Merge remote-tracking branch 'origin/cz/mergeAll' into fix/ui_adjustments
This commit is contained in:
Lucas Oliveira
commit
5b3a6551ae
434 changed files with 16538 additions and 10995 deletions
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*.ts text
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*.tsx text
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||||
*.md text
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||||
*.mdx text
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*.mdx text working-tree-encoding = UTF-8
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*.yaml text
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*.xml text
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*.csv text
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*.sh text
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*.Dockerfile text
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Dockerfile text
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*.svg binary
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.github/workflows/docker-build.yml
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.github/workflows/docker-build.yml
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@ -80,7 +80,10 @@ jobs:
|
|||
langflowai/langflow-frontend:1.0-alpha
|
||||
|
||||
restart-space:
|
||||
name: Restart HuggingFace Spaces
|
||||
if: ${{ inputs.release_type == 'main' }}
|
||||
runs-on: ubuntu-latest
|
||||
needs: docker_build
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
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|
@ -100,6 +103,4 @@ jobs:
|
|||
|
||||
- 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 ${{ secrets.HUGGINGFACE_API_TOKEN }}
|
||||
|
|
|
|||
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.github/workflows/pre-release.yml
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|||
with:
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||||
python-version: "3.10"
|
||||
cache: "poetry"
|
||||
- name: Set up Nodejs 20
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: "20"
|
||||
- name: Check Version
|
||||
id: check-version
|
||||
run: |
|
||||
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|
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|||
8
.vscode/launch.json
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.vscode/launch.json
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@ -3,7 +3,7 @@
|
|||
"configurations": [
|
||||
{
|
||||
"name": "Debug Backend",
|
||||
"type": "python",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"module": "uvicorn",
|
||||
"args": [
|
||||
|
|
@ -26,7 +26,7 @@
|
|||
},
|
||||
{
|
||||
"name": "Debug CLI",
|
||||
"type": "python",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"module": "langflow",
|
||||
"args": [
|
||||
|
|
@ -43,7 +43,7 @@
|
|||
},
|
||||
{
|
||||
"name": "Python: Remote Attach",
|
||||
"type": "python",
|
||||
"type": "debugpy",
|
||||
"request": "attach",
|
||||
"justMyCode": true,
|
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"connect": {
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||||
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@ -65,7 +65,7 @@
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|||
},
|
||||
{
|
||||
"name": "Python: Debug Tests",
|
||||
"type": "python",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${file}",
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||||
"purpose": ["debug-test"],
|
||||
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|
|
|||
1
Makefile
1
Makefile
|
|
@ -168,6 +168,7 @@ build_and_install:
|
|||
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -86,11 +86,12 @@ from langflow.load import run_flow_from_json
|
|||
|
||||
results = run_flow_from_json("path/to/flow.json", input_value="Hello, World!")
|
||||
```
|
||||
|
||||
# 部署
|
||||
|
||||
## 在Google Cloud Platform上部署Langflow
|
||||
|
||||
请按照我们的分步指南使用 Google Cloud Shell 在 Google Cloud Platform (GCP) 上部署 Langflow。该指南在 [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) 文档中提供。
|
||||
请按照我们的分步指南使用 Google Cloud Shell 在 Google Cloud Platform (GCP) 上部署 Langflow。该指南在 [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) 文档中提供。
|
||||
|
||||
或者，点击下面的 "Open in Cloud Shell" 按钮，启动 Google Cloud Shell，克隆 Langflow 仓库，并开始一个互动教程，该教程将指导您设置必要的资源并在 GCP 项目中部署 Langflow。
|
||||
|
||||
|
|
@ -168,4 +169,4 @@ langflow run [OPTIONS]
|
|||
|
||||
# 📄 许可证
|
||||
|
||||
Langflow 以 MIT 许可证发布。有关详细信息，请参阅 [LICENSE](LICENSE) 文件。
|
||||
Langflow 以 MIT 许可证发布。有关详细信息，请参阅 [LICENSE](LICENSE) 文件。
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
# syntax=docker/dockerfile:1
|
||||
# Keep this syntax directive! It's used to enable Docker BuildKit
|
||||
|
||||
|
||||
################################
|
||||
# BUILDER-BASE
|
||||
# Used to build deps + create our virtual environment
|
||||
|
|
@ -47,12 +48,10 @@ 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
|
||||
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. |
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ Global Variables are a useful feature of Langflow, allowing you to define reusab
|
|||
- All Credential Global Variables are encrypted and accessible only by you.
|
||||
- Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables.
|
||||
|
||||
|
||||
## Creating and Adding a Global Variable
|
||||
|
||||
To create and add a global variable, click the 🌐 button in a Text field, and then click **+ Add New Variable**.
|
||||
|
|
@ -25,18 +24,20 @@ To create and add a global variable, click the 🌐 button in a Text field, and
|
|||
Text fields are where you write text without opening a Text area, and are identified with the 🌐 icon.
|
||||
|
||||
For example, to create an environment variable for the **OpenAI** component:
|
||||
1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**.
|
||||
2. Enter `openai_api_key` in the **Variable Name** field.
|
||||
3. Paste your OpenAI API Key (`sk-...`) in the **Value** field.
|
||||
4. Select **Credential** for the **Type**.
|
||||
5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**.
|
||||
6. Click **Save Variable**.
|
||||
|
||||
1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**.
|
||||
2. Enter `openai_api_key` in the **Variable Name** field.
|
||||
3. Paste your OpenAI API Key (`sk-...`) in the **Value** field.
|
||||
4. Select **Credential** for the **Type**.
|
||||
5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**.
|
||||
6. Click **Save Variable**.
|
||||
|
||||
You now have a `openai_api_key` global environment variable for your Langflow project.
|
||||
Subsequently, clicking the 🌐 button in a Text field will display the new variable in the dropdown.
|
||||
|
||||
<Admonition type="tip">
|
||||
You can also create global variables in **Settings** > **Variables and Secrets**.
|
||||
You can also create global variables in **Settings** > **Variables and
|
||||
Secrets**.
|
||||
</Admonition>
|
||||
|
||||
<ZoomableImage
|
||||
|
|
@ -65,7 +66,8 @@ Setting `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `true` in your `.env` file (de
|
|||
These variables are accessible like any other Global Variable.
|
||||
|
||||
<Admonition type="tip">
|
||||
To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `false` in your `.env` file.
|
||||
To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to
|
||||
`false` in your `.env` file.
|
||||
</Admonition>
|
||||
|
||||
You can specify variables to get from the environment by listing them in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`.
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Agents
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may
|
||||
contain some rough edges. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of agency. LLM chains can only perform hardcoded sequences of actions, while agents use LLMs to reason through which actions to take, and in which order.
|
||||
|
|
@ -87,4 +89,4 @@ The `ZeroShotAgent` uses the ReAct framework to decide which tool to use based o
|
|||
**Parameters**:
|
||||
|
||||
- **Allowed Tools:** The tools accessible to the agent.
|
||||
- **LLM Chain:** The LLM Chain used by the agent.
|
||||
- **LLM Chain:** The LLM Chain used by the agent.
|
||||
|
|
|
|||
|
|
@ -6,11 +6,11 @@ import Admonition from "@theme/Admonition";
|
|||
# Chains
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
Thank you for your patience while we enhance our documentation. It may
|
||||
have some imperfections. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
Thank you for your patience while we enhance our documentation. It may have
|
||||
some imperfections. Share your feedback or report issues to help us improve!
|
||||
🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Chains, in the context of language models, refer to a series of calls made to a language model. This approach allows for using the output of one call as the input for another. Different chain types facilitate varying complexity levels, making them useful for creating pipelines and executing specific scenarios.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Data
|
||||
|
||||
|
|
|
|||
|
|
@ -4,113 +4,113 @@
|
|||
|
||||
Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|-----------------------------|-------------------|------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
||||
| `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | |
|
||||
| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | |
|
||||
| `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | |
|
||||
| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| -------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- |
|
||||
| `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | |
|
||||
| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | |
|
||||
| `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | |
|
||||
| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | |
|
||||
|
||||
## Cohere Embeddings
|
||||
|
||||
Used to load embedding models from [Cohere](https://cohere.com/).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `cohere_api_key` | `str` | API key required to authenticate with the Cohere service. | |
|
||||
| `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` |
|
||||
| `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ---------------- | -------- | ------------------------------------------------------------------------- | -------------------- |
|
||||
| `cohere_api_key` | `str` | API key required to authenticate with the Cohere service. | |
|
||||
| `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` |
|
||||
| `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` |
|
||||
|
||||
## Azure OpenAI Embeddings
|
||||
|
||||
Generate embeddings using Azure OpenAI models.
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | |
|
||||
| `Deployment Name` | `str` | The name of the deployment. | |
|
||||
| `API Version` | `str` | The API version to use, options include various dates. | |
|
||||
| `API Key` | `str` | The API key to access the Azure OpenAI service. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ----------------- | -------- | -------------------------------------------------------------------------------------------------- | ----------- |
|
||||
| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | |
|
||||
| `Deployment Name` | `str` | The name of the deployment. | |
|
||||
| `API Version` | `str` | The API version to use, options include various dates. | |
|
||||
| `API Key` | `str` | The API key to access the Azure OpenAI service. | |
|
||||
|
||||
## Hugging Face API Embeddings
|
||||
|
||||
Generate embeddings using Hugging Face Inference API models.
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `API Key` | `str` | API key for accessing the Hugging Face Inference API. | |
|
||||
| `API URL` | `str` | URL of the Hugging Face Inference API. | `http://localhost:8080` |
|
||||
| `Model Name` | `str` | Name of the model to use for embeddings. | `BAAI/bge-large-en-v1.5` |
|
||||
| `Cache Folder` | `str` | Folder path to cache Hugging Face models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------- | -------- | ----------------------------------------------------- | ------------------------ |
|
||||
| `API Key` | `str` | API key for accessing the Hugging Face Inference API. | |
|
||||
| `API URL` | `str` | URL of the Hugging Face Inference API. | `http://localhost:8080` |
|
||||
| `Model Name` | `str` | Name of the model to use for embeddings. | `BAAI/bge-large-en-v1.5` |
|
||||
| `Cache Folder` | `str` | Folder path to cache Hugging Face models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
|
||||
## Hugging Face Embeddings
|
||||
|
||||
Used to load embedding models from [HuggingFace](https://huggingface.co).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
|
||||
| `Cache Folder` | `str` | Folder path to cache HuggingFace models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------- | -------- | ---------------------------------------------- | ----------------------------------------- |
|
||||
| `Cache Folder` | `str` | Folder path to cache HuggingFace models. | |
|
||||
| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
|
||||
| `Model Kwargs` | `dict` | Additional arguments for the model. | |
|
||||
| `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` |
|
||||
| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
|
||||
|
||||
## OpenAI Embeddings
|
||||
|
||||
Used to load embedding models from [OpenAI](https://openai.com/).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
||||
| `OpenAI API Key` | `str` | The API key to use for accessing the OpenAI API. | |
|
||||
| `Default Headers` | `Dict[str, str]` | Default headers for the HTTP requests. | |
|
||||
| `Default Query` | `NestedDict` | Default query parameters for the HTTP requests. | |
|
||||
| `Allowed Special` | `List[str]` | Special tokens allowed for processing. | `[]` |
|
||||
| `Disallowed Special` | `List[str]` | Special tokens disallowed for processing. | `["all"]` |
|
||||
| `Chunk Size` | `int` | Chunk size for processing. | `1000` |
|
||||
| `Client` | `Any` | HTTP client for making requests. | |
|
||||
| `Deployment` | `str` | Deployment name for the model. | `text-embedding-3-small` |
|
||||
| `Embedding Context Length` | `int` | Length of embedding context. | `8191` |
|
||||
| `Max Retries` | `int` | Maximum number of retries for failed requests. | `6` |
|
||||
| `Model` | `str` | Name of the model to use. | `text-embedding-3-small` |
|
||||
| `Model Kwargs` | `NestedDict` | Additional keyword arguments for the model. | |
|
||||
| `OpenAI API Base` | `str` | Base URL of the OpenAI API. | |
|
||||
| `OpenAI API Type` | `str` | Type of the OpenAI API. | |
|
||||
| `OpenAI API Version` | `str` | Version of the OpenAI API. | |
|
||||
| `OpenAI Organization` | `str` | Organization associated with the API key. | |
|
||||
| `OpenAI Proxy` | `str` | Proxy server for the requests. | |
|
||||
| `Request Timeout` | `float` | Timeout for the HTTP requests. | |
|
||||
| `Show Progress Bar` | `bool` | Whether to show a progress bar for processing. | `False` |
|
||||
| `Skip Empty` | `bool` | Whether to skip empty inputs. | `False` |
|
||||
| `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` |
|
||||
| `TikToken Model Name` | `str` | Name of the TikToken model. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| -------------------------- | ---------------- | ------------------------------------------------ | ------------------------ |
|
||||
| `OpenAI API Key` | `str` | The API key to use for accessing the OpenAI API. | |
|
||||
| `Default Headers` | `Dict[str, str]` | Default headers for the HTTP requests. | |
|
||||
| `Default Query` | `NestedDict` | Default query parameters for the HTTP requests. | |
|
||||
| `Allowed Special` | `List[str]` | Special tokens allowed for processing. | `[]` |
|
||||
| `Disallowed Special` | `List[str]` | Special tokens disallowed for processing. | `["all"]` |
|
||||
| `Chunk Size` | `int` | Chunk size for processing. | `1000` |
|
||||
| `Client` | `Any` | HTTP client for making requests. | |
|
||||
| `Deployment` | `str` | Deployment name for the model. | `text-embedding-3-small` |
|
||||
| `Embedding Context Length` | `int` | Length of embedding context. | `8191` |
|
||||
| `Max Retries` | `int` | Maximum number of retries for failed requests. | `6` |
|
||||
| `Model` | `str` | Name of the model to use. | `text-embedding-3-small` |
|
||||
| `Model Kwargs` | `NestedDict` | Additional keyword arguments for the model. | |
|
||||
| `OpenAI API Base` | `str` | Base URL of the OpenAI API. | |
|
||||
| `OpenAI API Type` | `str` | Type of the OpenAI API. | |
|
||||
| `OpenAI API Version` | `str` | Version of the OpenAI API. | |
|
||||
| `OpenAI Organization` | `str` | Organization associated with the API key. | |
|
||||
| `OpenAI Proxy` | `str` | Proxy server for the requests. | |
|
||||
| `Request Timeout` | `float` | Timeout for the HTTP requests. | |
|
||||
| `Show Progress Bar` | `bool` | Whether to show a progress bar for processing. | `False` |
|
||||
| `Skip Empty` | `bool` | Whether to skip empty inputs. | `False` |
|
||||
| `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` |
|
||||
| `TikToken Model Name` | `str` | Name of the TikToken model. | |
|
||||
|
||||
## Ollama Embeddings
|
||||
|
||||
Generate embeddings using Ollama models.
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|---------------------|-------------------|--------------------------------------------------------------------------------------------------------------------|---------------------------|
|
||||
| `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` |
|
||||
| `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` |
|
||||
| `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| ------------------- | -------- | ---------------------------------------------------------------------------------------- | ------------------------ |
|
||||
| `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` |
|
||||
| `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` |
|
||||
| `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | |
|
||||
|
||||
## VertexAI Embeddings
|
||||
|
||||
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
|
||||
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
||||
| `credentials` | `Credentials` | The default custom credentials to use. | |
|
||||
| `location` | `str` | The default location to use when making API calls. | `us-central1`|
|
||||
| `max_output_tokens` | `int` | Token limit determines the maximum amount of text output from one prompt. | `128` |
|
||||
| `model_name` | `str` | The name of the Vertex AI large language model. | `text-bison`|
|
||||
| `project` | `str` | The default GCP project to use when making Vertex API calls. | |
|
||||
| `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` |
|
||||
| `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` |
|
||||
| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` |
|
||||
| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` |
|
||||
| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | |
|
||||
| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` |
|
||||
| **Parameter** | **Type** | **Description** | **Default** |
|
||||
| --------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------- |
|
||||
| `credentials` | `Credentials` | The default custom credentials to use. | |
|
||||
| `location` | `str` | The default location to use when making API calls. | `us-central1` |
|
||||
| `max_output_tokens` | `int` | Token limit determines the maximum amount of text output from one prompt. | `128` |
|
||||
| `model_name` | `str` | The name of the Vertex AI large language model. | `text-bison` |
|
||||
| `project` | `str` | The default GCP project to use when making Vertex API calls. | |
|
||||
| `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` |
|
||||
| `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` |
|
||||
| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` |
|
||||
| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` |
|
||||
| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | |
|
||||
| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` |
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Experimental
|
||||
|
||||
|
|
@ -29,10 +29,12 @@ This component extracts specified keys from a record.
|
|||
**Parameters**
|
||||
|
||||
- **Record:**
|
||||
|
||||
- **Display Name:** Record
|
||||
- **Info:** The record from which to extract keys.
|
||||
|
||||
- **Keys:**
|
||||
|
||||
- **Display Name:** Keys
|
||||
- **Info:** The keys to be extracted.
|
||||
|
||||
|
|
@ -54,6 +56,7 @@ This component turns a function running a flow into a Tool.
|
|||
**Parameters**
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
@ -61,10 +64,12 @@ This component turns a function running a flow into a Tool.
|
|||
- **Refresh Button:** True
|
||||
|
||||
- **Name:**
|
||||
|
||||
- **Display Name:** Name
|
||||
- **Description:** The tool's name.
|
||||
|
||||
- **Description:**
|
||||
|
||||
- **Display Name:** Description
|
||||
- **Description:** Describes the tool.
|
||||
|
||||
|
|
@ -127,10 +132,12 @@ This component generates a notification.
|
|||
**Parameters**
|
||||
|
||||
- **Name:**
|
||||
|
||||
- **Display Name:** Name
|
||||
- **Info:** The notification's name.
|
||||
|
||||
- **Record:**
|
||||
|
||||
- **Display Name:** Record
|
||||
- **Info:** Optionally, a record to store in the notification.
|
||||
|
||||
|
|
@ -151,10 +158,12 @@ This component runs a specified flow.
|
|||
**Parameters**
|
||||
|
||||
- **Input Value:**
|
||||
|
||||
- **Display Name:** Input Value
|
||||
- **Multiline:** True
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
@ -177,14 +186,17 @@ This component executes a specified runnable.
|
|||
**Parameters**
|
||||
|
||||
- **Input Key:**
|
||||
|
||||
- **Display Name:** Input Key
|
||||
- **Info:** The input key.
|
||||
|
||||
- **Inputs:**
|
||||
|
||||
- **Display Name:** Inputs
|
||||
- **Info:** Inputs for the runnable.
|
||||
|
||||
- **Runnable:**
|
||||
|
||||
- **Display Name:** Runnable
|
||||
- **Info:** The runnable to execute.
|
||||
|
||||
|
|
@ -205,14 +217,17 @@ This component executes an SQL query.
|
|||
**Parameters**
|
||||
|
||||
- **Database URL:**
|
||||
|
||||
- **Display Name:** Database URL
|
||||
- **Info:** The database's URL.
|
||||
|
||||
- **Include Columns:**
|
||||
|
||||
- **Display Name:** Include Columns
|
||||
- **Info:** Whether to include columns in the result.
|
||||
|
||||
- **Passthrough:**
|
||||
|
||||
- **Display Name:** Passthrough
|
||||
- **Info:** Returns the query instead of raising an exception if an error occurs.
|
||||
|
||||
|
|
@ -233,10 +248,12 @@ This component dynamically generates a tool from a flow.
|
|||
**Parameters**
|
||||
|
||||
- **Input Value:**
|
||||
|
||||
- **Display Name:** Input Value
|
||||
- **Multiline:** True
|
||||
|
||||
- **Flow Name:**
|
||||
|
||||
- **Display Name:** Flow Name
|
||||
- **Info:** Select the flow to run.
|
||||
- **Options:** List of available flows.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Helpers
|
||||
|
||||
|
|
@ -49,9 +49,10 @@ Use this component as a template to create your custom component.
|
|||
- **Parameter:** Describe the purpose of this parameter.
|
||||
|
||||
<Admonition type="info" title="Info">
|
||||
<p>
|
||||
Customize the <code>build_config</code> and <code>build</code> methods according to your requirements.
|
||||
</p>
|
||||
<p>
|
||||
Customize the <code>build_config</code> and <code>build</code> methods
|
||||
according to your requirements.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Learn more about creating custom components at [Custom Component](http://docs.langflow.org/components/custom).
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
# Inputs and Outputs
|
||||
|
|
@ -29,9 +29,8 @@ This component collects user input from the chat.
|
|||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data
|
||||
of the `Record` will be updated with the `Sender`, `Sender Name`, and
|
||||
`Session ID`.
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data of the
|
||||
`Record` will be updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
|
|
@ -112,9 +111,10 @@ This component sends a message to the chat.
|
|||
- **Message:** Specifies the text of the message.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data in the `Record` is updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
<p>
|
||||
If `As Record` is `true` and the `Message` is a `Record`, the data in the
|
||||
`Record` is updated with the `Sender`, `Sender Name`, and `Session ID`.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
### Text Output
|
||||
|
|
@ -125,7 +125,6 @@ This component displays text data to the user. It is useful when you want to sho
|
|||
|
||||
- **Value:** Specifies the text data to be displayed. Defaults to an empty string.
|
||||
|
||||
|
||||
The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow.
|
||||
|
||||
## Prompts
|
||||
|
|
@ -155,7 +154,8 @@ The `PromptTemplate` component enables users to create prompts and define variab
|
|||
|
||||
<Admonition type="info">
|
||||
After defining a variable in the prompt template, it acts as its own component
|
||||
input. See [Prompt Customization](../administration/prompt-customization) for more details.
|
||||
input. See [Prompt Customization](../administration/prompt-customization) for
|
||||
more details.
|
||||
</Admonition>
|
||||
|
||||
- **template:** The template used to format an individual request.
|
||||
- **template:** The template used to format an individual request.
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Memories
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
Thanks for your patience as we improve our documentation—it might have some rough edges. Share your feedback or report issues to help us enhance it! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
Thanks for your patience as we improve our documentation—it might have some
|
||||
rough edges. Share your feedback or report issues to help us enhance it!
|
||||
🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Memory is a concept in chat-based applications that allows the system to remember previous interactions. This capability helps maintain the context of the conversation and enables the system to understand new messages in light of past messages.
|
||||
|
|
@ -24,9 +26,13 @@ This component retrieves stored messages using various filters such as sender ty
|
|||
- **number_of_messages**: Specifies the number of messages to retrieve. Defaults to `5`. Determines the number of recent messages from the chat history to fetch.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
The component retrieves messages based on the provided criteria, including the specific file path for stored messages. If no specific criteria are provided, it returns the most recent messages up to the specified limit. This component can be used to review past interactions and analyze conversation flows.
|
||||
</p>
|
||||
<p>
|
||||
The component retrieves messages based on the provided criteria, including
|
||||
the specific file path for stored messages. If no specific criteria are
|
||||
provided, it returns the most recent messages up to the specified limit.
|
||||
This component can be used to review past interactions and analyze
|
||||
conversation flows.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
### ConversationBufferMemory
|
||||
|
|
@ -84,7 +90,8 @@ The `ConversationKGMemory` utilizes a knowledge graph to enhance memory capabili
|
|||
- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
|
||||
- **output_key**: Identifies the key under which the generated response
|
||||
|
||||
is stored, enabling retrieval using this key.
|
||||
is stored, enabling retrieval using this key.
|
||||
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
|
||||
---
|
||||
|
|
@ -124,4 +131,4 @@ The `VectorRetrieverMemory` retrieves vectors based on queries, facilitating vec
|
|||
- **Retriever**: The tool used to fetch documents.
|
||||
- **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation.
|
||||
- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Large Language Models (LLMs)
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
Thank you for your patience as we refine our documentation. You might encounter some inconsistencies. Please help us improve by sharing your feedback or reporting any issues! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
Thank you for your patience as we refine our documentation. You might
|
||||
encounter some inconsistencies. Please help us improve by sharing your
|
||||
feedback or reporting any issues! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
A Large Language Model (LLM) is a foundational component of Langflow. It provides a uniform interface for interacting with LLMs from various providers, including OpenAI, Cohere, and HuggingFace. Langflow extensively uses LLMs across its chains and agents, employing them to generate text based on specific prompts or inputs.
|
||||
|
|
@ -37,7 +39,9 @@ This is a wrapper for Anthropic's large language model designed for chat-based i
|
|||
`CTransformers` provides access to Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library.
|
||||
|
||||
<Admonition type="info">
|
||||
Ensure the `ctransformers` Python package is installed. Discover more about installation, supported models, and usage [here](https://github.com/marella/ctransformers).
|
||||
Ensure the `ctransformers` Python package is installed. Discover more about
|
||||
installation, supported models, and usage
|
||||
[here](https://github.com/marella/ctransformers).
|
||||
</Admonition>
|
||||
|
||||
- **config:** This configuration is for the Transformer models. Check the default settings and possible configurations at [config](https://github.com/marella/ctransformers#config).
|
||||
|
|
@ -128,7 +132,8 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
|
|||
|
||||
- **credentials**: Custom
|
||||
|
||||
credentials used for API interactions.
|
||||
credentials used for API interactions.
|
||||
|
||||
- **location**: The default location for API calls, defaulting to `us-central1`.
|
||||
- **max_output_tokens**: Limits the output tokens per prompt, defaulting to `128`.
|
||||
- **model_name**: The name of the Vertex AI model in use, defaulting to `text-bison`.
|
||||
|
|
@ -140,4 +145,4 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
|
|||
- **tuned_model_name**: Specifies a tuned model name, which overrides the default model name if provided.
|
||||
- **verbose**: Controls the output verbosity to assist in debugging and understanding the operational details, defaulting to `False`.
|
||||
|
||||
---
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Retrievers
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your patience as we enhance our documentation. It may have some imperfections. Please share your feedback or report issues to help us improve. 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
We appreciate your patience as we enhance our documentation. It may have
|
||||
some imperfections. Please share your feedback or report issues to help us
|
||||
improve. 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
A retriever is an interface that returns documents in response to an unstructured query. It's broader than a vector store because it doesn't need to store documents; it only needs to retrieve them.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
In Langflow 1.0, we added two main input and output types: `Text` and `Record`.
|
||||
|
||||
`Text` is a simple string input and output type, while ``Record`` is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
|
||||
`Text` is a simple string input and output type, while `Record` is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
|
||||
|
||||
We've created a few components to help you work with these types. Let's see how a few of them work.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,11 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Toolkits
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation - it may
|
||||
contain some rough edges. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Tools
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
Thanks for your patience as we refine our documentation. It might have some rough edges currently. Please share your feedback or report issues to help us enhance it! 🛠️📝
|
||||
</p>
|
||||
<p>
|
||||
Thanks for your patience as we refine our documentation. It might have some
|
||||
rough edges currently. Please share your feedback or report issues to help
|
||||
us enhance it! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
### SearchApi
|
||||
|
|
|
|||
|
|
@ -3,9 +3,9 @@ import Admonition from "@theme/Admonition";
|
|||
# Utilities
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="Zone Under Construction">
|
||||
We appreciate your understanding as we polish our documentation—it may
|
||||
contain some rough edges. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
We appreciate your understanding as we polish our documentation—it may contain
|
||||
some rough edges. Share your feedback or report issues to help us improve!
|
||||
🛠️📝
|
||||
</Admonition>
|
||||
|
||||
Utilities are a set of actions that can be used to perform common tasks in a flow. They are available in the **Utilities** section in the sidebar.
|
||||
|
|
@ -86,7 +86,11 @@ Generates a unique identifier (UUID) for each instance it is invoked, providing
|
|||
- Returns a unique identifier (UUID) as a string. This UUID is generated using Python's `uuid` module, ensuring that each identifier is unique and can be used as a reliable reference in your application.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
The Unique ID Generator is crucial for scenarios requiring distinct identifiers, such as session management, transaction tracking, or any context where different instances or entities must be uniquely identified. The generated UUID is provided as a hexadecimal string, offering a high level of uniqueness and security for identification purposes.
|
||||
The Unique ID Generator is crucial for scenarios requiring distinct
|
||||
identifiers, such as session management, transaction tracking, or any context
|
||||
where different instances or entities must be uniquely identified. The
|
||||
generated UUID is provided as a hexadecimal string, offering a high level of
|
||||
uniqueness and security for identification purposes.
|
||||
</Admonition>
|
||||
|
||||
For additional information and examples, please consult the [Langflow Components Custom Documentation](http://docs.langflow.org/components/custom).
|
||||
|
|
|
|||
|
|
@ -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 @@
|
|||
<svg width="18" height="18" viewBox="0 0 24 20" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
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Before Width: | Height: | Size: 1.1 KiB After Width: | Height: | Size: 430 B |
546
poetry.lock
generated
546
poetry.lock
generated
|
|
@ -261,13 +261,13 @@ extras = ["pyaudio (>=0.2.13)"]
|
|||
|
||||
[[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 = [
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||||
{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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]
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||||
|
||||
[package.dependencies]
|
||||
|
|
@ -367,13 +367,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "bce-python-sdk"
|
||||
version = "0.9.11"
|
||||
version = "0.9.14"
|
||||
description = "BCE SDK for python"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,<4,>=2.7"
|
||||
files = [
|
||||
{file = "bce_python_sdk-0.9.11-py3-none-any.whl", hash = "sha256:3afb9717f6c0c5f5fe3104a8bea4c111bf2ab3fe87ae73b05492566bc2b5d11a"},
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||||
{file = "bce_python_sdk-0.9.11.tar.gz", hash = "sha256:d9e977f059fef6466eebdbb34ad1e27b6f76ef90338807ab959693a78a761e7d"},
|
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{file = "bce_python_sdk-0.9.14-py3-none-any.whl", hash = "sha256:5704aa454151ee608b01ddda7531457433f9b4bb8afbd00706dd368f3b4339a1"},
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{file = "bce_python_sdk-0.9.14.tar.gz", hash = "sha256:7cbd182ec1e21034f10d3cdb812f3171d31908f1a783d6cf643039272942d8e8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -471,17 +471,17 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.34.119"
|
||||
version = "1.34.122"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "boto3-1.34.119-py3-none-any.whl", hash = "sha256:8f9c43c54b3dfaa36c4a0d7b42c417227a515bc7a2e163e62802780000a5a3e2"},
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{file = "boto3-1.34.119.tar.gz", hash = "sha256:cea2365a25b2b83a97e77f24ac6f922ef62e20636b42f9f6ee9f97188f9c1c03"},
|
||||
{file = "boto3-1.34.122-py3-none-any.whl", hash = "sha256:b2d7400ff84fa547e53b3d9acfa3c95d65d45b5886ba1ede1f7df4768d1cc0b1"},
|
||||
{file = "boto3-1.34.122.tar.gz", hash = "sha256:56840d8ce91654d182f1c113f0791fa2113c3aa43230c50b4481f235348a6037"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.34.119,<1.35.0"
|
||||
botocore = ">=1.34.122,<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 = "1.34.122"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "botocore-1.34.119-py3-none-any.whl", hash = "sha256:4bdf7926a1290b2650d62899ceba65073dd2693e61c35f5cdeb3a286a0aaa27b"},
|
||||
{file = "botocore-1.34.119.tar.gz", hash = "sha256:b253f15b24b87b070e176af48e8ef146516090429d30a7d8b136a4c079b28008"},
|
||||
{file = "botocore-1.34.122-py3-none-any.whl", hash = "sha256:6d75df3af831b62f0c7baa109728d987e0a8d34bfadf0476eb32e2f29a079a36"},
|
||||
{file = "botocore-1.34.122.tar.gz", hash = "sha256:9374e16a36f1062c3e27816e8599b53eba99315dfac71cc84fc3aee3f5d3cbe3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -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 = [
|
||||
{file = "cassio-0.1.7-py3-none-any.whl", hash = "sha256:08d1028a20d09bd207de0e17eaf7ae821b3c8e4788555e2d337aa440e0846d87"},
|
||||
{file = "cassio-0.1.7.tar.gz", hash = "sha256:44f705dff8a9a1c48527db2c9e968686358c960fa21ba940d9e66de00639ad78"},
|
||||
{file = "cassio-0.1.8-py3-none-any.whl", hash = "sha256:c09e7c884ba7227ff5277c86f3b0f31c523672ea407f56d093c7227e69c54d94"},
|
||||
{file = "cassio-0.1.8.tar.gz", hash = "sha256:4e09929506cb3dd6fad217e89846d0a1a59069afd24b82c72526ef6f2e9271af"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1450,13 +1450,13 @@ tests = ["pytest"]
|
|||
|
||||
[[package]]
|
||||
name = "dataclasses-json"
|
||||
version = "0.6.6"
|
||||
version = "0.6.7"
|
||||
description = "Easily serialize dataclasses to and from JSON."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.7"
|
||||
files = [
|
||||
{file = "dataclasses_json-0.6.6-py3-none-any.whl", hash = "sha256:e54c5c87497741ad454070ba0ed411523d46beb5da102e221efb873801b0ba85"},
|
||||
{file = "dataclasses_json-0.6.6.tar.gz", hash = "sha256:0c09827d26fffda27f1be2fed7a7a01a29c5ddcd2eb6393ad5ebf9d77e9deae8"},
|
||||
{file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
|
||||
{file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1590,6 +1590,23 @@ files = [
|
|||
[package.dependencies]
|
||||
packaging = "*"
|
||||
|
||||
[[package]]
|
||||
name = "dictdiffer"
|
||||
version = "0.9.0"
|
||||
description = "Dictdiffer is a library that helps you to diff and patch dictionaries."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "dictdiffer-0.9.0-py2.py3-none-any.whl", hash = "sha256:442bfc693cfcadaf46674575d2eba1c53b42f5e404218ca2c2ff549f2df56595"},
|
||||
{file = "dictdiffer-0.9.0.tar.gz", hash = "sha256:17bacf5fbfe613ccf1b6d512bd766e6b21fb798822a133aa86098b8ac9997578"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
all = ["Sphinx (>=3)", "check-manifest (>=0.42)", "mock (>=1.3.0)", "numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "sphinx-rtd-theme (>=0.2)", "tox (>=3.7.0)"]
|
||||
docs = ["Sphinx (>=3)", "sphinx-rtd-theme (>=0.2)"]
|
||||
numpy = ["numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)"]
|
||||
tests = ["check-manifest (>=0.42)", "mock (>=1.3.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "tox (>=3.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "dill"
|
||||
version = "0.3.7"
|
||||
|
|
@ -1824,13 +1841,13 @@ develop = ["aiohttp", "furo", "httpx", "mock", "opentelemetry-api", "opentelemet
|
|||
|
||||
[[package]]
|
||||
name = "elasticsearch"
|
||||
version = "8.13.2"
|
||||
version = "8.14.0"
|
||||
description = "Python client for Elasticsearch"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "elasticsearch-8.13.2-py3-none-any.whl", hash = "sha256:7412ceae9c0e437a72854ab3123aa1f37110d1635cc645366988b8c0fee98598"},
|
||||
{file = "elasticsearch-8.13.2.tar.gz", hash = "sha256:d51c93431a459b2b7c6c919b6e92a2adc8ac712758de9aeeb16cd4997fc148ad"},
|
||||
{file = "elasticsearch-8.14.0-py3-none-any.whl", hash = "sha256:cef8ef70a81af027f3da74a4f7d9296b390c636903088439087b8262a468c130"},
|
||||
{file = "elasticsearch-8.14.0.tar.gz", hash = "sha256:aa2490029dd96f4015b333c1827aa21fd6c0a4d223b00dfb0fe933b8d09a511b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2392,8 +2409,8 @@ files = [
|
|||
[package.dependencies]
|
||||
cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""}
|
||||
greenlet = [
|
||||
{version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
|
||||
{version = ">=3.0rc3", markers = "platform_python_implementation == \"CPython\" and python_version >= \"3.11\""},
|
||||
{version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
|
||||
]
|
||||
"zope.event" = "*"
|
||||
"zope.interface" = "*"
|
||||
|
|
@ -2520,12 +2537,12 @@ files = [
|
|||
google-auth = ">=2.14.1,<3.0.dev0"
|
||||
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
|
||||
grpcio = [
|
||||
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
grpcio-status = [
|
||||
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
proto-plus = ">=1.22.3,<2.0.0dev"
|
||||
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
|
||||
|
|
@ -2556,13 +2573,13 @@ uritemplate = ">=3.0.1,<5"
|
|||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.29.0"
|
||||
version = "2.30.0"
|
||||
description = "Google Authentication Library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
|
||||
{file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
|
||||
{file = "google-auth-2.30.0.tar.gz", hash = "sha256:ab630a1320f6720909ad76a7dbdb6841cdf5c66b328d690027e4867bdfb16688"},
|
||||
{file = "google_auth-2.30.0-py2.py3-none-any.whl", hash = "sha256:8df7da660f62757388b8a7f249df13549b3373f24388cb5d2f1dd91cc18180b5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2594,13 +2611,13 @@ httplib2 = ">=0.19.0"
|
|||
|
||||
[[package]]
|
||||
name = "google-cloud-aiplatform"
|
||||
version = "1.53.0"
|
||||
version = "1.54.1"
|
||||
description = "Vertex AI API client library"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "google-cloud-aiplatform-1.53.0.tar.gz", hash = "sha256:574cfad8ac5fa5d57ef717f5335ce05636a5fa9b8aeea0f5c325b46b9448e6b1"},
|
||||
{file = "google_cloud_aiplatform-1.53.0-py2.py3-none-any.whl", hash = "sha256:9dfb1f110e6d4795b45afcfab79108fc5c8ed9aa4eaf899e433bc2ca1b76c778"},
|
||||
{file = "google-cloud-aiplatform-1.54.1.tar.gz", hash = "sha256:01c231961cc1a1a3b049ea3ef71fb11e77b2d56d632d020ce09e419b27ff77f2"},
|
||||
{file = "google_cloud_aiplatform-1.54.1-py2.py3-none-any.whl", hash = "sha256:43f70fcd572f15317d769e5a0e04cfb7c0e259ead3fe581d2fba4f203ace5617"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2621,7 +2638,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
|
|||
cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
|
||||
datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
|
||||
endpoint = ["requests (>=2.28.1)"]
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||||
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@ -2897,13 +2914,13 @@ pydantic = ">=1.10,<3"
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||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3994,13 +4016,13 @@ 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"},
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||||
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|
||||
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|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4020,13 +4042,13 @@ adal = ["adal (>=1.0.2)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.2.2"
|
||||
version = "0.2.3"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"},
|
||||
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|
||||
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|
||||
{file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4042,20 +4064,6 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
|
||||
clarifai = ["clarifai (>=9.1.0)"]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
cohere = ["cohere (>=4,<6)"]
|
||||
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
|
||||
embeddings = ["sentence-transformers (>=2,<3)"]
|
||||
extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<6)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
javascript = ["esprima (>=4.0.1,<5.0.0)"]
|
||||
llms = ["clarifai (>=9.1.0)", "cohere (>=4,<6)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
|
||||
openai = ["openai (<2)", "tiktoken (>=0.7,<1.0)"]
|
||||
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
|
||||
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-anthropic"
|
||||
version = "0.1.15"
|
||||
|
|
@ -4122,13 +4130,13 @@ langchain-core = ">=0.1.42,<0.3"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.2.2"
|
||||
version = "0.2.4"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_community-0.2.2-py3-none-any.whl", hash = "sha256:470ee16e05f1acacb91a656b6d3c2cbf6fb6a8dcb00a13901cd1353cd29c2bb3"},
|
||||
{file = "langchain_community-0.2.2.tar.gz", hash = "sha256:fb09faf4640726a929932056dc55ff120e490aaf2e424fae8ddbb15605195447"},
|
||||
{file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"},
|
||||
{file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4143,19 +4151,15 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.4"
|
||||
version = "0.2.5"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"},
|
||||
{file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"},
|
||||
{file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"},
|
||||
{file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4166,9 +4170,6 @@ pydantic = ">=1,<3"
|
|||
PyYAML = ">=5.3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
extended-testing = ["jinja2 (>=3,<4)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-experimental"
|
||||
version = "0.0.60"
|
||||
|
|
@ -4189,37 +4190,37 @@ extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "pandas (>=2.
|
|||
|
||||
[[package]]
|
||||
name = "langchain-google-genai"
|
||||
version = "1.0.5"
|
||||
version = "1.0.6"
|
||||
description = "An integration package connecting Google's genai package and LangChain"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_google_genai-1.0.5-py3-none-any.whl", hash = "sha256:06b1af072e14fe2d4f9257be4bf883ccd544896094f847c2b1ab09b123ba3b9e"},
|
||||
{file = "langchain_google_genai-1.0.5.tar.gz", hash = "sha256:5b515192755fd396a1b61b33d1b08c77fb9b53394cc25954f9d7e9a0f615de9b"},
|
||||
{file = "langchain_google_genai-1.0.6-py3-none-any.whl", hash = "sha256:65188b3c2867efda78e09c29371499ab0d25c6a111b175365fdae2b5be1502e6"},
|
||||
{file = "langchain_google_genai-1.0.6.tar.gz", hash = "sha256:7c964117fa385c490b323ee50ab46907229823d3678b80bfacc8fa0a237fb0b9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-generativeai = ">=0.5.2,<0.6.0"
|
||||
langchain-core = ">=0.2.0,<0.3"
|
||||
langchain-core = ">=0.2.2,<0.3"
|
||||
|
||||
[package.extras]
|
||||
images = ["pillow (>=10.1.0,<11.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-google-vertexai"
|
||||
version = "1.0.4"
|
||||
version = "1.0.5"
|
||||
description = "An integration package connecting Google VertexAI and LangChain"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_google_vertexai-1.0.4-py3-none-any.whl", hash = "sha256:f9d217df2d5cfafb2e551ddd5f1c43611222f542ee0df0cc3b5faed82e657ee3"},
|
||||
{file = "langchain_google_vertexai-1.0.4.tar.gz", hash = "sha256:bb2d2e93cc2896b9bdc96789c2df247f6392184dffc0c3dddc06889f2b530465"},
|
||||
{file = "langchain_google_vertexai-1.0.5-py3-none-any.whl", hash = "sha256:38f4a39bf35927d744d0883907c4d4a59eef059e9b36f28bb5c737c2aae6963b"},
|
||||
{file = "langchain_google_vertexai-1.0.5.tar.gz", hash = "sha256:50005dc12ff9d66bbbab9e1ab660574b1584eee3e7b5a647dc8a009a94f0c500"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-cloud-aiplatform = ">=1.47.0,<2.0.0"
|
||||
google-cloud-storage = ">=2.14.0,<3.0.0"
|
||||
langchain-core = ">=0.1.42,<0.3"
|
||||
langchain-core = ">=0.2.2,<0.3"
|
||||
|
||||
[package.extras]
|
||||
anthropic = ["anthropic[vertexai] (>=0.23.0,<1)"]
|
||||
|
|
@ -4307,13 +4308,13 @@ extended-testing = ["beautifulsoup4 (>=4.12.3,<5.0.0)", "lxml (>=4.9.3,<6.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchainhub"
|
||||
version = "0.1.17"
|
||||
version = "0.1.18"
|
||||
description = "The LangChain Hub API client"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchainhub-0.1.17-py3-none-any.whl", hash = "sha256:4c609b3948252c71670f0d98f73413b515cfd2f6701a7b40ce959203e6133e04"},
|
||||
{file = "langchainhub-0.1.17.tar.gz", hash = "sha256:af7df0cb1cebc7a6e0864e8632ae48ecad39ed96568f699c78657b9d04e50b46"},
|
||||
{file = "langchainhub-0.1.18-py3-none-any.whl", hash = "sha256:11501f15e7f34715ecc8892587daa35c6f2a3005e1f2926c9bcabd31fc2c100c"},
|
||||
{file = "langchainhub-0.1.18.tar.gz", hash = "sha256:f2d0d8bf3abe4ca5e70511d8220bdc9ccea28d5267bcfd0e5ef9c53bd5bd3bad"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4322,7 +4323,7 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
|
|||
|
||||
[[package]]
|
||||
name = "langflow-base"
|
||||
version = "0.0.56"
|
||||
version = "0.0.60"
|
||||
description = "A Python package with a built-in web application"
|
||||
optional = false
|
||||
python-versions = ">=3.10,<3.13"
|
||||
|
|
@ -4379,13 +4380,13 @@ url = "src/backend/base"
|
|||
|
||||
[[package]]
|
||||
name = "langfuse"
|
||||
version = "2.33.1"
|
||||
version = "2.35.2"
|
||||
description = "A client library for accessing langfuse"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langfuse-2.33.1-py3-none-any.whl", hash = "sha256:61ff3ff4b9c9c195028c981cba892106fdf90028e3950209a15f0ae06a378a36"},
|
||||
{file = "langfuse-2.33.1.tar.gz", hash = "sha256:444a870e8b13ad37df710931389ecd3bad9997e550edf3c3178b5a0bd7ada013"},
|
||||
{file = "langfuse-2.35.2-py3-none-any.whl", hash = "sha256:d01a23842cab484594f03878aacb9732ef8fd361158eb819c7bf43f758a0954b"},
|
||||
{file = "langfuse-2.35.2.tar.gz", hash = "sha256:32b2e6c5bc71b4efdc430c6b964ab1c1e1ba1e105a4a73912c38b3959dc4502d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4403,13 +4404,13 @@ openai = ["openai (>=0.27.8)"]
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.71"
|
||||
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-0.1.71-py3-none-any.whl", hash = "sha256:a9979de2780442eb24eced31314e49f5ece6f807a0d70740b2c6c39217226794"},
|
||||
{file = "langsmith-0.1.71.tar.gz", hash = "sha256:bdb1037a08acf7c19b3969c085df09c1eecb65baca8400b3b76ae871e2c8a97e"},
|
||||
{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 +4420,13 @@ requests = ">=2,<3"
|
|||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.40.2"
|
||||
version = "1.40.7"
|
||||
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-1.40.7-py3-none-any.whl", hash = "sha256:c98dd8733e632aba16f14bf82e56f7159222097a6d085b242a3140b5d3e7baa4"},
|
||||
{file = "litellm-1.40.7.tar.gz", hash = "sha256:557bb19e8e484d0dfe8e4eaa9ccefc888617852988a46d6e7adc41585a2c0600"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4467,13 +4468,13 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
|
|||
|
||||
[[package]]
|
||||
name = "locust"
|
||||
version = "2.28.0"
|
||||
version = "2.29.0"
|
||||
description = "Developer-friendly load testing framework"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "locust-2.28.0-py3-none-any.whl", hash = "sha256:766be879db030c0118e7d9fca712f3538c4e628bdebf59468fa1c6c2fab217d3"},
|
||||
{file = "locust-2.28.0.tar.gz", hash = "sha256:260557eec866f7e34a767b6c916b5b278167562a280480aadb88f43d962fbdeb"},
|
||||
{file = "locust-2.29.0-py3-none-any.whl", hash = "sha256:aa9d94d3604ed9f2aab3248460d91e55d3de980a821dffdf8658b439b049d03f"},
|
||||
{file = "locust-2.29.0.tar.gz", hash = "sha256:649c99ce49d00720a3084c0109547035ad9021222835386599a8b545d31ebe51"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4487,7 +4488,10 @@ msgpack = ">=1.0.0"
|
|||
psutil = ">=5.9.1"
|
||||
pywin32 = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
pyzmq = ">=25.0.0"
|
||||
requests = ">=2.26.0"
|
||||
requests = [
|
||||
{version = ">=2.32.2", markers = "python_version > \"3.11\""},
|
||||
{version = ">=2.26.0", markers = "python_version <= \"3.11\""},
|
||||
]
|
||||
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
|
||||
Werkzeug = ">=2.0.0"
|
||||
|
||||
|
|
@ -4796,13 +4800,13 @@ 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 +5587,13 @@ sympy = "*"
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.31.0"
|
||||
version = "1.33.0"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
|
||||
files = [
|
||||
{file = "openai-1.31.0-py3-none-any.whl", hash = "sha256:82044ee3122113f2a468a1f308a8882324d09556ba5348687c535d3655ee331c"},
|
||||
{file = "openai-1.31.0.tar.gz", hash = "sha256:54ae0625b005d6a3b895db2b8438dae1059cffff0cd262a26e9015c13a29ab06"},
|
||||
{file = "openai-1.33.0-py3-none-any.whl", hash = "sha256:621163b56570897ab8389d187f686a53d4771fd6ce95d481c0a9611fe8bc4229"},
|
||||
{file = "openai-1.33.0.tar.gz", hash = "sha256:1169211a7b326ecbc821cafb427c29bfd0871f9a3e0947dd9e51acb3b0f1df78"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5915,9 +5919,9 @@ files = [
|
|||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
|
|
@ -6837,13 +6841,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
|
|||
|
||||
[[package]]
|
||||
name = "pydantic-settings"
|
||||
version = "2.3.0"
|
||||
version = "2.3.1"
|
||||
description = "Settings management using Pydantic"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pydantic_settings-2.3.0-py3-none-any.whl", hash = "sha256:26eeed27370a9c5e3f64e4a7d6602573cbedf05ed940f1d5b11c3f178427af7a"},
|
||||
{file = "pydantic_settings-2.3.0.tar.gz", hash = "sha256:78db28855a71503cfe47f39500a1dece523c640afd5280edb5c5c9c9cfa534c9"},
|
||||
{file = "pydantic_settings-2.3.1-py3-none-any.whl", hash = "sha256:acb2c213140dfff9669f4fe9f8180d43914f51626db28ab2db7308a576cce51a"},
|
||||
{file = "pydantic_settings-2.3.1.tar.gz", hash = "sha256:e34bbd649803a6bb3e2f0f58fb0edff1f0c7f556849fda106cc21bcce12c30ab"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -7575,13 +7579,13 @@ 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"},
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||||
{file = "redis-5.0.4.tar.gz", hash = "sha256:ec31f2ed9675cc54c21ba854cfe0462e6faf1d83c8ce5944709db8a4700b9c61"},
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||||
{file = "redis-5.0.5-py3-none-any.whl", hash = "sha256:30b47d4ebb6b7a0b9b40c1275a19b87bb6f46b3bed82a89012cf56dea4024ada"},
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{file = "redis-5.0.5.tar.gz", hash = "sha256:3417688621acf6ee368dec4a04dd95881be24efd34c79f00d31f62bb528800ae"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -7766,28 +7770,28 @@ pyasn1 = ">=0.1.3"
|
|||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.4.7"
|
||||
version = "0.4.8"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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|
||||
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|
||||
{file = "ruff-0.4.8-py3-none-win_amd64.whl", hash = "sha256:e14a3a095d07560a9d6769a72f781d73259655919d9b396c650fc98a8157555d"},
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||||
{file = "ruff-0.4.8-py3-none-win_arm64.whl", hash = "sha256:14019a06dbe29b608f6b7cbcec300e3170a8d86efaddb7b23405cb7f7dcaf780"},
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{file = "ruff-0.4.8.tar.gz", hash = "sha256:16d717b1d57b2e2fd68bd0bf80fb43931b79d05a7131aa477d66fc40fbd86268"},
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|
||||
[[package]]
|
||||
|
|
@ -8313,13 +8317,13 @@ 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"},
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{file = "storage3-0.7.5.tar.gz", hash = "sha256:ffe43f3877898b43a94024e68c2aaf4cebb3ad73dbbbd67747041d1d70bbf032"},
|
||||
{file = "storage3-0.7.6-py3-none-any.whl", hash = "sha256:d8c23bf87b3a88cafb03761b7f936e4e49daca67741d571513edf746e0f8ba72"},
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{file = "storage3-0.7.6.tar.gz", hash = "sha256:0b7781cea7fe6382e6b9349b84395808c5f4203dfcac31478304eedc2f81acf6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8381,13 +8385,13 @@ 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"},
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{file = "supafunc-0.4.5.tar.gz", hash = "sha256:a6466d78bdcaa58b7f0303793643103baae8106a87acd5d01e196179a9d0d024"},
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||||
{file = "supafunc-0.4.6-py3-none-any.whl", hash = "sha256:f7ca7b244365e171da7055a64edb462c2ec449cdaa210fc418cfccd132f4cf98"},
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{file = "supafunc-0.4.6.tar.gz", hash = "sha256:92db51f8f8568d1430285219c9c0072e44207409c416622d7387f609e31928a6"},
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]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8653,31 +8657,31 @@ files = [
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|||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.3.0"
|
||||
version = "2.3.1"
|
||||
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
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optional = true
|
||||
python-versions = ">=3.8.0"
|
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||||
{file = "torch-2.3.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:462d1c07dbf6bb5d9d2f3316fee73a24f3d12cd8dacf681ad46ef6418f7f6626"},
|
||||
{file = "torch-2.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:ff60bf7ce3de1d43ad3f6969983f321a31f0a45df3690921720bcad6a8596cc4"},
|
||||
{file = "torch-2.3.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:bee0bd33dc58aa8fc8a7527876e9b9a0e812ad08122054a5bff2ce5abf005b10"},
|
||||
{file = "torch-2.3.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:aaa872abde9a3d4f91580f6396d54888620f4a0b92e3976a6034759df4b961ad"},
|
||||
{file = "torch-2.3.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:3d7a7f7ef21a7520510553dc3938b0c57c116a7daee20736a9e25cbc0e832bdc"},
|
||||
{file = "torch-2.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:4777f6cefa0c2b5fa87223c213e7b6f417cf254a45e5829be4ccd1b2a4ee1011"},
|
||||
{file = "torch-2.3.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:2bb5af780c55be68fe100feb0528d2edebace1d55cb2e351de735809ba7391eb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8698,7 +8702,7 @@ nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"
|
|||
nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
sympy = "*"
|
||||
triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
|
||||
triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
|
||||
typing-extensions = ">=4.8.0"
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -8707,22 +8711,22 @@ 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]]
|
||||
|
|
@ -8830,17 +8834,17 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "2.3.0"
|
||||
version = "2.3.1"
|
||||
description = "A language and compiler for custom Deep Learning operations"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
|
||||
{file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"},
|
||||
{file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
|
||||
{file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
|
||||
{file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
|
||||
{file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
|
||||
{file = "triton-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c84595cbe5e546b1b290d2a58b1494df5a2ef066dd890655e5b8a8a92205c33"},
|
||||
{file = "triton-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d64ae33bcb3a7a18081e3a746e8cf87ca8623ca13d2c362413ce7a486f893e"},
|
||||
{file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"},
|
||||
{file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"},
|
||||
{file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"},
|
||||
{file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -9041,13 +9045,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.1"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
|
||||
{file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -10054,4 +10058,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "83c94ed0fa28b968553221385251b871139a7440ab0420f867efbe16568b8411"
|
||||
content-hash = "0ee3f3bef82d57be2ab4ae7b70215ebca67b5bd5223e6a9322ee1837516a3cc6"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a45"
|
||||
version = "1.0.0a49"
|
||||
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"
|
||||
|
|
@ -115,6 +115,7 @@ pytest-asyncio = "^0.23.0"
|
|||
pytest-profiling = "^1.7.0"
|
||||
pre-commit = "^3.7.0"
|
||||
vulture = "^2.11"
|
||||
dictdiffer = "^0.9.0"
|
||||
|
||||
[tool.poetry.extras]
|
||||
deploy = ["celery", "redis", "flower"]
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -86,6 +86,10 @@ def update_frontend_node_with_template_values(frontend_node, raw_frontend_node):
|
|||
|
||||
update_template_values(frontend_node["template"], raw_frontend_node["template"])
|
||||
|
||||
old_code = raw_frontend_node["template"]["code"]["value"]
|
||||
new_code = frontend_node["template"]["code"]["value"]
|
||||
frontend_node["edited"] = old_code != new_code
|
||||
|
||||
return frontend_node
|
||||
|
||||
|
||||
|
|
@ -204,16 +208,18 @@ def format_elapsed_time(elapsed_time: float) -> str:
|
|||
return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
|
||||
|
||||
|
||||
async def build_and_cache_graph_from_db(
|
||||
flow_id: str,
|
||||
session: Session,
|
||||
chat_service: "ChatService",
|
||||
):
|
||||
async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
|
||||
"""Build and cache the graph."""
|
||||
flow: Optional[Flow] = session.get(Flow, flow_id)
|
||||
if not flow or not flow.data:
|
||||
raise ValueError("Invalid flow ID")
|
||||
graph = Graph.from_payload(flow.data, flow_id)
|
||||
for vertex_id in graph._has_session_id_vertices:
|
||||
vertex = graph.get_vertex(vertex_id)
|
||||
if vertex is None:
|
||||
raise ValueError(f"Vertex {vertex_id} not found")
|
||||
if not vertex._raw_params.get("session_id"):
|
||||
vertex.update_raw_params({"session_id": flow_id})
|
||||
await chat_service.set_cache(flow_id, graph)
|
||||
return graph
|
||||
|
||||
|
|
@ -317,3 +323,4 @@ def parse_exception(exc):
|
|||
if hasattr(exc, "body"):
|
||||
return exc.body["message"]
|
||||
return str(exc)
|
||||
return str(exc)
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ from langflow.api.v1.schemas import (
|
|||
VertexBuildResponse,
|
||||
VerticesOrderResponse,
|
||||
)
|
||||
from langflow.schema.schema import Log
|
||||
from langflow.services.auth.utils import get_current_active_user
|
||||
from langflow.services.chat.service import ChatService
|
||||
from langflow.services.deps import get_chat_service, get_session, get_session_service
|
||||
|
|
@ -123,6 +124,7 @@ async def build_vertex(
|
|||
vertex_id: str,
|
||||
background_tasks: BackgroundTasks,
|
||||
inputs: Annotated[Optional[InputValueRequest], Body(embed=True)] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
chat_service: "ChatService" = Depends(get_chat_service),
|
||||
current_user=Depends(get_current_active_user),
|
||||
):
|
||||
|
|
@ -159,6 +161,7 @@ async def build_vertex(
|
|||
else:
|
||||
graph = cache.get("result")
|
||||
vertex = graph.get_vertex(vertex_id)
|
||||
|
||||
try:
|
||||
lock = chat_service._cache_locks[flow_id_str]
|
||||
(
|
||||
|
|
@ -175,19 +178,25 @@ async def build_vertex(
|
|||
vertex_id=vertex_id,
|
||||
user_id=current_user.id,
|
||||
inputs_dict=inputs.model_dump() if inputs else {},
|
||||
files=files,
|
||||
)
|
||||
log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
|
||||
result_data_response = ResultDataResponse(**result_dict.model_dump())
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error building vertex: {exc}")
|
||||
params = format_exception_message(exc)
|
||||
valid = False
|
||||
log_obj = Log(message=params, type="error")
|
||||
result_data_response = ResultDataResponse(results={})
|
||||
artifacts = {}
|
||||
# If there's an error building the vertex
|
||||
# we need to clear the cache
|
||||
await chat_service.clear_cache(flow_id_str)
|
||||
|
||||
result_data_response.message = artifacts
|
||||
result_data_response.logs.append(log_obj)
|
||||
|
||||
# Log the vertex build
|
||||
if not vertex.will_stream:
|
||||
background_tasks.add_task(
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import hashlib
|
|||
from http import HTTPStatus
|
||||
from io import BytesIO
|
||||
from uuid import UUID
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
|
@ -99,6 +100,47 @@ async def download_image(file_name: str, flow_id: UUID, storage_service: Storage
|
|||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/profile_pictures/{folder_name}/{file_name}")
|
||||
async def download_profile_picture(
|
||||
folder_name: str,
|
||||
file_name: str,
|
||||
storage_service: StorageService = Depends(get_storage_service),
|
||||
):
|
||||
try:
|
||||
extension = file_name.split(".")[-1]
|
||||
config_dir = get_storage_service().settings_service.settings.config_dir
|
||||
config_path = Path(config_dir)
|
||||
folder_path = config_path / 'profile_pictures' / folder_name
|
||||
content_type = build_content_type_from_extension(extension)
|
||||
file_content = await storage_service.get_file(flow_id=folder_path, file_name=file_name)
|
||||
return StreamingResponse(BytesIO(file_content), media_type=content_type)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/profile_pictures/list")
|
||||
async def list_profile_pictures(storage_service: StorageService = Depends(get_storage_service)):
|
||||
try:
|
||||
config_dir = get_storage_service().settings_service.settings.config_dir
|
||||
config_path = Path(config_dir)
|
||||
|
||||
people_path = config_path / "profile_pictures/People"
|
||||
space_path = config_path / "profile_pictures/Space"
|
||||
|
||||
people = await storage_service.list_files(flow_id=people_path)
|
||||
space = await storage_service.list_files(flow_id=space_path)
|
||||
|
||||
files = [Path("People") / i for i in people]
|
||||
files += [Path("Space") / i for i in space]
|
||||
|
||||
return {"files": files}
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
|
||||
@router.get("/list/{flow_id}")
|
||||
async def list_files(
|
||||
flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from loguru import logger
|
|||
from sqlmodel import Session, col, select
|
||||
|
||||
from langflow.api.utils import remove_api_keys, validate_is_component
|
||||
from langflow.api.v1.schemas import FlowListCreate, FlowListIds, FlowListRead
|
||||
from langflow.api.v1.schemas import FlowListCreate, FlowListRead
|
||||
from langflow.initial_setup.setup import STARTER_FOLDER_NAME
|
||||
from langflow.services.auth.utils import get_current_active_user
|
||||
from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate
|
||||
|
|
@ -258,9 +258,9 @@ async def download_file(
|
|||
return FlowListRead(flows=flows)
|
||||
|
||||
|
||||
@router.post("/multiple_delete/")
|
||||
@router.delete("/")
|
||||
async def delete_multiple_flows(
|
||||
flow_ids: FlowListIds, user: User = Depends(get_current_active_user), db: Session = Depends(get_session)
|
||||
flow_ids: List[UUID], user: User = Depends(get_current_active_user), db: Session = Depends(get_session)
|
||||
):
|
||||
"""
|
||||
Delete multiple flows by their IDs.
|
||||
|
|
@ -274,9 +274,7 @@ async def delete_multiple_flows(
|
|||
|
||||
"""
|
||||
try:
|
||||
deleted_flows = db.exec(
|
||||
select(Flow).where(col(Flow.id).in_(flow_ids.flow_ids)).where(Flow.user_id == user.id)
|
||||
).all()
|
||||
deleted_flows = db.exec(select(Flow).where(col(Flow.id).in_(flow_ids)).where(Flow.user_id == user.id)).all()
|
||||
for flow in deleted_flows:
|
||||
db.delete(flow)
|
||||
db.commit()
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ 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 +67,44 @@ 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: int,
|
||||
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),
|
||||
|
|
@ -79,6 +118,22 @@ async def get_transactions(
|
|||
dicts = monitor_service.get_transactions(
|
||||
source=source, target=target, status=status, order_by=order_by, flow_id=flow_id
|
||||
)
|
||||
return [TransactionModelResponse(**d) for d in dicts]
|
||||
result = []
|
||||
for d in dicts:
|
||||
d = TransactionModelResponse(
|
||||
index=d["index"],
|
||||
timestamp=d["timestamp"],
|
||||
vertex_id=d["vertex_id"],
|
||||
inputs=d["inputs"],
|
||||
outputs=d["outputs"],
|
||||
status=d["status"],
|
||||
error=d["error"],
|
||||
flow_id=d["flow_id"],
|
||||
source=d["vertex_id"],
|
||||
target=d["target_id"],
|
||||
)
|
||||
result.append(d)
|
||||
return result
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serial
|
|||
from langflow.graph.schema import RunOutputs
|
||||
from langflow.schema import dotdict
|
||||
from langflow.schema.graph import Tweaks
|
||||
from langflow.schema.schema import InputType, OutputType
|
||||
from langflow.schema.schema import InputType, Log, OutputType
|
||||
from langflow.services.database.models.api_key.model import ApiKeyRead
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
from langflow.services.database.models.flow import FlowCreate, FlowRead
|
||||
|
|
@ -245,6 +245,8 @@ class VerticesOrderResponse(BaseModel):
|
|||
|
||||
class ResultDataResponse(BaseModel):
|
||||
results: Optional[Any] = Field(default_factory=dict)
|
||||
logs: List[Log | None] = Field(default_factory=list)
|
||||
message: Optional[Any] = Field(default_factory=dict)
|
||||
artifacts: Optional[Any] = Field(default_factory=dict)
|
||||
timedelta: Optional[float] = None
|
||||
duration: Optional[str] = None
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from langchain_core.runnables import Runnable
|
|||
from langflow.base.agents.utils import get_agents_list, records_to_messages
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class LCAgentComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
|
|||
from langchain_core.tools import BaseTool
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
from .default_prompts import XML_AGENT_PROMPT
|
||||
|
||||
|
|
|
|||
|
|
@ -7,9 +7,11 @@ Constants:
|
|||
- FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields.
|
||||
"""
|
||||
|
||||
import orjson
|
||||
|
||||
STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
|
||||
|
||||
NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"]
|
||||
NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description", "output_types"]
|
||||
|
||||
|
||||
FIELD_FORMAT_ATTRIBUTES = [
|
||||
|
|
@ -27,3 +29,5 @@ FIELD_FORMAT_ATTRIBUTES = [
|
|||
"refresh_button_text",
|
||||
"options",
|
||||
]
|
||||
|
||||
ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS
|
||||
|
|
|
|||
|
|
@ -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(
|
||||
"ParsedArgs",
|
||||
[
|
||||
"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 :]
|
||||
|
|
|
|||
|
|
@ -1,12 +1,14 @@
|
|||
import json
|
||||
import unicodedata
|
||||
import xml.etree.ElementTree as ET
|
||||
from concurrent import futures
|
||||
from pathlib import Path
|
||||
from typing import Callable, List, Optional, Text
|
||||
|
||||
import chardet
|
||||
import orjson
|
||||
import yaml
|
||||
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
# Types of files that can be read simply by file.read()
|
||||
# and have 100% to be completely readable
|
||||
|
|
@ -31,6 +33,17 @@ TEXT_FILE_TYPES = [
|
|||
"tsx",
|
||||
]
|
||||
|
||||
IMG_FILE_TYPES = [
|
||||
"jpg",
|
||||
"jpeg",
|
||||
"png",
|
||||
"bmp",
|
||||
]
|
||||
|
||||
|
||||
def normalize_text(text):
|
||||
return unicodedata.normalize("NFKD", text)
|
||||
|
||||
|
||||
def is_hidden(path: Path) -> bool:
|
||||
return path.name.startswith(".")
|
||||
|
|
@ -92,7 +105,10 @@ 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-1252", "Windows-1254"]:
|
||||
encoding = "utf-8"
|
||||
|
||||
with open(file_path, "r", encoding=encoding) as f:
|
||||
return f.read()
|
||||
|
|
@ -121,9 +137,15 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
|
|||
text = read_docx_file(file_path)
|
||||
else:
|
||||
text = read_text_file(file_path)
|
||||
|
||||
# if file is json, yaml, or xml, we can parse it
|
||||
if file_path.endswith(".json"):
|
||||
text = json.loads(text)
|
||||
text = orjson.loads(text)
|
||||
if isinstance(text, dict):
|
||||
text = {k: normalize_text(v) if isinstance(v, str) else v for k, v in text.items()}
|
||||
elif isinstance(text, list):
|
||||
text = [normalize_text(item) if isinstance(item, str) else item for item in text]
|
||||
|
||||
elif file_path.endswith(".yaml") or file_path.endswith(".yml"):
|
||||
text = yaml.safe_load(text)
|
||||
elif file_path.endswith(".xml"):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import List
|
||||
|
||||
from langflow.graph.schema import ResultData, RunOutputs
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
def build_records_from_run_outputs(run_outputs: RunOutputs) -> List[Record]:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.memory import store_message
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatComponent(CustomComponent):
|
||||
|
|
@ -15,7 +15,7 @@ class ChatComponent(CustomComponent):
|
|||
return {
|
||||
"input_value": {
|
||||
"input_types": ["Text"],
|
||||
"display_name": "Message",
|
||||
"display_name": "Text",
|
||||
"multiline": True,
|
||||
},
|
||||
"sender": {
|
||||
|
|
@ -40,98 +40,45 @@ class ChatComponent(CustomComponent):
|
|||
"info": "In case of Message being a Record, this template will be used to convert it to text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"files": {
|
||||
"field_type": "file",
|
||||
"display_name": "Files",
|
||||
"file_types": TEXT_FILE_TYPES + IMG_FILE_TYPES,
|
||||
"info": "Files to be sent with the message.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def store_message(
|
||||
self,
|
||||
message: Union[str, Text, Record],
|
||||
session_id: Optional[str] = None,
|
||||
sender: Optional[str] = None,
|
||||
sender_name: Optional[str] = None,
|
||||
) -> list[Record]:
|
||||
records = store_message(
|
||||
message: Message,
|
||||
) -> list[Message]:
|
||||
messages = store_message(
|
||||
message,
|
||||
session_id=session_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
flow_id=self.graph.flow_id,
|
||||
)
|
||||
|
||||
self.status = records
|
||||
return records
|
||||
self.status = messages
|
||||
return messages
|
||||
|
||||
def build_with_record(
|
||||
self,
|
||||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[Union[str, Record]] = None,
|
||||
input_value: Optional[Union[str, Record, Message]] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
) -> Union[Text, Record]:
|
||||
input_value_record: Optional[Record] = None
|
||||
if return_record:
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
input_value.data["sender"] = sender
|
||||
input_value.data["sender_name"] = sender_name
|
||||
input_value.data["session_id"] = session_id
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
data={
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
input_value = records_to_text(template=record_template, records=input_value)
|
||||
if not input_value:
|
||||
input_value = ""
|
||||
if return_record and input_value_record:
|
||||
result: Union[Text, Record] = input_value_record
|
||||
else:
|
||||
result = input_value
|
||||
self.status = result
|
||||
if session_id and isinstance(result, (Record, str)):
|
||||
self.store_message(result, session_id, sender, sender_name)
|
||||
return result
|
||||
) -> Message:
|
||||
message: Message | None = None
|
||||
|
||||
def build_no_record(
|
||||
self,
|
||||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: str = "Text: {text}\nData: {data}",
|
||||
) -> Union[Text, Record]:
|
||||
input_value_record: Optional[Record] = None
|
||||
if return_record:
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
input_value.data["sender"] = sender
|
||||
input_value.data["sender_name"] = sender_name
|
||||
input_value.data["session_id"] = session_id
|
||||
else:
|
||||
input_value_record = Record(
|
||||
text=input_value,
|
||||
data={
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"session_id": session_id,
|
||||
},
|
||||
)
|
||||
elif isinstance(input_value, Record):
|
||||
input_value = records_to_text(template=record_template, records=input_value)
|
||||
if not input_value:
|
||||
input_value = ""
|
||||
if return_record and input_value_record:
|
||||
result: Union[Text, Record] = input_value_record
|
||||
if isinstance(input_value, Record):
|
||||
# Update the data of the record
|
||||
message = Message.from_record(input_value)
|
||||
else:
|
||||
result = input_value
|
||||
self.status = result
|
||||
if session_id and isinstance(result, (Record, str)):
|
||||
self.store_message(result, session_id, sender, sender_name)
|
||||
return result
|
||||
message = Message(
|
||||
text=input_value, sender=sender, sender_name=sender_name, files=files, session_id=session_id
|
||||
)
|
||||
self.status = message
|
||||
if session_id and isinstance(message, Message):
|
||||
self.store_message(message)
|
||||
return message
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class TextComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class BaseMemoryComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import warnings
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
|
|
@ -5,6 +6,7 @@ from langchain_core.language_models.llms import LLM
|
|||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing.prompt import Prompt
|
||||
|
||||
|
||||
class LCModelComponent(CustomComponent):
|
||||
|
|
@ -53,19 +55,28 @@ class LCModelComponent(CustomComponent):
|
|||
key in response_metadata["token_usage"] for key in inner_openai_keys
|
||||
):
|
||||
token_usage = response_metadata["token_usage"]
|
||||
completion_tokens = token_usage["completion_tokens"]
|
||||
prompt_tokens = token_usage["prompt_tokens"]
|
||||
total_tokens = token_usage["total_tokens"]
|
||||
finish_reason = response_metadata["finish_reason"]
|
||||
status_message = f"Tokens:\nInput: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}"
|
||||
status_message = {
|
||||
"tokens": {
|
||||
"input": token_usage["prompt_tokens"],
|
||||
"output": token_usage["completion_tokens"],
|
||||
"total": token_usage["total_tokens"],
|
||||
"stop_reason": response_metadata["finish_reason"],
|
||||
"response": content,
|
||||
}
|
||||
}
|
||||
|
||||
elif all(key in response_metadata for key in anthropic_keys) and all(
|
||||
key in response_metadata["usage"] for key in inner_anthropic_keys
|
||||
):
|
||||
usage = response_metadata["usage"]
|
||||
input_tokens = usage["input_tokens"]
|
||||
output_tokens = usage["output_tokens"]
|
||||
stop_reason = response_metadata["stop_reason"]
|
||||
status_message = f"Tokens:\nInput: {input_tokens}\nOutput: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
|
||||
status_message = {
|
||||
"tokens": {
|
||||
"input": usage["input_tokens"],
|
||||
"output": usage["output_tokens"],
|
||||
"stop_reason": response_metadata["stop_reason"],
|
||||
"response": content,
|
||||
}
|
||||
}
|
||||
else:
|
||||
status_message = f"Response: {content}"
|
||||
else:
|
||||
|
|
@ -73,7 +84,7 @@ class LCModelComponent(CustomComponent):
|
|||
return status_message
|
||||
|
||||
def get_chat_result(
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str | Prompt, system_message: Optional[str] = None
|
||||
):
|
||||
messages: list[Union[HumanMessage, SystemMessage]] = []
|
||||
if not input_value and not system_message:
|
||||
|
|
@ -81,11 +92,21 @@ class LCModelComponent(CustomComponent):
|
|||
if system_message:
|
||||
messages.append(SystemMessage(content=system_message))
|
||||
if input_value:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
if isinstance(input_value, Prompt):
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
if "prompt" in input_value:
|
||||
prompt = input_value.load_lc_prompt()
|
||||
runnable = prompt | runnable
|
||||
else:
|
||||
messages.append(input_value.to_lc_message())
|
||||
else:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
inputs = messages or {}
|
||||
if stream:
|
||||
return runnable.stream(messages)
|
||||
return runnable.stream(inputs)
|
||||
else:
|
||||
message = runnable.invoke(messages)
|
||||
message = runnable.invoke(inputs)
|
||||
result = message.content
|
||||
if isinstance(message, AIMessage):
|
||||
status_message = self.build_status_message(message)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from copy import deepcopy
|
||||
|
||||
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
def record_to_string(record: Record) -> str:
|
||||
|
|
@ -35,10 +35,14 @@ def dict_values_to_string(d: dict) -> dict:
|
|||
# it could be a list of records or documents or strings
|
||||
if isinstance(value, list):
|
||||
for i, item in enumerate(value):
|
||||
if isinstance(item, Record):
|
||||
if isinstance(item, Message):
|
||||
d_copy[key][i] = item.text
|
||||
elif isinstance(item, Record):
|
||||
d_copy[key][i] = record_to_string(item)
|
||||
elif isinstance(item, Document):
|
||||
d_copy[key][i] = document_to_string(item)
|
||||
elif isinstance(value, Message):
|
||||
d_copy[key] = value.text
|
||||
elif isinstance(value, Record):
|
||||
d_copy[key] = record_to_string(value)
|
||||
elif isinstance(value, Document):
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langchain_core.prompts import ChatPromptTemplate
|
|||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ToolCallingAgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
|
|
@ -3,10 +3,9 @@ from typing import List, Optional
|
|||
from langchain.agents import create_xml_agent
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class XMLAgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langchain_core.documents import Document
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class RetrievalQAComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ import uuid
|
|||
from typing import Any, Optional
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class WebhookComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessag
|
|||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class AgentComponent(LCAgentComponent):
|
||||
|
|
|
|||
|
|
@ -7,8 +7,8 @@ from langflow.custom import CustomComponent
|
|||
from langflow.field_typing import Tool
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.helpers.flow import get_flow_inputs
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class FlowToolComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from typing import List, Optional
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.memory import get_messages, store_message
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class StoreMessageComponent(CustomComponent):
|
||||
|
|
@ -31,12 +31,11 @@ class StoreMessageComponent(CustomComponent):
|
|||
sender_name: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
message: str = "",
|
||||
) -> List[Record]:
|
||||
) -> List[Message]:
|
||||
store_message(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
session_id=session_id,
|
||||
message=message,
|
||||
message=Message(
|
||||
text=message, sender=sender, sender_name=sender_name, flow_id=self.graph.flow_id, session_id=session_id
|
||||
)
|
||||
)
|
||||
|
||||
self.status = get_messages(session_id=session_id)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,9 @@ from typing import Optional
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.helpers.record import messages_to_text
|
||||
from langflow.memory import get_messages
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class MemoryComponent(BaseMemoryComponent):
|
||||
|
|
@ -43,7 +43,7 @@ class MemoryComponent(BaseMemoryComponent):
|
|||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
def get_messages(self, **kwargs) -> list[Message]:
|
||||
# Validate kwargs by checking if it contains the correct keys
|
||||
if "sender" not in kwargs:
|
||||
kwargs["sender"] = None
|
||||
|
|
@ -77,6 +77,6 @@ class MemoryComponent(BaseMemoryComponent):
|
|||
limit=n_messages,
|
||||
order=order,
|
||||
)
|
||||
messages_str = records_to_text(template=record_template or "", records=messages)
|
||||
messages_str = messages_to_text(template=record_template or "", messages=messages)
|
||||
self.status = messages_str
|
||||
return messages_str
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langflow.base.io.chat import ChatComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatInput(ChatComponent):
|
||||
|
|
@ -14,7 +13,7 @@ class ChatInput(ChatComponent):
|
|||
build_config = super().build_config()
|
||||
build_config["input_value"] = {
|
||||
"input_types": [],
|
||||
"display_name": "Message",
|
||||
"display_name": "Text",
|
||||
"multiline": True,
|
||||
}
|
||||
|
||||
|
|
@ -25,13 +24,13 @@ class ChatInput(ChatComponent):
|
|||
sender: Optional[str] = "User",
|
||||
sender_name: Optional[str] = "User",
|
||||
input_value: Optional[str] = None,
|
||||
files: Optional[list[str]] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
) -> Union[Text, Record]:
|
||||
return super().build_no_record(
|
||||
) -> Message:
|
||||
return super().build_with_record(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
input_value=input_value,
|
||||
files=files,
|
||||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Prompt, TemplateField, Text
|
||||
from langflow.field_typing import TemplateField
|
||||
from langflow.field_typing.prompt import Prompt
|
||||
|
||||
|
||||
class PromptComponent(CustomComponent):
|
||||
|
|
@ -15,19 +14,11 @@ class PromptComponent(CustomComponent):
|
|||
"code": TemplateField(advanced=True),
|
||||
}
|
||||
|
||||
def build(
|
||||
async def build(
|
||||
self,
|
||||
template: Prompt,
|
||||
**kwargs,
|
||||
) -> Text:
|
||||
from langflow.base.prompts.utils import dict_values_to_string
|
||||
|
||||
prompt_template = PromptTemplate.from_template(Text(template))
|
||||
kwargs = dict_values_to_string(kwargs)
|
||||
kwargs = {k: "\n".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}
|
||||
try:
|
||||
formated_prompt = prompt_template.format(**kwargs)
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error formatting prompt: {exc}") from exc
|
||||
self.status = f'Prompt:\n"{formated_prompt}"'
|
||||
return formated_prompt
|
||||
) -> Prompt:
|
||||
prompt = await Prompt.from_template_and_variables(template, kwargs)
|
||||
self.status = prompt.format_text()
|
||||
return prompt
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ class TextInput(TextComponent):
|
|||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Value",
|
||||
"display_name": "Text",
|
||||
"input_types": ["Record", "Text"],
|
||||
"info": "Text or Record to be passed as input.",
|
||||
},
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class AstraDBMessageReaderComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_astradb import AstraDBChatMessageHistory
|
||||
from langchain_core.messages import BaseMessage
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_astradb import AstraDBChatMessageHistory
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class AstraDBMessageWriterComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_community.chat_message_histories.zep import SearchScope, SearchTy
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ZepMessageReaderComponent(BaseMemoryComponent):
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Optional
|
|||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ class AmazonBedrockComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"cache": {"display_name": "Cache"},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ class AnthropicLLM(LCModelComponent):
|
|||
"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -78,7 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
|
|||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -81,7 +81,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
|
|||
"info": "Endpoint of the Qianfan LLM, required if custom model used.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
|
|||
"required": False,
|
||||
"default": False,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_cohere import ChatCohere
|
||||
from pydantic.v1 import SecretStr
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langchain_cohere import ChatCohere
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CohereComponent(LCModelComponent):
|
||||
|
|
@ -42,7 +43,7 @@ class CohereComponent(LCModelComponent):
|
|||
"type": "float",
|
||||
"show": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -69,3 +70,4 @@ class CohereComponent(LCModelComponent):
|
|||
temperature=temperature,
|
||||
)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,10 @@ from typing import Optional
|
|||
|
||||
from langchain_community.chat_models.huggingface import ChatHuggingFace
|
||||
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(LCModelComponent):
|
||||
|
|
@ -36,7 +37,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -72,3 +73,4 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
output = ChatHuggingFace(llm=llm)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class MistralAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -194,7 +194,7 @@ class ChatOllamaComponent(LCModelComponent):
|
|||
"info": "Template to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
@ -79,7 +79,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
input_value: Text,
|
||||
openai_api_key: str,
|
||||
temperature: float = 0.1,
|
||||
model_name: str = "gpt-4o",
|
||||
model_name: str = "gpt-3.5-turbo",
|
||||
max_tokens: Optional[int] = 256,
|
||||
model_kwargs: NestedDict = {},
|
||||
openai_api_base: Optional[str] = None,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import Optional
|
||||
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
|
@ -74,7 +73,7 @@ class ChatVertexAIComponent(LCModelComponent):
|
|||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langflow.base.io.chat import ChatComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
class ChatOutput(ChatComponent):
|
||||
|
|
@ -16,14 +15,12 @@ class ChatOutput(ChatComponent):
|
|||
sender_name: Optional[str] = "AI",
|
||||
input_value: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
return_record: Optional[bool] = False,
|
||||
record_template: Optional[str] = "{text}",
|
||||
) -> Union[Text, Record]:
|
||||
files: Optional[list[str]] = None,
|
||||
) -> Message:
|
||||
return super().build_with_record(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
input_value=input_value,
|
||||
session_id=session_id,
|
||||
return_record=return_record,
|
||||
record_template=record_template or "",
|
||||
files=files,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,18 @@ from langflow.custom import CustomComponent
|
|||
from langflow.schema import Record
|
||||
|
||||
|
||||
class RecordsOutput(CustomComponent):
|
||||
class RecordOutput(CustomComponent):
|
||||
display_name = "Records Output"
|
||||
description = "Display Records as a Table"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Records",
|
||||
"input_types": ["Record"],
|
||||
"info": "Record or Record list to be passed as input.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(self, input_value: Record) -> Record:
|
||||
return input_value
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ class TextOutput(TextComponent):
|
|||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Value",
|
||||
"display_name": "Text",
|
||||
"input_types": ["Record", "Text"],
|
||||
"info": "Text or Record to be passed as output.",
|
||||
},
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import List
|
|||
from langchain_text_splitters import CharacterTextSplitter
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.utils.util import unescape_string
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import List, Optional
|
|||
from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class LanguageRecursiveTextSplitterComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Optional
|
|||
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Redis import RedisComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -1,27 +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",
|
||||
]
|
||||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import List
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.pgvector import PGVectorComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Record
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
|
||||
|
|
|
|||
|
|
@ -1,11 +1,12 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from langchain_astradb import AstraDBVectorStore
|
||||
from langchain_astradb.utils.astradb import SetupMode
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings, VectorStore
|
||||
from langflow.schema import Record
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
|
||||
class AstraDBVectorStoreComponent(CustomComponent):
|
||||
|
|
@ -156,3 +157,4 @@ class AstraDBVectorStoreComponent(CustomComponent):
|
|||
)
|
||||
|
||||
return vector_store
|
||||
return vector_store
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from langchain_core.retrievers import BaseRetriever
|
|||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class ChromaComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,18 +1,16 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from langchain_community.vectorstores import CouchbaseVectorStore
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings, VectorStore
|
||||
from langflow.schema import Record
|
||||
|
||||
from datetime import timedelta
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from couchbase.auth import PasswordAuthenticator # type: ignore
|
||||
from couchbase.cluster import Cluster # type: ignore
|
||||
from couchbase.options import ClusterOptions # type: ignore
|
||||
from langchain_community.vectorstores import CouchbaseVectorStore
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings, VectorStore
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class CouchbaseComponent(CustomComponent):
|
||||
display_name = "Couchbase"
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class FAISSComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSea
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class MongoDBAtlasComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from langchain_pinecone.vectorstores import PineconeVectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class PineconeComponent(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
|
|||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Embeddings
|
||||
from langflow.schema.schema import Record
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class QdrantComponent(CustomComponent):
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue