file = "ijson-3.3.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:891f95c036df1bc95309951940f8eea8537f102fa65715cdc5aae20b8523813b"},
|
||||
{file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed1336a2a6e5c427f419da0154e775834abcbc8ddd703004108121c6dd9eba9d"},
|
||||
{file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f0c819f83e4f7b7f7463b2dc10d626a8be0c85fbc7b3db0edc098c2b16ac968e"},
|
||||
{file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33afc25057377a6a43c892de34d229a86f89ea6c4ca3dd3db0dcd17becae0dbb"},
|
||||
{file = "ijson-3.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7914d0cf083471856e9bc2001102a20f08e82311dfc8cf1a91aa422f9414a0d6"},
|
||||
{file = "ijson-3.3.0.tar.gz", hash = "sha256:7f172e6ba1bee0d4c8f8ebd639577bfe429dee0f3f96775a067b8bae4492d8a0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3994,13 +3999,13 @@ zookeeper = ["kazoo (>=2.8.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "kubernetes"
|
||||
version = "29.0.0"
|
||||
version = "30.1.0"
|
||||
description = "Kubernetes python client"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "kubernetes-29.0.0-py2.py3-none-any.whl", hash = "sha256:ab8cb0e0576ccdfb71886366efb102c6a20f268d817be065ce7f9909c631e43e"},
|
||||
{file = "kubernetes-29.0.0.tar.gz", hash = "sha256:c4812e227ae74d07d53c88293e564e54b850452715a59a927e7e1bc6b9a60459"},
|
||||
{file = "kubernetes-30.1.0-py2.py3-none-any.whl", hash = "sha256:e212e8b7579031dd2e512168b617373bc1e03888d41ac4e04039240a292d478d"},
|
||||
{file = "kubernetes-30.1.0.tar.gz", hash = "sha256:41e4c77af9f28e7a6c314e3bd06a8c6229ddd787cad684e0ab9f69b498e98ebc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4322,7 +4327,7 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
|
|||
|
||||
[[package]]
|
||||
name = "langflow-base"
|
||||
version = "0.0.57"
|
||||
version = "0.0.59"
|
||||
description = "A Python package with a built-in web application"
|
||||
optional = false
|
||||
python-versions = ">=3.10,<3.13"
|
||||
|
|
@ -4379,13 +4384,13 @@ url = "src/backend/base"
|
|||
|
||||
[[package]]
|
||||
name = "langfuse"
|
||||
version = "2.34.1"
|
||||
version = "2.35.0"
|
||||
description = "A client library for accessing langfuse"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langfuse-2.34.1-py3-none-any.whl", hash = "sha256:2bb76d8ead3837798fc1b43e74b012cfca6cf8f433be36e0d53e7498a8b9ba6f"},
|
||||
{file = "langfuse-2.34.1.tar.gz", hash = "sha256:c40220b66a8ba429a4b23d42e02fcfbbe9bd755615f6410854eef1454c36f6ff"},
|
||||
{file = "langfuse-2.35.0-py3-none-any.whl", hash = "sha256:e9df2474a01f8e167b7b13674c554915415b27064e48ad207054475f7fa8f82d"},
|
||||
{file = "langfuse-2.35.0.tar.gz", hash = "sha256:b1d4b478233eefbc8a6fc63ca00ca82f6afecf2b0fdc1835ca65e751cf901577"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4403,13 +4408,13 @@ openai = ["openai (>=0.27.8)"]
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.72"
|
||||
version = "0.1.75"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langsmith-0.1.72-py3-none-any.whl", hash = "sha256:a4456707669521bd75b7431b9205a6b99579fb9ff01bd338f52d29df11a7662d"},
|
||||
{file = "langsmith-0.1.72.tar.gz", hash = "sha256:262ae9e8aceaba50f3a0f5b6eb559d6110886f0afc6b0ed5270e7d3d3f1fd8d6"},
|
||||
{file = "langsmith-0.1.75-py3-none-any.whl", hash = "sha256:d08b08dd6b3fa4da170377f95123d77122ef4c52999d10fff4ae08ff70d07aed"},
|
||||
{file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4419,13 +4424,13 @@ requests = ">=2,<3"
|
|||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.40.2"
|
||||
version = "1.40.4"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
optional = false
|
||||
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
|
||||
files = [
|
||||
{file = "litellm-1.40.2-py3-none-any.whl", hash = "sha256:56ee777eed30ee9acb86e74401d090dcac4adb57b5c8a8714f791b0c97a34afc"},
|
||||
{file = "litellm-1.40.2.tar.gz", hash = "sha256:1f5dc4eab7100962c3a2985c7d8c13070ff5793b341540d19b98a2bd85955cb0"},
|
||||
{file = "litellm-1.40.4-py3-none-any.whl", hash = "sha256:b3b8e4401f717c3a18595446bfdb80fc6bb74974aac4eae537fb7b3be37fbf9e"},
|
||||
{file = "litellm-1.40.4.tar.gz", hash = "sha256:3edaa1189742afd7c7df2b122f77373d47154a8fb6df6187ff5875e188baa3e1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4796,13 +4801,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "marshmallow"
|
||||
version = "3.21.2"
|
||||
version = "3.21.3"
|
||||
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"},
|
||||
{file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
|
||||
{file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
|
||||
{file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5583,13 +5588,13 @@ sympy = "*"
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.31.1"
|
||||
version = "1.32.0"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
|
||||
files = [
|
||||
{file = "openai-1.31.1-py3-none-any.whl", hash = "sha256:a746cf070798a4048cfea00b0fc7cb9760ee7ead5a08c48115b914d1afbd1b53"},
|
||||
{file = "openai-1.31.1.tar.gz", hash = "sha256:a15266827de20f407d4bf9837030b168074b5b29acd54f10bb38d5f53e95f083"},
|
||||
{file = "openai-1.32.0-py3-none-any.whl", hash = "sha256:953d57669f309002044fd2f678aba9f07a43256d74b3b00cd04afb5b185568ea"},
|
||||
{file = "openai-1.32.0.tar.gz", hash = "sha256:a6df15a7ab9344b1bc2bc8d83639f68b7a7e2453c0f5e50c1666547eee86f0bd"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -7575,13 +7580,13 @@ websockets = ">=11,<13"
|
|||
|
||||
[[package]]
|
||||
name = "redis"
|
||||
version = "5.0.4"
|
||||
version = "5.0.5"
|
||||
description = "Python client for Redis database and key-value store"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "redis-5.0.4-py3-none-any.whl", hash = "sha256:7adc2835c7a9b5033b7ad8f8918d09b7344188228809c98df07af226d39dec91"},
|
||||
{file = "redis-5.0.4.tar.gz", hash = "sha256:ec31f2ed9675cc54c21ba854cfe0462e6faf1d83c8ce5944709db8a4700b9c61"},
|
||||
{file = "redis-5.0.5-py3-none-any.whl", hash = "sha256:30b47d4ebb6b7a0b9b40c1275a19b87bb6f46b3bed82a89012cf56dea4024ada"},
|
||||
{file = "redis-5.0.5.tar.gz", hash = "sha256:3417688621acf6ee368dec4a04dd95881be24efd34c79f00d31f62bb528800ae"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8313,13 +8318,13 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7
|
|||
|
||||
[[package]]
|
||||
name = "storage3"
|
||||
version = "0.7.5"
|
||||
version = "0.7.6"
|
||||
description = "Supabase Storage client for Python."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "storage3-0.7.5-py3-none-any.whl", hash = "sha256:a2d9fdacafdcbcdb6776a54987a7d84c3e3195a5e4782955c4ccfb36cb021f14"},
|
||||
{file = "storage3-0.7.5.tar.gz", hash = "sha256:ffe43f3877898b43a94024e68c2aaf4cebb3ad73dbbbd67747041d1d70bbf032"},
|
||||
{file = "storage3-0.7.6-py3-none-any.whl", hash = "sha256:d8c23bf87b3a88cafb03761b7f936e4e49daca67741d571513edf746e0f8ba72"},
|
||||
{file = "storage3-0.7.6.tar.gz", hash = "sha256:0b7781cea7fe6382e6b9349b84395808c5f4203dfcac31478304eedc2f81acf6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8381,13 +8386,13 @@ supafunc = ">=0.3.1,<0.5.0"
|
|||
|
||||
[[package]]
|
||||
name = "supafunc"
|
||||
version = "0.4.5"
|
||||
version = "0.4.6"
|
||||
description = "Library for Supabase Functions"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "supafunc-0.4.5-py3-none-any.whl", hash = "sha256:2208045f8f5c797924666f6a332efad75ad368f8030b2e4ceb9d2bf63f329373"},
|
||||
{file = "supafunc-0.4.5.tar.gz", hash = "sha256:a6466d78bdcaa58b7f0303793643103baae8106a87acd5d01e196179a9d0d024"},
|
||||
{file = "supafunc-0.4.6-py3-none-any.whl", hash = "sha256:f7ca7b244365e171da7055a64edb462c2ec449cdaa210fc418cfccd132f4cf98"},
|
||||
{file = "supafunc-0.4.6.tar.gz", hash = "sha256:92db51f8f8568d1430285219c9c0072e44207409c416622d7387f609e31928a6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8707,22 +8712,22 @@ optree = ["optree (>=0.9.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "tornado"
|
||||
version = "6.4"
|
||||
version = "6.4.1"
|
||||
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
|
||||
optional = false
|
||||
python-versions = ">= 3.8"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
|
||||
{file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
|
||||
{file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
|
||||
{file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
|
||||
{file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
|
||||
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
|
||||
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
|
||||
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
|
||||
{file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
|
||||
{file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
|
||||
{file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"},
|
||||
{file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"},
|
||||
{file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -10054,4 +10059,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "83c94ed0fa28b968553221385251b871139a7440ab0420f867efbe16568b8411"
|
||||
content-hash = "2ba268be17a69253c9631ec721ece465a85a22949c2df7c712b7aa12d1a002fa"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a46"
|
||||
version = "1.0.0a48"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
|
|
@ -66,7 +66,7 @@ qianfan = "0.3.5"
|
|||
pgvector = "^0.2.3"
|
||||
pyautogen = "^0.2.0"
|
||||
langchain-google-genai = "^1.0.1"
|
||||
langchain-cohere = "^0.1.0rc1"
|
||||
langchain-cohere = "^0.1.5"
|
||||
elasticsearch = "^8.12.0"
|
||||
pytube = "^15.0.0"
|
||||
dspy-ai = "^2.4.0"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import os
|
||||
import argparse
|
||||
|
||||
from huggingface_hub import HfApi, list_models
|
||||
from rich import print
|
||||
|
|
@ -6,11 +6,27 @@ from rich import print
|
|||
# Use root method
|
||||
models = list_models()
|
||||
|
||||
args = argparse.ArgumentParser(description="Restart a space in the Hugging Face Hub.")
|
||||
args.add_argument("--space", type=str, help="The space to restart.")
|
||||
args.add_argument("--token", type=str, help="The Hugging Face API token.")
|
||||
|
||||
parsed_args = args.parse_args()
|
||||
|
||||
space = parsed_args.space
|
||||
|
||||
if not space:
|
||||
print("Please provide a space to restart.")
|
||||
exit()
|
||||
|
||||
if not parsed_args.token:
|
||||
print("Please provide an API token.")
|
||||
exit()
|
||||
|
||||
# Or configure a HfApi client
|
||||
hf_api = HfApi(
|
||||
endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
|
||||
token=os.getenv("HUGGINFACE_API_TOKEN"),
|
||||
token=parsed_args.token,
|
||||
)
|
||||
|
||||
space_runtime = hf_api.restart_space("Langflow/Langflow-Preview", factory_reboot=True)
|
||||
space_runtime = hf_api.restart_space(space, factory_reboot=True)
|
||||
print(space_runtime)
|
||||
|
|
|
|||
|
|
@ -204,16 +204,18 @@ 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})
|
||||
await chat_service.set_cache(flow_id, graph)
|
||||
return graph
|
||||
|
||||
|
|
@ -317,3 +319,4 @@ 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)
|
||||
log_object = None
|
||||
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(
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
from typing import List
|
||||
|
||||
from langflow.helpers.flow import generate_unique_flow_name
|
||||
from langflow.helpers.folders import generate_unique_folder_name
|
||||
import orjson
|
||||
from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile, status
|
||||
from sqlalchemy import or_, update
|
||||
|
|
@ -203,16 +205,9 @@ 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 +227,8 @@ 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
|
||||
|
||||
|
|
|
|||
|
|
@ -71,9 +71,7 @@ async def login_to_get_access_token(
|
|||
|
||||
@router.get("/auto_login")
|
||||
async def auto_login(
|
||||
response: Response,
|
||||
db: Session = Depends(get_session),
|
||||
settings_service=Depends(get_settings_service)
|
||||
response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
|
||||
):
|
||||
auth_settings = settings_service.auth_settings
|
||||
if settings_service.auth_settings.AUTO_LOGIN:
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
|
||||
from langflow.services.deps import get_monitor_service
|
||||
from langflow.services.monitor.schema import (
|
||||
MessageModelRequest,
|
||||
MessageModelResponse,
|
||||
TransactionModelResponse,
|
||||
VertexBuildMapModel,
|
||||
|
|
@ -66,6 +66,44 @@ async def get_messages(
|
|||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.delete("/messages", status_code=204)
|
||||
async def delete_messages(
|
||||
message_ids: List[int],
|
||||
monitor_service: MonitorService = Depends(get_monitor_service),
|
||||
):
|
||||
try:
|
||||
monitor_service.delete_messages(message_ids=message_ids)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.post("/messages/{message_id}", response_model=MessageModelResponse)
|
||||
async def update_message(
|
||||
message_id: str,
|
||||
message: MessageModelRequest,
|
||||
monitor_service: MonitorService = Depends(get_monitor_service),
|
||||
):
|
||||
try:
|
||||
message_dict = message.model_dump(exclude_none=True)
|
||||
message_dict.pop("index", None)
|
||||
monitor_service.update_message(message_id=message_id, **message_dict)
|
||||
return MessageModelResponse(index=message_id, **message_dict)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.delete("/messages/session/{session_id}", status_code=204)
|
||||
async def delete_messages_session(
|
||||
session_id: str,
|
||||
monitor_service: MonitorService = Depends(get_monitor_service),
|
||||
):
|
||||
try:
|
||||
monitor_service.delete_messages_session(session_id=session_id)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/transactions", response_model=List[TransactionModelResponse])
|
||||
async def get_transactions(
|
||||
source: Optional[str] = Query(None),
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from datetime import datetime, timezone
|
|||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from typing_extensions import TypedDict
|
||||
from uuid import UUID
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serializer
|
||||
|
|
@ -9,11 +10,12 @@ 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
|
||||
from langflow.services.database.models.user import UserRead
|
||||
from langflow.utils.schemas import ChatOutputResponse
|
||||
|
||||
|
||||
class BuildStatus(Enum):
|
||||
|
|
@ -242,9 +244,10 @@ class VerticesOrderResponse(BaseModel):
|
|||
run_id: UUID
|
||||
vertices_to_run: List[str]
|
||||
|
||||
|
||||
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
|
||||
|
|
|
|||
|
|
@ -15,10 +15,25 @@ import shlex
|
|||
from collections import OrderedDict, namedtuple
|
||||
from http.cookies import SimpleCookie
|
||||
|
||||
from uncurl.api import parser # type: ignore
|
||||
|
||||
parser.add_argument("-x", "--proxy", default={})
|
||||
parser.add_argument("-U", "--proxy-user", default="")
|
||||
ParsedArgs = namedtuple(
|
||||
"ParsedContext",
|
||||
[
|
||||
"command",
|
||||
"url",
|
||||
"data",
|
||||
"data_binary",
|
||||
"method",
|
||||
"headers",
|
||||
"compressed",
|
||||
"insecure",
|
||||
"user",
|
||||
"include",
|
||||
"silent",
|
||||
"proxy",
|
||||
"proxy_user",
|
||||
"cookies",
|
||||
],
|
||||
)
|
||||
|
||||
ParsedContext = namedtuple("ParsedContext", ["method", "url", "data", "headers", "cookies", "verify", "auth", "proxy"])
|
||||
|
||||
|
|
@ -27,24 +42,90 @@ def normalize_newlines(multiline_text):
|
|||
return multiline_text.replace(" \\\n", " ")
|
||||
|
||||
|
||||
def parse_curl_command(curl_command):
|
||||
tokens = shlex.split(normalize_newlines(curl_command))
|
||||
tokens = [token for token in tokens if token and token != " "]
|
||||
if "curl" not in tokens[0]:
|
||||
raise ValueError("Invalid curl command")
|
||||
args_template = {
|
||||
"command": None,
|
||||
"url": None,
|
||||
"data": None,
|
||||
"data_binary": None,
|
||||
"method": "get",
|
||||
"headers": [],
|
||||
"compressed": False,
|
||||
"insecure": False,
|
||||
"user": (),
|
||||
"include": False,
|
||||
"silent": False,
|
||||
"proxy": None,
|
||||
"proxy_user": None,
|
||||
"cookies": {},
|
||||
}
|
||||
args = args_template.copy()
|
||||
method_on_curl = None
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
if token == "-X":
|
||||
i += 1
|
||||
args["method"] = tokens[i].lower()
|
||||
method_on_curl = tokens[i].lower()
|
||||
elif token in ("-d", "--data"):
|
||||
i += 1
|
||||
args["data"] = tokens[i]
|
||||
elif token in ("-b", "--data-binary", "--data-raw"):
|
||||
i += 1
|
||||
args["data_binary"] = tokens[i]
|
||||
elif token in ("-H", "--header"):
|
||||
i += 1
|
||||
args["headers"].append(tokens[i])
|
||||
elif token == "--compressed":
|
||||
args["compressed"] = True
|
||||
elif token in ("-k", "--insecure"):
|
||||
args["insecure"] = True
|
||||
elif token in ("-u", "--user"):
|
||||
i += 1
|
||||
args["user"] = tuple(tokens[i].split(":"))
|
||||
elif token in ("-I", "--include"):
|
||||
args["include"] = True
|
||||
elif token in ("-s", "--silent"):
|
||||
args["silent"] = True
|
||||
elif token in ("-x", "--proxy"):
|
||||
i += 1
|
||||
args["proxy"] = tokens[i]
|
||||
elif token in ("-U", "--proxy-user"):
|
||||
i += 1
|
||||
args["proxy_user"] = tokens[i]
|
||||
elif not token.startswith("-"):
|
||||
if args["command"] is None:
|
||||
args["command"] = token
|
||||
else:
|
||||
args["url"] = token
|
||||
i += 1
|
||||
|
||||
args["method"] = method_on_curl or args["method"]
|
||||
|
||||
return ParsedArgs(**args)
|
||||
|
||||
|
||||
def parse_context(curl_command):
|
||||
method = "get"
|
||||
|
||||
tokens = shlex.split(normalize_newlines(curl_command))
|
||||
tokens = [token for token in tokens if token and token != " "]
|
||||
parsed_args = parser.parse_args(tokens)
|
||||
parsed_args: ParsedArgs = parse_curl_command(curl_command)
|
||||
|
||||
post_data = parsed_args.data or parsed_args.data_binary
|
||||
if post_data:
|
||||
method = "post"
|
||||
|
||||
if parsed_args.X:
|
||||
method = parsed_args.X.lower()
|
||||
if parsed_args.method:
|
||||
method = parsed_args.method.lower()
|
||||
|
||||
cookie_dict = OrderedDict()
|
||||
quoted_headers = OrderedDict()
|
||||
|
||||
for curl_header in parsed_args.header:
|
||||
for curl_header in parsed_args.headers:
|
||||
if curl_header.startswith(":"):
|
||||
occurrence = [m.start() for m in re.finditer(":", curl_header)]
|
||||
header_key, header_value = curl_header[: occurrence[1]], curl_header[occurrence[1] + 1 :]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import xml.etree.ElementTree as ET
|
|||
from concurrent import futures
|
||||
from pathlib import Path
|
||||
from typing import Callable, List, Optional, Text
|
||||
import unicodedata
|
||||
import chardet
|
||||
import yaml
|
||||
|
||||
|
|
@ -31,6 +32,17 @@ 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(".")
|
||||
|
|
@ -92,7 +104,10 @@ def read_text_file(file_path: str) -> str:
|
|||
with open(file_path, "rb") as f:
|
||||
raw_data = f.read()
|
||||
result = chardet.detect(raw_data)
|
||||
encoding = result['encoding']
|
||||
encoding = result["encoding"]
|
||||
|
||||
if encoding in ["Windows-1254", "MacRoman"]:
|
||||
encoding = "utf-8"
|
||||
|
||||
with open(file_path, "r", encoding=encoding) as f:
|
||||
return f.read()
|
||||
|
|
@ -121,9 +136,15 @@ 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)
|
||||
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"):
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
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
|
||||
|
|
@ -40,6 +41,13 @@ 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(
|
||||
|
|
@ -65,6 +73,7 @@ class ChatComponent(CustomComponent):
|
|||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[Union[str, Record]] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
|
|
@ -76,6 +85,7 @@ class ChatComponent(CustomComponent):
|
|||
input_value.data["sender"] = sender
|
||||
input_value.data["sender_name"] = sender_name
|
||||
input_value.data["session_id"] = session_id
|
||||
input_value.data["files"] = files
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
|
|
@ -83,6 +93,7 @@ class ChatComponent(CustomComponent):
|
|||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
"files": files,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
|
|
@ -103,17 +114,21 @@ class ChatComponent(CustomComponent):
|
|||
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,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
) -> Union[Text, Record]:
|
||||
input_value_record: Optional[Record] = None
|
||||
if files and not return_record:
|
||||
raise ValueError("Files can only be provided when Return Record is enabled.")
|
||||
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
|
||||
input_value.data["files"] = files
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
|
|
@ -121,6 +136,7 @@ class ChatComponent(CustomComponent):
|
|||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
"files": files,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,13 @@
|
|||
import warnings
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.load import load
|
||||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class LCModelComponent(CustomComponent):
|
||||
|
|
@ -53,19 +56,28 @@ 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 +85,7 @@ 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 | Record, system_message: Optional[str] = None
|
||||
):
|
||||
messages: list[Union[HumanMessage, SystemMessage]] = []
|
||||
if not input_value and not system_message:
|
||||
|
|
@ -81,7 +93,16 @@ 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, Record):
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
if "prompt" in input_value:
|
||||
prompt = load(input_value.prompt)
|
||||
runnable = prompt | runnable
|
||||
else:
|
||||
messages.append(input_value.to_lc_message())
|
||||
else:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
if stream:
|
||||
return runnable.stream(messages)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
import base64
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.schema import Record
|
||||
from langflow.services.deps import get_storage_service
|
||||
|
||||
|
||||
def record_to_string(record: Record) -> str:
|
||||
|
|
@ -19,7 +20,7 @@ def record_to_string(record: Record) -> str:
|
|||
return record.get_text()
|
||||
|
||||
|
||||
def dict_values_to_string(d: dict) -> dict:
|
||||
async def dict_values_to_string(d: dict) -> dict:
|
||||
"""
|
||||
Converts the values of a dictionary to strings.
|
||||
|
||||
|
|
@ -36,16 +37,43 @@ def dict_values_to_string(d: dict) -> dict:
|
|||
if isinstance(value, list):
|
||||
for i, item in enumerate(value):
|
||||
if isinstance(item, Record):
|
||||
d_copy[key][i] = record_to_string(item)
|
||||
d_copy[key][i] = item.to_lc_message()
|
||||
elif isinstance(item, Document):
|
||||
d_copy[key][i] = document_to_string(item)
|
||||
elif isinstance(value, Record):
|
||||
d_copy[key] = record_to_string(value)
|
||||
if "files" in value and value.files:
|
||||
files = await get_file_paths(value.files)
|
||||
value.files = files
|
||||
d_copy[key] = value.to_lc_message()
|
||||
elif isinstance(value, Document):
|
||||
d_copy[key] = document_to_string(value)
|
||||
return d_copy
|
||||
|
||||
|
||||
async def get_file_paths(files: list[str]):
|
||||
storage_service = get_storage_service()
|
||||
file_paths = []
|
||||
for file in files:
|
||||
flow_id, file_name = file.split("/")
|
||||
file_paths.append(storage_service.build_full_path(flow_id=flow_id, file_name=file_name))
|
||||
return file_paths
|
||||
|
||||
|
||||
async def get_files(
|
||||
file_paths: str,
|
||||
convert_to_base64: bool = False,
|
||||
):
|
||||
storage_service = get_storage_service()
|
||||
file_objects = []
|
||||
for file_path in file_paths:
|
||||
flow_id, file_name = file_path.split("/")
|
||||
file_object = await storage_service.get_file(flow_id=flow_id, file_name=file_name)
|
||||
if convert_to_base64:
|
||||
file_object = base64.b64encode(file_object).decode("utf-8")
|
||||
file_objects.append(file_object)
|
||||
return file_objects
|
||||
|
||||
|
||||
def document_to_string(document: Document) -> str:
|
||||
"""
|
||||
Convert a document to a string.
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ 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]:
|
||||
|
|
@ -32,6 +33,7 @@ class ChatInput(ChatComponent):
|
|||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
input_value=input_value,
|
||||
files=files,
|
||||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
from langflow.base.prompts.utils import dict_values_to_string
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Prompt, TemplateField, Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class PromptComponent(CustomComponent):
|
||||
|
|
@ -15,19 +17,14 @@ 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
|
||||
) -> Record:
|
||||
prompt_template = ChatPromptTemplate.from_template(Text(template))
|
||||
kwargs = await dict_values_to_string(kwargs)
|
||||
messages = list(kwargs.values())
|
||||
prompt = prompt_template + messages
|
||||
self.status = f'Prompt:\n"{template}"'
|
||||
return Record(data={"prompt": prompt.to_json()})
|
||||
|
|
|
|||
|
|
@ -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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"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"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -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"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ class ChatOutput(ChatComponent):
|
|||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: Optional[str] = "{text}",
|
||||
files: Optional[list[str]] = None,
|
||||
) -> Union[Text, Record]:
|
||||
return super().build_with_record(
|
||||
sender=sender,
|
||||
|
|
@ -26,4 +27,5 @@ class ChatOutput(ChatComponent):
|
|||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
record_template=record_template or "",
|
||||
files=files,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,27 +0,0 @@
|
|||
from .AstraDBSearch import AstraDBSearchComponent
|
||||
from .ChromaSearch import ChromaSearchComponent
|
||||
from .FAISSSearch import FAISSSearchComponent
|
||||
from .MongoDBAtlasVectorSearch import MongoDBAtlasSearchComponent
|
||||
from .PineconeSearch import PineconeSearchComponent
|
||||
from .QdrantSearch import QdrantSearchComponent
|
||||
from .RedisSearch import RedisSearchComponent
|
||||
from .SupabaseVectorStoreSearch import SupabaseSearchComponent
|
||||
from .VectaraSearch import VectaraSearchComponent
|
||||
from .WeaviateSearch import WeaviateSearchVectorStore
|
||||
from .pgvectorSearch import PGVectorSearchComponent
|
||||
from .Couchbase import CouchbaseSearchComponent # type: ignore
|
||||
|
||||
__all__ = [
|
||||
"AstraDBSearchComponent",
|
||||
"ChromaSearchComponent",
|
||||
"CouchbaseSearchComponent",
|
||||
"FAISSSearchComponent",
|
||||
"MongoDBAtlasSearchComponent",
|
||||
"PineconeSearchComponent",
|
||||
"QdrantSearchComponent",
|
||||
"RedisSearchComponent",
|
||||
"SupabaseSearchComponent",
|
||||
"VectaraSearchComponent",
|
||||
"WeaviateSearchVectorStore",
|
||||
"PGVectorSearchComponent",
|
||||
]
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
from .AstraDB import AstraDBVectorStoreComponent
|
||||
from .Chroma import ChromaComponent
|
||||
from .FAISS import FAISSComponent
|
||||
from .MongoDBAtlasVector import MongoDBAtlasComponent
|
||||
from .Pinecone import PineconeComponent
|
||||
from .Qdrant import QdrantComponent
|
||||
from .Redis import RedisComponent
|
||||
from .SupabaseVectorStore import SupabaseComponent
|
||||
from .Vectara import VectaraComponent
|
||||
from .Weaviate import WeaviateVectorStoreComponent
|
||||
from .pgvector import PGVectorComponent
|
||||
from .Couchbase import CouchbaseComponent
|
||||
|
||||
__all__ = [
|
||||
"AstraDBVectorStoreComponent",
|
||||
"ChromaComponent",
|
||||
"CouchbaseComponent",
|
||||
"FAISSComponent",
|
||||
"MongoDBAtlasComponent",
|
||||
"PineconeComponent",
|
||||
"QdrantComponent",
|
||||
"RedisComponent",
|
||||
"SupabaseComponent",
|
||||
"VectaraComponent",
|
||||
"WeaviateVectorStoreComponent",
|
||||
"base",
|
||||
"PGVectorComponent",
|
||||
]
|
||||
|
|
@ -297,7 +297,7 @@ class CodeParser:
|
|||
bases = self.execute_and_inspect_classes(self.code)
|
||||
except Exception as e:
|
||||
# If the code cannot be executed, return an empty list
|
||||
logger.exception(e)
|
||||
logger.debug(e)
|
||||
bases = []
|
||||
raise e
|
||||
return bases
|
||||
|
|
|
|||
|
|
@ -78,7 +78,8 @@ class DirectoryReader:
|
|||
component_tuple = (*build_component(component), component)
|
||||
components.append(component_tuple)
|
||||
except Exception as e:
|
||||
logger.error(f"Error while loading component { component['name']}: {e}")
|
||||
logger.debug(f"Error while loading component { component['name']}")
|
||||
logger.debug(e)
|
||||
continue
|
||||
items.append({"name": menu["name"], "path": menu["path"], "components": components})
|
||||
filtered = [menu for menu in items if menu["components"]]
|
||||
|
|
@ -266,8 +267,7 @@ class DirectoryReader:
|
|||
if validation_result:
|
||||
try:
|
||||
output_types = self.get_output_types_from_code(result_content)
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error while getting output types from code: {str(exc)}")
|
||||
except Exception:
|
||||
output_types = [component_name_camelcase]
|
||||
else:
|
||||
output_types = [component_name_camelcase]
|
||||
|
|
|
|||
|
|
@ -710,6 +710,7 @@ class Graph:
|
|||
chat_service: ChatService,
|
||||
vertex_id: str,
|
||||
inputs_dict: Optional[Dict[str, str]] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
user_id: Optional[str] = None,
|
||||
fallback_to_env_vars: bool = False,
|
||||
):
|
||||
|
|
@ -737,7 +738,9 @@ class Graph:
|
|||
# Check the cache for the vertex
|
||||
cached_result = await chat_service.get_cache(key=vertex.id)
|
||||
if isinstance(cached_result, CacheMiss):
|
||||
await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
|
||||
await vertex.build(
|
||||
user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
|
||||
)
|
||||
await chat_service.set_cache(key=vertex.id, data=vertex)
|
||||
else:
|
||||
cached_vertex = cached_result["result"]
|
||||
|
|
@ -751,7 +754,9 @@ class Graph:
|
|||
vertex.result.used_frozen_result = True
|
||||
|
||||
else:
|
||||
await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
|
||||
await vertex.build(
|
||||
user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
|
||||
)
|
||||
|
||||
if vertex.result is not None:
|
||||
params = f"{vertex._built_object_repr()}{params}"
|
||||
|
|
|
|||
|
|
@ -1,15 +1,17 @@
|
|||
from enum import Enum
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Field, field_serializer
|
||||
from pydantic import BaseModel, Field, field_serializer, model_validator
|
||||
|
||||
from langflow.graph.utils import serialize_field
|
||||
from langflow.schema.schema import Log, StreamURL
|
||||
from langflow.utils.schemas import ChatOutputResponse, ContainsEnumMeta
|
||||
|
||||
|
||||
class ResultData(BaseModel):
|
||||
results: Optional[Any] = Field(default_factory=dict)
|
||||
artifacts: Optional[Any] = Field(default_factory=dict)
|
||||
logs: Optional[List[dict]] = Field(default_factory=list)
|
||||
messages: Optional[list[ChatOutputResponse]] = Field(default_factory=list)
|
||||
timedelta: Optional[float] = None
|
||||
duration: Optional[str] = None
|
||||
|
|
@ -23,6 +25,19 @@ class ResultData(BaseModel):
|
|||
return {key: serialize_field(val) for key, val in value.items()}
|
||||
return serialize_field(value)
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_model(cls, values):
|
||||
if not values.get("logs") and values.get("artifacts"):
|
||||
# Build the log from the artifacts
|
||||
message = values["artifacts"]
|
||||
if "stream_url" in message and "type" in message:
|
||||
stream_url = StreamURL(location=message["stream_url"])
|
||||
values["logs"] = [Log(message=stream_url, type=message["type"])]
|
||||
elif "type" in message:
|
||||
values["logs"] = [Log(message=message, type=message["type"])]
|
||||
return values
|
||||
|
||||
|
||||
class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta):
|
||||
# ChatInput and ChatOutput are the only ones that are
|
||||
|
|
|
|||
|
|
@ -1,9 +1,12 @@
|
|||
from typing import Any, Union
|
||||
from enum import Enum
|
||||
from typing import Any, Generator, Union
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langflow.schema.schema import Record
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class UnbuiltObject:
|
||||
|
|
@ -14,6 +17,15 @@ class UnbuiltResult:
|
|||
pass
|
||||
|
||||
|
||||
class ArtifactType(str, Enum):
|
||||
TEXT = "text"
|
||||
RECORD = "record"
|
||||
OBJECT = "object"
|
||||
ARRAY = "array"
|
||||
STREAM = "stream"
|
||||
UNKNOWN = "unknown"
|
||||
|
||||
|
||||
def validate_prompt(prompt: str):
|
||||
"""Validate prompt."""
|
||||
if extract_input_variables_from_prompt(prompt):
|
||||
|
|
@ -50,3 +62,33 @@ def serialize_field(value):
|
|||
elif isinstance(value, str):
|
||||
return {"result": value}
|
||||
return value
|
||||
|
||||
|
||||
def get_artifact_type(custom_component, build_result) -> str:
|
||||
result = ArtifactType.UNKNOWN
|
||||
value = custom_component.repr_value
|
||||
match value:
|
||||
case Record():
|
||||
result = ArtifactType.RECORD
|
||||
|
||||
case str():
|
||||
result = ArtifactType.TEXT
|
||||
|
||||
case dict():
|
||||
result = ArtifactType.OBJECT
|
||||
|
||||
case list():
|
||||
result = ArtifactType.ARRAY
|
||||
|
||||
if result == ArtifactType.UNKNOWN:
|
||||
if isinstance(build_result, Generator):
|
||||
result = ArtifactType.STREAM
|
||||
|
||||
return result.value
|
||||
|
||||
|
||||
def post_process_raw(raw, artifact_type: str):
|
||||
if artifact_type == ArtifactType.STREAM.value:
|
||||
raw = ""
|
||||
|
||||
return raw
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@ from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator,
|
|||
from loguru import logger
|
||||
|
||||
from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
|
||||
from langflow.graph.utils import UnbuiltObject, UnbuiltResult
|
||||
from langflow.graph.vertex.utils import log_transaction
|
||||
from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
|
||||
from langflow.interface.initialize import loading
|
||||
from langflow.interface.listing import lazy_load_dict
|
||||
from langflow.schema.schema import INPUT_FIELD_NAME
|
||||
from langflow.services.deps import get_storage_service
|
||||
from langflow.services.monitor.utils import log_transaction
|
||||
from langflow.utils.constants import DIRECT_TYPES
|
||||
from langflow.utils.schemas import ChatOutputResponse
|
||||
from langflow.utils.util import sync_to_async, unescape_string
|
||||
|
|
@ -63,6 +63,8 @@ class Vertex:
|
|||
self._built_result = None
|
||||
self._built = False
|
||||
self.artifacts: Dict[str, Any] = {}
|
||||
self.artifacts_raw: Any = None
|
||||
self.artifacts_type: Optional[str] = None
|
||||
self.steps: List[Callable] = [self._build]
|
||||
self.steps_ran: List[Callable] = []
|
||||
self.task_id: Optional[str] = None
|
||||
|
|
@ -371,7 +373,7 @@ class Vertex:
|
|||
self.load_from_db_fields = load_from_db_fields
|
||||
self._raw_params = params.copy()
|
||||
|
||||
def update_raw_params(self, new_params: Dict[str, str], overwrite: bool = False):
|
||||
def update_raw_params(self, new_params: Dict[str, str | list[str]], overwrite: bool = False):
|
||||
"""
|
||||
Update the raw parameters of the vertex with the given new parameters.
|
||||
|
||||
|
|
@ -426,7 +428,10 @@ class Vertex:
|
|||
sender=artifacts.get("sender"),
|
||||
sender_name=artifacts.get("sender_name"),
|
||||
session_id=artifacts.get("session_id"),
|
||||
stream_url=artifacts.get("stream_url"),
|
||||
files=[{"path": file} if isinstance(file, str) else file for file in artifacts.get("files", [])],
|
||||
component_id=self.id,
|
||||
type=self.artifacts_type,
|
||||
).model_dump(exclude_none=True)
|
||||
]
|
||||
except KeyError:
|
||||
|
|
@ -444,7 +449,6 @@ class Vertex:
|
|||
messages = self.extract_messages_from_artifacts(artifacts)
|
||||
else:
|
||||
messages = []
|
||||
|
||||
result_dict = ResultData(
|
||||
results=result_dict,
|
||||
artifacts=artifacts,
|
||||
|
|
@ -526,11 +530,11 @@ class Vertex:
|
|||
The built result if use_result is True, else the built object.
|
||||
"""
|
||||
if not self._built:
|
||||
log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
|
||||
log_transaction(vertex=self, target=requester, status="error")
|
||||
raise ValueError(f"Component {self.display_name} has not been built yet")
|
||||
|
||||
result = self._built_result if self.use_result else self._built_object
|
||||
log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success")
|
||||
log_transaction(vertex=self, target=requester, status="success")
|
||||
return result
|
||||
|
||||
async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
|
||||
|
|
@ -624,6 +628,8 @@ class Vertex:
|
|||
self._built_object, self.artifacts = result
|
||||
elif len(result) == 3:
|
||||
self._custom_component, self._built_object, self.artifacts = result
|
||||
self.artifacts_raw = self.artifacts.get("raw", None)
|
||||
self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
|
||||
else:
|
||||
self._built_object = result
|
||||
|
||||
|
|
@ -664,6 +670,7 @@ class Vertex:
|
|||
self,
|
||||
user_id=None,
|
||||
inputs: Optional[Dict[str, Any]] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
requester: Optional["Vertex"] = None,
|
||||
**kwargs,
|
||||
) -> Any:
|
||||
|
|
@ -681,9 +688,14 @@ class Vertex:
|
|||
return await self.get_requester_result(requester)
|
||||
self._reset()
|
||||
|
||||
if self._is_chat_input() and inputs:
|
||||
inputs = {"input_value": inputs.get(INPUT_FIELD_NAME, "")}
|
||||
self.update_raw_params(inputs, overwrite=True)
|
||||
if self._is_chat_input() and (inputs or files):
|
||||
chat_input = {}
|
||||
if inputs:
|
||||
chat_input.update({"input_value": inputs.get(INPUT_FIELD_NAME, "")})
|
||||
if files:
|
||||
chat_input.update({"files": files})
|
||||
|
||||
self.update_raw_params(chat_input, overwrite=True)
|
||||
|
||||
# Run steps
|
||||
for step in self.steps:
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@ import json
|
|||
from typing import AsyncIterator, Dict, Iterator, List
|
||||
|
||||
import yaml
|
||||
from langchain_core.messages import AIMessage
|
||||
from langchain_core.messages import AIMessage, AIMessageChunk
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes
|
||||
from langflow.graph.utils import UnbuiltObject, serialize_field
|
||||
from langflow.graph.utils import ArtifactType, UnbuiltObject, serialize_field
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.schema import INPUT_FIELD_NAME
|
||||
|
|
@ -83,10 +83,11 @@ class InterfaceVertex(Vertex):
|
|||
sender = self.params.get("sender", None)
|
||||
sender_name = self.params.get("sender_name", None)
|
||||
message = self.params.get(INPUT_FIELD_NAME, None)
|
||||
files = [{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])]
|
||||
if isinstance(message, str):
|
||||
message = unescape_string(message)
|
||||
stream_url = None
|
||||
if isinstance(self._built_object, AIMessage):
|
||||
if isinstance(self._built_object, (AIMessage, AIMessageChunk)):
|
||||
artifacts = ChatOutputResponse.from_message(
|
||||
self._built_object,
|
||||
sender=sender,
|
||||
|
|
@ -108,12 +109,14 @@ class InterfaceVertex(Vertex):
|
|||
# it means that it is a stream of messages
|
||||
else:
|
||||
message = self._built_object
|
||||
|
||||
artifact_type = ArtifactType.STREAM if stream_url is not None else ArtifactType.OBJECT
|
||||
artifacts = ChatOutputResponse(
|
||||
message=message,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
stream_url=stream_url,
|
||||
files=files,
|
||||
type=artifact_type,
|
||||
)
|
||||
|
||||
self.will_stream = stream_url is not None
|
||||
|
|
@ -195,6 +198,8 @@ class InterfaceVertex(Vertex):
|
|||
message=complete_message,
|
||||
sender=self.params.get("sender", ""),
|
||||
sender_name=self.params.get("sender_name", ""),
|
||||
files=[{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])],
|
||||
type=ArtifactType.OBJECT.value,
|
||||
).model_dump()
|
||||
self.params[INPUT_FIELD_NAME] = complete_message
|
||||
self._built_object = Record(text=complete_message, data=self.artifacts)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,5 @@
|
|||
from typing import TYPE_CHECKING
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from langflow.services.deps import get_monitor_service
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
|
|
@ -21,34 +17,3 @@ def build_clean_params(target: "Vertex") -> dict:
|
|||
if isinstance(value, list):
|
||||
params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))]
|
||||
return params
|
||||
|
||||
|
||||
def log_transaction(source: "Vertex", target: "Vertex", flow_id, status, error=None):
|
||||
"""
|
||||
Logs a transaction between two vertices.
|
||||
|
||||
Args:
|
||||
source (Vertex): The source vertex of the transaction.
|
||||
target (Vertex): The target vertex of the transaction.
|
||||
status: The status of the transaction.
|
||||
error (Optional): Any error associated with the transaction.
|
||||
|
||||
Raises:
|
||||
Exception: If there is an error while logging the transaction.
|
||||
|
||||
"""
|
||||
try:
|
||||
monitor_service = get_monitor_service()
|
||||
clean_params = build_clean_params(target)
|
||||
data = {
|
||||
"source": source.vertex_type,
|
||||
"target": target.vertex_type,
|
||||
"target_args": clean_params,
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"status": status,
|
||||
"error": error,
|
||||
"flow_id": flow_id,
|
||||
}
|
||||
monitor_service.add_row(table_name="transactions", data=data)
|
||||
except Exception as e:
|
||||
logger.error(f"Error logging transaction: {e}")
|
||||
|
|
|
|||
|
|
@ -90,7 +90,9 @@ async def run_flow(
|
|||
|
||||
fallback_to_env_vars = get_settings_service().settings.fallback_to_env_var
|
||||
|
||||
return await graph.arun(inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars)
|
||||
return await graph.arun(
|
||||
inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars
|
||||
)
|
||||
|
||||
|
||||
def generate_function_for_flow(
|
||||
|
|
@ -257,3 +259,24 @@ def get_flow_by_id_or_endpoint_name(
|
|||
raise HTTPException(status_code=404, detail=f"Flow identifier {flow_id_or_name} not found")
|
||||
|
||||
return flow
|
||||
|
||||
|
||||
def generate_unique_flow_name(flow_name, user_id, session):
|
||||
original_name = flow_name
|
||||
n = 1
|
||||
while True:
|
||||
# Check if a flow with the given name exists
|
||||
existing_flow = session.exec(
|
||||
select(Flow).where(
|
||||
Flow.name == flow_name,
|
||||
Flow.user_id == user_id,
|
||||
)
|
||||
).first()
|
||||
|
||||
# If no flow with the given name exists, return the name
|
||||
if not existing_flow:
|
||||
return flow_name
|
||||
|
||||
# If a flow with the name already exists, append (n) to the name and increment n
|
||||
flow_name = f"{original_name} ({n})"
|
||||
n += 1
|
||||
23
src/backend/base/langflow/helpers/folders.py
Normal file
23
src/backend/base/langflow/helpers/folders.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
from langflow.services.database.models.folder.model import Folder
|
||||
from sqlalchemy import select
|
||||
|
||||
|
||||
def generate_unique_folder_name(folder_name, user_id, session):
|
||||
original_name = folder_name
|
||||
n = 1
|
||||
while True:
|
||||
# Check if a folder with the given name exists
|
||||
existing_folder = session.exec(
|
||||
select(Folder).where(
|
||||
Folder.name == folder_name,
|
||||
Folder.user_id == user_id,
|
||||
)
|
||||
).first()
|
||||
|
||||
# If no folder with the given name exists, return the name
|
||||
if not existing_folder:
|
||||
return folder_name
|
||||
|
||||
# If a folder with the name already exists, append (n) to the name and increment n
|
||||
folder_name = f"{original_name} ({n})"
|
||||
n += 1
|
||||
|
|
@ -20,7 +20,7 @@ from langflow.services.database.models.user.crud import get_user_by_username
|
|||
from langflow.services.deps import get_settings_service, session_scope
|
||||
|
||||
from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist
|
||||
from langflow.services.deps import get_settings_service, session_scope, get_variable_service
|
||||
from langflow.services.deps import get_variable_service
|
||||
|
||||
|
||||
STARTER_FOLDER_NAME = "Starter Projects"
|
||||
|
|
@ -221,6 +221,7 @@ def _is_valid_uuid(val):
|
|||
return False
|
||||
return str(uuid_obj) == val
|
||||
|
||||
|
||||
def load_flows_from_directory():
|
||||
settings_service = get_settings_service()
|
||||
flows_path = settings_service.settings.load_flows_path
|
||||
|
|
@ -262,6 +263,7 @@ def load_flows_from_directory():
|
|||
session.add(flow)
|
||||
session.commit()
|
||||
|
||||
|
||||
def find_existing_flow(session, flow_id, flow_endpoint_name):
|
||||
if flow_endpoint_name:
|
||||
stmt = select(Flow).where(Flow.endpoint_name == flow_endpoint_name)
|
||||
|
|
@ -271,6 +273,8 @@ def find_existing_flow(session, flow_id, flow_endpoint_name):
|
|||
if existing := session.exec(stmt).first():
|
||||
return existing
|
||||
return None
|
||||
|
||||
|
||||
def create_or_update_starter_projects():
|
||||
components_paths = get_settings_service().settings.components_path
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -140,7 +140,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -149,7 +149,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -392,7 +392,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -571,7 +571,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n files: Optional[list[str]] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n files=files,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -260,7 +260,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -444,7 +444,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -453,7 +453,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -262,7 +262,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n files: Optional[list[str]] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n files=files,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -421,7 +421,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -589,7 +589,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -598,7 +598,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n files: Optional[list[str]] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n files=files,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -182,7 +182,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -524,7 +524,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -670,7 +670,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -679,7 +679,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -130,7 +130,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -236,7 +236,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -411,7 +411,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -789,7 +789,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -798,7 +798,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1146,7 +1146,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -1155,7 +1155,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n files: Optional[list[str]] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n files=files,\n session_id=session_id,\n return_record=return_record,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -784,7 +784,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -793,7 +793,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1034,7 +1034,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1170,7 +1170,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
|
||||
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n files: Optional[list[str]] = None,\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n files=files,\n )\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ import orjson
|
|||
from loguru import logger
|
||||
|
||||
from langflow.custom.eval import eval_custom_component_code
|
||||
from langflow.graph.utils import get_artifact_type, post_process_raw
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -124,4 +125,14 @@ async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_
|
|||
custom_repr = build_result
|
||||
if not isinstance(custom_repr, str):
|
||||
custom_repr = str(custom_repr)
|
||||
return custom_component, build_result, {"repr": custom_repr}
|
||||
raw = custom_component.repr_value
|
||||
if hasattr(raw, "data"):
|
||||
raw = raw.data
|
||||
|
||||
elif hasattr(raw, "model_dump"):
|
||||
raw = raw.model_dump()
|
||||
|
||||
artifact_type = get_artifact_type(custom_component, build_result)
|
||||
raw = post_process_raw(raw, artifact_type)
|
||||
artifact = {"repr": custom_repr, "raw": raw, "type": artifact_type}
|
||||
return custom_component, build_result, artifact
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ async def run_graph_internal(
|
|||
outputs or [],
|
||||
stream=stream,
|
||||
session_id=session_id_str or "",
|
||||
fallback_to_env_vars=fallback_to_env_vars
|
||||
fallback_to_env_vars=fallback_to_env_vars,
|
||||
)
|
||||
if session_id_str and session_service:
|
||||
await session_service.update_session(session_id_str, (graph, artifacts))
|
||||
|
|
|
|||
|
|
@ -1,10 +1,12 @@
|
|||
import copy
|
||||
import json
|
||||
from typing import Literal, Optional, cast
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from pydantic import BaseModel, model_validator
|
||||
from langchain_core.prompts.image import ImagePromptTemplate
|
||||
from pydantic import BaseModel, model_serializer, model_validator
|
||||
|
||||
|
||||
class Record(BaseModel):
|
||||
|
|
@ -29,6 +31,11 @@ class Record(BaseModel):
|
|||
values["data"][key] = values[key]
|
||||
return values
|
||||
|
||||
@model_serializer(mode="plain", when_used="json")
|
||||
def serialize_model(self):
|
||||
data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
|
||||
return data
|
||||
|
||||
def get_text(self):
|
||||
"""
|
||||
Retrieves the text value from the data dictionary.
|
||||
|
|
@ -102,7 +109,9 @@ class Record(BaseModel):
|
|||
text = self.data.pop(self.text_key, self.default_value)
|
||||
return Document(page_content=text, metadata=self.data)
|
||||
|
||||
def to_lc_message(self) -> BaseMessage:
|
||||
def to_lc_message(
|
||||
self,
|
||||
) -> BaseMessage:
|
||||
"""
|
||||
Converts the Record to a BaseMessage.
|
||||
|
||||
|
|
@ -118,8 +127,22 @@ class Record(BaseModel):
|
|||
raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
|
||||
sender = self.data.get("sender", "Machine")
|
||||
text = self.data.get("text", "")
|
||||
files = self.data.get("files", [])
|
||||
if sender == "User":
|
||||
return HumanMessage(content=text)
|
||||
if files:
|
||||
contents = [{"type": "text", "text": text}]
|
||||
for file_path in files:
|
||||
image_template = ImagePromptTemplate()
|
||||
image_prompt_value = image_template.invoke(input={"path": file_path})
|
||||
contents.append({"type": "image_url", "image_url": image_prompt_value.image_url})
|
||||
human_message = HumanMessage(content=contents)
|
||||
else:
|
||||
human_message = HumanMessage(
|
||||
content=[{"type": "text", "text": text}],
|
||||
)
|
||||
|
||||
return human_message
|
||||
|
||||
return AIMessage(content=text)
|
||||
|
||||
def __getattr__(self, key):
|
||||
|
|
@ -169,11 +192,26 @@ class Record(BaseModel):
|
|||
|
||||
def __str__(self) -> str:
|
||||
# return a JSON string representation of the Record atributes
|
||||
try:
|
||||
data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
|
||||
return json.dumps(data, indent=4)
|
||||
except Exception:
|
||||
return str(self.data)
|
||||
|
||||
return json.dumps(self.data)
|
||||
def __contains__(self, key):
|
||||
return key in self.data
|
||||
|
||||
|
||||
INPUT_FIELD_NAME = "input_value"
|
||||
|
||||
InputType = Literal["chat", "text", "any"]
|
||||
OutputType = Literal["chat", "text", "any", "debug"]
|
||||
|
||||
|
||||
class StreamURL(TypedDict):
|
||||
location: str
|
||||
|
||||
|
||||
class Log(TypedDict):
|
||||
message: str | dict | StreamURL
|
||||
type: str
|
||||
|
|
|
|||
|
|
@ -215,10 +215,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID
|
|||
username = settings_service.auth_settings.SUPERUSER
|
||||
super_user = get_user_by_username(db, username)
|
||||
if not super_user:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="Super user hasn't been created"
|
||||
)
|
||||
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Super user hasn't been created")
|
||||
access_token_expires_longterm = timedelta(days=365)
|
||||
access_token = create_token(
|
||||
data={"sub": str(super_user.id)},
|
||||
|
|
|
|||
|
|
@ -23,6 +23,9 @@ class CacheMiss:
|
|||
def __repr__(self):
|
||||
return "<CACHE_MISS>"
|
||||
|
||||
def __bool__(self):
|
||||
return False
|
||||
|
||||
|
||||
def create_cache_folder(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
|
|
|
|||
|
|
@ -55,6 +55,7 @@ class ApiKeyRead(ApiKeyBase):
|
|||
id: UUID
|
||||
api_key: str = Field(schema_extra={"validate_default": True})
|
||||
user_id: UUID = Field()
|
||||
created_at: datetime = Field()
|
||||
|
||||
@field_validator("api_key")
|
||||
@classmethod
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ class FlowBase(SQLModel):
|
|||
is_component: Optional[bool] = Field(default=False, nullable=True)
|
||||
updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True)
|
||||
webhook: Optional[bool] = Field(default=False, nullable=True, description="Can be used on the webhook endpoint")
|
||||
folder_id: Optional[UUID] = Field(default=None, nullable=True)
|
||||
endpoint_name: Optional[str] = Field(default=None, nullable=True, index=True)
|
||||
|
||||
@field_validator("endpoint_name")
|
||||
|
|
|
|||
|
|
@ -11,10 +11,10 @@ if TYPE_CHECKING:
|
|||
class TransactionModel(BaseModel):
|
||||
index: Optional[int] = Field(default=None)
|
||||
timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
|
||||
flow_id: str
|
||||
source: str
|
||||
target: str
|
||||
target_args: dict
|
||||
vertex_id: str
|
||||
target_id: str | None = None
|
||||
inputs: dict
|
||||
outputs: dict
|
||||
status: str
|
||||
error: Optional[str] = None
|
||||
|
||||
|
|
@ -23,13 +23,13 @@ class TransactionModel(BaseModel):
|
|||
populate_by_name = True
|
||||
|
||||
# validate target_args in case it is a JSON
|
||||
@field_validator("target_args", mode="before")
|
||||
@field_validator("outputs", "inputs", mode="before")
|
||||
def validate_target_args(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
return v
|
||||
|
||||
@field_serializer("target_args")
|
||||
@field_serializer("outputs", "inputs")
|
||||
def serialize_target_args(v):
|
||||
if isinstance(v, dict):
|
||||
return json.dumps(v)
|
||||
|
|
@ -39,10 +39,9 @@ class TransactionModel(BaseModel):
|
|||
class TransactionModelResponse(BaseModel):
|
||||
index: Optional[int] = Field(default=None)
|
||||
timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
|
||||
flow_id: str
|
||||
source: str
|
||||
target: str
|
||||
target_args: dict
|
||||
vertex_id: str
|
||||
inputs: dict
|
||||
outputs: dict
|
||||
status: str
|
||||
error: Optional[str] = None
|
||||
|
||||
|
|
@ -51,7 +50,7 @@ class TransactionModelResponse(BaseModel):
|
|||
populate_by_name = True
|
||||
|
||||
# validate target_args in case it is a JSON
|
||||
@field_validator("target_args", mode="before")
|
||||
@field_validator("outputs", "inputs", mode="before")
|
||||
def validate_target_args(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
|
|
@ -75,14 +74,14 @@ class MessageModel(BaseModel):
|
|||
sender_name: str
|
||||
session_id: str
|
||||
message: str
|
||||
artifacts: dict
|
||||
files: list[str] = []
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
populate_by_name = True
|
||||
|
||||
@field_validator("artifacts", mode="before")
|
||||
def validate_target_args(cls, v):
|
||||
@field_validator("files", mode="before")
|
||||
def validate_files(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
return v
|
||||
|
|
@ -97,6 +96,7 @@ class MessageModel(BaseModel):
|
|||
sender_name=record.sender_name,
|
||||
message=record.text,
|
||||
session_id=record.session_id,
|
||||
files=record.files or [],
|
||||
artifacts=record.artifacts or {},
|
||||
timestamp=record.timestamp,
|
||||
flow_id=flow_id,
|
||||
|
|
@ -106,12 +106,6 @@ class MessageModel(BaseModel):
|
|||
class MessageModelResponse(MessageModel):
|
||||
index: Optional[int] = Field(default=None)
|
||||
|
||||
@field_validator("artifacts", mode="before")
|
||||
def serialize_artifacts(v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
return v
|
||||
|
||||
@field_validator("index", mode="before")
|
||||
def validate_id(cls, v):
|
||||
if isinstance(v, float):
|
||||
|
|
@ -122,6 +116,13 @@ class MessageModelResponse(MessageModel):
|
|||
return v
|
||||
|
||||
|
||||
class MessageModelRequest(MessageModel):
|
||||
message: str = Field(default="")
|
||||
sender: str = Field(default="")
|
||||
sender_name: str = Field(default="")
|
||||
session_id: str = Field(default="")
|
||||
|
||||
|
||||
class VertexBuildModel(BaseModel):
|
||||
index: Optional[int] = Field(default=None, alias="index", exclude=True)
|
||||
id: Optional[str] = Field(default=None, alias="id")
|
||||
|
|
|
|||
|
|
@ -32,6 +32,10 @@ class MonitorService(Service):
|
|||
except Exception as e:
|
||||
logger.exception(f"Error initializing monitor service: {e}")
|
||||
|
||||
def exec_query(self, query: str):
|
||||
with duckdb.connect(str(self.db_path)) as conn:
|
||||
return conn.execute(query).df()
|
||||
|
||||
def to_df(self, table_name):
|
||||
return self.load_table_as_dataframe(table_name)
|
||||
|
||||
|
|
@ -69,7 +73,7 @@ class MonitorService(Service):
|
|||
valid: Optional[bool] = None,
|
||||
order_by: Optional[str] = "timestamp",
|
||||
):
|
||||
query = "SELECT index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
|
||||
query = "SELECT id, index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
|
||||
conditions = []
|
||||
if flow_id:
|
||||
conditions.append(f"flow_id = '{flow_id}'")
|
||||
|
|
@ -88,6 +92,8 @@ class MonitorService(Service):
|
|||
with duckdb.connect(str(self.db_path)) as conn:
|
||||
df = conn.execute(query).df()
|
||||
|
||||
print(query)
|
||||
|
||||
return df.to_dict(orient="records")
|
||||
|
||||
def delete_vertex_builds(self, flow_id: Optional[str] = None):
|
||||
|
|
@ -98,11 +104,22 @@ class MonitorService(Service):
|
|||
with duckdb.connect(str(self.db_path)) as conn:
|
||||
conn.execute(query)
|
||||
|
||||
def delete_messages(self, session_id: str):
|
||||
def delete_messages_session(self, session_id: str):
|
||||
query = f"DELETE FROM messages WHERE session_id = '{session_id}'"
|
||||
|
||||
with duckdb.connect(str(self.db_path)) as conn:
|
||||
conn.execute(query)
|
||||
return self.exec_query(query)
|
||||
|
||||
def delete_messages(self, message_ids: list[int]):
|
||||
query = f"DELETE FROM messages WHERE index IN ({','.join(map(str, message_ids))})"
|
||||
|
||||
return self.exec_query(query)
|
||||
|
||||
def update_message(self, message_id: int, **kwargs):
|
||||
query = (
|
||||
f"""UPDATE messages SET {', '.join(f"{k} = '{v}'" for k, v in kwargs.items())} WHERE index = {message_id}"""
|
||||
)
|
||||
|
||||
return self.exec_query(query)
|
||||
|
||||
def add_message(self, message: MessageModel):
|
||||
self.add_row("messages", message)
|
||||
|
|
@ -117,7 +134,7 @@ class MonitorService(Service):
|
|||
order: Optional[str] = "DESC",
|
||||
limit: Optional[int] = None,
|
||||
):
|
||||
query = "SELECT index, flow_id, sender_name, sender, session_id, message, artifacts, timestamp FROM messages"
|
||||
query = "SELECT index, flow_id, sender_name, sender, session_id, message, timestamp FROM messages"
|
||||
conditions = []
|
||||
if sender:
|
||||
conditions.append(f"sender = '{sender}'")
|
||||
|
|
|
|||
|
|
@ -119,21 +119,16 @@ async def log_message(
|
|||
sender_name: str,
|
||||
message: str,
|
||||
session_id: str,
|
||||
artifacts: Optional[dict] = None,
|
||||
files: Optional[list] = None,
|
||||
flow_id: Optional[str] = None,
|
||||
):
|
||||
try:
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
if isinstance(session_id, Vertex):
|
||||
session_id = await session_id.build() # type: ignore
|
||||
|
||||
monitor_service = get_monitor_service()
|
||||
row = {
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"message": message,
|
||||
"artifacts": artifacts or {},
|
||||
"files": files or [],
|
||||
"session_id": session_id,
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"flow_id": flow_id,
|
||||
|
|
@ -183,14 +178,15 @@ def build_clean_params(target: "Vertex") -> dict:
|
|||
return params
|
||||
|
||||
|
||||
def log_transaction(vertex: "Vertex", status, error=None):
|
||||
def log_transaction(vertex: "Vertex", status, target: Optional["Vertex"] = None, error=None):
|
||||
try:
|
||||
monitor_service = get_monitor_service()
|
||||
clean_params = build_clean_params(vertex)
|
||||
data = {
|
||||
"vertex_id": vertex.id,
|
||||
"vertex_id": str(vertex.id),
|
||||
"target_id": str(target.id) if target else None,
|
||||
"inputs": clean_params,
|
||||
"output": str(vertex.result),
|
||||
"outputs": vertex.result.model_dump_json(),
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"status": status,
|
||||
"error": error,
|
||||
|
|
|
|||
|
|
@ -70,7 +70,7 @@ class Settings(BaseSettings):
|
|||
"""Database URL for Langflow. If not provided, Langflow will use a SQLite database."""
|
||||
pool_size: int = 10
|
||||
"""The number of connections to keep open in the connection pool. If not provided, the default is 10."""
|
||||
max_overflow: int = 10
|
||||
max_overflow: int = 20
|
||||
"""The number of connections to allow that can be opened beyond the pool size. If not provided, the default is 10."""
|
||||
cache_type: str = "async"
|
||||
remove_api_keys: bool = False
|
||||
|
|
@ -78,7 +78,6 @@ class Settings(BaseSettings):
|
|||
langchain_cache: str = "InMemoryCache"
|
||||
load_flows_path: Optional[str] = None
|
||||
|
||||
|
||||
# Redis
|
||||
redis_host: str = "localhost"
|
||||
redis_port: int = 6379
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
import os
|
||||
from typing import Optional
|
||||
|
||||
import yaml
|
||||
from loguru import logger
|
||||
|
|
@ -8,6 +7,7 @@ from langflow.services.base import Service
|
|||
from langflow.services.settings.auth import AuthSettings
|
||||
from langflow.services.settings.base import Settings
|
||||
|
||||
|
||||
class SettingsService(Service):
|
||||
name = "settings_service"
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,18 @@ import enum
|
|||
from typing import Dict, List, Optional, Union
|
||||
|
||||
from langchain_core.messages import BaseMessage
|
||||
from pydantic import BaseModel, model_validator
|
||||
from pydantic import BaseModel, field_validator, model_validator
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
|
||||
|
||||
|
||||
class File(TypedDict):
|
||||
"""File schema."""
|
||||
|
||||
path: str
|
||||
name: str
|
||||
type: str
|
||||
|
||||
|
||||
class ChatOutputResponse(BaseModel):
|
||||
|
|
@ -14,6 +25,47 @@ class ChatOutputResponse(BaseModel):
|
|||
session_id: Optional[str] = None
|
||||
stream_url: Optional[str] = None
|
||||
component_id: Optional[str] = None
|
||||
files: List[File] = []
|
||||
type: str
|
||||
|
||||
@field_validator("files", mode="before")
|
||||
def validate_files(cls, files):
|
||||
"""Validate files."""
|
||||
if not files:
|
||||
return files
|
||||
|
||||
for file in files:
|
||||
if not isinstance(file, dict):
|
||||
raise ValueError("Files must be a list of dictionaries.")
|
||||
|
||||
if not all(key in file for key in ["path", "name", "type"]):
|
||||
# If any of the keys are missing, we should extract the
|
||||
# values from the file path
|
||||
path = file.get("path")
|
||||
if not path:
|
||||
raise ValueError("File path is required.")
|
||||
|
||||
name = file.get("name")
|
||||
if not name:
|
||||
name = path.split("/")[-1]
|
||||
file["name"] = name
|
||||
_type = file.get("type")
|
||||
if not _type:
|
||||
# get the file type from the path
|
||||
extension = path.split(".")[-1]
|
||||
file_types = set(TEXT_FILE_TYPES + IMG_FILE_TYPES)
|
||||
if extension and extension in file_types:
|
||||
_type = extension
|
||||
else:
|
||||
for file_type in file_types:
|
||||
if file_type in path:
|
||||
_type = file_type
|
||||
break
|
||||
if not _type:
|
||||
raise ValueError("File type is required.")
|
||||
file["type"] = _type
|
||||
|
||||
return files
|
||||
|
||||
@classmethod
|
||||
def from_message(
|
||||
|
|
|
|||
12
src/backend/base/poetry.lock
generated
12
src/backend/base/poetry.lock
generated
|
|
@ -1296,13 +1296,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.72"
|
||||
version = "0.1.75"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langsmith-0.1.72-py3-none-any.whl", hash = "sha256:a4456707669521bd75b7431b9205a6b99579fb9ff01bd338f52d29df11a7662d"},
|
||||
{file = "langsmith-0.1.72.tar.gz", hash = "sha256:262ae9e8aceaba50f3a0f5b6eb559d6110886f0afc6b0ed5270e7d3d3f1fd8d6"},
|
||||
{file = "langsmith-0.1.75-py3-none-any.whl", hash = "sha256:d08b08dd6b3fa4da170377f95123d77122ef4c52999d10fff4ae08ff70d07aed"},
|
||||
{file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1600,13 +1600,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "marshmallow"
|
||||
version = "3.21.2"
|
||||
version = "3.21.3"
|
||||
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"},
|
||||
{file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
|
||||
{file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
|
||||
{file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow-base"
|
||||
version = "0.0.57"
|
||||
version = "0.0.59"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
|
|
|
|||
13972
src/frontend/package-lock.json
generated
Normal file
13972
src/frontend/package-lock.json
generated
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -22,6 +22,7 @@
|
|||
"@radix-ui/react-slot": "^1.0.2",
|
||||
"@radix-ui/react-switch": "^1.0.3",
|
||||
"@radix-ui/react-tabs": "^1.0.4",
|
||||
"@radix-ui/react-toggle": "^1.0.3",
|
||||
"@radix-ui/react-tooltip": "^1.0.6",
|
||||
"@tabler/icons-react": "^2.32.0",
|
||||
"@tailwindcss/forms": "^0.5.6",
|
||||
|
|
@ -36,9 +37,9 @@
|
|||
"class-variance-authority": "^0.6.1",
|
||||
"clsx": "^1.2.1",
|
||||
"cmdk": "^1.0.0",
|
||||
"debounce-promise": "^3.1.2",
|
||||
"dompurify": "^3.0.5",
|
||||
"dotenv": "^16.4.5",
|
||||
"emoji-regex": "^10.3.0",
|
||||
"esbuild": "^0.17.19",
|
||||
"file-saver": "^2.0.5",
|
||||
"framer-motion": "^11.0.6",
|
||||
|
|
@ -47,6 +48,7 @@
|
|||
"million": "^3.0.6",
|
||||
"moment": "^2.29.4",
|
||||
"openseadragon": "^4.1.1",
|
||||
"p-debounce": "^4.0.0",
|
||||
"playwright": "^1.42.0",
|
||||
"react": "^18.2.21",
|
||||
"react-ace": "^10.1.0",
|
||||
|
|
|
|||
|
|
@ -45,6 +45,9 @@ export default defineConfig({
|
|||
name: "chromium",
|
||||
use: {
|
||||
...devices["Desktop Chrome"],
|
||||
launchOptions: {
|
||||
// headless: false,
|
||||
},
|
||||
contextOptions: {
|
||||
// chromium-specific permissions
|
||||
permissions: ["clipboard-read", "clipboard-write"],
|
||||
|
|
@ -57,6 +60,7 @@ export default defineConfig({
|
|||
// use: {
|
||||
// ...devices["Desktop Firefox"],
|
||||
// launchOptions: {
|
||||
// headless: false,
|
||||
// firefoxUserPrefs: {
|
||||
// "dom.events.asyncClipboard.readText": true,
|
||||
// "dom.events.testing.asyncClipboard": true,
|
||||
|
|
|
|||
|
|
@ -164,3 +164,13 @@ body {
|
|||
.ag-body-vertical-scroll-viewport::-webkit-scrollbar-thumb:hover {
|
||||
background-color: #bbb;
|
||||
}
|
||||
|
||||
/* This CSS is to not apply the border for the column having 'no-border' class */
|
||||
.no-border.ag-cell:focus {
|
||||
border: none !important;
|
||||
outline: none;
|
||||
}
|
||||
.no-border.ag-cell {
|
||||
border: none !important;
|
||||
outline: none;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import axios from "axios";
|
||||
import { useContext, useEffect, useState } from "react";
|
||||
import { ErrorBoundary } from "react-error-boundary";
|
||||
import { useNavigate } from "react-router-dom";
|
||||
|
|
|
|||
|
|
@ -0,0 +1,12 @@
|
|||
import { Textarea } from "../../../../../../../components/ui/textarea";
|
||||
|
||||
export default function ErrorOutput({ value }: { value: string }) {
|
||||
return (
|
||||
<Textarea
|
||||
className={`h-full w-full text-destructive custom-scroll`}
|
||||
placeholder={"Empty"}
|
||||
value={value}
|
||||
readOnly
|
||||
/>
|
||||
);
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
export const convertToTableRows = (obj: Object) => {
|
||||
const tokensArray = [Object.values(obj)[0]];
|
||||
return tokensArray;
|
||||
};
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
import ForwardedIconComponent from "../../../../../../components/genericIconComponent";
|
||||
import RecordsOutputComponent from "../../../../../../components/recordsOutputComponent";
|
||||
import {
|
||||
Alert,
|
||||
AlertDescription,
|
||||
AlertTitle,
|
||||
} from "../../../../../../components/ui/alert";
|
||||
import { Case } from "../../../../../../shared/components/caseComponent";
|
||||
import TextOutputView from "../../../../../../shared/components/textOutputView";
|
||||
import useFlowStore from "../../../../../../stores/flowStore";
|
||||
import ErrorOutput from "./components";
|
||||
|
||||
export default function SwitchOutputView(nodeId): JSX.Element {
|
||||
const nodeIdentity = nodeId.nodeId;
|
||||
|
||||
const nodes = useFlowStore((state) => state.nodes);
|
||||
const flowPool = useFlowStore((state) => state.flowPool);
|
||||
const node = nodes.find((node) => node?.id === nodeIdentity);
|
||||
|
||||
const flowPoolNode = (flowPool[nodeIdentity] ?? [])[
|
||||
(flowPool[nodeIdentity]?.length ?? 1) - 1
|
||||
];
|
||||
|
||||
const results = flowPoolNode?.data?.logs[0] ?? "";
|
||||
const resultType = results?.type;
|
||||
let resultMessage = results?.message;
|
||||
if (resultMessage.raw) {
|
||||
resultMessage = resultMessage.raw;
|
||||
}
|
||||
console.log("resultType", results);
|
||||
return (
|
||||
<>
|
||||
<Case condition={!resultType || resultType === "unknown"}>
|
||||
<div>NO OUTPUT</div>
|
||||
</Case>
|
||||
<Case condition={resultType === "ValueError"}>
|
||||
<ErrorOutput value={resultMessage}></ErrorOutput>
|
||||
</Case>
|
||||
|
||||
<Case condition={node && resultType === "text"}>
|
||||
<TextOutputView left={false} value={resultMessage} />
|
||||
</Case>
|
||||
|
||||
<Case condition={resultType === "record"}>
|
||||
<RecordsOutputComponent
|
||||
rows={[resultMessage] ?? []}
|
||||
pagination={true}
|
||||
columnMode="union"
|
||||
/>
|
||||
</Case>
|
||||
|
||||
<Case condition={resultType === "object"}>
|
||||
<RecordsOutputComponent
|
||||
rows={[resultMessage]}
|
||||
pagination={true}
|
||||
columnMode="union"
|
||||
/>
|
||||
</Case>
|
||||
{Array.isArray(resultMessage) && (
|
||||
<Case condition={resultType === "array"}>
|
||||
<RecordsOutputComponent
|
||||
rows={
|
||||
(resultMessage as Array<any>).every((item) => item.data)
|
||||
? (resultMessage as Array<any>).map((item) => item.data)
|
||||
: resultMessage
|
||||
}
|
||||
pagination={true}
|
||||
columnMode="union"
|
||||
/>
|
||||
</Case>
|
||||
)}
|
||||
<Case condition={resultType === "stream"}>
|
||||
<div className="flex h-full w-full items-center justify-center align-middle">
|
||||
<Alert variant={"default"} className="w-fit">
|
||||
<ForwardedIconComponent
|
||||
name="AlertCircle"
|
||||
className="h-5 w-5 text-primary"
|
||||
/>
|
||||
<AlertTitle>{"Streaming is not supported"}</AlertTitle>
|
||||
<AlertDescription>
|
||||
{
|
||||
"Use the playground to interact with components that stream data"
|
||||
}
|
||||
</AlertDescription>
|
||||
</Alert>
|
||||
</div>
|
||||
</Case>
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
import { Button } from "../../../../components/ui/button";
|
||||
import BaseModal from "../../../../modals/baseModal";
|
||||
import SwitchOutputView from "./components/switchOutputView";
|
||||
|
||||
export default function OutputModal({ open, setOpen, nodeId }): JSX.Element {
|
||||
return (
|
||||
<BaseModal open={open} setOpen={setOpen} size="medium">
|
||||
<BaseModal.Header description="Inspect the output of the component below.">
|
||||
<div className="flex items-center">
|
||||
<span className="pr-2">Component Output</span>
|
||||
</div>
|
||||
</BaseModal.Header>
|
||||
<BaseModal.Content>
|
||||
<SwitchOutputView nodeId={nodeId} />
|
||||
</BaseModal.Content>
|
||||
<BaseModal.Footer>
|
||||
<div className="flex w-full justify-end pt-2">
|
||||
<Button className="flex gap-2 px-3" onClick={() => setOpen(false)}>
|
||||
Close
|
||||
</Button>
|
||||
</div>
|
||||
</BaseModal.Footer>
|
||||
</BaseModal>
|
||||
);
|
||||
}
|
||||
|
|
@ -22,7 +22,6 @@ import {
|
|||
TOOLTIP_EMPTY,
|
||||
} from "../../../../constants/constants";
|
||||
import { Case } from "../../../../shared/components/caseComponent";
|
||||
import useAlertStore from "../../../../stores/alertStore";
|
||||
import useFlowStore from "../../../../stores/flowStore";
|
||||
import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
|
||||
import { useTypesStore } from "../../../../stores/typesStore";
|
||||
|
|
@ -45,6 +44,7 @@ import useFetchDataOnMount from "../../../hooks/use-fetch-data-on-mount";
|
|||
import useHandleOnNewValue from "../../../hooks/use-handle-new-value";
|
||||
import useHandleNodeClass from "../../../hooks/use-handle-node-class";
|
||||
import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons";
|
||||
import OutputModal from "../outputModal";
|
||||
import TooltipRenderComponent from "../tooltipRenderComponent";
|
||||
import { TEXT_FIELD_TYPES } from "./constants";
|
||||
|
||||
|
|
@ -67,7 +67,6 @@ export default function ParameterComponent({
|
|||
const ref = useRef<HTMLDivElement>(null);
|
||||
const refHtml = useRef<HTMLDivElement & ReactNode>(null);
|
||||
const infoHtml = useRef<HTMLDivElement & ReactNode>(null);
|
||||
const setErrorData = useAlertStore((state) => state.setErrorData);
|
||||
const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
|
||||
const nodes = useFlowStore((state) => state.nodes);
|
||||
const edges = useFlowStore((state) => state.edges);
|
||||
|
|
@ -80,6 +79,16 @@ export default function ParameterComponent({
|
|||
const flow = currentFlow?.data?.nodes ?? null;
|
||||
const groupedEdge = useRef(null);
|
||||
const setFilterEdge = useFlowStore((state) => state.setFilterEdge);
|
||||
const [openOutputModal, setOpenOutputModal] = useState(false);
|
||||
const flowPool = useFlowStore((state) => state.flowPool);
|
||||
|
||||
const displayOutputPreview = !!flowPool[data.id];
|
||||
|
||||
const unknownOutput = !!(
|
||||
flowPool[data.id] &&
|
||||
flowPool[data.id][flowPool[data.id].length - 1]?.data?.logs[0]?.type ===
|
||||
"unknown"
|
||||
);
|
||||
|
||||
const { handleOnNewValue: handleOnNewValueHook } = useHandleOnNewValue(
|
||||
data,
|
||||
|
|
@ -251,9 +260,38 @@ export default function ParameterComponent({
|
|||
</span>
|
||||
</ShadTooltip>
|
||||
) : (
|
||||
<span className={!left && data.node?.frozen ? " text-ice" : ""}>
|
||||
{title}
|
||||
</span>
|
||||
<div className="flex gap-2">
|
||||
<span className={!left && data.node?.frozen ? " text-ice" : ""}>
|
||||
{title}
|
||||
</span>
|
||||
{!left && (
|
||||
<ShadTooltip
|
||||
content={
|
||||
displayOutputPreview
|
||||
? unknownOutput
|
||||
? "Output can't be displayed"
|
||||
: "Inspect Output"
|
||||
: "Please build the component first"
|
||||
}
|
||||
>
|
||||
<button
|
||||
disabled={!displayOutputPreview || unknownOutput}
|
||||
onClick={() => setOpenOutputModal(true)}
|
||||
data-testid={`output-inspection-${title.toLowerCase()}`}
|
||||
>
|
||||
<IconComponent
|
||||
className={classNames(
|
||||
"h-5 w-5 rounded-md",
|
||||
displayOutputPreview && !unknownOutput
|
||||
? " hover:bg-secondary-foreground/5 hover:text-medium-indigo"
|
||||
: " cursor-not-allowed text-muted-foreground",
|
||||
)}
|
||||
name={"ScanEye"}
|
||||
/>
|
||||
</button>
|
||||
</ShadTooltip>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
<span className={(required ? "ml-2 " : "") + "text-status-red"}>
|
||||
{required ? "*" : ""}
|
||||
|
|
@ -392,7 +430,7 @@ export default function ParameterComponent({
|
|||
});
|
||||
}}
|
||||
name={name}
|
||||
data={data}
|
||||
data={data.node?.template[name]!}
|
||||
/>
|
||||
</div>
|
||||
{data.node?.template[name]?.refresh_button && (
|
||||
|
|
@ -448,8 +486,8 @@ export default function ParameterComponent({
|
|||
data.node?.template[name]?.real_time_refresh)
|
||||
}
|
||||
>
|
||||
<div className="mt-2 flex w-full items-center">
|
||||
<div className="w-5/6 flex-grow">
|
||||
<div className="mt-2 flex w-full items-center gap-2">
|
||||
<div className="flex-1">
|
||||
<Dropdown
|
||||
disabled={disabled}
|
||||
isLoading={isLoading}
|
||||
|
|
@ -467,7 +505,6 @@ export default function ParameterComponent({
|
|||
name={name}
|
||||
data={data}
|
||||
button_text={data.node?.template[name]?.refresh_button_text}
|
||||
className="extra-side-bar-buttons ml-2 mt-1"
|
||||
handleUpdateValues={handleRefreshButtonPress}
|
||||
id={"refresh-button-" + name}
|
||||
/>
|
||||
|
|
@ -580,6 +617,13 @@ export default function ParameterComponent({
|
|||
/>
|
||||
</div>
|
||||
</Case>
|
||||
{openOutputModal && (
|
||||
<OutputModal
|
||||
open={openOutputModal}
|
||||
nodeId={data.id}
|
||||
setOpen={setOpenOutputModal}
|
||||
/>
|
||||
)}
|
||||
</>
|
||||
</div>
|
||||
);
|
||||
|
|
@ -1,32 +1,36 @@
|
|||
import { cloneDeep } from "lodash";
|
||||
import { useCallback, useEffect, useMemo, useState } from "react";
|
||||
import emojiRegex from "emoji-regex";
|
||||
import { useEffect, useMemo, useState } from "react";
|
||||
import { NodeToolbar, useUpdateNodeInternals } from "reactflow";
|
||||
import IconComponent from "../../components/genericIconComponent";
|
||||
import InputComponent from "../../components/inputComponent";
|
||||
import ShadTooltip from "../../components/shadTooltipComponent";
|
||||
import { Button } from "../../components/ui/button";
|
||||
import Checkmark from "../../components/ui/checkmark";
|
||||
import Loading from "../../components/ui/loading";
|
||||
import { Textarea } from "../../components/ui/textarea";
|
||||
import Xmark from "../../components/ui/xmark";
|
||||
import {
|
||||
RUN_TIMESTAMP_PREFIX,
|
||||
STATUS_BUILD,
|
||||
STATUS_BUILDING,
|
||||
} from "../../constants/constants";
|
||||
import { BuildStatus } from "../../constants/enums";
|
||||
import { countHandlesFn } from "../helpers/count-handles";
|
||||
import { getSpecificClassFromBuildStatus } from "../helpers/get-class-from-build-status";
|
||||
import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent";
|
||||
import useAlertStore from "../../stores/alertStore";
|
||||
import { useDarkStore } from "../../stores/darkStore";
|
||||
import useFlowStore from "../../stores/flowStore";
|
||||
import useFlowsManagerStore from "../../stores/flowsManagerStore";
|
||||
import { useTypesStore } from "../../stores/typesStore";
|
||||
import { APIClassType } from "../../types/api";
|
||||
import { validationStatusType } from "../../types/components";
|
||||
import { VertexBuildTypeAPI } from "../../types/api";
|
||||
import { NodeDataType } from "../../types/flow";
|
||||
import { handleKeyDown, scapedJSONStringfy } from "../../utils/reactflowUtils";
|
||||
import { nodeColors, nodeIconsLucide } from "../../utils/styleUtils";
|
||||
import { classNames, cn } from "../../utils/utils";
|
||||
import useCheckCodeValidity from "../hooks/use-check-code-validity";
|
||||
import useIconNodeRender from "../hooks/use-icon-render";
|
||||
import useIconStatus from "../hooks/use-icons-status";
|
||||
import useUpdateNodeCode from "../hooks/use-update-node-code";
|
||||
import useUpdateValidationStatus from "../hooks/use-update-validation-status";
|
||||
import useValidationStatusString from "../hooks/use-validation-status-string";
|
||||
import getFieldTitle from "../utils/get-field-title";
|
||||
import sortFields from "../utils/sort-fields";
|
||||
import isWrappedWithClass from "../../pages/FlowPage/components/PageComponent/utils/is-wrapped-with-class";
|
||||
|
|
@ -34,14 +38,13 @@ import ParameterComponent from "./components/parameterComponent";
|
|||
|
||||
export default function GenericNode({
|
||||
data,
|
||||
xPos,
|
||||
yPos,
|
||||
|
||||
selected,
|
||||
}: {
|
||||
data: NodeDataType;
|
||||
selected: boolean;
|
||||
xPos: number;
|
||||
yPos: number;
|
||||
xPos?: number;
|
||||
yPos?: number;
|
||||
}): JSX.Element {
|
||||
const types = useTypesStore((state) => state.types);
|
||||
const templates = useTypesStore((state) => state.templates);
|
||||
|
|
@ -51,7 +54,15 @@ export default function GenericNode({
|
|||
const setNode = useFlowStore((state) => state.setNode);
|
||||
const updateNodeInternals = useUpdateNodeInternals();
|
||||
const setErrorData = useAlertStore((state) => state.setErrorData);
|
||||
const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
|
||||
const isDark = useDarkStore((state) => state.dark);
|
||||
const buildStatus = useFlowStore(
|
||||
(state) => state.flowBuildStatus[data.id]?.status,
|
||||
);
|
||||
const lastRunTime = useFlowStore(
|
||||
(state) => state.flowBuildStatus[data.id]?.timestamp,
|
||||
);
|
||||
const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
|
||||
|
||||
const [inputName, setInputName] = useState(false);
|
||||
const [nodeName, setNodeName] = useState(data.node!.display_name);
|
||||
const [inputDescription, setInputDescription] = useState(false);
|
||||
|
|
@ -59,185 +70,25 @@ export default function GenericNode({
|
|||
data.node?.description!,
|
||||
);
|
||||
const [isOutdated, setIsOutdated] = useState(false);
|
||||
const buildStatus = useFlowStore(
|
||||
(state) => state.flowBuildStatus[data.id]?.status,
|
||||
);
|
||||
const lastRunTime = useFlowStore(
|
||||
(state) => state.flowBuildStatus[data.id]?.timestamp,
|
||||
);
|
||||
const [validationStatus, setValidationStatus] =
|
||||
useState<validationStatusType | null>(null);
|
||||
useState<VertexBuildTypeAPI | null>(null);
|
||||
const [handles, setHandles] = useState<number>(0);
|
||||
|
||||
const [validationString, setValidationString] = useState<string>("");
|
||||
|
||||
const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
|
||||
|
||||
useEffect(() => {
|
||||
// This one should run only once
|
||||
// first check if data.type in NATIVE_CATEGORIES
|
||||
// if not return
|
||||
if (!data.node?.template?.code?.value) return;
|
||||
const thisNodeTemplate = templates[data.type]?.template;
|
||||
// if the template does not have a code key
|
||||
// return
|
||||
if (!thisNodeTemplate?.code) return;
|
||||
const currentCode = thisNodeTemplate.code?.value;
|
||||
const thisNodesCode = data.node!.template?.code?.value;
|
||||
const componentsToIgnore = ["Custom Component"];
|
||||
if (
|
||||
currentCode !== thisNodesCode &&
|
||||
!componentsToIgnore.includes(data.node!.display_name)
|
||||
) {
|
||||
setIsOutdated(true);
|
||||
} else {
|
||||
setIsOutdated(false);
|
||||
}
|
||||
// template.code can be undefined
|
||||
}, [data.node?.template?.code?.value]);
|
||||
|
||||
const updateNodeCode = useCallback(
|
||||
(newNodeClass: APIClassType, code: string, name: string) => {
|
||||
setNode(data.id, (oldNode) => {
|
||||
let newNode = cloneDeep(oldNode);
|
||||
|
||||
newNode.data = {
|
||||
...newNode.data,
|
||||
node: newNodeClass,
|
||||
description: newNodeClass.description ?? data.node!.description,
|
||||
display_name: newNodeClass.display_name ?? data.node!.display_name,
|
||||
};
|
||||
|
||||
newNode.data.node.template[name].value = code;
|
||||
setIsOutdated(false);
|
||||
|
||||
return newNode;
|
||||
});
|
||||
|
||||
updateNodeInternals(data.id);
|
||||
},
|
||||
[data.id, data.node, setNode, setIsOutdated],
|
||||
);
|
||||
|
||||
if (!data.node!.template) {
|
||||
setErrorData({
|
||||
title: `Error in component ${data.node!.display_name}`,
|
||||
list: [
|
||||
`The component ${data.node!.display_name} has no template.`,
|
||||
`Please contact the developer of the component to fix this issue.`,
|
||||
],
|
||||
});
|
||||
takeSnapshot();
|
||||
deleteNode(data.id);
|
||||
}
|
||||
|
||||
function countHandles(): void {
|
||||
let count = Object.keys(data.node!.template)
|
||||
.filter((templateField) => templateField.charAt(0) !== "_")
|
||||
.map((templateCamp) => {
|
||||
const { template } = data.node!;
|
||||
if (template[templateCamp].input_types) return true;
|
||||
if (!template[templateCamp].show) return false;
|
||||
switch (template[templateCamp].type) {
|
||||
case "str":
|
||||
case "bool":
|
||||
case "float":
|
||||
case "code":
|
||||
case "prompt":
|
||||
case "file":
|
||||
case "int":
|
||||
return false;
|
||||
default:
|
||||
return true;
|
||||
}
|
||||
})
|
||||
.reduce((total, value) => total + (value ? 1 : 0), 0);
|
||||
|
||||
setHandles(count);
|
||||
}
|
||||
useEffect(() => {
|
||||
countHandles();
|
||||
}, [data, data.node]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!selected) {
|
||||
setInputName(false);
|
||||
setInputDescription(false);
|
||||
}
|
||||
}, [selected]);
|
||||
|
||||
const iconStatus = useIconStatus(buildStatus, validationStatus);
|
||||
const [showNode, setShowNode] = useState(data.showNode ?? true);
|
||||
// State for outline color
|
||||
const isBuilding = useFlowStore((state) => state.isBuilding);
|
||||
|
||||
// should be empty string if no duration
|
||||
// else should be `Duration: ${duration}`
|
||||
const getDurationString = (duration: number | undefined): string => {
|
||||
if (duration === undefined) {
|
||||
return "";
|
||||
} else {
|
||||
return `${duration}`;
|
||||
}
|
||||
};
|
||||
const durationString = getDurationString(validationStatus?.data.duration);
|
||||
const updateNodeCode = useUpdateNodeCode(
|
||||
data?.id,
|
||||
data.node!,
|
||||
setNode,
|
||||
setIsOutdated,
|
||||
updateNodeInternals,
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
setNodeDescription(data.node!.description);
|
||||
}, [data.node!.description]);
|
||||
|
||||
useEffect(() => {
|
||||
setNodeName(data.node!.display_name);
|
||||
}, [data.node!.display_name]);
|
||||
|
||||
useEffect(() => {
|
||||
const relevantData =
|
||||
flowPool[data.id] && flowPool[data.id]?.length > 0
|
||||
? flowPool[data.id][flowPool[data.id].length - 1]
|
||||
: null;
|
||||
if (relevantData) {
|
||||
// Extract validation information from relevantData and update the validationStatus state
|
||||
setValidationStatus(relevantData);
|
||||
} else {
|
||||
setValidationStatus(null);
|
||||
}
|
||||
}, [flowPool[data.id], data.id]);
|
||||
|
||||
useEffect(() => {
|
||||
if (validationStatus?.params) {
|
||||
// if it is not a string turn it into a string
|
||||
let newValidationString = validationStatus.params;
|
||||
if (typeof newValidationString !== "string") {
|
||||
newValidationString = JSON.stringify(validationStatus.params);
|
||||
}
|
||||
|
||||
setValidationString(newValidationString);
|
||||
}
|
||||
}, [validationStatus, validationStatus?.params]);
|
||||
|
||||
const [showNode, setShowNode] = useState(data.showNode ?? true);
|
||||
|
||||
useEffect(() => {
|
||||
setShowNode(data.showNode ?? true);
|
||||
}, [data.showNode]);
|
||||
|
||||
const nameEditable = true;
|
||||
|
||||
const emojiRegex = /\p{Emoji}/u;
|
||||
const isEmoji = emojiRegex.test(data?.node?.icon!);
|
||||
|
||||
const iconNodeRender = useCallback(() => {
|
||||
const iconElement = data?.node?.icon;
|
||||
const iconColor = nodeColors[types[data.type]];
|
||||
const iconName =
|
||||
iconElement || (data.node?.flow ? "group_components" : name);
|
||||
const iconClassName = `generic-node-icon ${
|
||||
!showNode ? " absolute inset-x-6 h-12 w-12 " : ""
|
||||
}`;
|
||||
if (iconElement && isEmoji) {
|
||||
return nodeIconFragment(iconElement);
|
||||
} else {
|
||||
return checkNodeIconFragment(iconColor, iconName, iconClassName);
|
||||
}
|
||||
}, [data, isEmoji, name, showNode]);
|
||||
const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
|
||||
|
||||
const nodeIconFragment = (icon) => {
|
||||
return <span className="text-lg">{icon}</span>;
|
||||
|
|
@ -253,79 +104,24 @@ export default function GenericNode({
|
|||
);
|
||||
};
|
||||
|
||||
const isDark = useDarkStore((state) => state.dark);
|
||||
const renderIconStatus = (
|
||||
buildStatus: BuildStatus | undefined,
|
||||
validationStatus: validationStatusType | null,
|
||||
) => {
|
||||
if (buildStatus === BuildStatus.BUILDING) {
|
||||
return <Loading className="text-medium-indigo" />;
|
||||
} else {
|
||||
return (
|
||||
<>
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-medium-indigo opacity-0 transition-all group-hover:opacity-100"
|
||||
/>
|
||||
{validationStatus && validationStatus.valid ? (
|
||||
<Checkmark
|
||||
className="absolute ml-0.5 h-5 stroke-2 text-status-green opacity-100 transition-all group-hover:opacity-0"
|
||||
isVisible={true}
|
||||
/>
|
||||
) : validationStatus &&
|
||||
!validationStatus.valid &&
|
||||
buildStatus === BuildStatus.INACTIVE ? (
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-green opacity-30 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
) : buildStatus === BuildStatus.ERROR ||
|
||||
(validationStatus && !validationStatus.valid) ? (
|
||||
<Xmark
|
||||
isVisible={true}
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-red opacity-100 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
) : (
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-muted-foreground opacity-100 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
};
|
||||
const getSpecificClassFromBuildStatus = (
|
||||
buildStatus: BuildStatus | undefined,
|
||||
validationStatus: validationStatusType | null,
|
||||
) => {
|
||||
let isInvalid = validationStatus && !validationStatus.valid;
|
||||
|
||||
if (buildStatus === BuildStatus.INACTIVE) {
|
||||
// INACTIVE should have its own class
|
||||
return "inactive-status";
|
||||
}
|
||||
if (
|
||||
(buildStatus === BuildStatus.BUILT && isInvalid) ||
|
||||
buildStatus === BuildStatus.ERROR
|
||||
) {
|
||||
return isDark ? "built-invalid-status-dark" : "built-invalid-status";
|
||||
} else if (buildStatus === BuildStatus.BUILDING) {
|
||||
return "building-status";
|
||||
} else {
|
||||
return "";
|
||||
}
|
||||
const renderIconStatus = () => {
|
||||
return (
|
||||
<div className="generic-node-status-position flex items-center justify-center">
|
||||
{iconStatus}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
const getNodeBorderClassName = (
|
||||
selected: boolean,
|
||||
showNode: boolean,
|
||||
buildStatus: BuildStatus | undefined,
|
||||
validationStatus: validationStatusType | null,
|
||||
validationStatus: VertexBuildTypeAPI | null,
|
||||
) => {
|
||||
const specificClassFromBuildStatus = getSpecificClassFromBuildStatus(
|
||||
buildStatus,
|
||||
validationStatus,
|
||||
isDark,
|
||||
);
|
||||
|
||||
const baseBorderClass = getBaseBorderClass(selected);
|
||||
|
|
@ -333,7 +129,7 @@ export default function GenericNode({
|
|||
const names = classNames(
|
||||
baseBorderClass,
|
||||
nodeSizeClass,
|
||||
"generic-node-div",
|
||||
"generic-node-div group/node",
|
||||
specificClassFromBuildStatus,
|
||||
);
|
||||
return names;
|
||||
|
|
@ -350,6 +146,64 @@ export default function GenericNode({
|
|||
const getNodeSizeClass = (showNode) =>
|
||||
showNode ? "w-96 rounded-lg" : "w-26 h-26 rounded-full";
|
||||
|
||||
const nameEditable = true;
|
||||
const isEmoji = emojiRegex().test(data?.node?.icon!);
|
||||
|
||||
if (!data.node!.template) {
|
||||
setErrorData({
|
||||
title: `Error in component ${data.node!.display_name}`,
|
||||
list: [
|
||||
`The component ${data.node!.display_name} has no template.`,
|
||||
`Please contact the developer of the component to fix this issue.`,
|
||||
],
|
||||
});
|
||||
takeSnapshot();
|
||||
deleteNode(data.id);
|
||||
}
|
||||
|
||||
useCheckCodeValidity(data, templates, setIsOutdated, types);
|
||||
useValidationStatusString(validationStatus, setValidationString);
|
||||
useUpdateValidationStatus(data?.id, flowPool, setValidationStatus);
|
||||
|
||||
const iconNodeRender = useIconNodeRender(
|
||||
data,
|
||||
types,
|
||||
nodeColors,
|
||||
name,
|
||||
showNode,
|
||||
isEmoji,
|
||||
nodeIconFragment,
|
||||
checkNodeIconFragment,
|
||||
);
|
||||
|
||||
function countHandles(): void {
|
||||
const count = countHandlesFn(data);
|
||||
setHandles(count);
|
||||
}
|
||||
|
||||
useEffect(() => {
|
||||
countHandles();
|
||||
}, [data, data.node]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!selected) {
|
||||
setInputName(false);
|
||||
setInputDescription(false);
|
||||
}
|
||||
}, [selected]);
|
||||
|
||||
useEffect(() => {
|
||||
setNodeDescription(data.node!.description);
|
||||
}, [data.node!.description]);
|
||||
|
||||
useEffect(() => {
|
||||
setNodeName(data.node!.display_name);
|
||||
}, [data.node!.display_name]);
|
||||
|
||||
useEffect(() => {
|
||||
setShowNode(data.showNode ?? true);
|
||||
}, [data.showNode]);
|
||||
|
||||
const memoizedNodeToolbarComponent = useMemo(() => {
|
||||
return (
|
||||
<NodeToolbar>
|
||||
|
|
@ -593,67 +447,56 @@ export default function GenericNode({
|
|||
)}
|
||||
</div>
|
||||
{showNode && (
|
||||
<ShadTooltip
|
||||
content={
|
||||
buildStatus === BuildStatus.BUILDING ? (
|
||||
<span> {STATUS_BUILDING} </span>
|
||||
) : !validationStatus ? (
|
||||
<span className="flex">{STATUS_BUILD}</span>
|
||||
) : (
|
||||
<div className="max-h-100 p-2">
|
||||
<div>
|
||||
{lastRunTime && (
|
||||
<div className="justify-left flex font-normal text-muted-foreground">
|
||||
<div>{RUN_TIMESTAMP_PREFIX}</div>
|
||||
<div className="ml-1 text-status-blue">
|
||||
{lastRunTime}
|
||||
<>
|
||||
<ShadTooltip
|
||||
content={
|
||||
buildStatus === BuildStatus.BUILDING ? (
|
||||
<span> {STATUS_BUILDING} </span>
|
||||
) : !validationStatus ? (
|
||||
<span className="flex">{STATUS_BUILD}</span>
|
||||
) : (
|
||||
<div className="max-h-100 p-2">
|
||||
<div>
|
||||
{lastRunTime && (
|
||||
<div className="justify-left flex font-normal text-muted-foreground">
|
||||
<div>{RUN_TIMESTAMP_PREFIX}</div>
|
||||
<div className="ml-1 text-status-blue">
|
||||
{lastRunTime}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<div className="justify-left flex font-normal text-muted-foreground">
|
||||
<div>Duration:</div>
|
||||
<div className="ml-1 text-status-blue">
|
||||
{validationStatus?.data.duration}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<div className="justify-left flex font-normal text-muted-foreground">
|
||||
<div>Duration:</div>
|
||||
<div className="mb-3 ml-1 text-status-blue">
|
||||
{validationStatus?.data.duration}
|
||||
</div>
|
||||
</div>
|
||||
<hr />
|
||||
<span className="mb-2 mt-2 flex justify-center font-semibold text-muted-foreground">
|
||||
Output
|
||||
</span>
|
||||
<div className="max-h-96 overflow-auto font-normal custom-scroll">
|
||||
{validationString.split("\n").map((line, index) => (
|
||||
<div className="font-normal" key={index}>
|
||||
{line}
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
side="bottom"
|
||||
>
|
||||
<Button
|
||||
onClick={() => {
|
||||
if (buildStatus === BuildStatus.BUILDING || isBuilding)
|
||||
return;
|
||||
setValidationStatus(null);
|
||||
buildFlow({ stopNodeId: data.id });
|
||||
}}
|
||||
variant="secondary"
|
||||
className={"group h-9 px-1.5"}
|
||||
)
|
||||
}
|
||||
side="bottom"
|
||||
>
|
||||
<div
|
||||
data-testid={
|
||||
`button_run_` + data?.node?.display_name.toLowerCase()
|
||||
}
|
||||
<Button
|
||||
onClick={() => {
|
||||
if (buildStatus === BuildStatus.BUILDING || isBuilding)
|
||||
return;
|
||||
setValidationStatus(null);
|
||||
buildFlow({ stopNodeId: data.id });
|
||||
}}
|
||||
variant="secondary"
|
||||
className={"group h-9 px-1.5"}
|
||||
>
|
||||
<div className="generic-node-status-position flex items-center justify-center">
|
||||
{renderIconStatus(buildStatus, validationStatus)}
|
||||
<div
|
||||
data-testid={
|
||||
`button_run_` + data?.node?.display_name.toLowerCase()
|
||||
}
|
||||
>
|
||||
{renderIconStatus()}
|
||||
</div>
|
||||
</div>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
26
src/frontend/src/CustomNodes/helpers/count-handles.ts
Normal file
26
src/frontend/src/CustomNodes/helpers/count-handles.ts
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
import { NodeDataType } from "../../types/flow";
|
||||
|
||||
export function countHandlesFn(data: NodeDataType): number {
|
||||
let count = Object.keys(data.node!.template)
|
||||
.filter((templateField) => templateField.charAt(0) !== "_")
|
||||
.map((templateCamp) => {
|
||||
const { template } = data.node!;
|
||||
if (template[templateCamp].input_types) return true;
|
||||
if (!template[templateCamp].show) return false;
|
||||
switch (template[templateCamp].type) {
|
||||
case "str":
|
||||
case "bool":
|
||||
case "float":
|
||||
case "code":
|
||||
case "prompt":
|
||||
case "file":
|
||||
case "int":
|
||||
return false;
|
||||
default:
|
||||
return true;
|
||||
}
|
||||
})
|
||||
.reduce((total, value) => total + (value ? 1 : 0), 0);
|
||||
|
||||
return count;
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
import { BuildStatus } from "../../constants/enums";
|
||||
import { VertexBuildTypeAPI } from "../../types/api";
|
||||
|
||||
export const getSpecificClassFromBuildStatus = (
|
||||
buildStatus: BuildStatus | undefined,
|
||||
validationStatus: VertexBuildTypeAPI | null,
|
||||
isDark: boolean,
|
||||
) => {
|
||||
let isInvalid = validationStatus && !validationStatus.valid;
|
||||
|
||||
if (buildStatus === BuildStatus.INACTIVE) {
|
||||
// INACTIVE should have its own class
|
||||
return "inactive-status";
|
||||
}
|
||||
if (
|
||||
(buildStatus === BuildStatus.BUILT && isInvalid) ||
|
||||
buildStatus === BuildStatus.ERROR
|
||||
) {
|
||||
return isDark ? "built-invalid-status-dark" : "built-invalid-status";
|
||||
} else if (buildStatus === BuildStatus.BUILDING) {
|
||||
return "building-status";
|
||||
} else {
|
||||
return "";
|
||||
}
|
||||
};
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
import { useEffect } from "react";
|
||||
import { NATIVE_CATEGORIES } from "../../constants/constants";
|
||||
import { NodeDataType } from "../../types/flow";
|
||||
|
||||
const useCheckCodeValidity = (
|
||||
data: NodeDataType,
|
||||
templates: { [key: string]: any },
|
||||
setIsOutdated: (value: boolean) => void,
|
||||
types,
|
||||
) => {
|
||||
useEffect(() => {
|
||||
// This one should run only once
|
||||
// first check if data.type in NATIVE_CATEGORIES
|
||||
// if not return
|
||||
if (
|
||||
!NATIVE_CATEGORIES.includes(types[data.type]) ||
|
||||
!data.node?.template?.code?.value
|
||||
)
|
||||
return;
|
||||
const thisNodeTemplate = templates[data.type].template;
|
||||
// if the template does not have a code key
|
||||
// return
|
||||
if (!thisNodeTemplate.code) return;
|
||||
const currentCode = thisNodeTemplate.code?.value;
|
||||
const thisNodesCode = data.node!.template?.code?.value;
|
||||
const componentsToIgnore = ["Custom Component", "Prompt"];
|
||||
if (
|
||||
currentCode !== thisNodesCode &&
|
||||
!componentsToIgnore.includes(data.node!.display_name)
|
||||
) {
|
||||
setIsOutdated(true);
|
||||
} else {
|
||||
setIsOutdated(false);
|
||||
}
|
||||
// template.code can be undefined
|
||||
}, [data.node?.template?.code?.value, templates, setIsOutdated]);
|
||||
};
|
||||
|
||||
export default useCheckCodeValidity;
|
||||
45
src/frontend/src/CustomNodes/hooks/use-icon-render.tsx
Normal file
45
src/frontend/src/CustomNodes/hooks/use-icon-render.tsx
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
import { useCallback } from "react";
|
||||
import { NodeDataType } from "../../types/flow";
|
||||
|
||||
const useIconNodeRender = (
|
||||
data: NodeDataType,
|
||||
types: { [key: string]: string },
|
||||
nodeColors: { [key: string]: string },
|
||||
name: string,
|
||||
showNode: boolean,
|
||||
isEmoji: boolean,
|
||||
nodeIconFragment: (iconElement: string) => JSX.Element,
|
||||
checkNodeIconFragment: (
|
||||
iconColor: string,
|
||||
iconName: string,
|
||||
iconClassName: string,
|
||||
) => JSX.Element,
|
||||
) => {
|
||||
const iconNodeRender = useCallback(() => {
|
||||
const iconElement = data?.node?.icon;
|
||||
const iconColor = nodeColors[types[data.type]];
|
||||
const iconName =
|
||||
iconElement || (data.node?.flow ? "group_components" : name);
|
||||
const iconClassName = `generic-node-icon ${
|
||||
!showNode ? " absolute inset-x-6 h-12 w-12 " : ""
|
||||
}`;
|
||||
if (iconElement && isEmoji) {
|
||||
return nodeIconFragment(iconElement);
|
||||
} else {
|
||||
return checkNodeIconFragment(iconColor, iconName, iconClassName);
|
||||
}
|
||||
}, [
|
||||
data,
|
||||
types,
|
||||
nodeColors,
|
||||
name,
|
||||
showNode,
|
||||
isEmoji,
|
||||
nodeIconFragment,
|
||||
checkNodeIconFragment,
|
||||
]);
|
||||
|
||||
return iconNodeRender;
|
||||
};
|
||||
|
||||
export default useIconNodeRender;
|
||||
54
src/frontend/src/CustomNodes/hooks/use-icons-status.tsx
Normal file
54
src/frontend/src/CustomNodes/hooks/use-icons-status.tsx
Normal file
|
|
@ -0,0 +1,54 @@
|
|||
import IconComponent from "../../components/genericIconComponent";
|
||||
import Checkmark from "../../components/ui/checkmark";
|
||||
import Loading from "../../components/ui/loading";
|
||||
import Xmark from "../../components/ui/xmark";
|
||||
import { BuildStatus } from "../../constants/enums";
|
||||
import { VertexBuildTypeAPI } from "../../types/api";
|
||||
|
||||
const useIconStatus = (
|
||||
buildStatus: BuildStatus | undefined,
|
||||
validationStatus: VertexBuildTypeAPI | null,
|
||||
) => {
|
||||
const renderIconStatus = () => {
|
||||
if (buildStatus === BuildStatus.BUILDING) {
|
||||
return <Loading className="text-medium-indigo" />;
|
||||
} else {
|
||||
return (
|
||||
<>
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-medium-indigo opacity-0 transition-all group-hover:opacity-100"
|
||||
/>
|
||||
{validationStatus && validationStatus.valid ? (
|
||||
<Checkmark
|
||||
className="absolute ml-0.5 h-5 stroke-2 text-status-green opacity-100 transition-all group-hover:opacity-0"
|
||||
isVisible={true}
|
||||
/>
|
||||
) : validationStatus &&
|
||||
!validationStatus.valid &&
|
||||
buildStatus === BuildStatus.INACTIVE ? (
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-green opacity-30 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
) : buildStatus === BuildStatus.ERROR ||
|
||||
(validationStatus && !validationStatus.valid) ? (
|
||||
<Xmark
|
||||
isVisible={true}
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-red opacity-100 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
) : (
|
||||
<IconComponent
|
||||
name="Play"
|
||||
className="absolute ml-0.5 h-5 fill-current stroke-2 text-muted-foreground opacity-100 transition-all group-hover:opacity-0"
|
||||
/>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
return renderIconStatus();
|
||||
};
|
||||
|
||||
export default useIconStatus;
|
||||
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