diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 000000000..c2c42d27d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,28 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**Browser and Version** + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 5a94a09e7..a807555c4 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -6,7 +6,7 @@ on: pull_request: env: - POETRY_VERSION: "1.3.1" + POETRY_VERSION: "1.4.0" jobs: build: diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index e48b80203..3955f2bb5 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -10,7 +10,7 @@ on: - "pyproject.toml" env: - POETRY_VERSION: "1.3.1" + POETRY_VERSION: "1.4.0" jobs: if_release: diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 000000000..52109f944 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,33 @@ +name: test + +on: + push: + branches: [main] + pull_request: + branches: [dev] + +env: + POETRY_VERSION: "1.4.0" + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.10" + - "3.11" + steps: + - uses: actions/checkout@v3 + - name: Install poetry + run: pipx install poetry==$POETRY_VERSION + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: "poetry" + - name: Install dependencies + run: poetry install + - name: Run unit tests + run: | + make test diff --git a/.gitignore b/.gitignore index 96512b650..c2ffb8276 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,11 @@ lerna-debug.log* # Mac .DS_Store +# VSCode +.vscode +.chroma +.ruff_cache + # Diagnostic reports (https://nodejs.org/api/report.html) report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json @@ -233,5 +238,5 @@ venv.bak/ .dmypy.json dmypy.json -# Poetry -.testenv/* \ No newline at end of file +# Poetry +.testenv/* diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 98dddaa84..f98336ba9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,6 +1,6 @@ # Contributing to LangFlow -Hello there! I appreciate your interest in contributing to LangFlow. +Hello there! We appreciate your interest in contributing to LangFlow. As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation. diff --git a/GCP_DEPLOYMENT.md b/GCP_DEPLOYMENT.md new file mode 100644 index 000000000..edb7e043c --- /dev/null +++ b/GCP_DEPLOYMENT.md @@ -0,0 +1,28 @@ +# Run Langflow from a New Google Cloud Project + +This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell. + +> **Note**: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment. + + +## Standard VM +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) + +This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. +
+ +## Spot/Preemptible Instance + +[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) + +When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts. + +## Pricing (approximate) +> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) +
+ +| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | +| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- | +| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | +| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | +| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | diff --git a/Makefile b/Makefile index 70407436b..328ac8580 100644 --- a/Makefile +++ b/Makefile @@ -42,14 +42,13 @@ build: dev: make install_frontend ifeq ($(build),1) - @echo 'Running docker compose up with build' - docker compose up --build + @echo 'Running docker compose up with build' + docker compose $(if $(debug),-f docker-compose.debug.yml) up --build else - @echo 'Running docker compose up without build' - docker compose up + @echo 'Running docker compose up without build' + docker compose $(if $(debug),-f docker-compose.debug.yml) up endif - publish: make build poetry publish diff --git a/README.md b/README.md index 8de32742a..9b0a0cb3a 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ ~ A User Interface For [LangChain](https://github.com/hwchase17/langchain) ~

+HuggingFace Spaces GitHub Contributors GitHub Last Commit @@ -19,14 +20,31 @@ LangFlow is a GUI for [LangChain](https://github.com/hwchase17/langchain), designed with [react-flow](https://github.com/wbkd/react-flow) to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat box. ## 📦 Installation - +### Locally You can install LangFlow from pip: -`pip install langflow` +```shell +pip install langflow +``` Next, run: -`langflow` +```shell +python -m langflow +``` +or +```shell +langflow +``` + +### Deploy Langflow on Google Cloud Platform + +Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) document. + +Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Cloud Shell, clone the Langflow repository, and start an **interactive tutorial** that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project. + +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) + ## 🎨 Creating Flows @@ -49,7 +67,7 @@ flow("Hey, have you heard of LangFlow?") ## 👋 Contributing -We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make LangFlow more accessible. +We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make LangFlow more accessible. [![Star History Chart](https://api.star-history.com/svg?repos=logspace-ai/langflow&type=Timeline)](https://star-history.com/#logspace-ai/langflow&Date) diff --git a/dev.Dockerfile b/dev.Dockerfile index 0f559a0cf..b38929db2 100644 --- a/dev.Dockerfile +++ b/dev.Dockerfile @@ -3,7 +3,7 @@ FROM python:3.10-slim WORKDIR /app # Install Poetry -RUN apt-get update && apt-get install gcc curl -y +RUN apt-get update && apt-get install gcc g++ curl build-essential postgresql-server-dev-all -y RUN curl -sSL https://install.python-poetry.org | python3 - # # Add Poetry to PATH ENV PATH="${PATH}:/root/.local/bin" @@ -15,4 +15,4 @@ COPY ./ ./ # Install dependencies RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi -CMD ["uvicorn", "langflow.main:app", "--host", "0.0.0.0", "--port", "5003", "--reload"] \ No newline at end of file +CMD ["uvicorn", "langflow.main:app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"] \ No newline at end of file diff --git a/docker-compose.debug.yml b/docker-compose.debug.yml new file mode 100644 index 000000000..581bdc6da --- /dev/null +++ b/docker-compose.debug.yml @@ -0,0 +1,28 @@ +version: '3.4' + +services: + backend: + volumes: + - ./:/app + build: + context: ./ + dockerfile: ./dev.Dockerfile + command: ["sh", "-c", "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn langflow.main:app --host 0.0.0.0 --port 7860 --reload"] + ports: + - 7860:7860 + - 5678:5678 + restart: on-failure + + frontend: + build: + context: ./src/frontend + dockerfile: ./dev.Dockerfile + args: + - BACKEND_URL=http://backend:7860 + ports: + - "3000:3000" + volumes: + - ./src/frontend/public:/home/node/app/public + - ./src/frontend/src:/home/node/app/src + - ./src/frontend/package.json:/home/node/app/package.json + restart: on-failure \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 7f6365839..e6dd6bf7d 100644 --- a/poetry.lock +++ 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+[package.extras] +cffi = ["cffi (>=1.11)"] + [metadata] lock-version = "2.0" python-versions = "^3.9" diff --git a/pyproject.toml b/pyproject.toml index 4150cf0d3..96c4e087d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langflow" -version = "0.0.46" +version = "0.0.58" description = "A Python package with a built-in web application" authors = ["Logspace "] maintainers = [ @@ -32,12 +32,34 @@ gunicorn = "^20.1.0" langchain = "~0.0.113" openai = "^0.27.2" types-pyyaml = "^6.0.12.8" +dill = "^0.3.6" +pandas = "^1.5.3" +chromadb = "^0.3.21" +huggingface-hub = "^0.13.3" +rich = "^13.3.3" +llama-cpp-python = "0.1.23" +networkx = "^3.1" +unstructured = "^0.5.11" +pypdf = "^3.7.1" +lxml = "^4.9.2" +pysrt = "^1.1.2" +fake-useragent = "^1.1.3" +docstring-parser = "^0.15" +psycopg2-binary = "^2.9.6" +pyarrow = "^11.0.0" [tool.poetry.group.dev.dependencies] black = "^23.1.0" ipykernel = "^6.21.2" mypy = "^1.1.1" ruff = "^0.0.254" +httpx = "^0.23.3" +pytest = "^7.2.2" +types-requests = "^2.28.11" +requests = "^2.28.0" + +[tool.ruff] +line-length = 120 [build-system] requires = ["poetry-core"] diff --git a/scripts/deploy_langflow_gcp.sh b/scripts/deploy_langflow_gcp.sh new file mode 100644 index 000000000..2c3dc0420 --- /dev/null +++ b/scripts/deploy_langflow_gcp.sh @@ -0,0 +1,89 @@ +# Set the VM, image, and networking configuration +VM_NAME="langflow-dev" +IMAGE_FAMILY="debian-11" +IMAGE_PROJECT="debian-cloud" +BOOT_DISK_SIZE="100GB" +ZONE="us-central1-a" +REGION="us-central1" +VPC_NAME="default" +SUBNET_NAME="default" +SUBNET_RANGE="10.128.0.0/20" +NAT_GATEWAY_NAME="nat-gateway" +CLOUD_ROUTER_NAME="nat-client" + +# Set the GCP project's compute region +gcloud config set compute/region $REGION + +# Check if the VPC exists, and create it if not +vpc_exists=$(gcloud compute networks list --filter="name=$VPC_NAME" --format="value(name)") +if [[ -z "$vpc_exists" ]]; then + gcloud compute networks create $VPC_NAME --subnet-mode=custom +fi + +# Check if the subnet exists, and create it if not +subnet_exists=$(gcloud compute networks subnets list --filter="name=$SUBNET_NAME AND region=$REGION" --format="value(name)") +if [[ -z "$subnet_exists" ]]; then + gcloud compute networks subnets create $SUBNET_NAME --network=$VPC_NAME --region=$REGION --range=$SUBNET_RANGE +fi + +# Create a firewall rule to allow TCP port 8080 for all instances in the VPC +firewall_8080_exists=$(gcloud compute firewall-rules list --filter="name=allow-tcp-8080" --format="value(name)") +if [[ -z "$firewall_8080_exists" ]]; then + gcloud compute firewall-rules create allow-tcp-8080 --network $VPC_NAME --allow tcp:8080 --source-ranges 0.0.0.0/0 --direction INGRESS +fi + +# Create a firewall rule to allow IAP traffic +firewall_iap_exists=$(gcloud compute firewall-rules list --filter="name=allow-iap" --format="value(name)") +if [[ -z "$firewall_iap_exists" ]]; then + gcloud compute firewall-rules create allow-iap --network $VPC_NAME --allow tcp:80,tcp:443 --source-ranges 35.235.240.0/20 --direction INGRESS +fi + +# Define the startup script as a multiline Bash here-doc +STARTUP_SCRIPT=$(cat <<'EOF' +#!/bin/bash + +# Update and upgrade the system +apt -y update +apt -y upgrade + +# Install Python 3 pip, Langflow, and Nginx +apt -y install python3-pip +pip install langflow +apt-get -y install nginx + +# Configure Nginx for Langflow +touch /etc/nginx/sites-available/langflow-app +echo "server { + listen 0.0.0.0:8080; + + location / { + proxy_pass http://127.0.0.1:7860; + proxy_set_header Host "\$host"; + proxy_set_header X-Real-IP "\$remote_addr"; + proxy_set_header X-Forwarded-For "\$proxy_add_x_forwarded_for"; + } +}" >> /etc/nginx/sites-available/langflow-app +ln -s /etc/nginx/sites-available/langflow-app /etc/nginx/sites-enabled/ +sudo nginx -t +sudo systemctl restart nginx +langflow +EOF +) + +# Create a temporary file to store the startup script +tempfile=$(mktemp) +echo "$STARTUP_SCRIPT" > $tempfile + +# Create the VM instance with the specified configuration and startup script +gcloud compute instances create $VM_NAME \ + --image-family $IMAGE_FAMILY \ + --image-project $IMAGE_PROJECT \ + --boot-disk-size $BOOT_DISK_SIZE \ + --machine-type=n1-standard-4 \ + --metadata-from-file startup-script=$tempfile \ + --zone $ZONE \ + --network $VPC_NAME \ + --subnet $SUBNET_NAME + +# Remove the temporary file after the VM is created +rm $tempfile diff --git a/scripts/deploy_langflow_gcp_spot.sh b/scripts/deploy_langflow_gcp_spot.sh new file mode 100644 index 000000000..065b6013f --- /dev/null +++ b/scripts/deploy_langflow_gcp_spot.sh @@ -0,0 +1,90 @@ +# Set the VM, image, and networking configuration +VM_NAME="langflow-dev" +IMAGE_FAMILY="debian-11" +IMAGE_PROJECT="debian-cloud" +BOOT_DISK_SIZE="100GB" +ZONE="us-central1-a" +REGION="us-central1" +VPC_NAME="default" +SUBNET_NAME="default" +SUBNET_RANGE="10.128.0.0/20" +NAT_GATEWAY_NAME="nat-gateway" +CLOUD_ROUTER_NAME="nat-client" + +# Set the GCP project's compute region +gcloud config set compute/region $REGION + +# Check if the VPC exists, and create it if not +vpc_exists=$(gcloud compute networks list --filter="name=$VPC_NAME" --format="value(name)") +if [[ -z "$vpc_exists" ]]; then + gcloud compute networks create $VPC_NAME --subnet-mode=custom +fi + +# Check if the subnet exists, and create it if not +subnet_exists=$(gcloud compute networks subnets list --filter="name=$SUBNET_NAME AND region=$REGION" --format="value(name)") +if [[ -z "$subnet_exists" ]]; then + gcloud compute networks subnets create $SUBNET_NAME --network=$VPC_NAME --region=$REGION --range=$SUBNET_RANGE +fi + +# Create a firewall rule to allow TCP port 8080 for all instances in the VPC +firewall_8080_exists=$(gcloud compute firewall-rules list --filter="name=allow-tcp-8080" --format="value(name)") +if [[ -z "$firewall_8080_exists" ]]; then + gcloud compute firewall-rules create allow-tcp-8080 --network $VPC_NAME --allow tcp:8080 --source-ranges 0.0.0.0/0 --direction INGRESS +fi + +# Create a firewall rule to allow IAP traffic +firewall_iap_exists=$(gcloud compute firewall-rules list --filter="name=allow-iap" --format="value(name)") +if [[ -z "$firewall_iap_exists" ]]; then + gcloud compute firewall-rules create allow-iap --network $VPC_NAME --allow tcp:80,tcp:443 --source-ranges 35.235.240.0/20 --direction INGRESS +fi + +# Define the startup script as a multiline Bash here-doc +STARTUP_SCRIPT=$(cat <<'EOF' +#!/bin/bash + +# Update and upgrade the system +apt -y update +apt -y upgrade + +# Install Python 3 pip, Langflow, and Nginx +apt -y install python3-pip +pip install langflow +apt-get -y install nginx + +# Configure Nginx for Langflow +touch /etc/nginx/sites-available/langflow-app +echo "server { + listen 0.0.0.0:8080; + + location / { + proxy_pass http://127.0.0.1:7860; + proxy_set_header Host "\$host"; + proxy_set_header X-Real-IP "\$remote_addr"; + proxy_set_header X-Forwarded-For "\$proxy_add_x_forwarded_for"; + } +}" >> /etc/nginx/sites-available/langflow-app +ln -s /etc/nginx/sites-available/langflow-app /etc/nginx/sites-enabled/ +sudo nginx -t +sudo systemctl restart nginx +langflow +EOF +) + +# Create a temporary file to store the startup script +tempfile=$(mktemp) +echo "$STARTUP_SCRIPT" > $tempfile + +# Create the VM instance with the specified configuration and startup script +gcloud compute instances create $VM_NAME \ + --image-family $IMAGE_FAMILY \ + --image-project $IMAGE_PROJECT \ + --boot-disk-size $BOOT_DISK_SIZE \ + --machine-type=n1-standard-4 \ + --metadata-from-file startup-script=$tempfile \ + --zone $ZONE \ + --network $VPC_NAME \ + --subnet $SUBNET_NAME \ + -preemptible + +# Remove the temporary file after the VM is created +rm $tempfile diff --git a/scripts/walkthroughtutorial.md b/scripts/walkthroughtutorial.md new file mode 100644 index 000000000..fa6e3c11d --- /dev/null +++ b/scripts/walkthroughtutorial.md @@ -0,0 +1,86 @@ +# Deploy Langflow on Google Cloud Platform + +**Duration**: 45 minutes +**Author**: [Robert Wilkins III](https://www.linkedin.com/in/robertwilkinsiii) + +## Introduction + +In this tutorial, you will learn how to deploy Langflow on [Google Cloud Platform](https://cloud.google.com/) (GCP) using Google Cloud Shell. + +This tutorial assumes you have a GCP account and basic knowledge of Google Cloud Shell. If you're not familiar with Cloud Shell, you can review the [Cloud Shell documentation](https://cloud.google.com/shell/docs). + +## Set up your environment + +Before you start, make sure you have the following prerequisites: + +- A GCP account with the necessary permissions to create resources +- A project on GCP where you want to deploy Langflow + +[**Select your GCP project**] + + + +In the next step, you'll configure the GCP environment and deploy Langflow. + +## Configure the GCP environment and deploy Langflow +Run the deploy_langflow_gcp.sh script to configure the GCP environment and deploy Langflow: + +```sh +gcloud config set project +bash ./deploy_langflow_gcp.sh +``` + +The script will: + +1. Check if the required resources (VPC, subnet, firewall rules, and Cloud Router) exist and create them if needed +2. Create a startup script to install Python, Langflow, and Nginx +3. Create a Compute Engine VM instance with the specified configuration and startup script +4. Configure Nginx to serve Langflow on TCP port 8080 + + +> The process may take approximately 30 minutes to complete. Rest assured that progress is being made, and you'll be able to proceed once the process is finished. + +In the next step, you'll learn how to connect to the Langflow VM. + +## Connect to the Langflow VM +To connect to your new Langflow VM, follow these steps: + +1. Navigate to the [VM instances](https://console.cloud.google.com/compute/instances) page and click on the external IP for your VM. Make sure to use HTTP and set the port to 8080 +
**or** +3. Run the following command to display the URL for your Langflow environment: +```bash +export LANGFLOW_IP=$(gcloud compute instances list --filter="NAME=langflow-dev" --format="value(EXTERNAL_IP)") + +echo http://$LANGFLOW_IP:8080 +``` + +4. Click on the Langflow URL in cloudshell to be greeted by the Langflow Dev environment + +Congratulations! You have successfully deployed Langflow on Google Cloud Platform. + + + +## Cleanup +If you want to remove the resources created during this tutorial, you can use the following commands: + +```sql +gcloud compute instances delete langflow-dev --zone us-central1-a --quiet +``` +The following network settings and services are used during this walkthrough. If you plan to continue using the project after the walkthrough, you may keep these configurations in place. + +However, if you decide to remove them after completing the walkthrough, you can use the following gcloud commands: + + +> These commands will delete the firewall rules and network configurations created during the walkthrough. Make sure to run them only if you no longer need these settings. + +``` +gcloud compute firewall-rules delete allow-tcp-8080 --quiet + +gcloud compute firewall-rules delete allow-iap --quiet + +gcloud compute networks subnets delete default --region us-central1 --quiet + +gcloud compute networks delete default --quiet +``` diff --git a/scripts/walkthroughtutorial_spot.md b/scripts/walkthroughtutorial_spot.md new file mode 100644 index 000000000..751f03d78 --- /dev/null +++ b/scripts/walkthroughtutorial_spot.md @@ -0,0 +1,83 @@ +# Deploy Langflow on Google Cloud Platform + +**Duration**: 45 minutes +**Author**: [Robert Wilkins III](https://www.linkedin.com/in/robertwilkinsiii) + +## Introduction + +In this tutorial, you will learn how to deploy Langflow on [Google Cloud Platform](https://cloud.google.com/) (GCP) using Google Cloud Shell. + +This tutorial assumes you have a GCP account and basic knowledge of Google Cloud Shell. If you're not familiar with Cloud Shell, you can review the [Cloud Shell documentation](https://cloud.google.com/shell/docs). + +## Set up your environment + +Before you start, make sure you have the following prerequisites: + +- A GCP account with the necessary permissions to create resources +- A project on GCP where you want to deploy Langflow + +[**Select your GCP project**] + + + +In the next step, you'll configure the GCP environment and deploy Langflow. + +## Configure the GCP environment and deploy Langflow +Run the deploy_langflow_gcp_spot.sh script to configure the GCP environment and deploy Langflow: + +```sh +gcloud config set project +bash ./deploy_langflow_gcp.sh +``` + +The script will: + +1. Check if the required resources (VPC, subnet, firewall rules, and Cloud Router) exist and create them if needed +2. Create a startup script to install Python, Langflow, and Nginx +3. Create a Compute Engine VM instance with the specified configuration and startup script +4. Configure Nginx to serve Langflow on TCP port 8080 + +> The process may take approximately 30 minutes to complete. Rest assured that progress is being made, and you'll be able to proceed once the process is finished. + +In the next step, you'll learn how to connect to the Langflow VM. + +## Connect to the Langflow VM +To connect to your new Langflow VM, follow these steps: + +1. Navigate to the [VM instances](https://console.cloud.google.com/compute/instances) page and click on the external IP for your VM. Make sure to use HTTP and set the port to 8080 +
**or** +3. Run the following command to display the URL for your Langflow environment: +```bash +export LANGFLOW_IP=$(gcloud compute instances list --filter="NAME=langflow-dev" --format="value(EXTERNAL_IP)") + +echo http://$LANGFLOW_IP:8080 +``` + +4. Click on the Langflow URL in cloudshell to be greeted by the Langflow Dev environment + +Congratulations! You have successfully deployed Langflow on Google Cloud Platform. + + + +## Cleanup +If you want to remove the resources created during this tutorial, you can use the following commands: + +```sql +gcloud compute instances delete langflow-dev --zone us-central1-a --quiet +``` +The following network settings and services are used during this walkthrough. If you plan to continue using the project after the walkthrough, you may keep these configurations in place. + +However, if you decide to remove them after completing the walkthrough, you can use the following gcloud commands: +> These commands will delete the firewall rules and network configurations created during the walkthrough. Make sure to run them only if you no longer need these settings. + +``` +gcloud compute firewall-rules delete allow-tcp-8080 --quiet + +gcloud compute firewall-rules delete allow-iap --quiet + +gcloud compute networks subnets delete default --region us-central1 --quiet + +gcloud compute networks delete default --quiet +``` diff --git a/src/backend/langflow/__main__.py b/src/backend/langflow/__main__.py index ea9386d7f..1f16744ae 100644 --- a/src/backend/langflow/__main__.py +++ b/src/backend/langflow/__main__.py @@ -1,4 +1,3 @@ -import logging import multiprocessing import platform from pathlib import Path @@ -7,8 +6,10 @@ import typer from fastapi.staticfiles import StaticFiles from langflow.main import create_app +from langflow.settings import settings +from langflow.utils.logger import configure -logger = logging.getLogger(__name__) +app = typer.Typer() def get_number_of_workers(workers=None): @@ -17,9 +18,28 @@ def get_number_of_workers(workers=None): return workers +def update_settings(config: str): + """Update the settings from a config file.""" + if config: + settings.update_from_yaml(config) + + +@app.command() def serve( - host: str = "127.0.0.1", workers: int = 1, timeout: int = 60, port: int = 7860 + host: str = typer.Option("127.0.0.1", help="Host to bind the server to."), + workers: int = typer.Option(1, help="Number of worker processes."), + timeout: int = typer.Option(60, help="Worker timeout in seconds."), + port: int = typer.Option(7860, help="Port to listen on."), + config: str = typer.Option("config.yaml", help="Path to the configuration file."), + log_level: str = typer.Option("info", help="Logging level."), + log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file."), ): + """ + Run the Langflow server. + """ + + configure(log_level=log_level, log_file=log_file) + update_settings(config) app = create_app() # get the directory of the current file path = Path(__file__).parent @@ -39,10 +59,10 @@ def serve( if platform.system() in ["Darwin", "Windows"]: # Run using uvicorn on MacOS and Windows # Windows doesn't support gunicorn - # MacOS requires a env variable to be set to use gunicorn + # MacOS requires an env variable to be set to use gunicorn import uvicorn - uvicorn.run(app, host=host, port=port, log_level="info") + uvicorn.run(app, host=host, port=port, log_level=log_level) else: from langflow.server import LangflowApplication @@ -50,7 +70,7 @@ def serve( def main(): - typer.run(serve) + app() if __name__ == "__main__": diff --git a/src/backend/langflow/api/base.py b/src/backend/langflow/api/base.py new file mode 100644 index 000000000..084e04d65 --- /dev/null +++ b/src/backend/langflow/api/base.py @@ -0,0 +1,80 @@ +from pydantic import BaseModel, validator + +from langflow.graph.utils import extract_input_variables_from_prompt + + +class Code(BaseModel): + code: str + + +class Prompt(BaseModel): + template: str + + +# Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}} +class CodeValidationResponse(BaseModel): + imports: dict + function: dict + + @validator("imports") + def validate_imports(cls, v): + return v or {"errors": []} + + @validator("function") + def validate_function(cls, v): + return v or {"errors": []} + + +class PromptValidationResponse(BaseModel): + input_variables: list + + +INVALID_CHARACTERS = { + " ", + ",", + ".", + ":", + ";", + "!", + "?", + "/", + "\\", + "(", + ")", + "[", + "]", + "{", + "}", +} + + +def validate_prompt(template: str): + input_variables = extract_input_variables_from_prompt(template) + + # Check if there are invalid characters in the input_variables + input_variables = check_input_variables(input_variables) + + return PromptValidationResponse(input_variables=input_variables) + + +def check_input_variables(input_variables: list): + invalid_chars = [] + fixed_variables = [] + for variable in input_variables: + new_var = variable + for char in INVALID_CHARACTERS: + if char in variable: + invalid_chars.append(char) + new_var = new_var.replace(char, "") + fixed_variables.append(new_var) + if new_var != variable: + input_variables.remove(variable) + input_variables.append(new_var) + # If any of the input_variables is not in the fixed_variables, then it means that + # there are invalid characters in the input_variables + if any(var not in fixed_variables for var in input_variables): + raise ValueError( + f"Invalid input variables: {input_variables}. Please, use something like {fixed_variables} instead." + ) + + return input_variables diff --git a/src/backend/langflow/api/endpoints.py b/src/backend/langflow/api/endpoints.py index 7214ec6a6..b8290e691 100644 --- a/src/backend/langflow/api/endpoints.py +++ b/src/backend/langflow/api/endpoints.py @@ -1,12 +1,14 @@ +import logging from typing import Any, Dict from fastapi import APIRouter, HTTPException -from langflow.interface.run import process_data_graph +from langflow.interface.run import process_graph_cached from langflow.interface.types import build_langchain_types_dict # build router router = APIRouter() +logger = logging.getLogger(__name__) @router.get("/all") @@ -17,6 +19,8 @@ def get_all(): @router.post("/predict") def get_load(data: Dict[str, Any]): try: - return process_data_graph(data) + return process_graph_cached(data) except Exception as e: - return HTTPException(status_code=500, detail=str(e)) + # Log stack trace + logger.exception(e) + raise HTTPException(status_code=500, detail=str(e)) from e diff --git a/src/backend/langflow/api/list_endpoints.py b/src/backend/langflow/api/list_endpoints.py deleted file mode 100644 index 15946a2db..000000000 --- a/src/backend/langflow/api/list_endpoints.py +++ /dev/null @@ -1,58 +0,0 @@ -from fastapi import APIRouter - -from langflow.interface.listing import list_type - -# build router -router = APIRouter( - prefix="/list", - tags=["list"], -) - - -@router.get("/") -def read_items(): - """List all components""" - return [ - "chains", - "agents", - "prompts", - "llms", - "tools", - ] - - -@router.get("/chains") -def list_chains(): - """List all chain types""" - return list_type("chains") - - -@router.get("/agents") -def list_agents(): - """List all agent types""" - # return list(agents.loading.AGENT_TO_CLASS.keys()) - return list_type("agents") - - -@router.get("/prompts") -def list_prompts(): - """List all prompt types""" - return list_type("prompts") - - -@router.get("/llms") -def list_llms(): - """List all llm types""" - return list_type("llms") - - -@router.get("/memories") -def list_memories(): - """List all memory types""" - return list_type("memories") - - -@router.get("/tools") -def list_tools(): - """List all load tools""" - return list_type("tools") diff --git a/src/backend/langflow/api/signature.py b/src/backend/langflow/api/signature.py deleted file mode 100644 index 96b654dbe..000000000 --- a/src/backend/langflow/api/signature.py +++ /dev/null @@ -1,63 +0,0 @@ -from fastapi import APIRouter, HTTPException - -from langflow.interface.signature import get_signature - -# build router -router = APIRouter( - prefix="/signatures", - tags=["signatures"], -) - - -@router.get("/chain") -def get_chain(name: str): - """Get the signature of a chain.""" - try: - return get_signature(name, "chains") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Chain not found") from exc - - -@router.get("/agent") -def get_agent(name: str): - """Get the signature of an agent.""" - try: - return get_signature(name, "agents") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Agent not found") from exc - - -@router.get("/prompt") -def get_prompt(name: str): - """Get the signature of a prompt.""" - try: - return get_signature(name, "prompts") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Prompt not found") from exc - - -@router.get("/llm") -def get_llm(name: str): - """Get the signature of an llm.""" - try: - return get_signature(name, "llms") - except ValueError as exc: - raise HTTPException(status_code=404, detail="LLM not found") from exc - - -@router.get("/memory") -def get_memory(name: str): - """Get the signature of a memory.""" - try: - return get_signature(name, "memories") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Memory not found") from exc - - -@router.get("/tool") -def get_tool(name: str): - """Get the signature of a tool.""" - try: - return get_signature(name, "tools") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Tool not found") from exc diff --git a/src/backend/langflow/api/validate.py b/src/backend/langflow/api/validate.py new file mode 100644 index 000000000..a60bcc506 --- /dev/null +++ b/src/backend/langflow/api/validate.py @@ -0,0 +1,35 @@ +from fastapi import APIRouter, HTTPException + +from langflow.api.base import ( + Code, + CodeValidationResponse, + Prompt, + PromptValidationResponse, + validate_prompt, +) +from langflow.utils.logger import logger +from langflow.utils.validate import validate_code + +# build router +router = APIRouter(prefix="/validate", tags=["validate"]) + + +@router.post("/code", status_code=200, response_model=CodeValidationResponse) +def post_validate_code(code: Code): + try: + errors = validate_code(code.code) + return CodeValidationResponse( + imports=errors.get("imports", {}), + function=errors.get("function", {}), + ) + except Exception as e: + return HTTPException(status_code=500, detail=str(e)) + + +@router.post("/prompt", status_code=200, response_model=PromptValidationResponse) +def post_validate_prompt(prompt: Prompt): + try: + return validate_prompt(prompt.template) + except Exception as e: + logger.exception(e) + raise HTTPException(status_code=500, detail=str(e)) from e diff --git a/src/backend/langflow/cache/__init__.py b/src/backend/langflow/cache/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/cache/utils.py b/src/backend/langflow/cache/utils.py new file mode 100644 index 000000000..310f3be80 --- /dev/null +++ b/src/backend/langflow/cache/utils.py @@ -0,0 +1,149 @@ +import base64 +import contextlib +import functools +import hashlib +import json +import os +import tempfile +from collections import OrderedDict +from pathlib import Path + +import dill # type: ignore + + +def create_cache_folder(func): + def wrapper(*args, **kwargs): + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + + # Create the destination folder if it doesn't exist + os.makedirs(cache_path, exist_ok=True) + + return func(*args, **kwargs) + + return wrapper + + +def memoize_dict(maxsize=128): + cache = OrderedDict() + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + hashed = compute_dict_hash(args[0]) + key = (func.__name__, hashed, frozenset(kwargs.items())) + if key not in cache: + result = func(*args, **kwargs) + cache[key] = result + if len(cache) > maxsize: + cache.popitem(last=False) + else: + result = cache[key] + return result + + def clear_cache(): + cache.clear() + + wrapper.clear_cache = clear_cache + return wrapper + + return decorator + + +PREFIX = "langflow_cache" + + +@create_cache_folder +def clear_old_cache_files(max_cache_size: int = 3): + cache_dir = Path(tempfile.gettempdir()) / PREFIX + cache_files = list(cache_dir.glob("*.dill")) + + if len(cache_files) > max_cache_size: + cache_files_sorted_by_mtime = sorted( + cache_files, key=lambda x: x.stat().st_mtime, reverse=True + ) + + for cache_file in cache_files_sorted_by_mtime[max_cache_size:]: + with contextlib.suppress(OSError): + os.remove(cache_file) + + +def compute_dict_hash(graph_data): + graph_data = filter_json(graph_data) + + cleaned_graph_json = json.dumps(graph_data, sort_keys=True) + return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() + + +def filter_json(json_data): + filtered_data = json_data.copy() + + # Remove 'viewport' and 'chatHistory' keys + if "viewport" in filtered_data: + del filtered_data["viewport"] + if "chatHistory" in filtered_data: + del filtered_data["chatHistory"] + + # Filter nodes + if "nodes" in filtered_data: + for node in filtered_data["nodes"]: + if "position" in node: + del node["position"] + if "positionAbsolute" in node: + del node["positionAbsolute"] + if "selected" in node: + del node["selected"] + if "dragging" in node: + del node["dragging"] + + return filtered_data + + +@create_cache_folder +def save_binary_file(content: str, file_name: str, accepted_types: list[str]) -> str: + """ + Save a binary file to the specified folder. + + Args: + content: The content of the file as a bytes object. + file_name: The name of the file, including its extension. + + Returns: + The path to the saved file. + """ + if not any(file_name.endswith(suffix) for suffix in accepted_types): + raise ValueError(f"File {file_name} is not accepted") + + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + + data = content.split(",")[1] + decoded_bytes = base64.b64decode(data) + + # Create the full file path + file_path = os.path.join(cache_path, file_name) + + # Save the binary content to the file + with open(file_path, "wb") as file: + file.write(decoded_bytes) + + return file_path + + +@create_cache_folder +def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" + with cache_path.open("wb") as cache_file: + dill.dump(chat_data, cache_file) + + if clean_old_cache_files: + clear_old_cache_files() + + +@create_cache_folder +def load_cache(hash_val): + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" + if cache_path.exists(): + with cache_path.open("rb") as cache_file: + return dill.load(cache_file) + return None diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 9b03aaeb1..9236d5996 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -1,27 +1,128 @@ chains: - LLMChain - LLMMathChain - - LLMChecker - # - ConversationChain + - LLMCheckerChain + - ConversationChain + - SeriesCharacterChain + - MidJourneyPromptChain + - TimeTravelGuideChain + - SQLDatabaseChain agents: - ZeroShotAgent + - JsonAgent + - CSVAgent + - initialize_agent + - VectorStoreAgent + - VectorStoreRouterAgent + - SQLAgent prompts: - PromptTemplate - FewShotPromptTemplate + - ZeroShotPrompt + # Wait more tests + # - ChatPromptTemplate + # - SystemMessagePromptTemplate + # - HumanMessagePromptTemplate llms: - OpenAI - - OpenAIChat + # - AzureOpenAI + - ChatOpenAI + - HuggingFaceHub + - LlamaCpp tools: - Search - PAL-MATH - Calculator - Serper Search + - Tool + - PythonFunction + - JsonSpec + - News API + - TMDB API + - Podcast API + - QuerySQLDataBaseTool + - InfoSQLDatabaseTool + - ListSQLDatabaseTool + # - QueryCheckerTool + - BingSearchRun + - GoogleSearchRun + - GoogleSearchResults + - JsonListKeysTool + - JsonGetValueTool + - PythonREPLTool + - PythonAstREPLTool + - RequestsGetTool + - RequestsPostTool + - RequestsPatchTool + - RequestsPutTool + - RequestsDeleteTool + - WikipediaQueryRun + - WolframAlphaQueryRun + +wrappers: + - RequestsWrapper + +toolkits: + - OpenAPIToolkit + - JsonToolkit + - VectorStoreInfo + - VectorStoreRouterToolkit memories: - # - ConversationBufferMemory + - ConversationBufferMemory + - ConversationSummaryMemory + - ConversationKGMemory + +embeddings: + - OpenAIEmbeddings + +vectorstores: + - Chroma + +documentloaders: + - AirbyteJSONLoader + - CoNLLULoader + - CSVLoader + - UnstructuredEmailLoader + - EverNoteLoader + - FacebookChatLoader + - GutenbergLoader + - BSHTMLLoader + - UnstructuredHTMLLoader + # - UnstructuredImageLoader # Issue with Python 3.11 (https://github.com/Unstructured-IO/unstructured-inference/issues/83) + - UnstructuredMarkdownLoader + - PyPDFLoader + - UnstructuredPowerPointLoader + - SRTLoader + - TelegramChatLoader + - TextLoader + - UnstructuredWordDocumentLoader + - WebBaseLoader + - AZLyricsLoader + - CollegeConfidentialLoader + - HNLoader + - IFixitLoader + - IMSDbLoader + - GitbookLoader + - ReadTheDocsLoader + +textsplitters: + - CharacterTextSplitter + +utilities: + - BingSearchAPIWrapper + - GoogleSearchAPIWrapper + - GoogleSerperAPIWrapper + - SearxResults + - SearxSearchWrapper + - SerpAPIWrapper + - WikipediaAPIWrapper + - WolframAlphaAPIWrapper + # - ZapierNLAWrapper + - SQLDatabase dev: false diff --git a/src/backend/langflow/custom/customs.py b/src/backend/langflow/custom/customs.py index cea81ac1a..d45221be7 100644 --- a/src/backend/langflow/custom/customs.py +++ b/src/backend/langflow/custom/customs.py @@ -1,42 +1,23 @@ -from langchain.agents.mrkl import prompt +from langflow.template import nodes + +# These should always be instantiated +CUSTOM_NODES = { + "prompts": {"ZeroShotPrompt": nodes.ZeroShotPromptNode()}, + "tools": {"PythonFunction": nodes.PythonFunctionNode(), "Tool": nodes.ToolNode()}, + "agents": { + "JsonAgent": nodes.JsonAgentNode(), + "CSVAgent": nodes.CSVAgentNode(), + "initialize_agent": nodes.InitializeAgentNode(), + "VectorStoreAgent": nodes.VectorStoreAgentNode(), + "VectorStoreRouterAgent": nodes.VectorStoreRouterAgentNode(), + "SQLAgent": nodes.SQLAgentNode(), + }, + "utilities": { + "SQLDatabase": nodes.SQLDatabaseNode(), + }, +} -def get_custom_prompts(): - """Get custom prompts.""" - - return { - "ZeroShotPrompt": { - "template": { - "_type": "zero_shot", - "prefix": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.PREFIX, - }, - "suffix": { - "type": "str", - "required": True, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.SUFFIX, - }, - "format_instructions": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.FORMAT_INSTRUCTIONS, - }, - }, - "description": "Prompt template for Zero Shot Agent.", - "base_classes": ["BasePromptTemplate"], - } - } +def get_custom_nodes(node_type: str): + """Get custom nodes.""" + return CUSTOM_NODES.get(node_type, {}) diff --git a/src/backend/langflow/graph/__init__.py b/src/backend/langflow/graph/__init__.py new file mode 100644 index 000000000..097b7a695 --- /dev/null +++ b/src/backend/langflow/graph/__init__.py @@ -0,0 +1,4 @@ +from langflow.graph.base import Edge, Node +from langflow.graph.graph import Graph + +__all__ = ["Graph", "Node", "Edge"] diff --git a/src/backend/langflow/graph/base.py b/src/backend/langflow/graph/base.py new file mode 100644 index 000000000..6d998eed6 --- /dev/null +++ b/src/backend/langflow/graph/base.py @@ -0,0 +1,270 @@ +# Description: Graph class for building a graph of nodes and edges +# Insights: +# - Defer prompts building to the last moment or when they have all the tools +# - Build each inner agent first, then build the outer agent + +import contextlib +import types +import warnings +from copy import deepcopy +from typing import Any, Dict, List, Optional + +from langflow.cache import utils as cache_utils +from langflow.graph.constants import DIRECT_TYPES +from langflow.interface import loading +from langflow.interface.listing import ALL_TYPES_DICT +from langflow.utils.logger import logger + + +class Node: + def __init__(self, data: Dict, base_type: Optional[str] = None) -> None: + self.id: str = data["id"] + self._data = data + self.edges: List[Edge] = [] + self.base_type: Optional[str] = base_type + self._parse_data() + self._built_object = None + self._built = False + + def _parse_data(self) -> None: + self.data = self._data["data"] + self.output = self.data["node"]["base_classes"] + template_dicts = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + + self.required_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if value["required"] + ] + self.optional_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if not value["required"] + ] + + template_dict = self.data["node"]["template"] + self.node_type = ( + self.data["type"] if "Tool" not in self.output else template_dict["_type"] + ) + if self.base_type is None: + for base_type, value in ALL_TYPES_DICT.items(): + if self.node_type in value: + self.base_type = base_type + break + + def _build_params(self): + # Some params are required, some are optional + # but most importantly, some params are python base classes + # like str and others are LangChain objects like LLMChain, BasePromptTemplate + # so we need to be able to distinguish between the two + + # The dicts with "type" == "str" are the ones that are python base classes + # and most likely have a "value" key + + # So for each key besides "_type" in the template dict, we have a dict + # with a "type" key. If the type is not "str", then we need to get the + # edge that connects to that node and get the Node with the required data + # and use that as the value for the param + # If the type is "str", then we need to get the value of the "value" key + # and use that as the value for the param + template_dict = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + params = {} + for key, value in template_dict.items(): + if key == "_type": + continue + # If the type is not transformable to a python base class + # then we need to get the edge that connects to this node + if value.get("type") == "file": + # Load the type in value.get('suffixes') using + # what is inside value.get('content') + # value.get('value') is the file name + file_name = value.get("value") + content = value.get("content") + type_to_load = value.get("suffixes") + file_path = cache_utils.save_binary_file( + content=content, file_name=file_name, accepted_types=type_to_load + ) + + params[key] = file_path + + elif value.get("type") not in DIRECT_TYPES: + # Get the edge that connects to this node + edges = [ + edge + for edge in self.edges + if edge.target == self and edge.matched_type in value["type"] + ] + + # Get the output of the node that the edge connects to + # if the value['list'] is True, then there will be more + # than one time setting to params[key] + # so we need to append to a list if it exists + # or create a new list if it doesn't + + if value["required"] and not edges: + # If a required parameter is not found, raise an error + raise ValueError( + f"Required input {key} for module {self.node_type} not found" + ) + elif value["list"]: + # If this is a list parameter, append all sources to a list + params[key] = [edge.source for edge in edges] + elif edges: + # If a single parameter is found, use its source + params[key] = edges[0].source + + elif value["required"] or value.get("value"): + # If value does not have value this still passes + # but then gives a keyError + # so we need to check if value has value + new_value = value.get("value") + if new_value is None: + warnings.warn(f"Value for {key} in {self.node_type} is None. ") + if value.get("type") == "int": + with contextlib.suppress(TypeError, ValueError): + new_value = int(new_value) # type: ignore + params[key] = new_value + + # Add _type to params + self.params = params + + def _build(self): + # The params dict is used to build the module + # it contains values and keys that point to nodes which + # have their own params dict + # When build is called, we iterate through the params dict + # and if the value is a node, we call build on that node + # and use the output of that build as the value for the param + # if the value is not a node, then we use the value as the param + # and continue + # Another aspect is that the node_type is the class that we need to import + # and instantiate with these built params + logger.debug(f"Building {self.node_type}") + # Build each node in the params dict + for key, value in self.params.copy().items(): + # Check if Node or list of Nodes and not self + # to avoid recursion + if isinstance(value, Node): + if value == self: + del self.params[key] + continue + result = value.build() + # If the key is "func", then we need to use the run method + if key == "func" and not isinstance(result, types.FunctionType): + # func can be PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n') + # so we need to check if there is an attribute called run + if hasattr(result, "run"): + result = result.run # type: ignore + elif hasattr(result, "get_function"): + result = result.get_function() # type: ignore + + self.params[key] = result + elif isinstance(value, list) and all( + isinstance(node, Node) for node in value + ): + self.params[key] = [node.build() for node in value] # type: ignore + + # Get the class from LANGCHAIN_TYPES_DICT + # and instantiate it with the params + # and return the instance + + try: + self._built_object = loading.instantiate_class( + node_type=self.node_type, + base_type=self.base_type, + params=self.params, + ) + except Exception as exc: + raise ValueError( + f"Error building node {self.node_type}: {str(exc)}" + ) from exc + + if self._built_object is None: + raise ValueError(f"Node type {self.node_type} not found") + + self._built = True + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + + #! Deepcopy is breaking for vectorstores + if self.base_type in [ + "vectorstores", + "VectorStoreRouterAgent", + "VectorStoreAgent", + "VectorStoreInfo", + ] or self.node_type in [ + "VectorStoreInfo", + "VectorStoreRouterToolkit", + "SQLDatabase", + ]: + return self._built_object + return deepcopy(self._built_object) + + def add_edge(self, edge: "Edge") -> None: + self.edges.append(edge) + + def __repr__(self) -> str: + return f"Node(id={self.id}, data={self.data})" + + def __eq__(self, __o: object) -> bool: + return self.id == __o.id if isinstance(__o, Node) else False + + def __hash__(self) -> int: + return id(self) + + +class Edge: + def __init__(self, source: "Node", target: "Node"): + self.source: "Node" = source + self.target: "Node" = target + self.validate_edge() + + def validate_edge(self) -> None: + # Validate that the outputs of the source node are valid inputs + # for the target node + self.source_types = self.source.output + self.target_reqs = self.target.required_inputs + self.target.optional_inputs + # Both lists contain strings and sometimes a string contains the value we are + # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] + # so we need to check if any of the strings in source_types is in target_reqs + self.valid = any( + output in target_req + for output in self.source_types + for target_req in self.target_reqs + ) + # Get what type of input the target node is expecting + + self.matched_type = next( + ( + output + for output in self.source_types + for target_req in self.target_reqs + if output in target_req + ), + None, + ) + no_matched_type = self.matched_type is None + if no_matched_type: + logger.debug(self.source_types) + logger.debug(self.target_reqs) + if no_matched_type: + raise ValueError( + f"Edge between {self.source.node_type} and {self.target.node_type} " + f"has no matched type" + ) + + def __repr__(self) -> str: + return ( + f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}" + f", matched_type={self.matched_type})" + ) diff --git a/src/backend/langflow/graph/constants.py b/src/backend/langflow/graph/constants.py new file mode 100644 index 000000000..8372e13a7 --- /dev/null +++ b/src/backend/langflow/graph/constants.py @@ -0,0 +1 @@ +DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"] diff --git a/src/backend/langflow/graph/graph.py b/src/backend/langflow/graph/graph.py new file mode 100644 index 000000000..b289d5c31 --- /dev/null +++ b/src/backend/langflow/graph/graph.py @@ -0,0 +1,166 @@ +from typing import Dict, List, Type, Union + +from langflow.graph.base import Edge, Node +from langflow.graph.nodes import ( + AgentNode, + ChainNode, + DocumentLoaderNode, + EmbeddingNode, + FileToolNode, + LLMNode, + MemoryNode, + PromptNode, + TextSplitterNode, + ToolkitNode, + ToolNode, + VectorStoreNode, + WrapperNode, +) +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.tools.constants import FILE_TOOLS +from langflow.interface.vector_store.base import vectorstore_creator +from langflow.interface.wrappers.base import wrapper_creator +from langflow.utils import payload + + +class Graph: + def __init__( + self, + nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], + edges: List[Dict[str, str]], + ) -> None: + self._nodes = nodes + self._edges = edges + self._build_graph() + + def _build_graph(self) -> None: + self.nodes = self._build_nodes() + self.edges = self._build_edges() + for edge in self.edges: + edge.source.add_edge(edge) + edge.target.add_edge(edge) + + # This is a hack to make sure that the LLM node is sent to + # the toolkit node + llm_node = None + for node in self.nodes: + node._build_params() + + if isinstance(node, LLMNode): + llm_node = node + + for node in self.nodes: + if isinstance(node, ToolkitNode): + node.params["llm"] = llm_node + # remove invalid nodes + self.nodes = [ + node + for node in self.nodes + if self._validate_node(node) + or (len(self.nodes) == 1 and len(self.edges) == 0) + ] + + def _validate_node(self, node: Node) -> bool: + # All nodes that do not have edges are invalid + return len(node.edges) > 0 + + def get_node(self, node_id: str) -> Union[None, Node]: + return next((node for node in self.nodes if node.id == node_id), None) + + def get_nodes_with_target(self, node: Node) -> List[Node]: + connected_nodes: List[Node] = [ + edge.source for edge in self.edges if edge.target == node + ] + return connected_nodes + + def build(self) -> List[Node]: + # Get root node + root_node = payload.get_root_node(self) + if root_node is None: + raise ValueError("No root node found") + return root_node.build() + + def get_node_neighbors(self, node: Node) -> Dict[Node, int]: + neighbors: Dict[Node, int] = {} + for edge in self.edges: + if edge.source == node: + neighbor = edge.target + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + elif edge.target == node: + neighbor = edge.source + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + return neighbors + + def _build_edges(self) -> List[Edge]: + # Edge takes two nodes as arguments, so we need to build the nodes first + # and then build the edges + # if we can't find a node, we raise an error + + edges: List[Edge] = [] + for edge in self._edges: + source = self.get_node(edge["source"]) + target = self.get_node(edge["target"]) + if source is None: + raise ValueError(f"Source node {edge['source']} not found") + if target is None: + raise ValueError(f"Target node {edge['target']} not found") + edges.append(Edge(source, target)) + return edges + + def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]: + node_type_map: Dict[str, Type[Node]] = { + **{t: PromptNode for t in prompt_creator.to_list()}, + **{t: AgentNode for t in agent_creator.to_list()}, + **{t: ChainNode for t in chain_creator.to_list()}, + **{t: ToolNode for t in tool_creator.to_list()}, + **{t: ToolkitNode for t in toolkits_creator.to_list()}, + **{t: WrapperNode for t in wrapper_creator.to_list()}, + **{t: LLMNode for t in llm_creator.to_list()}, + **{t: MemoryNode for t in memory_creator.to_list()}, + **{t: EmbeddingNode for t in embedding_creator.to_list()}, + **{t: VectorStoreNode for t in vectorstore_creator.to_list()}, + **{t: DocumentLoaderNode for t in documentloader_creator.to_list()}, + **{t: TextSplitterNode for t in textsplitter_creator.to_list()}, + } + + if node_type in FILE_TOOLS: + return FileToolNode + if node_type in node_type_map: + return node_type_map[node_type] + if node_lc_type in node_type_map: + return node_type_map[node_lc_type] + return Node + + def _build_nodes(self) -> List[Node]: + nodes: List[Node] = [] + for node in self._nodes: + node_data = node["data"] + node_type: str = node_data["type"] # type: ignore + node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore + + NodeClass = self._get_node_class(node_type, node_lc_type) + nodes.append(NodeClass(node)) + + return nodes + + def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]: + children = [] + node_types = [node.data["type"]] + if "node" in node.data: + node_types += node.data["node"]["base_classes"] + if node_type in node_types: + children.append(node) + return children diff --git a/src/backend/langflow/graph/nodes.py b/src/backend/langflow/graph/nodes.py new file mode 100644 index 000000000..018174334 --- /dev/null +++ b/src/backend/langflow/graph/nodes.py @@ -0,0 +1,160 @@ +from copy import deepcopy +from typing import Any, Dict, List, Optional, Union + +from langflow.graph.base import Node +from langflow.graph.utils import extract_input_variables_from_prompt + + +class AgentNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="agents") + + self.tools: List[ToolNode] = [] + self.chains: List[ChainNode] = [] + + def _set_tools_and_chains(self) -> None: + for edge in self.edges: + source_node = edge.source + if isinstance(source_node, ToolNode): + self.tools.append(source_node) + elif isinstance(source_node, ChainNode): + self.chains.append(source_node) + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._set_tools_and_chains() + # First, build the tools + for tool_node in self.tools: + tool_node.build() + + # Next, build the chains and the rest + for chain_node in self.chains: + chain_node.build(tools=self.tools) + + self._build() + + #! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents + if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]: + return self._built_object + return deepcopy(self._built_object) + + +class ToolNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="tools") + + +class PromptNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="prompts") + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + if ( + "input_variables" not in self.params + or self.params["input_variables"] is None + ): + self.params["input_variables"] = [] + # Check if it is a ZeroShotPrompt and needs a tool + if "ShotPrompt" in self.node_type: + tools = ( + [tool_node.build() for tool_node in tools] + if tools is not None + else [] + ) + self.params["tools"] = tools + prompt_params = [ + key + for key, value in self.params.items() + if isinstance(value, str) and key != "format_instructions" + ] + else: + prompt_params = ["template"] + for param in prompt_params: + prompt_text = self.params[param] + variables = extract_input_variables_from_prompt(prompt_text) + self.params["input_variables"].extend(variables) + self.params["input_variables"] = list(set(self.params["input_variables"])) + + self._build() + return deepcopy(self._built_object) + + +class ChainNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="chains") + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + # Check if the chain requires a PromptNode + for key, value in self.params.items(): + if isinstance(value, PromptNode): + # Build the PromptNode, passing the tools if available + self.params[key] = value.build(tools=tools, force=force) + + self._build() + + #! Cannot deepcopy SQLDatabaseChain + if self.node_type in ["SQLDatabaseChain"]: + return self._built_object + return deepcopy(self._built_object) + + +class LLMNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="llms") + + +class ToolkitNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="toolkits") + + +class FileToolNode(ToolNode): + def __init__(self, data: Dict): + super().__init__(data) + + +class WrapperNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="wrappers") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + if "headers" in self.params: + self.params["headers"] = eval(self.params["headers"]) + self._build() + return deepcopy(self._built_object) + + +class DocumentLoaderNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="documentloaders") + + +class EmbeddingNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="embeddings") + + +class VectorStoreNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="vectorstores") + + +class MemoryNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="memory") + + +class TextSplitterNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="textsplitters") diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py new file mode 100644 index 000000000..6d56e933e --- /dev/null +++ b/src/backend/langflow/graph/utils.py @@ -0,0 +1,19 @@ +import re + + +def validate_prompt(prompt: str): + """Validate prompt.""" + if extract_input_variables_from_prompt(prompt): + return prompt + + return fix_prompt(prompt) + + +def fix_prompt(prompt: str): + """Fix prompt.""" + return prompt + " {input}" + + +def extract_input_variables_from_prompt(prompt: str) -> list[str]: + """Extract input variables from prompt.""" + return re.findall(r"{(.*?)}", prompt) diff --git a/src/backend/langflow/interface/agents/__init__.py b/src/backend/langflow/interface/agents/__init__.py new file mode 100644 index 000000000..df15bc39b --- /dev/null +++ b/src/backend/langflow/interface/agents/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.agents.base import AgentCreator + +__all__ = ["AgentCreator"] diff --git a/src/backend/langflow/interface/agents/base.py b/src/backend/langflow/interface/agents/base.py new file mode 100644 index 000000000..1d23b481e --- /dev/null +++ b/src/backend/langflow/interface/agents/base.py @@ -0,0 +1,53 @@ +from typing import Dict, List, Optional + +from langchain.agents import loading + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.agents.custom import CUSTOM_AGENTS +from langflow.interface.base import LangChainTypeCreator +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class AgentCreator(LangChainTypeCreator): + type_name: str = "agents" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = loading.AGENT_TO_CLASS + # Add JsonAgent to the list of agents + for name, agent in CUSTOM_AGENTS.items(): + # TODO: validate AgentType + self.type_dict[name] = agent # type: ignore + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class( + name, self.type_to_loader_dict, add_function=True + ) + except ValueError as exc: + raise ValueError("Agent not found") from exc + except AttributeError as exc: + logger.error(f"Agent {name} not loaded: {exc}") + return None + + # Now this is a generator + def to_list(self) -> List[str]: + names = [] + for _, agent in self.type_to_loader_dict.items(): + agent_name = ( + agent.function_name() + if hasattr(agent, "function_name") + else agent.__name__ + ) + if agent_name in settings.agents or settings.dev: + names.append(agent_name) + return names + + +agent_creator = AgentCreator() diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py new file mode 100644 index 000000000..27159d22a --- /dev/null +++ b/src/backend/langflow/interface/agents/custom.py @@ -0,0 +1,306 @@ +from typing import Any, List, Optional + +from langchain import LLMChain +from langchain.agents import ( + AgentExecutor, + Tool, + ZeroShotAgent, + initialize_agent, +) +from langchain.agents.agent_toolkits import ( + SQLDatabaseToolkit, + VectorStoreInfo, + VectorStoreRouterToolkit, + VectorStoreToolkit, +) +from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX +from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit +from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX +from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX +from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX +from langchain.agents.agent_toolkits.vectorstore.prompt import ( + PREFIX as VECTORSTORE_PREFIX, +) +from langchain.agents.agent_toolkits.vectorstore.prompt import ( + ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX, +) +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS as SQL_FORMAT_INSTRUCTIONS +from langchain.llms.base import BaseLLM +from langchain.memory.chat_memory import BaseChatMemory +from langchain.schema import BaseLanguageModel +from langchain.sql_database import SQLDatabase +from langchain.tools.python.tool import PythonAstREPLTool +from langchain.tools.sql_database.prompt import QUERY_CHECKER + + +class JsonAgent(AgentExecutor): + """Json agent""" + + @staticmethod + def function_name(): + return "JsonAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): + tools = toolkit.get_tools() + tool_names = [tool.name for tool in tools] + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=JSON_PREFIX, + suffix=JSON_SUFFIX, + format_instructions=FORMAT_INSTRUCTIONS, + input_variables=None, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class CSVAgent(AgentExecutor): + """CSV agent""" + + @staticmethod + def function_name(): + return "CSVAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, + path: str, + llm: BaseLanguageModel, + pandas_kwargs: Optional[dict] = None, + **kwargs: Any + ): + import pandas as pd # type: ignore + + _kwargs = pandas_kwargs or {} + df = pd.read_csv(path, **_kwargs) + + tools = [PythonAstREPLTool(locals={"df": df})] # type: ignore + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=PANDAS_PREFIX, + suffix=PANDAS_SUFFIX, + input_variables=["df", "input", "agent_scratchpad"], + ) + partial_prompt = prompt.partial(df=str(df.head())) + llm_chain = LLMChain( + llm=llm, + prompt=partial_prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class VectorStoreAgent(AgentExecutor): + """Vector Store agent""" + + @staticmethod + def function_name(): + return "VectorStoreAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, llm: BaseLLM, vectorstoreinfo: VectorStoreInfo, **kwargs: Any + ): + """Construct a vectorstore agent from an LLM and tools.""" + + toolkit = VectorStoreToolkit(vectorstore_info=vectorstoreinfo, llm=llm) + + tools = toolkit.get_tools() + prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_PREFIX) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, tools=tools, verbose=True + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class SQLAgent(AgentExecutor): + """SQL agent""" + + @staticmethod + def function_name(): + return "SQLAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, llm: BaseLLM, database_uri: str, **kwargs: Any): + """Construct a sql agent from an LLM and tools.""" + db = SQLDatabase.from_uri(database_uri) + toolkit = SQLDatabaseToolkit(db=db) + + # The right code should be this, but there is a problem with tools = toolkit.get_tools() + # related to `OPENAI_API_KEY` + # return create_sql_agent(llm=llm, toolkit=toolkit, verbose=True) + from langchain.prompts import PromptTemplate + from langchain.tools.sql_database.tool import ( + InfoSQLDatabaseTool, + ListSQLDatabaseTool, + QueryCheckerTool, + QuerySQLDataBaseTool, + ) + + llmchain = LLMChain( + llm=llm, + prompt=PromptTemplate( + template=QUERY_CHECKER, input_variables=["query", "dialect"] + ), + ) + + tools = [ + QuerySQLDataBaseTool(db=db), # type: ignore + InfoSQLDatabaseTool(db=db), # type: ignore + ListSQLDatabaseTool(db=db), # type: ignore + QueryCheckerTool(db=db, llm_chain=llmchain), # type: ignore + ] + + prefix = SQL_PREFIX.format(dialect=toolkit.dialect, top_k=10) + prompt = ZeroShotAgent.create_prompt( + tools=tools, # type: ignore + prefix=prefix, + suffix=SQL_SUFFIX, + format_instructions=SQL_FORMAT_INSTRUCTIONS, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] # type: ignore + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, + tools=tools, # type: ignore + verbose=True, + max_iterations=15, + early_stopping_method="force", + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class VectorStoreRouterAgent(AgentExecutor): + """Vector Store Router Agent""" + + @staticmethod + def function_name(): + return "VectorStoreRouterAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, + llm: BaseLanguageModel, + vectorstoreroutertoolkit: VectorStoreRouterToolkit, + **kwargs: Any + ): + """Construct a vector store router agent from an LLM and tools.""" + + tools = vectorstoreroutertoolkit.get_tools() + prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_ROUTER_PREFIX) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, tools=tools, verbose=True + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class InitializeAgent(AgentExecutor): + """Implementation of initialize_agent function""" + + @staticmethod + def function_name(): + return "initialize_agent" + + @classmethod + def initialize( + cls, + llm: BaseLLM, + tools: List[Tool], + agent: str, + memory: Optional[BaseChatMemory] = None, + ): + return initialize_agent( + tools=tools, + llm=llm, + # LangChain now uses Enum for agent, but we still support string + agent=agent, # type: ignore + memory=memory, + return_intermediate_steps=True, + ) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +CUSTOM_AGENTS = { + "JsonAgent": JsonAgent, + "CSVAgent": CSVAgent, + "initialize_agent": InitializeAgent, + "VectorStoreAgent": VectorStoreAgent, + "VectorStoreRouterAgent": VectorStoreRouterAgent, + "SQLAgent": SQLAgent, +} diff --git a/src/backend/langflow/interface/agents/prebuilt.py b/src/backend/langflow/interface/agents/prebuilt.py new file mode 100644 index 000000000..e20ec3bde --- /dev/null +++ b/src/backend/langflow/interface/agents/prebuilt.py @@ -0,0 +1,45 @@ +from langchain import LLMChain +from langchain.agents import AgentExecutor, ZeroShotAgent +from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX +from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS +from langchain.schema import BaseLanguageModel + + +class MalfoyAgent(AgentExecutor): + """Json agent""" + + prefix = "Malfoy: " + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): + tools = toolkit.get_tools() + tool_names = [tool.name for tool in tools] + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=JSON_PREFIX, + suffix=JSON_SUFFIX, + format_instructions=FORMAT_INSTRUCTIONS, + input_variables=None, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +PREBUILT_AGENTS = { + "MalfoyAgent": MalfoyAgent, +} diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py new file mode 100644 index 000000000..663700ffd --- /dev/null +++ b/src/backend/langflow/interface/base.py @@ -0,0 +1,80 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Type, Union + +from pydantic import BaseModel + +from langflow.template.base import FrontendNode, Template, TemplateField +from langflow.utils.logger import logger + +# Assuming necessary imports for Field, Template, and FrontendNode classes + + +class LangChainTypeCreator(BaseModel, ABC): + type_name: str + type_dict: Optional[Dict] = None + + @property + def frontend_node_class(self) -> Type[FrontendNode]: + """The class type of the FrontendNode created in frontend_node.""" + return FrontendNode + + @property + @abstractmethod + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + raise NotImplementedError + return self.type_dict + + @abstractmethod + def get_signature(self, name: str) -> Union[Optional[Dict[Any, Any]], FrontendNode]: + pass + + @abstractmethod + def to_list(self) -> List[str]: + pass + + def to_dict(self) -> Dict: + result: Dict = {self.type_name: {}} + + for name in self.to_list(): + # frontend_node.to_dict() returns a dict with the following structure: + # {name: {template: {fields}, description: str}} + # so we should update the result dict + node = self.frontend_node(name) + if node is not None: + node = node.to_dict() + result[self.type_name].update(node) + + return result + + def frontend_node(self, name) -> Union[FrontendNode, None]: + signature = self.get_signature(name) + if signature is None: + logger.error(f"Node {name} not loaded") + return None + if isinstance(signature, FrontendNode): + return signature + fields = [ + TemplateField( + name=key, + field_type=value["type"], + required=value.get("required", False), + placeholder=value.get("placeholder", ""), + is_list=value.get("list", False), + show=value.get("show", True), + multiline=value.get("multiline", False), + value=value.get("value", None), + suffixes=value.get("suffixes", []), + file_types=value.get("fileTypes", []), + content=value.get("content", None), + ) + for key, value in signature["template"].items() + if key != "_type" + ] + template = Template(type_name=name, fields=fields) + return self.frontend_node_class( + template=template, + description=signature.get("description", ""), + base_classes=signature["base_classes"], + name=name, + ) diff --git a/src/backend/langflow/interface/chains/__init__.py b/src/backend/langflow/interface/chains/__init__.py new file mode 100644 index 000000000..2e5570b3c --- /dev/null +++ b/src/backend/langflow/interface/chains/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.chains.base import ChainCreator + +__all__ = ["ChainCreator"] diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py new file mode 100644 index 000000000..9dc8ded3f --- /dev/null +++ b/src/backend/langflow/interface/chains/base.py @@ -0,0 +1,54 @@ +from typing import Dict, List, Optional, Type + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import chain_type_to_cls_dict +from langflow.settings import settings +from langflow.template.nodes import ChainFrontendNode +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + +# Assuming necessary imports for Field, Template, and FrontendNode classes + + +class ChainCreator(LangChainTypeCreator): + type_name: str = "chains" + + @property + def frontend_node_class(self) -> Type[ChainFrontendNode]: + return ChainFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = chain_type_to_cls_dict + from langflow.interface.chains.custom import CUSTOM_CHAINS + + self.type_dict.update(CUSTOM_CHAINS) + # Filter according to settings.chains + self.type_dict = { + name: chain + for name, chain in self.type_dict.items() + if name in settings.chains or settings.dev + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Chain not found") from exc + except AttributeError as exc: + logger.error(f"Chain {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + custom_chains = list(get_custom_nodes("chains").keys()) + default_chains = list(self.type_to_loader_dict.keys()) + + return default_chains + custom_chains + + +chain_creator = ChainCreator() diff --git a/src/backend/langflow/interface/chains/custom.py b/src/backend/langflow/interface/chains/custom.py new file mode 100644 index 000000000..cb76a53c8 --- /dev/null +++ b/src/backend/langflow/interface/chains/custom.py @@ -0,0 +1,101 @@ +from typing import Dict, Optional, Type + +from langchain.chains import ConversationChain +from langchain.memory.buffer import ConversationBufferMemory +from langchain.schema import BaseMemory +from pydantic import Field, root_validator + +from langflow.graph.utils import extract_input_variables_from_prompt + +DEFAULT_SUFFIX = """" +Current conversation: +{history} +Human: {input} +{ai_prefix}""" + + +class BaseCustomChain(ConversationChain): + """BaseCustomChain is a chain you can use to have a conversation with a custom character.""" + + template: Optional[str] + + ai_prefix_value: Optional[str] + """Field to use as the ai_prefix. It needs to be set and has to be in the template""" + + @root_validator(pre=False) + def build_template(cls, values): + format_dict = {} + input_variables = extract_input_variables_from_prompt(values["template"]) + + if values.get("ai_prefix_value", None) is None: + values["ai_prefix_value"] = values["memory"].ai_prefix + + for key in input_variables: + new_value = values.get(key, f"{{{key}}}") + format_dict[key] = new_value + if key == values.get("ai_prefix_value", None): + values["memory"].ai_prefix = new_value + + values["template"] = values["template"].format(**format_dict) + + values["template"] = values["template"] + values["input_variables"] = extract_input_variables_from_prompt( + values["template"] + ) + values["prompt"].template = values["template"] + values["prompt"].input_variables = values["input_variables"] + return values + + +class SeriesCharacterChain(BaseCustomChain): + """SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.""" + + character: str + series: str + template: Optional[ + str + ] = """I want you to act like {character} from {series}. +I want you to respond and answer like {character}. do not write any explanations. only answer like {character}. +You must know all of the knowledge of {character}. +Current conversation: +{history} +Human: {input} +{character}:""" + memory: BaseMemory = Field(default_factory=ConversationBufferMemory) + ai_prefix_value: Optional[str] = "character" + """Default memory store.""" + + +class MidJourneyPromptChain(BaseCustomChain): + """MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.""" + + template: Optional[ + str + ] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program. + Your job is to provide detailed and creative descriptions that will inspire unique and interesting images from the AI. + Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible. + For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures. + The more detailed and imaginative your description, the more interesting the resulting image will be. Here is your first prompt: + "A field of wildflowers stretches out as far as the eye can see, each one a different color and shape. In the distance, a massive tree towers over the landscape, its branches reaching up to the sky like tentacles.\" + + Current conversation: + {history} + Human: {input} + AI:""" # noqa: E501 + + +class TimeTravelGuideChain(BaseCustomChain): + template: Optional[ + str + ] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information. + Current conversation: + {history} + Human: {input} + AI:""" # noqa: E501 + + +CUSTOM_CHAINS: Dict[str, Type[ConversationChain]] = { + "SeriesCharacterChain": SeriesCharacterChain, + "MidJourneyPromptChain": MidJourneyPromptChain, + "TimeTravelGuideChain": TimeTravelGuideChain, +} diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py index 3e8a800f9..f07b03f04 100644 --- a/src/backend/langflow/interface/custom_lists.py +++ b/src/backend/langflow/interface/custom_lists.py @@ -1,43 +1,86 @@ -## LLM +import inspect from typing import Any -from langchain import llms -from langchain.llms.openai import OpenAIChat +from langchain import ( + chains, + document_loaders, + embeddings, + llms, + memory, + requests, + text_splitter, + utilities, + vectorstores, +) +from langchain.agents import agent_toolkits +from langchain.chat_models import ChatOpenAI +from langchain.sql_database import SQLDatabase +from langflow.interface.importing.utils import import_class + +## LLMs llm_type_to_cls_dict = llms.type_to_cls_dict -llm_type_to_cls_dict["openai-chat"] = OpenAIChat +llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore - -## Memory - -# from langchain.memory.buffer_window import ConversationBufferWindowMemory -# from langchain.memory.chat_memory import ChatMessageHistory -# from langchain.memory.combined import CombinedMemory -# from langchain.memory.entity import ConversationEntityMemory -# from langchain.memory.kg import ConversationKGMemory -# from langchain.memory.readonly import ReadOnlySharedMemory -# from langchain.memory.simple import SimpleMemory -# from langchain.memory.summary import ConversationSummaryMemory -# from langchain.memory.summary_buffer import ConversationSummaryBufferMemory - -memory_type_to_cls_dict: dict[str, Any] = { - # "CombinedMemory": CombinedMemory, - # "ConversationBufferWindowMemory": ConversationBufferWindowMemory, - # "ConversationBufferMemory": ConversationBufferMemory, - # "SimpleMemory": SimpleMemory, - # "ConversationSummaryBufferMemory": ConversationSummaryBufferMemory, - # "ConversationKGMemory": ConversationKGMemory, - # "ConversationEntityMemory": ConversationEntityMemory, - # "ConversationSummaryMemory": ConversationSummaryMemory, - # "ChatMessageHistory": ChatMessageHistory, - # "ConversationStringBufferMemory": ConversationStringBufferMemory, - # "ReadOnlySharedMemory": ReadOnlySharedMemory, +## Chains +chain_type_to_cls_dict: dict[str, Any] = { + chain_name: import_class(f"langchain.chains.{chain_name}") + for chain_name in chains.__all__ } +## Toolkits +toolkit_type_to_loader_dict: dict[str, Any] = { + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") + # if toolkit_name is lower case it is a loader + for toolkit_name in agent_toolkits.__all__ + if toolkit_name.islower() +} -## Chain -# from langchain.chains.loading import type_to_loader_dict -# from langchain.chains.conversation.base import ConversationChain +toolkit_type_to_cls_dict: dict[str, Any] = { + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") + # if toolkit_name is not lower case it is a class + for toolkit_name in agent_toolkits.__all__ + if not toolkit_name.islower() +} -# chain_type_to_cls_dict = type_to_loader_dict -# chain_type_to_cls_dict["conversation_chain"] = ConversationChain +## Memories +memory_type_to_cls_dict: dict[str, Any] = { + memory_name: import_class(f"langchain.memory.{memory_name}") + for memory_name in memory.__all__ +} + +## Wrappers +wrapper_type_to_cls_dict: dict[str, Any] = { + wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper] +} + +## Embeddings +embedding_type_to_cls_dict: dict[str, Any] = { + embedding_name: import_class(f"langchain.embeddings.{embedding_name}") + for embedding_name in embeddings.__all__ +} + +## Vector Stores +vectorstores_type_to_cls_dict: dict[str, Any] = { + vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}") + for vectorstore_name in vectorstores.__all__ +} + +## Document Loaders +documentloaders_type_to_cls_dict: dict[str, Any] = { + documentloader_name: import_class( + f"langchain.document_loaders.{documentloader_name}" + ) + for documentloader_name in document_loaders.__all__ +} + +## Text Splitters +textsplitter_type_to_cls_dict: dict[str, Any] = dict( + inspect.getmembers(text_splitter, inspect.isclass) +) + +## Utilities +utility_type_to_cls_dict: dict[str, Any] = dict( + inspect.getmembers(utilities, inspect.isclass) +) +utility_type_to_cls_dict["SQLDatabase"] = SQLDatabase diff --git a/src/backend/langflow/interface/document_loaders/__init__.py b/src/backend/langflow/interface/document_loaders/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/document_loaders/base.py b/src/backend/langflow/interface/document_loaders/base.py new file mode 100644 index 000000000..aab017c0f --- /dev/null +++ b/src/backend/langflow/interface/document_loaders/base.py @@ -0,0 +1,146 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import documentloaders_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +def build_file_path_template( + suffixes: list, fileTypes: list, name: str = "file_path" +) -> Dict: + """Build a file path template for a document loader.""" + return { + "type": "file", + "required": True, + "show": True, + "name": name, + "value": "", + "suffixes": suffixes, + "fileTypes": fileTypes, + } + + +class DocumentLoaderCreator(LangChainTypeCreator): + type_name: str = "documentloaders" + + @property + def type_to_loader_dict(self) -> Dict: + return documentloaders_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a document loader.""" + try: + signature = build_template_from_class( + name, documentloaders_type_to_cls_dict + ) + + file_path_templates = { + "AirbyteJSONLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "CoNLLULoader": build_file_path_template( + suffixes=[".csv"], fileTypes=["csv"] + ), + "CSVLoader": build_file_path_template( + suffixes=[".csv"], fileTypes=["csv"] + ), + "UnstructuredEmailLoader": build_file_path_template( + suffixes=[".eml"], fileTypes=["eml"] + ), + "EverNoteLoader": build_file_path_template( + suffixes=[".xml"], fileTypes=["xml"] + ), + "FacebookChatLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "GutenbergLoader": build_file_path_template( + suffixes=[".txt"], fileTypes=["txt"] + ), + "BSHTMLLoader": build_file_path_template( + suffixes=[".html"], fileTypes=["html"] + ), + "UnstructuredHTMLLoader": build_file_path_template( + suffixes=[".html"], fileTypes=["html"] + ), + "UnstructuredImageLoader": build_file_path_template( + suffixes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], + fileTypes=["jpg", "jpeg", "png", "gif", "bmp"], + ), + "UnstructuredMarkdownLoader": build_file_path_template( + suffixes=[".md"], fileTypes=["md"] + ), + "PyPDFLoader": build_file_path_template( + suffixes=[".pdf"], fileTypes=["pdf"] + ), + "UnstructuredPowerPointLoader": build_file_path_template( + suffixes=[".pptx", ".ppt"], fileTypes=["pptx", "ppt"] + ), + "SRTLoader": build_file_path_template( + suffixes=[".srt"], fileTypes=["srt"] + ), + "TelegramChatLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "TextLoader": build_file_path_template( + suffixes=[".txt"], fileTypes=["txt"] + ), + "UnstructuredWordDocumentLoader": build_file_path_template( + suffixes=[".docx", ".doc"], fileTypes=["docx", "doc"] + ), + } + + if name in file_path_templates: + signature["template"]["file_path"] = file_path_templates[name] + elif name in { + "WebBaseLoader", + "AZLyricsLoader", + "CollegeConfidentialLoader", + "HNLoader", + "IFixitLoader", + "IMSDbLoader", + }: + signature["template"]["web_path"] = { + "type": "str", + "required": True, + "show": True, + "name": "web_path", + "value": "", + "display_name": "Web Page", + } + elif name in {"GitbookLoader"}: + signature["template"]["web_page"] = { + "type": "str", + "required": True, + "show": True, + "name": "web_page", + "value": "", + "display_name": "Web Page", + } + elif name in {"ReadTheDocsLoader"}: + signature["template"]["path"] = { + "type": "str", + "required": True, + "show": True, + "name": "path", + "value": "", + "display_name": "Web Page", + } + + return signature + except ValueError as exc: + raise ValueError(f"Documment Loader {name} not found") from exc + except AttributeError as exc: + logger.error(f"Documment Loader {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + documentloader.__name__ + for documentloader in self.type_to_loader_dict.values() + if documentloader.__name__ in settings.documentloaders or settings.dev + ] + + +documentloader_creator = DocumentLoaderCreator() diff --git a/src/backend/langflow/interface/embeddings/__init__.py b/src/backend/langflow/interface/embeddings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/embeddings/base.py b/src/backend/langflow/interface/embeddings/base.py new file mode 100644 index 000000000..061b1d3b5 --- /dev/null +++ b/src/backend/langflow/interface/embeddings/base.py @@ -0,0 +1,36 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import embedding_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class EmbeddingCreator(LangChainTypeCreator): + type_name: str = "embeddings" + + @property + def type_to_loader_dict(self) -> Dict: + return embedding_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an embedding.""" + try: + return build_template_from_class(name, embedding_type_to_cls_dict) + except ValueError as exc: + raise ValueError(f"Embedding {name} not found") from exc + + except AttributeError as exc: + logger.error(f"Embedding {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + embedding.__name__ + for embedding in self.type_to_loader_dict.values() + if embedding.__name__ in settings.embeddings or settings.dev + ] + + +embedding_creator = EmbeddingCreator() diff --git a/src/backend/langflow/interface/importing/__init__.py b/src/backend/langflow/interface/importing/__init__.py new file mode 100644 index 000000000..317849f8e --- /dev/null +++ b/src/backend/langflow/interface/importing/__init__.py @@ -0,0 +1,7 @@ +from langflow.interface.importing.utils import import_by_type # noqa: F401 + +# This module is used to import any langchain class by name. + +ALL = [ + "import_by_type", +] diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py new file mode 100644 index 000000000..e303da0eb --- /dev/null +++ b/src/backend/langflow/interface/importing/utils.py @@ -0,0 +1,147 @@ +# This module is used to import any langchain class by name. + +import importlib +from typing import Any, Type + +from langchain import PromptTemplate +from langchain.agents import Agent +from langchain.chains.base import Chain +from langchain.chat_models.base import BaseChatModel +from langchain.llms.base import BaseLLM +from langchain.tools import BaseTool + +from langflow.interface.tools.base import tool_creator + + +def import_module(module_path: str) -> Any: + """Import module from module path""" + if "from" not in module_path: + # Import the module using the module path + return importlib.import_module(module_path) + # Split the module path into its components + _, module_path, _, object_name = module_path.split() + + # Import the module using the module path + module = importlib.import_module(module_path) + + return getattr(module, object_name) + + +def import_by_type(_type: str, name: str) -> Any: + """Import class by type and name""" + if _type is None: + raise ValueError(f"Type cannot be None. Check if {name} is in the config file.") + func_dict = { + "agents": import_agent, + "prompts": import_prompt, + "llms": {"llm": import_llm, "chat": import_chat_llm}, + "tools": import_tool, + "chains": import_chain, + "toolkits": import_toolkit, + "wrappers": import_wrapper, + "memory": import_memory, + "embeddings": import_embedding, + "vectorstores": import_vectorstore, + "documentloaders": import_documentloader, + "textsplitters": import_textsplitter, + "utilities": import_utility, + } + if _type == "llms": + key = "chat" if "chat" in name.lower() else "llm" + loaded_func = func_dict[_type][key] # type: ignore + else: + loaded_func = func_dict[_type] + + return loaded_func(name) + + +def import_chat_llm(llm: str) -> BaseChatModel: + """Import chat llm from llm name""" + return import_class(f"langchain.chat_models.{llm}") + + +def import_memory(memory: str) -> Any: + """Import memory from memory name""" + return import_module(f"from langchain.memory import {memory}") + + +def import_class(class_path: str) -> Any: + """Import class from class path""" + module_path, class_name = class_path.rsplit(".", 1) + module = import_module(module_path) + return getattr(module, class_name) + + +def import_prompt(prompt: str) -> Type[PromptTemplate]: + from langflow.interface.prompts.custom import CUSTOM_PROMPTS + + """Import prompt from prompt name""" + if prompt == "ZeroShotPrompt": + return import_class("langchain.prompts.PromptTemplate") + elif prompt in CUSTOM_PROMPTS: + return CUSTOM_PROMPTS[prompt] + return import_class(f"langchain.prompts.{prompt}") + + +def import_wrapper(wrapper: str) -> Any: + """Import wrapper from wrapper name""" + return import_module(f"from langchain.requests import {wrapper}") + + +def import_toolkit(toolkit: str) -> Any: + """Import toolkit from toolkit name""" + return import_module(f"from langchain.agents.agent_toolkits import {toolkit}") + + +def import_agent(agent: str) -> Agent: + """Import agent from agent name""" + # check for custom agent + + return import_class(f"langchain.agents.{agent}") + + +def import_llm(llm: str) -> BaseLLM: + """Import llm from llm name""" + return import_class(f"langchain.llms.{llm}") + + +def import_tool(tool: str) -> BaseTool: + """Import tool from tool name""" + + return tool_creator.type_to_loader_dict[tool]["fcn"] + + +def import_chain(chain: str) -> Type[Chain]: + """Import chain from chain name""" + from langflow.interface.chains.custom import CUSTOM_CHAINS + + if chain in CUSTOM_CHAINS: + return CUSTOM_CHAINS[chain] + return import_class(f"langchain.chains.{chain}") + + +def import_embedding(embedding: str) -> Any: + """Import embedding from embedding name""" + return import_class(f"langchain.embeddings.{embedding}") + + +def import_vectorstore(vectorstore: str) -> Any: + """Import vectorstore from vectorstore name""" + return import_class(f"langchain.vectorstores.{vectorstore}") + + +def import_documentloader(documentloader: str) -> Any: + """Import documentloader from documentloader name""" + return import_class(f"langchain.document_loaders.{documentloader}") + + +def import_textsplitter(textsplitter: str) -> Any: + """Import textsplitter from textsplitter name""" + return import_class(f"langchain.text_splitter.{textsplitter}") + + +def import_utility(utility: str) -> Any: + """Import utility from utility name""" + if utility == "SQLDatabase": + return import_class(f"langchain.sql_database.{utility}") + return import_class(f"langchain.utilities.{utility}") diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py index 21e763de4..3d73105c2 100644 --- a/src/backend/langflow/interface/listing.py +++ b/src/backend/langflow/interface/listing.py @@ -1,82 +1,41 @@ -from langchain import agents, chains, prompts -from langchain.agents.load_tools import get_all_tool_names - -from langflow.custom import customs -from langflow.interface.custom_lists import ( - llm_type_to_cls_dict, - memory_type_to_cls_dict, -) -from langflow.settings import settings -from langflow.utils import util +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.utilities.base import utility_creator +from langflow.interface.vector_store.base import vectorstore_creator +from langflow.interface.wrappers.base import wrapper_creator -def list_type(object_type: str): - """List all components""" +def get_type_dict(): return { - "chains": list_chain_types, - "agents": list_agents, - "prompts": list_prompts, - "llms": list_llms, - "memories": list_memories, - "tools": list_tools, - }.get(object_type, lambda: "Invalid type")() + "agents": agent_creator.to_list(), + "prompts": prompt_creator.to_list(), + "llms": llm_creator.to_list(), + "tools": tool_creator.to_list(), + "chains": chain_creator.to_list(), + "memory": memory_creator.to_list(), + "toolkits": toolkits_creator.to_list(), + "wrappers": wrapper_creator.to_list(), + "documentLoaders": documentloader_creator.to_list(), + "vectorStore": vectorstore_creator.to_list(), + "embeddings": embedding_creator.to_list(), + "textSplitters": textsplitter_creator.to_list(), + "utilities": utility_creator.to_list(), + } -def list_agents(): - """List all agent types""" - return [ - agent.__name__ - for agent in agents.loading.AGENT_TO_CLASS.values() - if agent.__name__ in settings.agents or settings.dev - ] +LANGCHAIN_TYPES_DICT = get_type_dict() +# Now we'll build a dict with Langchain types and ours -def list_prompts(): - """List all prompt types""" - custom_prompts = customs.get_custom_prompts() - library_prompts = [ - prompt.__annotations__["return"].__name__ - for prompt in prompts.loading.type_to_loader_dict.values() - if prompt.__annotations__["return"].__name__ in settings.prompts or settings.dev - ] - return library_prompts + list(custom_prompts.keys()) - - -def list_tools(): - """List all load tools""" - - tools = [] - - for tool in get_all_tool_names(): - tool_params = util.get_tool_params(util.get_tools_dict(tool)) - if tool_params and tool_params["name"] in settings.tools or settings.dev: - tools.append(tool_params["name"]) - - return tools - - -def list_llms(): - """List all llm types""" - return [ - llm.__name__ - for llm in llm_type_to_cls_dict.values() - if llm.__name__ in settings.llms or settings.dev - ] - - -def list_chain_types(): - """List all chain types""" - return [ - chain.__annotations__["return"].__name__ - for chain in chains.loading.type_to_loader_dict.values() - if chain.__annotations__["return"].__name__ in settings.chains or settings.dev - ] - - -def list_memories(): - """List all memory types""" - return [ - memory.__name__ - for memory in memory_type_to_cls_dict.values() - if memory.__name__ in settings.memories or settings.dev - ] +ALL_TYPES_DICT = { + **LANGCHAIN_TYPES_DICT, + "Custom": ["Custom Tool", "Python Function"], +} diff --git a/src/backend/langflow/interface/llms/__init__.py b/src/backend/langflow/interface/llms/__init__.py new file mode 100644 index 000000000..c5d7186fb --- /dev/null +++ b/src/backend/langflow/interface/llms/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.llms.base import LLMCreator + +__all__ = ["LLMCreator"] diff --git a/src/backend/langflow/interface/llms/base.py b/src/backend/langflow/interface/llms/base.py new file mode 100644 index 000000000..04a36eb2d --- /dev/null +++ b/src/backend/langflow/interface/llms/base.py @@ -0,0 +1,43 @@ +from typing import Dict, List, Optional, Type + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import llm_type_to_cls_dict +from langflow.settings import settings +from langflow.template.nodes import LLMFrontendNode +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class LLMCreator(LangChainTypeCreator): + type_name: str = "llms" + + @property + def frontend_node_class(self) -> Type[LLMFrontendNode]: + return LLMFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = llm_type_to_cls_dict + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an llm.""" + try: + return build_template_from_class(name, llm_type_to_cls_dict) + except ValueError as exc: + raise ValueError("LLM not found") from exc + + except AttributeError as exc: + logger.error(f"LLM {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + llm.__name__ + for llm in self.type_to_loader_dict.values() + if llm.__name__ in settings.llms or settings.dev + ] + + +llm_creator = LLMCreator() diff --git a/src/backend/langflow/interface/loading.py b/src/backend/langflow/interface/loading.py index 619264988..4cf702ea2 100644 --- a/src/backend/langflow/interface/loading.py +++ b/src/backend/langflow/interface/loading.py @@ -1,7 +1,10 @@ import json -from typing import Any, Dict, Optional +from typing import Any, Callable, Dict, Optional +from langchain.agents import ZeroShotAgent +from langchain.agents import agent as agent_module from langchain.agents.agent import AgentExecutor +from langchain.agents.agent_toolkits.base import BaseToolkit from langchain.agents.load_tools import ( _BASE_TOOLS, _EXTRA_LLM_TOOLS, @@ -15,29 +18,95 @@ from langchain.chains.loading import load_chain_from_config from langchain.llms.base import BaseLLM from langchain.llms.loading import load_llm_from_config +from langflow.interface.agents.custom import CUSTOM_AGENTS +from langflow.interface.importing.utils import import_by_type +from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.types import get_type_list -from langflow.utils import payload, util +from langflow.interface.utils import load_file_into_dict +from langflow.utils import util, validate -def load_flow_from_json(path: str): +def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: + """Instantiate class from module type and key, and params""" + if node_type in CUSTOM_AGENTS: + if custom_agent := CUSTOM_AGENTS.get(node_type): + return custom_agent.initialize(**params) # type: ignore + + class_object = import_by_type(_type=base_type, name=node_type) + + if base_type == "agents": + # We need to initialize it differently + return load_agent_executor(class_object, params) + elif base_type == "prompts": + if node_type == "ZeroShotPrompt": + if "tools" not in params: + params["tools"] = [] + return ZeroShotAgent.create_prompt(**params) + elif base_type == "tools": + if node_type == "JsonSpec": + params["dict_"] = load_file_into_dict(params.pop("path")) + return class_object(**params) + elif node_type == "PythonFunction": + # If the node_type is "PythonFunction" + # we need to get the function from the params + # which will be a str containing a python function + # and then we need to compile it and return the function + # as the instance + function_string = params["code"] + if isinstance(function_string, str): + return validate.eval_function(function_string) + raise ValueError("Function should be a string") + elif node_type.lower() == "tool": + return class_object(**params) + elif base_type == "toolkits": + loaded_toolkit = class_object(**params) + # Check if node_type has a loader + if toolkits_creator.has_create_function(node_type): + return load_toolkits_executor(node_type, loaded_toolkit, params) + return loaded_toolkit + elif base_type == "embeddings": + params.pop("model") + return class_object(**params) + elif base_type == "vectorstores": + if len(params.get("documents", [])) == 0: + # Error when the pdf or other source was not correctly + # loaded. + raise ValueError( + "The source you provided did not load correctly or was empty." + "This may cause an error in the vectorstore." + ) + return class_object.from_documents(**params) + elif base_type == "documentloaders": + return class_object(**params).load() + elif base_type == "textsplitters": + documents = params.pop("documents") + text_splitter = class_object(**params) + return text_splitter.split_documents(documents) + elif base_type == "utilities": + if node_type == "SQLDatabase": + return class_object.from_uri(params.pop("uri")) + + return class_object(**params) + + +def load_flow_from_json(path: str, build=True): + # This is done to avoid circular imports + from langflow.graph import Graph + """Load flow from json file""" - with open(path, "r") as f: + with open(path, "r", encoding="utf-8") as f: flow_graph = json.load(f) data_graph = flow_graph["data"] - extracted_json = extract_json(data_graph) - return load_langchain_type_from_config(config=extracted_json) - - -def extract_json(data_graph): nodes = data_graph["nodes"] # Substitute ZeroShotPrompt with PromptTemplate - nodes = replace_zero_shot_prompt_with_prompt_template(nodes) + # nodes = replace_zero_shot_prompt_with_prompt_template(nodes) # Add input variables - nodes = payload.extract_input_variables(nodes) + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node edges = data_graph["edges"] - root = payload.get_root_node(nodes, edges) - return payload.build_json(root, nodes, edges) + graph = Graph(nodes, edges) + return graph.build() if build else graph def replace_zero_shot_prompt_with_prompt_template(nodes): @@ -92,6 +161,25 @@ def load_agent_executor_from_config( ) +def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs): + """Load agent executor from agent class, tools and chain""" + allowed_tools = params["allowed_tools"] + llm_chain = params["llm_chain"] + tool_names = [tool.name for tool in allowed_tools] + agent = agent_class(allowed_tools=tool_names, llm_chain=llm_chain) + return AgentExecutor.from_agent_and_tools( + agent=agent, + tools=allowed_tools, + **kwargs, + ) + + +def load_toolkits_executor(node_type: str, toolkit: BaseToolkit, params: dict): + create_function: Callable = toolkits_creator.get_create_function(node_type) + if llm := params.get("llm"): + return create_function(llm=llm, toolkit=toolkit) + + def load_tools_from_config(tool_list: list[dict]) -> list: """Load tools based on a config list. diff --git a/src/backend/langflow/interface/memories/__init__.py b/src/backend/langflow/interface/memories/__init__.py new file mode 100644 index 000000000..845eb29fe --- /dev/null +++ b/src/backend/langflow/interface/memories/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.memories.base import MemoryCreator + +__all__ = ["MemoryCreator"] diff --git a/src/backend/langflow/interface/memories/base.py b/src/backend/langflow/interface/memories/base.py new file mode 100644 index 000000000..f26b09351 --- /dev/null +++ b/src/backend/langflow/interface/memories/base.py @@ -0,0 +1,44 @@ +from typing import Dict, List, Optional, Type + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import memory_type_to_cls_dict +from langflow.settings import settings +from langflow.template.base import FrontendNode +from langflow.template.nodes import MemoryFrontendNode +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class MemoryCreator(LangChainTypeCreator): + type_name: str = "memories" + + @property + def frontend_node_class(self) -> Type[FrontendNode]: + """The class type of the FrontendNode created in frontend_node.""" + return MemoryFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = memory_type_to_cls_dict + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a memory.""" + try: + return build_template_from_class(name, memory_type_to_cls_dict) + except ValueError as exc: + raise ValueError("Memory not found") from exc + except AttributeError as exc: + logger.error(f"Memory {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + memory.__name__ + for memory in self.type_to_loader_dict.values() + if memory.__name__ in settings.memories or settings.dev + ] + + +memory_creator = MemoryCreator() diff --git a/src/backend/langflow/interface/prompts/__init__.py b/src/backend/langflow/interface/prompts/__init__.py new file mode 100644 index 000000000..2a81e8bf0 --- /dev/null +++ b/src/backend/langflow/interface/prompts/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.prompts.base import PromptCreator + +__all__ = ["PromptCreator"] diff --git a/src/backend/langflow/interface/prompts/base.py b/src/backend/langflow/interface/prompts/base.py new file mode 100644 index 000000000..5f83a5412 --- /dev/null +++ b/src/backend/langflow/interface/prompts/base.py @@ -0,0 +1,64 @@ +from typing import Dict, List, Optional, Type + +from langchain import prompts + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.importing.utils import import_class +from langflow.settings import settings +from langflow.template.nodes import PromptFrontendNode +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class PromptCreator(LangChainTypeCreator): + type_name: str = "prompts" + + @property + def frontend_node_class(self) -> Type[PromptFrontendNode]: + return PromptFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + prompt_name: import_class(f"langchain.prompts.{prompt_name}") + # if prompt_name is not lower case it is a class + for prompt_name in prompts.__all__ + } + # Merge CUSTOM_PROMPTS into self.type_dict + from langflow.interface.prompts.custom import CUSTOM_PROMPTS + + self.type_dict.update(CUSTOM_PROMPTS) + # Now filter according to settings.prompts + self.type_dict = { + name: prompt + for name, prompt in self.type_dict.items() + if name in settings.prompts or settings.dev + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + # raise ValueError("Prompt not found") from exc + logger.error(f"Prompt {name} not found: {exc}") + except AttributeError as exc: + logger.error(f"Prompt {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + custom_prompts = get_custom_nodes("prompts") + # library_prompts = [ + # prompt.__annotations__["return"].__name__ + # for prompt in self.type_to_loader_dict.values() + # if prompt.__annotations__["return"].__name__ in settings.prompts + # or settings.dev + # ] + return list(self.type_to_loader_dict.keys()) + list(custom_prompts.keys()) + + +prompt_creator = PromptCreator() diff --git a/src/backend/langflow/interface/prompts/custom.py b/src/backend/langflow/interface/prompts/custom.py new file mode 100644 index 000000000..b1dbef370 --- /dev/null +++ b/src/backend/langflow/interface/prompts/custom.py @@ -0,0 +1,77 @@ +from typing import Dict, List, Optional, Type + +from langchain.prompts import PromptTemplate +from pydantic import root_validator + +from langflow.graph.utils import extract_input_variables_from_prompt + +# Steps to create a BaseCustomPrompt: +# 1. Create a prompt template that endes with: +# Current conversation: +# {history} +# Human: {input} +# {ai_prefix}: +# 2. Create a class that inherits from BaseCustomPrompt +# 3. Add the following class attributes: +# template: str = "" +# description: Optional[str] +# ai_prefix: Optional[str] = "{ai_prefix}" +# 3.1. The ai_prefix should be a value in input_variables +# SeriesCharacterPrompt is a working example +# If used in a LLMChain, with a Memory module, it will work as expected +# We should consider creating ConversationalChains that expose custom parameters +# That way it will be easier to create custom prompts + + +class BaseCustomPrompt(PromptTemplate): + template: str = "" + description: Optional[str] + ai_prefix: Optional[str] + + @root_validator(pre=False) + def build_template(cls, values): + format_dict = {} + ai_prefix_format_dict = {} + for key in values.get("input_variables", []): + new_value = values.get(key, f"{{{key}}}") + format_dict[key] = new_value + if key in values["ai_prefix"]: + ai_prefix_format_dict[key] = new_value + + values["ai_prefix"] = values["ai_prefix"].format(**ai_prefix_format_dict) + values["template"] = values["template"].format(**format_dict) + + values["template"] = values["template"] + values["input_variables"] = extract_input_variables_from_prompt( + values["template"] + ) + return values + + +class SeriesCharacterPrompt(BaseCustomPrompt): + # Add a very descriptive description for the prompt generator + description: Optional[ + str + ] = "A prompt that asks the AI to act like a character from a series." + character: str + series: str + template: str = """I want you to act like {character} from {series}. +I want you to respond and answer like {character}. do not write any explanations. only answer like {character}. +You must know all of the knowledge of {character}. + +Current conversation: +{history} +Human: {input} +{character}:""" + + ai_prefix: str = "{character}" + input_variables: List[str] = ["character", "series"] + + +CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = { + "SeriesCharacterPrompt": SeriesCharacterPrompt +} + +if __name__ == "__main__": + prompt = SeriesCharacterPrompt(character="Harry Potter", series="Harry Potter") + print(prompt.template) diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index 0844e2073..047ba733e 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -1,47 +1,286 @@ import contextlib import io -import re from typing import Any, Dict +from chromadb.errors import NotEnoughElementsException # type: ignore +from langflow.cache.utils import compute_dict_hash, load_cache, memoize_dict +from langflow.graph.graph import Graph from langflow.interface import loading +from langflow.utils.logger import logger -def process_data_graph(data_graph: Dict[str, Any]): +def load_langchain_object(data_graph, is_first_message=False): """ - Process data graph by extracting input variables and replacing ZeroShotPrompt + Load langchain object from cache if it exists, otherwise build it. + """ + computed_hash = compute_dict_hash(data_graph) + if is_first_message: + langchain_object = build_langchain_object(data_graph) + else: + logger.debug("Loading langchain object from cache") + langchain_object = load_cache(computed_hash) + + return computed_hash, langchain_object + + +def load_or_build_langchain_object(data_graph, is_first_message=False): + """ + Load langchain object from cache if it exists, otherwise build it. + """ + if is_first_message: + build_langchain_object_with_caching.clear_cache() + return build_langchain_object_with_caching(data_graph) + + +@memoize_dict(maxsize=1) +def build_langchain_object_with_caching(data_graph): + """ + Build langchain object from data_graph. + """ + + logger.debug("Building langchain object") + nodes = data_graph["nodes"] + # Add input variables + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node + edges = data_graph["edges"] + graph = Graph(nodes, edges) + + return graph.build() + + +def build_langchain_object(data_graph): + """ + Build langchain object from data_graph. + """ + + logger.debug("Building langchain object") + nodes = data_graph["nodes"] + # Add input variables + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node + edges = data_graph["edges"] + graph = Graph(nodes, edges) + + return graph.build() + + +def process_graph(data_graph: Dict[str, Any]): + """ + Process graph by extracting input variables and replacing ZeroShotPrompt with PromptTemplate,then run the graph and return the result and thought. """ + # Load langchain object + logger.debug("Loading langchain object") + message = data_graph.pop("message", "") + is_first_message = len(data_graph.get("chatHistory", [])) == 0 + computed_hash, langchain_object = load_langchain_object( + data_graph, is_first_message + ) + logger.debug("Loaded langchain object") - extracted_json = loading.extract_json(data_graph) + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) - message = data_graph["message"] + # Generate result and thought + logger.debug("Generating result and thought") + result, thought = get_result_and_thought_using_graph(langchain_object, message) + logger.debug("Generated result and thought") - # Process json - result, thought = get_result_and_thought(extracted_json, message) + # Save langchain_object to cache + # We have to save it here because if the + # memory is updated we need to keep the new values + logger.debug("Saving langchain object to cache") + # save_cache(computed_hash, langchain_object, is_first_message) + logger.debug("Saved langchain object to cache") + return {"result": str(result), "thought": thought.strip()} - return { - "result": result, - "thought": re.sub( - r"\x1b\[([0-9,A-Z]{1,2}(;[0-9,A-Z]{1,2})?)?[m|K]", "", thought - ).strip(), + +def process_graph_cached(data_graph: Dict[str, Any]): + """ + Process graph by extracting input variables and replacing ZeroShotPrompt + with PromptTemplate,then run the graph and return the result and thought. + """ + # Load langchain object + message = data_graph.pop("message", "") + is_first_message = len(data_graph.get("chatHistory", [])) == 0 + langchain_object = load_or_build_langchain_object(data_graph, is_first_message) + logger.debug("Loaded langchain object") + + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) + + # Generate result and thought + logger.debug("Generating result and thought") + result, thought = get_result_and_thought_using_graph(langchain_object, message) + logger.debug("Generated result and thought") + return {"result": str(result), "thought": thought.strip()} + + +def get_memory_key(langchain_object): + """ + Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. + It then checks if the key exists in a dictionary of known memory keys and returns the corresponding key, + or None if the current key is not recognized. + """ + mem_key_dict = { + "chat_history": "history", + "history": "chat_history", } + memory_key = langchain_object.memory.memory_key + return mem_key_dict.get(memory_key) + + +def update_memory_keys(langchain_object, possible_new_mem_key): + """ + Given a LangChain object and a possible new memory key, this function updates the input and output keys in the + object's memory attribute to exclude the current memory key and the possible new key. It then sets the memory key + to the possible new key. + """ + input_key = [ + key + for key in langchain_object.input_keys + if key not in [langchain_object.memory.memory_key, possible_new_mem_key] + ][0] + + output_key = [ + key + for key in langchain_object.output_keys + if key not in [langchain_object.memory.memory_key, possible_new_mem_key] + ][0] + + langchain_object.memory.input_key = input_key + langchain_object.memory.output_key = output_key + langchain_object.memory.memory_key = possible_new_mem_key + + +def fix_memory_inputs(langchain_object): + """ + Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the + object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the + get_memory_key function and updates the memory keys using the update_memory_keys function. + """ + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + try: + if langchain_object.memory.memory_key in langchain_object.input_variables: + return + except AttributeError: + input_variables = ( + langchain_object.prompt.input_variables + if hasattr(langchain_object, "prompt") + else langchain_object.input_keys + ) + if langchain_object.memory.memory_key in input_variables: + return + + possible_new_mem_key = get_memory_key(langchain_object) + if possible_new_mem_key is not None: + update_memory_keys(langchain_object, possible_new_mem_key) + + +def get_result_and_thought_using_graph(langchain_object, message: str): + """Get result and thought from extracted json""" + try: + if hasattr(langchain_object, "verbose"): + langchain_object.verbose = True + chat_input = None + memory_key = "" + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + memory_key = langchain_object.memory.memory_key + + if hasattr(langchain_object, "input_keys"): + for key in langchain_object.input_keys: + if key not in [memory_key, "chat_history"]: + chat_input = {key: message} + else: + chat_input = message # type: ignore + + if hasattr(langchain_object, "return_intermediate_steps"): + # https://github.com/hwchase17/langchain/issues/2068 + # Deactivating until we have a frontend solution + # to display intermediate steps + langchain_object.return_intermediate_steps = False + + fix_memory_inputs(langchain_object) + + with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): + try: + output = langchain_object(chat_input) + except ValueError as exc: + # make the error message more informative + logger.debug(f"Error: {str(exc)}") + output = langchain_object.run(chat_input) + + intermediate_steps = ( + output.get("intermediate_steps", []) if isinstance(output, dict) else [] + ) + + result = ( + output.get(langchain_object.output_keys[0]) + if isinstance(output, dict) + else output + ) + if intermediate_steps: + thought = format_intermediate_steps(intermediate_steps) + else: + thought = output_buffer.getvalue() + + except NotEnoughElementsException as exc: + raise ValueError( + "Error: Not enough documents for ChromaDB to index. Try reducing chunk size in TextSplitter." + ) from exc + except Exception as exc: + raise ValueError(f"Error: {str(exc)}") from exc + return result, thought def get_result_and_thought(extracted_json: Dict[str, Any], message: str): """Get result and thought from extracted json""" try: - loaded_langchain = loading.load_langchain_type_from_config( + langchain_object = loading.load_langchain_type_from_config( config=extracted_json ) with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): - result = loaded_langchain(message) - result = ( - result.get(loaded_langchain.output_keys[0]) - if isinstance(result, dict) - else result + output = langchain_object(message) + intermediate_steps = ( + output.get("intermediate_steps", []) if isinstance(output, dict) else [] ) - thought = output_buffer.getvalue() + result = ( + output.get(langchain_object.output_keys[0]) + if isinstance(output, dict) + else output + ) + + if intermediate_steps: + thought = format_intermediate_steps(intermediate_steps) + else: + thought = output_buffer.getvalue() + except Exception as e: result = f"Error: {str(e)}" thought = "" return result, thought + + +def format_intermediate_steps(intermediate_steps): + formatted_chain = "> Entering new AgentExecutor chain...\n" + for step in intermediate_steps: + action = step[0] + observation = step[1] + + formatted_chain += ( + f" {action.log}\nAction: {action.tool}\nAction Input: {action.tool_input}\n" + ) + formatted_chain += f"Observation: {observation}\n" + + final_answer = f"Final Answer: {observation}\n" + formatted_chain += f"Thought: I now know the final answer\n{final_answer}\n" + formatted_chain += "> Finished chain.\n" + + return formatted_chain diff --git a/src/backend/langflow/interface/signature.py b/src/backend/langflow/interface/signature.py deleted file mode 100644 index 69e83fad6..000000000 --- a/src/backend/langflow/interface/signature.py +++ /dev/null @@ -1,133 +0,0 @@ -from typing import Any, Dict # noqa: F401 - -from langchain import agents, chains, prompts -from langchain.agents.load_tools import ( - _BASE_TOOLS, - _EXTRA_LLM_TOOLS, - _EXTRA_OPTIONAL_TOOLS, - _LLM_TOOLS, - get_all_tool_names, -) - -from langflow.custom import customs -from langflow.interface.custom_lists import ( - llm_type_to_cls_dict, - memory_type_to_cls_dict, -) -from langflow.utils import util - - -def get_signature(name: str, object_type: str): - """Get the signature of an object.""" - return { - "chains": get_chain_signature, - "agents": get_agent_signature, - "prompts": get_prompt_signature, - "llms": get_llm_signature, - "memories": get_memory_signature, - "tools": get_tool_signature, - }.get(object_type, lambda name: f"Invalid type: {name}")(name) - - -def get_chain_signature(name: str): - """Get the chain type by signature.""" - try: - return util.build_template_from_function( - name, chains.loading.type_to_loader_dict - ) - except ValueError as exc: - raise ValueError("Chain not found") from exc - - -def get_agent_signature(name: str): - """Get the signature of an agent.""" - try: - return util.build_template_from_class(name, agents.loading.AGENT_TO_CLASS) - except ValueError as exc: - raise ValueError("Agent not found") from exc - - -def get_prompt_signature(name: str): - """Get the signature of a prompt.""" - try: - if name in customs.get_custom_prompts().keys(): - return customs.get_custom_prompts()[name] - return util.build_template_from_function( - name, prompts.loading.type_to_loader_dict - ) - except ValueError as exc: - raise ValueError("Prompt not found") from exc - - -def get_llm_signature(name: str): - """Get the signature of an llm.""" - try: - return util.build_template_from_class(name, llm_type_to_cls_dict) - except ValueError as exc: - raise ValueError("LLM not found") from exc - - -def get_memory_signature(name: str): - """Get the signature of a memory.""" - try: - return util.build_template_from_class(name, memory_type_to_cls_dict) - except ValueError as exc: - raise ValueError("Memory not found") from exc - - -def get_tool_signature(name: str): - """Get the signature of a tool.""" - - all_tools = {} - for tool in get_all_tool_names(): - if tool_params := util.get_tool_params(util.get_tools_dict(tool)): - all_tools[tool_params["name"]] = tool - - # Raise error if name is not in tools - if name not in all_tools.keys(): - raise ValueError("Tool not found") - - type_dict = { - "str": { - "type": "str", - "required": True, - "list": False, - "show": True, - "placeholder": "", - "value": "", - }, - "llm": {"type": "BaseLLM", "required": True, "list": False, "show": True}, - } - - tool_type = all_tools[name] - - if tool_type in _BASE_TOOLS: - params = [] - elif tool_type in _LLM_TOOLS: - params = ["llm"] - elif tool_type in _EXTRA_LLM_TOOLS: - _, extra_keys = _EXTRA_LLM_TOOLS[tool_type] - params = ["llm"] + extra_keys - elif tool_type in _EXTRA_OPTIONAL_TOOLS: - _, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type] - params = extra_keys - else: - params = [] - - template = { - param: (type_dict[param].copy() if param == "llm" else type_dict["str"].copy()) - for param in params - } - - # Remove required from aiosession - if "aiosession" in template.keys(): - template["aiosession"]["required"] = False - template["aiosession"]["show"] = False - - template["_type"] = tool_type # type: ignore - - return { - "template": template, - **util.get_tool_params(util.get_tools_dict(tool_type)), - "base_classes": ["Tool"], - } diff --git a/src/backend/langflow/interface/text_splitters/__init__.py b/src/backend/langflow/interface/text_splitters/__init__.py new file mode 100644 index 000000000..4bb9dd1b0 --- /dev/null +++ b/src/backend/langflow/interface/text_splitters/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.text_splitters.base import TextSplitterCreator + +__all__ = ["TextSplitterCreator"] diff --git a/src/backend/langflow/interface/text_splitters/base.py b/src/backend/langflow/interface/text_splitters/base.py new file mode 100644 index 000000000..e58a5bfa6 --- /dev/null +++ b/src/backend/langflow/interface/text_splitters/base.py @@ -0,0 +1,71 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import textsplitter_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class TextSplitterCreator(LangChainTypeCreator): + type_name: str = "textsplitters" + + @property + def type_to_loader_dict(self) -> Dict: + return textsplitter_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a text splitter.""" + try: + signature = build_template_from_class(name, textsplitter_type_to_cls_dict) + + signature["template"]["documents"] = { + "type": "BaseLoader", + "required": True, + "show": True, + "name": "documents", + } + + signature["template"]["separator"] = { + "type": "str", + "required": True, + "show": True, + "value": ".", + "name": "separator", + "display_name": "Separator", + } + + signature["template"]["chunk_size"] = { + "type": "int", + "required": True, + "show": True, + "value": 4000, + "name": "chunk_size", + "display_name": "Chunk Size", + } + + signature["template"]["chunk_overlap"] = { + "type": "int", + "required": True, + "show": True, + "value": 200, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + } + + return signature + except ValueError as exc: + raise ValueError(f"Text Splitter {name} not found") from exc + except AttributeError as exc: + logger.error(f"Text Splitter {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + textsplitter.__name__ + for textsplitter in self.type_to_loader_dict.values() + if textsplitter.__name__ in settings.textsplitters or settings.dev + ] + + +textsplitter_creator = TextSplitterCreator() diff --git a/src/backend/langflow/interface/toolkits/__init__.py b/src/backend/langflow/interface/toolkits/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py new file mode 100644 index 000000000..cbe625f0d --- /dev/null +++ b/src/backend/langflow/interface/toolkits/base.py @@ -0,0 +1,69 @@ +from typing import Callable, Dict, List, Optional + +from langchain.agents import agent_toolkits + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.importing.utils import import_class, import_module +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class ToolkitCreator(LangChainTypeCreator): + type_name: str = "toolkits" + all_types: List[str] = agent_toolkits.__all__ + create_functions: Dict = { + "JsonToolkit": [], + "SQLDatabaseToolkit": [], + "OpenAPIToolkit": ["create_openapi_agent"], + "VectorStoreToolkit": [ + "create_vectorstore_agent", + "create_vectorstore_router_agent", + "VectorStoreInfo", + ], + "ZapierToolkit": [], + "PandasToolkit": ["create_pandas_dataframe_agent"], + "CSVToolkit": ["create_csv_agent"], + } + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + toolkit_name: import_class( + f"langchain.agents.agent_toolkits.{toolkit_name}" + ) + # if toolkit_name is not lower case it is a class + for toolkit_name in agent_toolkits.__all__ + if not toolkit_name.islower() and toolkit_name in settings.toolkits + } + + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Prompt not found") from exc + except AttributeError as exc: + logger.error(f"Prompt {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return list(self.type_to_loader_dict.keys()) + + def get_create_function(self, name: str) -> Callable: + if loader_name := self.create_functions.get(name, None): + # import loader + return import_module( + f"from langchain.agents.agent_toolkits import {loader_name[0]}" + ) + else: + raise ValueError("Loader not found") + + def has_create_function(self, name: str) -> bool: + # check if the function list is not empty + return bool(self.create_functions.get(name, None)) + + +toolkits_creator = ToolkitCreator() diff --git a/src/backend/langflow/interface/toolkits/custom.py b/src/backend/langflow/interface/toolkits/custom.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/tools/__init__.py b/src/backend/langflow/interface/tools/__init__.py new file mode 100644 index 000000000..148892e90 --- /dev/null +++ b/src/backend/langflow/interface/tools/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.tools.base import ToolCreator + +__all__ = ["ToolCreator"] diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py new file mode 100644 index 000000000..e756ecbd6 --- /dev/null +++ b/src/backend/langflow/interface/tools/base.py @@ -0,0 +1,164 @@ +from typing import Dict, List, Optional + +from langchain.agents.load_tools import ( + _EXTRA_LLM_TOOLS, + _EXTRA_OPTIONAL_TOOLS, + _LLM_TOOLS, +) + +from langflow.custom import customs +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.tools.constants import ( + ALL_TOOLS_NAMES, + CUSTOM_TOOLS, + FILE_TOOLS, + OTHER_TOOLS, +) +from langflow.interface.tools.util import get_tool_params +from langflow.settings import settings +from langflow.template.base import Template, TemplateField +from langflow.utils import util +from langflow.utils.util import build_template_from_class + +TOOL_INPUTS = { + "str": TemplateField( + field_type="str", + required=True, + is_list=False, + show=True, + placeholder="", + value="", + ), + "llm": TemplateField(field_type="BaseLLM", required=True, is_list=False, show=True), + "func": TemplateField( + field_type="function", + required=True, + is_list=False, + show=True, + multiline=True, + ), + "code": TemplateField( + field_type="str", + required=True, + is_list=False, + show=True, + value="", + multiline=True, + ), + "path": TemplateField( + field_type="file", + required=True, + is_list=False, + show=True, + value="", + suffixes=[".json", ".yaml", ".yml"], + fileTypes=["json", "yaml", "yml"], + ), +} + + +class ToolCreator(LangChainTypeCreator): + type_name: str = "tools" + tools_dict: Optional[Dict] = None + + @property + def type_to_loader_dict(self) -> Dict: + if self.tools_dict is None: + all_tools = {} + for tool, tool_fcn in ALL_TOOLS_NAMES.items(): + tool_params = get_tool_params(tool_fcn) + tool_name = tool_params.get("name", tool) + + if tool_name in settings.tools or settings.dev: + if tool_name == "JsonSpec": + tool_params["path"] = tool_params.pop("dict_") # type: ignore + all_tools[tool_name] = { + "type": tool, + "params": tool_params, + "fcn": tool_fcn, + } + + self.tools_dict = all_tools + + return self.tools_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a tool.""" + + base_classes = ["Tool"] + fields = [] + params = [] + tool_params = {} + + # Raise error if name is not in tools + if name not in self.type_to_loader_dict.keys(): + raise ValueError("Tool not found") + + tool_type: str = self.type_to_loader_dict[name]["type"] # type: ignore + + # if tool_type in _BASE_TOOLS.keys(): + # params = [] + if tool_type in _LLM_TOOLS.keys(): + params = ["llm"] + elif tool_type in _EXTRA_LLM_TOOLS.keys(): + extra_keys = _EXTRA_LLM_TOOLS[tool_type][1] + params = ["llm"] + extra_keys + elif tool_type in _EXTRA_OPTIONAL_TOOLS.keys(): + extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type][1] + params = extra_keys + # elif tool_type == "Tool": + # params = ["name", "description", "func"] + elif tool_type in CUSTOM_TOOLS: + # Get custom tool params + params = self.type_to_loader_dict[name]["params"] # type: ignore + base_classes = ["function"] + if node := customs.get_custom_nodes("tools").get(tool_type): + return node + elif tool_type in FILE_TOOLS: + params = self.type_to_loader_dict[name]["params"] # type: ignore + base_classes += [name] + elif tool_type in OTHER_TOOLS: + tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) + fields = tool_dict["template"] + + # Pop unnecessary fields and add name + fields.pop("_type") # type: ignore + fields.pop("return_direct") # type: ignore + fields.pop("verbose") # type: ignore + + tool_params = { + "name": fields.pop("name")["value"], # type: ignore + "description": fields.pop("description")["value"], # type: ignore + } + + fields = [ + TemplateField(name=name, field_type=field["type"], **field) + for name, field in fields.items() # type: ignore + ] + base_classes += tool_dict["base_classes"] + + # Copy the field and add the name + for param in params: + field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() + field.name = param + if param == "aiosession": + field.show = False + field.required = False + fields.append(field) + + template = Template(fields=fields, type_name=tool_type) + + tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]} + return { + "template": util.format_dict(template.to_dict()), + **tool_params, + "base_classes": base_classes, + } + + def to_list(self) -> List[str]: + """List all load tools""" + + return list(self.type_to_loader_dict.keys()) + + +tool_creator = ToolCreator() diff --git a/src/backend/langflow/interface/tools/constants.py b/src/backend/langflow/interface/tools/constants.py new file mode 100644 index 000000000..34890a684 --- /dev/null +++ b/src/backend/langflow/interface/tools/constants.py @@ -0,0 +1,60 @@ +from langchain.agents import Tool +from langchain.agents.load_tools import ( + _BASE_TOOLS, + _EXTRA_LLM_TOOLS, + _EXTRA_OPTIONAL_TOOLS, + _LLM_TOOLS, +) +from langchain.tools.bing_search.tool import BingSearchRun +from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun +from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec +from langchain.tools.python.tool import PythonAstREPLTool, PythonREPLTool +from langchain.tools.requests.tool import ( + RequestsDeleteTool, + RequestsGetTool, + RequestsPatchTool, + RequestsPostTool, + RequestsPutTool, +) +from langchain.tools.sql_database.tool import ( + InfoSQLDatabaseTool, + ListSQLDatabaseTool, + QueryCheckerTool, + QuerySQLDataBaseTool, +) +from langchain.tools.wikipedia.tool import WikipediaQueryRun +from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun + +from langflow.interface.tools.custom import PythonFunction + +FILE_TOOLS = {"JsonSpec": JsonSpec} +CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction} +OTHER_TOOLS = { + "QuerySQLDataBaseTool": QuerySQLDataBaseTool, + "InfoSQLDatabaseTool": InfoSQLDatabaseTool, + "ListSQLDatabaseTool": ListSQLDatabaseTool, + "QueryCheckerTool": QueryCheckerTool, + "BingSearchRun": BingSearchRun, + "GoogleSearchRun": GoogleSearchRun, + "GoogleSearchResults": GoogleSearchResults, + "JsonListKeysTool": JsonListKeysTool, + "JsonGetValueTool": JsonGetValueTool, + "PythonREPLTool": PythonREPLTool, + "PythonAstREPLTool": PythonAstREPLTool, + "RequestsGetTool": RequestsGetTool, + "RequestsPostTool": RequestsPostTool, + "RequestsPatchTool": RequestsPatchTool, + "RequestsPutTool": RequestsPutTool, + "RequestsDeleteTool": RequestsDeleteTool, + "WikipediaQueryRun": WikipediaQueryRun, + "WolframAlphaQueryRun": WolframAlphaQueryRun, +} +ALL_TOOLS_NAMES = { + **_BASE_TOOLS, + **_LLM_TOOLS, # type: ignore + **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore + **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, + **CUSTOM_TOOLS, + **FILE_TOOLS, # type: ignore + **OTHER_TOOLS, +} diff --git a/src/backend/langflow/interface/tools/custom.py b/src/backend/langflow/interface/tools/custom.py new file mode 100644 index 000000000..4c641f388 --- /dev/null +++ b/src/backend/langflow/interface/tools/custom.py @@ -0,0 +1,37 @@ +from typing import Callable, Optional + +from pydantic import BaseModel, validator + +from langflow.utils import validate + + +class Function(BaseModel): + code: str + function: Optional[Callable] = None + imports: Optional[str] = None + + # Eval code and store the function + def __init__(self, **data): + super().__init__(**data) + + # Validate the function + @validator("code") + def validate_func(cls, v): + try: + validate.eval_function(v) + except Exception as e: + raise e + + return v + + def get_function(self): + """Get the function""" + function_name = validate.extract_function_name(self.code) + + return validate.create_function(self.code, function_name) + + +class PythonFunction(Function): + """Python function""" + + code: str diff --git a/src/backend/langflow/interface/tools/util.py b/src/backend/langflow/interface/tools/util.py new file mode 100644 index 000000000..f1d66696a --- /dev/null +++ b/src/backend/langflow/interface/tools/util.py @@ -0,0 +1,97 @@ +import ast +import inspect +from typing import Dict, Union + +from langchain.agents.tools import Tool + + +def get_func_tool_params(func, **kwargs) -> Union[Dict, None]: + tree = ast.parse(inspect.getsource(func)) + + # Iterate over the statements in the abstract syntax tree + for node in ast.walk(tree): + # Find the first return statement + if isinstance(node, ast.Return): + tool = node.value + if isinstance(tool, ast.Call): + if isinstance(tool.func, ast.Name) and tool.func.id == "Tool": + if tool.keywords: + tool_params = {} + for keyword in tool.keywords: + if keyword.arg == "name": + tool_params["name"] = ast.literal_eval(keyword.value) + elif keyword.arg == "description": + tool_params["description"] = ast.literal_eval( + keyword.value + ) + + return tool_params + return { + "name": ast.literal_eval(tool.args[0]), + "description": ast.literal_eval(tool.args[2]), + } + # + else: + # get the class object from the return statement + try: + class_obj = eval( + compile(ast.Expression(tool), "", "eval") + ) + except Exception: + return None + + return { + "name": getattr(class_obj, "name"), + "description": getattr(class_obj, "description"), + } + # Return None if no return statement was found + return None + + +def get_class_tool_params(cls, **kwargs) -> Union[Dict, None]: + tree = ast.parse(inspect.getsource(cls)) + + tool_params = {} + + # Iterate over the statements in the abstract syntax tree + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + # Find the class definition and look for methods + for stmt in node.body: + if isinstance(stmt, ast.FunctionDef) and stmt.name == "__init__": + # There is no assignment statements in the __init__ method + # So we need to get the params from the function definition + for arg in stmt.args.args: + if arg.arg == "name": + # It should be the name of the class + tool_params[arg.arg] = cls.__name__ + elif arg.arg == "self": + continue + # If there is not default value, set it to an empty string + else: + try: + annotation = ast.literal_eval(arg.annotation) # type: ignore + tool_params[arg.arg] = annotation + except ValueError: + tool_params[arg.arg] = "" + # Get the attribute name and the annotation + elif cls != Tool and isinstance(stmt, ast.AnnAssign): + # Get the attribute name and the annotation + tool_params[stmt.target.id] = "" # type: ignore + + return tool_params + + +def get_tool_params(tool, **kwargs) -> Dict: + # Parse the function code into an abstract syntax tree + # Define if it is a function or a class + if inspect.isfunction(tool): + return get_func_tool_params(tool, **kwargs) or {} + elif inspect.isclass(tool): + # Get the parameters necessary to + # instantiate the class + + return get_class_tool_params(tool, **kwargs) or {} + + else: + raise ValueError("Tool must be a function or class.") diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index 57f3a2578..085537756 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -1,12 +1,23 @@ -from langflow.interface.listing import list_type -from langflow.interface.signature import get_signature +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.utilities.base import utility_creator +from langflow.interface.vector_store.base import vectorstore_creator +from langflow.interface.wrappers.base import wrapper_creator def get_type_list(): """Get a list of all langchain types""" all_types = build_langchain_types_dict() - all_types.pop("tools") + # all_types.pop("tools") for key, value in all_types.items(): all_types[key] = [item["template"]["_type"] for item in value.values()] @@ -14,23 +25,30 @@ def get_type_list(): return all_types -def build_langchain_types_dict(): +def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union """Build a dictionary of all langchain types""" - return { - "chains": { - chain: get_signature(chain, "chains") for chain in list_type("chains") - }, - "agents": { - agent: get_signature(agent, "agents") for agent in list_type("agents") - }, - "prompts": { - prompt: get_signature(prompt, "prompts") for prompt in list_type("prompts") - }, - "llms": {llm: get_signature(llm, "llms") for llm in list_type("llms")}, - "memories": { - memory: get_signature(memory, "memories") - for memory in list_type("memories") - }, - "tools": {tool: get_signature(tool, "tools") for tool in list_type("tools")}, - } + all_types = {} + + creators = [ + chain_creator, + agent_creator, + prompt_creator, + llm_creator, + memory_creator, + tool_creator, + toolkits_creator, + wrapper_creator, + embedding_creator, + vectorstore_creator, + documentloader_creator, + textsplitter_creator, + utility_creator, + ] + + all_types = {} + for creator in creators: + created_types = creator.to_dict() + if created_types[creator.type_name].values(): + all_types.update(created_types) + return all_types diff --git a/src/backend/langflow/interface/utilities/__init__.py b/src/backend/langflow/interface/utilities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/utilities/base.py b/src/backend/langflow/interface/utilities/base.py new file mode 100644 index 000000000..e60e344ad --- /dev/null +++ b/src/backend/langflow/interface/utilities/base.py @@ -0,0 +1,39 @@ +from typing import Dict, List, Optional + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import utility_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class UtilityCreator(LangChainTypeCreator): + type_name: str = "utilities" + + @property + def type_to_loader_dict(self) -> Dict: + return utility_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a utility.""" + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class(name, utility_type_to_cls_dict) + except ValueError as exc: + raise ValueError(f"Utility {name} not found") from exc + + except AttributeError as exc: + logger.error(f"Utility {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + utility.__name__ + for utility in self.type_to_loader_dict.values() + if utility.__name__ in settings.utilities or settings.dev + ] + + +utility_creator = UtilityCreator() diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py new file mode 100644 index 000000000..b3b154790 --- /dev/null +++ b/src/backend/langflow/interface/utils.py @@ -0,0 +1,22 @@ +import json +import os + +import yaml + + +def load_file_into_dict(file_path: str) -> dict: + if not os.path.exists(file_path): + raise FileNotFoundError(f"File not found: {file_path}") + + file_extension = os.path.splitext(file_path)[1].lower() + + if file_extension == ".json": + with open(file_path, "r") as json_file: + data = json.load(json_file) + elif file_extension in [".yaml", ".yml"]: + with open(file_path, "r") as yaml_file: + data = yaml.safe_load(yaml_file) + else: + raise ValueError("Unsupported file type. Please provide a JSON or YAML file.") + + return data diff --git a/src/backend/langflow/interface/vector_store/__init__.py b/src/backend/langflow/interface/vector_store/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/vector_store/base.py b/src/backend/langflow/interface/vector_store/base.py new file mode 100644 index 000000000..7fca2ba0c --- /dev/null +++ b/src/backend/langflow/interface/vector_store/base.py @@ -0,0 +1,55 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import vectorstores_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class VectorstoreCreator(LangChainTypeCreator): + type_name: str = "vectorstores" + + @property + def type_to_loader_dict(self) -> Dict: + return vectorstores_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an embedding.""" + try: + signature = build_template_from_class(name, vectorstores_type_to_cls_dict) + + # TODO: Use FrontendendNode class to build the signature + signature["template"] = { + "documents": { + "type": "TextSplitter", + "required": True, + "show": True, + "name": "documents", + "display_name": "Text Splitter", + }, + "embedding": { + "type": "Embeddings", + "required": True, + "show": True, + "name": "embedding", + "display_name": "Embedding", + }, + } + return signature + + except ValueError as exc: + raise ValueError(f"Vector Store {name} not found") from exc + except AttributeError as exc: + logger.error(f"Vector Store {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + vectorstore + for vectorstore in self.type_to_loader_dict.keys() + if vectorstore in settings.vectorstores or settings.dev + ] + + +vectorstore_creator = VectorstoreCreator() diff --git a/src/backend/langflow/interface/wrappers/__init__.py b/src/backend/langflow/interface/wrappers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py new file mode 100644 index 000000000..f5773d07a --- /dev/null +++ b/src/backend/langflow/interface/wrappers/base.py @@ -0,0 +1,34 @@ +from typing import Dict, List, Optional + +from langchain import requests + +from langflow.interface.base import LangChainTypeCreator +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class WrapperCreator(LangChainTypeCreator): + type_name: str = "wrappers" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + wrapper.__name__: wrapper for wrapper in [requests.TextRequestsWrapper] + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Wrapper not found") from exc + except AttributeError as exc: + logger.error(f"Wrapper {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return list(self.type_to_loader_dict.keys()) + + +wrapper_creator = WrapperCreator() diff --git a/src/backend/langflow/main.py b/src/backend/langflow/main.py index a2a02465e..176e46236 100644 --- a/src/backend/langflow/main.py +++ b/src/backend/langflow/main.py @@ -2,8 +2,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from langflow.api.endpoints import router as endpoints_router -from langflow.api.list_endpoints import router as list_router -from langflow.api.signature import router as signatures_router +from langflow.api.validate import router as validate_router def create_app(): @@ -23,8 +22,7 @@ def create_app(): ) app.include_router(endpoints_router) - app.include_router(list_router) - app.include_router(signatures_router) + app.include_router(validate_router) return app diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/settings.py index f4dd4ae30..48aa5939d 100644 --- a/src/backend/langflow/settings.py +++ b/src/backend/langflow/settings.py @@ -1,29 +1,51 @@ import os -from typing import List, Optional +from typing import List import yaml -from pydantic import BaseSettings, Field, root_validator +from pydantic import BaseSettings, root_validator class Settings(BaseSettings): - chains: Optional[List[str]] = Field(...) - agents: Optional[List[str]] = Field(...) - prompts: Optional[List[str]] = Field(...) - llms: Optional[List[str]] = Field(...) - tools: Optional[List[str]] = Field(...) - memories: Optional[List[str]] = Field(...) - dev: bool = Field(...) + chains: List[str] = [] + agents: List[str] = [] + prompts: List[str] = [] + llms: List[str] = [] + tools: List[str] = [] + memories: List[str] = [] + embeddings: List[str] = [] + vectorstores: List[str] = [] + documentloaders: List[str] = [] + wrappers: List[str] = [] + toolkits: List[str] = [] + textsplitters: List[str] = [] + utilities: List[str] = [] + dev: bool = False class Config: validate_assignment = True + extra = "ignore" - @root_validator + @root_validator(allow_reuse=True) def validate_lists(cls, values): for key, value in values.items(): if key != "dev" and not value: values[key] = [] return values + def update_from_yaml(self, file_path: str): + new_settings = load_settings_from_yaml(file_path) + self.chains = new_settings.chains or [] + self.agents = new_settings.agents or [] + self.prompts = new_settings.prompts or [] + self.llms = new_settings.llms or [] + self.tools = new_settings.tools or [] + self.memories = new_settings.memories or [] + self.wrappers = new_settings.wrappers or [] + self.toolkits = new_settings.toolkits or [] + self.textsplitters = new_settings.textsplitters or [] + self.utilities = new_settings.utilities or [] + self.dev = new_settings.dev or False + def save_settings_to_yaml(settings: Settings, file_path: str): with open(file_path, "w") as f: @@ -41,9 +63,8 @@ def load_settings_from_yaml(file_path: str) -> Settings: with open(file_path, "r") as f: settings_dict = yaml.safe_load(f) - a = Settings.parse_obj(settings_dict) - return a + return Settings(**settings_dict) settings = load_settings_from_yaml("config.yaml") diff --git a/src/backend/langflow/template/__init__.py b/src/backend/langflow/template/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/template/base.py b/src/backend/langflow/template/base.py new file mode 100644 index 000000000..ecadde108 --- /dev/null +++ b/src/backend/langflow/template/base.py @@ -0,0 +1,234 @@ +from abc import ABC +from typing import Any, Callable, Dict, Optional, Union + +from pydantic import BaseModel + +from langflow.template.constants import FORCE_SHOW_FIELDS +from langflow.utils import constants + + +class TemplateFieldCreator(BaseModel, ABC): + field_type: str = "str" + required: bool = False + placeholder: str = "" + is_list: bool = False + show: bool = True + multiline: bool = False + value: Any = None + suffixes: list[str] = [] + fileTypes: list[str] = [] + file_types: list[str] = [] + content: Union[str, None] = None + password: bool = False + options: list[str] = [] + name: str = "" + display_name: Optional[str] = None + + def to_dict(self): + result = self.dict() + # Remove key if it is None + for key in list(result.keys()): + if result[key] is None or result[key] == []: + del result[key] + result["type"] = result.pop("field_type") + result["list"] = result.pop("is_list") + + if result.get("file_types"): + result["fileTypes"] = result.pop("file_types") + + if self.field_type == "file": + result["content"] = self.content + return result + + def process_field( + self, key: str, value: Dict[str, Any], name: Optional[str] = None + ) -> None: + _type = value["type"] + + # Remove 'Optional' wrapper + if "Optional" in _type: + _type = _type.replace("Optional[", "")[:-1] + + # Check for list type + if "List" in _type: + _type = _type.replace("List[", "")[:-1] + self.is_list = True + + # Replace 'Mapping' with 'dict' + if "Mapping" in _type: + _type = _type.replace("Mapping", "dict") + + # Change type from str to Tool + self.field_type = "Tool" if key in {"allowed_tools"} else self.field_type + + self.field_type = "int" if key in {"max_value_length"} else self.field_type + + # Show or not field + self.show = bool( + (self.required and key not in ["input_variables"]) + or key in FORCE_SHOW_FIELDS + or "api_key" in key + ) + + # Add password field + self.password = any( + text in key.lower() for text in {"password", "token", "api", "key"} + ) + + # Add multline + self.multiline = key in { + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + } + + # Replace dict type with str + if "dict" in self.field_type.lower(): + self.field_type = "code" + + if key == "dict_": + self.field_type = "file" + self.suffixes = [".json", ".yaml", ".yml"] + self.file_types = ["json", "yaml", "yml"] + + # Replace default value with actual value + if "default" in value: + self.value = value["default"] + + if key == "headers": + self.value = """{'Authorization': + 'Bearer '}""" + + # Add options to openai + if name == "OpenAI" and key == "model_name": + self.options = constants.OPENAI_MODELS + self.is_list = True + elif name == "ChatOpenAI" and key == "model_name": + self.options = constants.CHAT_OPENAI_MODELS + self.is_list = True + + +class TemplateField(TemplateFieldCreator): + pass + + +class Template(BaseModel): + type_name: str + fields: list[TemplateField] + + def process_fields( + self, + name: Optional[str] = None, + format_field_func: Union[Callable, None] = None, + ): + if format_field_func: + for field in self.fields: + format_field_func(field, name) + + def to_dict(self, format_field_func=None): + self.process_fields(self.type_name, format_field_func) + result = {field.name: field.to_dict() for field in self.fields} + result["_type"] = self.type_name # type: ignore + return result + + +class FrontendNode(BaseModel): + template: Template + description: str + base_classes: list + name: str = "" + + def to_dict(self): + return { + self.name: { + "template": self.template.to_dict(self.format_field), + "description": self.description, + "base_classes": self.base_classes, + } + } + + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + key = field.name + value = field.to_dict() + _type = value["type"] + + # Remove 'Optional' wrapper + if "Optional" in _type: + _type = _type.replace("Optional[", "")[:-1] + + # Check for list type + if "List" in _type: + _type = _type.replace("List[", "")[:-1] + field.is_list = True + + # Replace 'Mapping' with 'dict' + if "Mapping" in _type: + _type = _type.replace("Mapping", "dict") + + # Change type from str to Tool + field.field_type = "Tool" if key in {"allowed_tools"} else field.field_type + + field.field_type = "int" if key in {"max_value_length"} else field.field_type + + # Show or not field + field.show = bool( + (field.required and key not in ["input_variables"]) + or key in FORCE_SHOW_FIELDS + or "api" in key + or ("key" in key and "input" not in key and "output" not in key) + ) + + # Add password field + field.password = ( + any(text in key.lower() for text in {"password", "token", "api", "key"}) + and field.show + ) + + # Add multline + field.multiline = key in { + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + } + + # Replace dict type with str + if "dict" in field.field_type.lower(): + field.field_type = "code" + + if key == "dict_": + field.field_type = "file" + field.suffixes = [".json", ".yaml", ".yml"] + field.file_types = ["json", "yaml", "yml"] + + # Replace default value with actual value + if "default" in value: + field.value = value["default"] + + if key == "headers": + field.value = """{'Authorization': + 'Bearer '}""" + + # Add options to openai + if name == "OpenAI" and key == "model_name": + field.options = constants.OPENAI_MODELS + field.is_list = True + elif name == "ChatOpenAI": + if key == "model_name": + field.options = constants.CHAT_OPENAI_MODELS + field.is_list = True + if "api_key" in key and "OpenAI" in str(name): + field.display_name = "OpenAI API Key" + field.required = False + if field.value is None: + field.value = "" + # If the field.name contains api or api and key, then it might be an api key + # other conditions are to make sure that it is not an input or output variable + if "api" in key.lower() and "key" in key.lower(): + field.required = False diff --git a/src/backend/langflow/template/constants.py b/src/backend/langflow/template/constants.py new file mode 100644 index 000000000..ae08d3691 --- /dev/null +++ b/src/backend/langflow/template/constants.py @@ -0,0 +1,32 @@ +FORCE_SHOW_FIELDS = [ + "allowed_tools", + "memory", + "prefix", + "examples", + "temperature", + "model_name", + "headers", + "max_value_length", + "max_tokens", +] + +DEFAULT_PROMPT = """ +I want you to act as a naming consultant for new companies. + +Here are some examples of good company names: + +- search engine, Google +- social media, Facebook +- video sharing, YouTube + +The name should be short, catchy and easy to remember. + +What is a good name for a company that makes {product}? +""" + +SYSTEM_PROMPT = """ +You are a helpful assistant that talks casually about life in general. +You are a good listener and you can talk about anything. +""" + +HUMAN_PROMPT = "{input}" diff --git a/src/backend/langflow/template/fields.py b/src/backend/langflow/template/fields.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/template/nodes.py b/src/backend/langflow/template/nodes.py new file mode 100644 index 000000000..b174a9363 --- /dev/null +++ b/src/backend/langflow/template/nodes.py @@ -0,0 +1,461 @@ +from typing import Optional + +from langchain.agents import loading +from langchain.agents.mrkl import prompt + +from langflow.template.base import FrontendNode, Template, TemplateField +from langflow.template.constants import DEFAULT_PROMPT, HUMAN_PROMPT, SYSTEM_PROMPT +from langflow.utils.constants import DEFAULT_PYTHON_FUNCTION + +NON_CHAT_AGENTS = { + agent_type: agent_class + for agent_type, agent_class in loading.AGENT_TO_CLASS.items() + if "chat" not in agent_type.value +} + + +class BasePromptFrontendNode(FrontendNode): + name: str + template: Template + description: str + base_classes: list[str] + + def to_dict(self): + return super().to_dict() + + +class ZeroShotPromptNode(BasePromptFrontendNode): + name: str = "ZeroShotPrompt" + template: Template = Template( + type_name="zero_shot", + fields=[ + TemplateField( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.PREFIX, + name="prefix", + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.SUFFIX, + name="suffix", + ), + TemplateField( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.FORMAT_INSTRUCTIONS, + name="format_instructions", + ), + ], + ) + description: str = "Prompt template for Zero Shot Agent." + base_classes: list[str] = ["BasePromptTemplate"] + + def to_dict(self): + return super().to_dict() + + +class PromptTemplateNode(FrontendNode): + name: str = "PromptTemplate" + template: Template + description: str + base_classes: list[str] = ["BasePromptTemplate"] + + def to_dict(self): + return super().to_dict() + + +class PythonFunctionNode(FrontendNode): + name: str = "PythonFunction" + template: Template = Template( + type_name="python_function", + fields=[ + TemplateField( + field_type="code", + required=True, + placeholder="", + is_list=False, + show=True, + value=DEFAULT_PYTHON_FUNCTION, + name="code", + ) + ], + ) + description: str = "Python function to be executed." + base_classes: list[str] = ["function"] + + def to_dict(self): + return super().to_dict() + + +class ToolNode(FrontendNode): + name: str = "Tool" + template: Template = Template( + type_name="Tool", + fields=[ + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="name", + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="description", + ), + TemplateField( + name="func", + field_type="function", + required=True, + is_list=False, + show=True, + multiline=True, + ), + TemplateField( + field_type="bool", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=False, + value=False, + name="return_direct", + ), + ], + ) + description: str = "Tool to be used in the flow." + base_classes: list[str] = ["Tool"] + + def to_dict(self): + return super().to_dict() + + +class JsonAgentNode(FrontendNode): + name: str = "JsonAgent" + template: Template = Template( + type_name="json_agent", + fields=[ + TemplateField( + field_type="BaseToolkit", + required=True, + show=True, + name="toolkit", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from an LLM and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class InitializeAgentNode(FrontendNode): + name: str = "initialize_agent" + template: Template = Template( + type_name="initailize_agent", + fields=[ + TemplateField( + field_type="str", + required=True, + is_list=True, + show=True, + multiline=False, + options=list(NON_CHAT_AGENTS.keys()), + value=list(NON_CHAT_AGENTS.keys())[0], + name="agent", + ), + TemplateField( + field_type="BaseChatMemory", + required=False, + show=True, + name="memory", + ), + TemplateField( + field_type="Tool", + required=False, + show=True, + name="tools", + is_list=True, + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from an LLM and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + # do nothing and don't return anything + pass + + +class CSVAgentNode(FrontendNode): + name: str = "CSVAgent" + template: Template = Template( + type_name="csv_agent", + fields=[ + TemplateField( + field_type="file", + required=True, + show=True, + name="path", + value="", + suffixes=[".csv"], + fileTypes=["csv"], + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from a CSV and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class SQLDatabaseNode(FrontendNode): + name: str = "SQLDatabase" + template: Template = Template( + type_name="sql_database", + fields=[ + TemplateField( + field_type="str", + required=True, + is_list=False, + show=True, + multiline=False, + value="", + name="uri", + ), + ], + ) + description: str = """SQLAlchemy wrapper around a database.""" + base_classes: list[str] = ["SQLDatabase"] + + def to_dict(self): + return super().to_dict() + + +class VectorStoreAgentNode(FrontendNode): + name: str = "VectorStoreAgent" + template: Template = Template( + type_name="vectorstore_agent", + fields=[ + TemplateField( + field_type="VectorStoreInfo", + required=True, + show=True, + name="vectorstoreinfo", + display_name="Vector Store Info", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class VectorStoreRouterAgentNode(FrontendNode): + name: str = "VectorStoreRouterAgent" + template: Template = Template( + type_name="vectorstorerouter_agent", + fields=[ + TemplateField( + field_type="VectorStoreRouterToolkit", + required=True, + show=True, + name="vectorstoreroutertoolkit", + display_name="Vector Store Router Toolkit", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store Router.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class SQLAgentNode(FrontendNode): + name: str = "SQLAgent" + template: Template = Template( + type_name="sql_agent", + fields=[ + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=False, + value="", + name="database_uri", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store Router.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class PromptFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + # if field.field_type == "StringPromptTemplate" + # change it to str + PROMPT_FIELDS = [ + "template", + "suffix", + "prefix", + "examples", + ] + if field.field_type == "StringPromptTemplate" and "Message" in str(name): + field.field_type = "prompt" + field.multiline = True + field.value = HUMAN_PROMPT if "Human" in field.name else SYSTEM_PROMPT + if field.name == "template" and field.value == "": + field.value = DEFAULT_PROMPT + + if field.name in PROMPT_FIELDS: + field.field_type = "prompt" + + if ( + "Union" in field.field_type + and "BaseMessagePromptTemplate" in field.field_type + ): + field.field_type = "BaseMessagePromptTemplate" + + # All prompt fields should be password=False + field.password = False + + +class MemoryFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + FrontendNode.format_field(field, name) + + if not isinstance(field.value, str): + field.value = None + if field.name == "k": + field.required = True + field.show = True + field.field_type = "int" + field.value = 10 + field.display_name = "Memory Size" + field.password = False + + +class ChainFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + FrontendNode.format_field(field, name) + + if "key" in field.name: + field.password = False + field.show = False + if field.name in ["input_key", "output_key"]: + field.required = True + field.show = True + # Separated for possible future changes + if field.name == "prompt": + # if no prompt is provided, use the default prompt + field.required = False + field.show = True + + +class LLMFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + display_names_dict = { + "huggingfacehub_api_token": "HuggingFace Hub API Token", + } + FrontendNode.format_field(field, name) + SHOW_FIELDS = ["repo_id", "task", "model_kwargs"] + if field.name in SHOW_FIELDS: + field.show = True + + if "api" in field.name and ("key" in field.name or "token" in field.name): + field.password = True + field.show = True + # Required should be False to support + # loading the API key from environment variables + field.required = False + + if field.name == "task": + field.required = True + field.show = True + field.is_list = True + field.options = ["text-generation", "text2text-generation"] + + if display_name := display_names_dict.get(field.name): + field.display_name = display_name + if field.name == "model_kwargs": + field.field_type = "code" diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py index 73a50ce40..2d101ab98 100644 --- a/src/backend/langflow/utils/constants.py +++ b/src/backend/langflow/utils/constants.py @@ -6,3 +6,10 @@ OPENAI_MODELS = [ "text-ada-001", ] CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"] + + +DEFAULT_PYTHON_FUNCTION = """ +def python_function(text: str) -> str: + \"\"\"This is a default python function that returns the input text\"\"\" + return text +""" diff --git a/src/backend/langflow/utils/logger.py b/src/backend/langflow/utils/logger.py new file mode 100644 index 000000000..b70a451d4 --- /dev/null +++ b/src/backend/langflow/utils/logger.py @@ -0,0 +1,30 @@ +import logging +from pathlib import Path + +from rich.logging import RichHandler + +logger = logging.getLogger("langflow") + + +def configure(log_level: str = "INFO", log_file: Path = None): # type: ignore + log_format = "%(asctime)s - %(levelname)s - %(message)s" + log_level_value = getattr(logging, log_level.upper(), logging.INFO) + + logging.basicConfig( + level=log_level_value, + format=log_format, + datefmt="[%X]", + handlers=[RichHandler(rich_tracebacks=True)], + ) + + if log_file: + log_file = Path(log_file) + log_file.parent.mkdir(parents=True, exist_ok=True) + + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter(logging.Formatter(log_format)) + logger.addHandler(file_handler) + + logger.info(f"Logger set up with log level: {log_level_value}({log_level})") + if log_file: + logger.info(f"Log file: {log_file}") diff --git a/src/backend/langflow/utils/payload.py b/src/backend/langflow/utils/payload.py index e7d38139e..cac23a0d6 100644 --- a/src/backend/langflow/utils/payload.py +++ b/src/backend/langflow/utils/payload.py @@ -1,5 +1,6 @@ import contextlib import re +from typing import Dict def extract_input_variables(nodes): @@ -27,48 +28,63 @@ def extract_input_variables(nodes): return nodes -def get_root_node(nodes, edges): +def get_root_node(graph): """ Returns the root node of the template. """ - incoming_edges = {edge["source"] for edge in edges} - return next((node for node in nodes if node["id"] not in incoming_edges), None) + incoming_edges = {edge.source for edge in graph.edges} + + if not incoming_edges and len(graph.nodes) == 1: + return graph.nodes[0] + + return next((node for node in graph.nodes if node not in incoming_edges), None) -def build_json(root, nodes, edges): - """ - Builds a json from the nodes and edges - """ - edge_ids = [edge["source"] for edge in edges if edge["target"] == root["id"]] - local_nodes = [node for node in nodes if node["id"] in edge_ids] +def build_json(root, graph) -> Dict: + if "node" not in root.data: + # If the root node has no "node" key, then it has only one child, + # which is the target of the single outgoing edge + edge = root.edges[0] + local_nodes = [edge.target] + else: + # Otherwise, find all children whose type matches the type + # specified in the template + node_type = root.node_type + local_nodes = graph.get_nodes_with_target(root) - if "node" not in root["data"]: - return build_json(local_nodes[0], nodes, edges) - - final_dict = root["data"]["node"]["template"].copy() + if len(local_nodes) == 1: + return build_json(local_nodes[0], graph) + # Build a dictionary from the template + template = root.data["node"]["template"] + final_dict = template.copy() for key, value in final_dict.items(): if key == "_type": continue - module_type = value["type"] + node_type = value["type"] if "value" in value and value["value"] is not None: + # If the value is specified, use it value = value["value"] - elif "dict" in module_type: + elif "dict" in node_type: + # If the value is a dictionary, create an empty dictionary value = {} else: + # Otherwise, recursively build the child nodes children = [] - for c in local_nodes: - module_types = [c["data"]["type"]] - if "node" in c["data"]: - module_types += c["data"]["node"]["base_classes"] - if module_type in module_types: - children.append(c) + for local_node in local_nodes: + node_children = graph.get_children_by_node_type(local_node, node_type) + children.extend(node_children) if value["required"] and not children: - raise ValueError(f"No child with type {module_type} found") - values = [build_json(child, nodes, edges) for child in children] - value = list(values) if value["list"] else next(iter(values), None) + raise ValueError(f"No child with type {node_type} found") + values = [build_json(child, graph) for child in children] + value = ( + list(values) + if value["list"] + else next(iter(values), None) # type: ignore + ) final_dict[key] = value + return final_dict diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index 4710114ed..b31a3bed1 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -1,20 +1,60 @@ -import ast import importlib import inspect import re from typing import Dict, Optional -from langchain.agents.load_tools import ( - _BASE_TOOLS, - _EXTRA_LLM_TOOLS, - _EXTRA_OPTIONAL_TOOLS, - _LLM_TOOLS, -) +from docstring_parser import parse # type: ignore +from langflow.template.constants import FORCE_SHOW_FIELDS from langflow.utils import constants -def build_template_from_function(name: str, type_to_loader_dict: Dict): +def build_template_from_parameters( + name: str, type_to_loader_dict: Dict, add_function: bool = False +): + # Retrieve the function that matches the provided name + func = None + for _, v in type_to_loader_dict.items(): + if v.__name__ == name: + func = v + break + + if func is None: + raise ValueError(f"{name} not found") + + # Process parameters + parameters = func.__annotations__ + variables = {} + for param_name, param_type in parameters.items(): + if param_name in ["return", "kwargs"]: + continue + + variables[param_name] = { + "type": param_type.__name__, + "default": parameters[param_name].__repr_args__()[0][1], + # Op + "placeholder": "", + } + + # Get the base classes of the return type + return_type = parameters.get("return") + base_classes = get_base_classes(return_type) if return_type else [] + if add_function: + base_classes.append("function") + + # Get the function's docstring + docs = inspect.getdoc(func) or "" + + return { + "template": format_dict(variables, name), + "description": docs["Description"], # type: ignore + "base_classes": base_classes, + } + + +def build_template_from_function( + name: str, type_to_loader_dict: Dict, add_function: bool = False +): classes = [ item.__annotations__["return"].__name__ for item in type_to_loader_dict.values() ] @@ -27,52 +67,8 @@ def build_template_from_function(name: str, type_to_loader_dict: Dict): if v.__annotations__["return"].__name__ == name: _class = v.__annotations__["return"] - docs = get_class_doc(_class) - - variables = {"_type": _type} - for class_field_items, value in _class.__fields__.items(): - if class_field_items in ["callback_manager", "requests_wrapper"]: - continue - variables[class_field_items] = {} - for name_, value_ in value.__repr_args__(): - if name_ == "default_factory": - try: - variables[class_field_items][ - "default" - ] = get_default_factory( - module=_class.__base__.__module__, function=value_ - ) - except Exception: - variables[class_field_items]["default"] = None - elif name_ not in ["name"]: - variables[class_field_items][name_] = value_ - - variables[class_field_items]["placeholder"] = ( - docs["Attributes"][class_field_items] - if class_field_items in docs["Attributes"] - else "" - ) - - return { - "template": format_dict(variables, name), - "description": docs["Description"], - "base_classes": get_base_classes(_class), - } - - -def build_template_from_class(name: str, type_to_cls_dict: Dict): - classes = [item.__name__ for item in type_to_cls_dict.values()] - - # Raise error if name is not in chains - if name not in classes: - raise ValueError(f"{name} not found.") - - for _type, v in type_to_cls_dict.items(): - if v.__name__ == name: - _class = v - # Get the docstring - docs = get_class_doc(_class) + docs = parse(_class.__doc__) variables = {"_type": _type} for class_field_items, value in _class.__fields__.items(): @@ -93,30 +89,97 @@ def build_template_from_class(name: str, type_to_cls_dict: Dict): variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = ( - docs["Attributes"][class_field_items] - if class_field_items in docs["Attributes"] + docs.params[class_field_items] + if class_field_items in docs.params else "" ) + # Adding function to base classes to allow + # the output to be a function + base_classes = get_base_classes(_class) + if add_function: + base_classes.append("function") return { "template": format_dict(variables, name), - "description": docs["Description"], - "base_classes": get_base_classes(_class), + "description": docs.short_description or "", + "base_classes": base_classes, + } + + +def build_template_from_class( + name: str, type_to_cls_dict: Dict, add_function: bool = False +): + classes = [item.__name__ for item in type_to_cls_dict.values()] + + # Raise error if name is not in chains + if name not in classes: + raise ValueError(f"{name} not found.") + + for _type, v in type_to_cls_dict.items(): + if v.__name__ == name: + _class = v + + # Get the docstring + docs = parse(_class.__doc__) + + variables = {"_type": _type} + + if "__fields__" in _class.__dict__: + for class_field_items, value in _class.__fields__.items(): + if class_field_items in ["callback_manager"]: + continue + variables[class_field_items] = {} + for name_, value_ in value.__repr_args__(): + if name_ == "default_factory": + try: + variables[class_field_items][ + "default" + ] = get_default_factory( + module=_class.__base__.__module__, function=value_ + ) + except Exception: + variables[class_field_items]["default"] = None + elif name_ not in ["name"]: + variables[class_field_items][name_] = value_ + + variables[class_field_items]["placeholder"] = ( + docs.params[class_field_items] + if class_field_items in docs.params + else "" + ) + base_classes = get_base_classes(_class) + # Adding function to base classes to allow + # the output to be a function + if add_function: + base_classes.append("function") + return { + "template": format_dict(variables, name), + "description": docs.short_description or "", + "base_classes": base_classes, } def get_base_classes(cls): - bases = cls.__bases__ - if not bases: - return [] - else: + """Get the base classes of a class. + These are used to determine the output of the nodes. + """ + if bases := cls.__bases__: result = [] for base in bases: if any(type in base.__module__ for type in ["pydantic", "abc"]): continue result.append(base.__name__) - result.extend(get_base_classes(base)) - return result + base_classes = get_base_classes(base) + # check if the base_classes are in the result + # if not, add them + for base_class in base_classes: + if base_class not in result: + result.append(base_class) + else: + result = [cls.__name__] + if not result: + result = [cls.__name__] + return list(set(result + [cls.__name__])) def get_default_factory(module: str, function: str): @@ -128,114 +191,6 @@ def get_default_factory(module: str, function: str): return None -def get_tools_dict(name: Optional[str] = None): - """Get the tools dictionary.""" - tools = { - **_BASE_TOOLS, - **_LLM_TOOLS, # type: ignore - **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore - **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, - } - return tools[name] if name else tools - - -def get_tool_params(func, **kwargs): - # Parse the function code into an abstract syntax tree - tree = ast.parse(inspect.getsource(func)) - - # Iterate over the statements in the abstract syntax tree - for node in ast.walk(tree): - # Find the first return statement - if isinstance(node, ast.Return): - tool = node.value - if isinstance(tool, ast.Call): - if tool.func.id == "Tool": - if tool.keywords: - tool_params = {} - for keyword in tool.keywords: - if keyword.arg == "name": - tool_params["name"] = ast.literal_eval(keyword.value) - elif keyword.arg == "description": - tool_params["description"] = ast.literal_eval( - keyword.value - ) - return tool_params - return { - "name": ast.literal_eval(tool.args[0]), - "description": ast.literal_eval(tool.args[2]), - } - else: - # get the class object from the return statement - try: - class_obj = eval( - compile(ast.Expression(tool), "", "eval") - ) - except Exception: - return None - - return { - "name": getattr(class_obj, "name"), - "description": getattr(class_obj, "description"), - } - - # Return None if no return statement was found - return None - - -def get_class_doc(class_name): - """ - Extracts information from the docstring of a given class. - - Args: - class_name: the class to extract information from - - Returns: - A dictionary containing the extracted information, with keys - for 'Description', 'Parameters', 'Attributes', and 'Returns'. - """ - # Template - data = { - "Description": "", - "Parameters": {}, - "Attributes": {}, - "Example": [], - "Returns": {}, - } - - # Get the class docstring - docstring = class_name.__doc__ - - if not docstring: - return data - - # Parse the docstring to extract information - lines = docstring.split("\n") - - current_section = "Description" - - for line in lines: - line = line.strip() - - if not line: - continue - - if ( - line.startswith(tuple(data.keys())) - and len(line.split()) == 1 - and line.endswith(":") - ): - current_section = line[:-1] - continue - - if current_section in ["Description", "Example"]: - data[current_section] += line - else: - param, desc = line.split(":") - data[current_section][param.strip()] = desc.strip() - - return data - - def format_dict(d, name: Optional[str] = None): """ Formats a dictionary by removing certain keys and modifying the @@ -272,41 +227,58 @@ def format_dict(d, name: Optional[str] = None): _type = _type.replace("Mapping", "dict") # Change type from str to Tool - value["type"] = "Tool" if key == "allowed_tools" else _type + value["type"] = "Tool" if key in ["allowed_tools"] else _type + + value["type"] = "int" if key in ["max_value_length"] else value["type"] # Show or not field value["show"] = bool( (value["required"] and key not in ["input_variables"]) - or key - in [ - "allowed_tools", - "memory", - "prefix", - "examples", - "temperature", - "model_name", - ] + or key in FORCE_SHOW_FIELDS or "api_key" in key ) # Add password field value["password"] = any( - text in key for text in ["password", "token", "api", "key"] + text in key.lower() for text in ["password", "token", "api", "key"] ) # Add multline - value["multiline"] = key in ["suffix", "prefix", "template", "examples"] + value["multiline"] = key in [ + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + ] + + # Replace dict type with str + if "dict" in value["type"].lower(): + value["type"] = "code" + + if key == "dict_": + value["type"] = "file" + value["suffixes"] = [".json", ".yaml", ".yml"] + value["fileTypes"] = ["json", "yaml", "yml"] # Replace default value with actual value if "default" in value: value["value"] = value["default"] value.pop("default") + if key == "headers": + value[ + "value" + ] = """{'Authorization': + 'Bearer '}""" # Add options to openai if name == "OpenAI" and key == "model_name": value["options"] = constants.OPENAI_MODELS - elif name == "OpenAIChat" and key == "model_name": + value["list"] = True + elif name == "ChatOpenAI" and key == "model_name": value["options"] = constants.CHAT_OPENAI_MODELS + value["list"] = True return d diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py new file mode 100644 index 000000000..d1353bd77 --- /dev/null +++ b/src/backend/langflow/utils/validate.py @@ -0,0 +1,173 @@ +import ast +import importlib +import types +from typing import Dict + + +def add_type_ignores(): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + +def validate_code(code): + # Initialize the errors dictionary + errors = {"imports": {"errors": []}, "function": {"errors": []}} + + # Parse the code string into an abstract syntax tree (AST) + try: + tree = ast.parse(code) + except Exception as e: + errors["function"]["errors"].append(str(e)) + return errors + + # Add a dummy type_ignores field to the AST + add_type_ignores() + tree.type_ignores = [] + + # Evaluate the import statements + for node in tree.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + importlib.import_module(alias.name) + except ModuleNotFoundError as e: + errors["imports"]["errors"].append(str(e)) + + # Evaluate the function definition + for node in tree.body: + if isinstance(node, ast.FunctionDef): + code_obj = compile( + ast.Module(body=[node], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj) + except Exception as e: + errors["function"]["errors"].append(str(e)) + + # Return the errors dictionary + return errors + + +def eval_function(function_string: str): + # Create an empty dictionary to serve as a separate namespace + namespace: Dict = {} + + # Execute the code string in the new namespace + exec(function_string, namespace) + function_object = next( + ( + obj + for name, obj in namespace.items() + if isinstance(obj, types.FunctionType) + and obj.__code__.co_filename == "" + ), + None, + ) + if function_object is None: + raise ValueError("Function string does not contain a function") + return function_object + + +def execute_function(code, function_name, *args, **kwargs): + add_type_ignores() + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec( + f"{alias.asname or alias.name} = importlib.import_module('{alias.name}')", + exec_globals, + locals(), + ) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception as exc: + raise ValueError("Function string does not contain a function") from exc + + # Add the function to the exec_globals dictionary + exec_globals[function_name] = locals()[function_name] + + return exec_globals[function_name](*args, **kwargs) + + +def create_function(code, function_name): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception: + pass + + exec_globals[function_name] = locals()[function_name] + + # Return a function that imports necessary modules and calls the target function + def wrapped_function(*args, **kwargs): + for module_name, module in exec_globals.items(): + if isinstance(module, type(importlib)): + globals()[module_name] = module + + return exec_globals[function_name](*args, **kwargs) + + return wrapped_function + + +def extract_function_name(code): + module = ast.parse(code) + for node in module.body: + if isinstance(node, ast.FunctionDef): + return node.name + raise ValueError("No function definition found in the code string") diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index f0a40a452..35d27c5f4 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -14,6 +14,7 @@ "@heroicons/react": "^2.0.15", "@mui/material": "^5.11.9", "@tailwindcss/forms": "^0.5.3", + "@tailwindcss/line-clamp": "^0.4.4", "@testing-library/jest-dom": "^5.16.5", "@testing-library/react": "^13.4.0", "@testing-library/user-event": "^13.5.0", @@ -21,9 +22,12 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", + "ansi-to-html": "^0.7.2", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", @@ -3927,6 +3931,14 @@ "tailwindcss": ">=3.0.0 || >= 3.0.0-alpha.1" } }, + "node_modules/@tailwindcss/line-clamp": { + "version": "0.4.4", + "resolved": "https://registry.npmjs.org/@tailwindcss/line-clamp/-/line-clamp-0.4.4.tgz", + "integrity": "sha512-5U6SY5z8N42VtrCrKlsTAA35gy2VSyYtHWCsg1H87NU1SXnEfekTVlrga9fzUDrrHcGi2Lb5KenUWb4lRQT5/g==", + "peerDependencies": { + "tailwindcss": ">=2.0.0 || >=3.0.0 || >=3.0.0-alpha.1" + } + }, "node_modules/@testing-library/dom": { "version": "8.20.0", "resolved": "https://registry.npmjs.org/@testing-library/dom/-/dom-8.20.0.tgz", @@ -5098,6 +5110,11 @@ "node": ">= 0.6" } }, + "node_modules/ace-builds": { + "version": "1.16.0", + "resolved": "https://registry.npmjs.org/ace-builds/-/ace-builds-1.16.0.tgz", + "integrity": "sha512-EriMhoxdfhh0zKm7icSt8EXekODAOVsYh9fpnlru9ALwf0Iw7J7bpuqLjhi3QRxvVKR7P0teQdJwTvjVMcYHuw==" + }, "node_modules/acorn": { "version": "8.8.2", "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.8.2.tgz", @@ -5308,6 +5325,20 @@ "node": ">=4" } }, + "node_modules/ansi-to-html": { + "version": "0.7.2", + "resolved": "https://registry.npmjs.org/ansi-to-html/-/ansi-to-html-0.7.2.tgz", + "integrity": "sha512-v6MqmEpNlxF+POuyhKkidusCHWWkaLcGRURzivcU3I9tv7k4JVhFcnukrM5Rlk2rUywdZuzYAZ+kbZqWCnfN3g==", + "dependencies": { + "entities": "^2.2.0" + }, + "bin": { + "ansi-to-html": "bin/ansi-to-html" + }, + "engines": { + "node": ">=8.0.0" + } + }, "node_modules/anymatch": { "version": "3.1.3", "resolved": "https://registry.npmjs.org/anymatch/-/anymatch-3.1.3.tgz", @@ -7205,6 +7236,11 @@ "resolved": "https://registry.npmjs.org/didyoumean/-/didyoumean-1.2.2.tgz", "integrity": "sha512-gxtyfqMg7GKyhQmb056K7M3xszy/myH8w+B4RT+QXBQsvAOdc3XymqDDPHx1BgPgsdAA5SIifona89YtRATDzw==" }, + "node_modules/diff-match-patch": { + "version": "1.0.5", + "resolved": "https://registry.npmjs.org/diff-match-patch/-/diff-match-patch-1.0.5.tgz", + "integrity": "sha512-IayShXAgj/QMXgB0IWmKx+rOPuGMhqm5w6jvFxmVenXKIzRqTAAsbBPT3kWQeGANj3jGgvcvv4yK6SxqYmikgw==" + }, "node_modules/diff-sequences": { "version": "27.5.1", "resolved": "https://registry.npmjs.org/diff-sequences/-/diff-sequences-27.5.1.tgz", @@ -12409,6 +12445,16 @@ "resolved": "https://registry.npmjs.org/lodash.debounce/-/lodash.debounce-4.0.8.tgz", "integrity": "sha512-FT1yDzDYEoYWhnSGnpE/4Kj1fLZkDFyqRb7fNt6FdYOSxlUWAtp42Eh6Wb0rGIv/m9Bgo7x4GhQbm5Ys4SG5ow==" }, + "node_modules/lodash.get": { + "version": "4.4.2", + "resolved": "https://registry.npmjs.org/lodash.get/-/lodash.get-4.4.2.tgz", + "integrity": "sha512-z+Uw/vLuy6gQe8cfaFWD7p0wVv8fJl3mbzXh33RS+0oW2wvUqiRXiQ69gLWSLpgB5/6sU+r6BlQR0MBILadqTQ==" + }, + "node_modules/lodash.isequal": { + "version": "4.5.0", + "resolved": "https://registry.npmjs.org/lodash.isequal/-/lodash.isequal-4.5.0.tgz", + "integrity": "sha512-pDo3lu8Jhfjqls6GkMgpahsF9kCyayhgykjyLMNFTKWrpVdAQtYyB4muAMWozBB4ig/dtWAmsMxLEI8wuz+DYQ==" + }, "node_modules/lodash.memoize": { "version": "4.1.2", "resolved": "https://registry.npmjs.org/lodash.memoize/-/lodash.memoize-4.1.2.tgz", @@ -14803,6 +14849,22 @@ "node": ">=0.10.0" } }, + "node_modules/react-ace": { + "version": "10.1.0", + "resolved": "https://registry.npmjs.org/react-ace/-/react-ace-10.1.0.tgz", + "integrity": "sha512-VkvUjZNhdYTuKOKQpMIZi7uzZZVgzCjM7cLYu6F64V0mejY8a2XTyPUIMszC6A4trbeMIHbK5fYFcT/wkP/8VA==", + "dependencies": { + "ace-builds": "^1.4.14", + "diff-match-patch": "^1.0.5", + "lodash.get": "^4.4.2", + "lodash.isequal": "^4.5.0", + "prop-types": "^15.7.2" + }, + "peerDependencies": { + "react": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0", + "react-dom": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0" + } + }, "node_modules/react-app-polyfill": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/react-app-polyfill/-/react-app-polyfill-3.0.0.tgz", diff --git a/src/frontend/package.json b/src/frontend/package.json index 312e52051..6eba117fc 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -9,6 +9,7 @@ "@heroicons/react": "^2.0.15", "@mui/material": "^5.11.9", "@tailwindcss/forms": "^0.5.3", + "@tailwindcss/line-clamp": "^0.4.4", "@testing-library/jest-dom": "^5.16.5", "@testing-library/react": "^13.4.0", "@testing-library/user-event": "^13.5.0", @@ -16,9 +17,12 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", + "ansi-to-html": "^0.7.2", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", diff --git a/src/frontend/public/favicon.ico b/src/frontend/public/favicon.ico new file mode 100644 index 000000000..0612ba9b3 Binary files /dev/null and b/src/frontend/public/favicon.ico differ diff --git a/src/frontend/public/index.html b/src/frontend/public/index.html index 57757fb21..b2e9c4b82 100644 --- a/src/frontend/public/index.html +++ b/src/frontend/public/index.html @@ -4,7 +4,8 @@ - LangFLow + + LangFlow diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css index 60ff72aba..319e2ba39 100644 --- a/src/frontend/src/App.css +++ b/src/frontend/src/App.css @@ -40,3 +40,8 @@ transform: rotate(360deg); } } + +@font-face{ + font-family: text-security-disc; + src: url("assets/text-security-disc.woff") format("woff"); +} \ No newline at end of file diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index c1a6d5f5f..1a2eeb9e6 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -10,6 +10,11 @@ import { typesContext } from "../../../../contexts/typesContext"; import { ParameterComponentType } from "../../../../types/components"; import FloatComponent from "../../../../components/floatComponent"; import Dropdown from "../../../../components/dropdownComponent"; +import CodeAreaComponent from "../../../../components/codeAreaComponent"; +import InputFileComponent from "../../../../components/inputFileComponent"; +import { TabsContext } from "../../../../contexts/tabsContext"; +import IntComponent from "../../../../components/intComponent"; +import PromptAreaComponent from "../../../../components/promptComponent"; export default function ParameterComponent({ left, @@ -42,6 +47,7 @@ export default function ParameterComponent({ const { reactFlowInstance } = useContext(typesContext); let disabled = reactFlowInstance?.getEdges().some((e) => e.targetHandle === id) ?? false; + const { save } = useContext(TabsContext); return (

<> -
+
{title} {required ? " *" : ""}
- {left && (type === "str" || type === "bool" || type === "float") ? ( + {left && + (type === "str" || + type === "bool" || + type === "float" || + type === "code" || + type === "prompt" || + type === "file" || + type === "int") ? ( <> ) : ( @@ -91,6 +104,7 @@ export default function ParameterComponent({ } onChange={(t: string[]) => { data.node.template[name].value = t; + save(); }} /> ) : data.node.template[name].multiline ? ( @@ -99,15 +113,17 @@ export default function ParameterComponent({ value={data.node.template[name].value ?? ""} onChange={(t: string) => { data.node.template[name].value = t; + save(); }} /> ) : ( { data.node.template[name].value = t; + save(); }} /> )} @@ -120,6 +136,7 @@ export default function ParameterComponent({ setEnabled={(t) => { data.node.template[name].value = t; setEnabled(t); + save(); }} />
@@ -129,6 +146,7 @@ export default function ParameterComponent({ value={data.node.template[name].value ?? ""} onChange={(t) => { data.node.template[name].value = t; + save(); }} /> ) : left === true && @@ -136,12 +154,51 @@ export default function ParameterComponent({ data.node.template[name].options ? ( data.node.template[name].value=newValue} - value={data.node.template[name].value??"chose an option"} + onSelect={(newValue) => (data.node.template[name].value = newValue)} + value={data.node.template[name].value ?? "Choose an option"} > - ) : ( - <> - )} + ) : left === true && type === "code" ? ( + { + data.node.template[name].value = t; + save(); + }} + /> + ) : left === true && type === "file" ? ( + { + data.node.template[name].value = t; + }} + fileTypes={data.node.template[name].fileTypes} + suffixes={data.node.template[name].suffixes} + onFileChange={(t: string) => { + data.node.template[name].content = t; + save(); + }} + > + ) : left === true && type === "int" ? ( + { + data.node.template[name].value = t; + save(); + }} + /> + ) : left === true && type === "prompt" ? ( + { + data.node.template[name].value = t; + save(); + }} + /> + ):(<>)}
); diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index ddb9524ee..ff13af901 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -23,7 +23,6 @@ export default function GenericNode({ const { types, deleteNode } = useContext(typesContext); const Icon = nodeIcons[types[data.type]]; if (!Icon) { - console.log(data); if (showError.current) { setErrorData({ title: data.type @@ -32,9 +31,9 @@ export default function GenericNode({ }); showError.current = false; } + deleteNode(data.id); return; } - return (
(
{idx === 0 ? ( -
- Inputs: +
+ !key.startsWith("_") && data.node.template[key].show + ).length === 0 + ? "hidden" + : "" + )} + > + Inputs
) : ( <> @@ -85,7 +94,13 @@ export default function GenericNode({ nodeColors[types[data.node.template[t].type]] ?? nodeColors.unknown } - title={snakeToNormalCase(t)} + title={ + data.node.template[t].display_name + ? data.node.template[t].display_name + : data.node.template[t].name + ? snakeToNormalCase(data.node.template[t].name) + : snakeToNormalCase(t) + } name={t} tooltipTitle={ "Type: " + @@ -103,7 +118,7 @@ export default function GenericNode({
))}
- Output: + Output
diff --git a/src/frontend/src/assets/text-security-disc.woff b/src/frontend/src/assets/text-security-disc.woff new file mode 100644 index 000000000..bc3cd0e63 Binary files /dev/null and b/src/frontend/src/assets/text-security-disc.woff differ diff --git a/src/frontend/src/components/TooltipComponent/index.tsx b/src/frontend/src/components/TooltipComponent/index.tsx index a111d047b..65c8d0e5f 100644 --- a/src/frontend/src/components/TooltipComponent/index.tsx +++ b/src/frontend/src/components/TooltipComponent/index.tsx @@ -1,6 +1,7 @@ import { ReactElement } from "react"; import { LightTooltip } from "../LightTooltipComponent"; +import { TooltipComponentType } from "../../types/components"; -export default function Tooltip({ children, title }:{children:ReactElement,title:string}) { - return {children}; +export default function Tooltip({ children, title,placement }:TooltipComponentType) { + return {children}; } diff --git a/src/frontend/src/components/chatComponent/chatMessage/index.tsx b/src/frontend/src/components/chatComponent/chatMessage/index.tsx index e26a8c05d..aa2a0d080 100644 --- a/src/frontend/src/components/chatComponent/chatMessage/index.tsx +++ b/src/frontend/src/components/chatComponent/chatMessage/index.tsx @@ -2,6 +2,8 @@ import { ChatBubbleLeftEllipsisIcon, ChatBubbleOvalLeftEllipsisIcon, PlusSmallIc import { useState } from "react"; import { ChatMessageType } from "../../../types/chat"; import { nodeColors } from "../../../utils"; +var Convert = require('ansi-to-html'); +var convert = new Convert({newline:true}); export default function ChatMessage({ chat }: { chat: ChatMessageType }) { const [hidden, setHidden] = useState(true); @@ -27,7 +29,7 @@ export default function ChatMessage({ chat }: { chat: ChatMessageType }) { style={{ backgroundColor: nodeColors["thought"] }} className=" text-start inline-block w-full pb-3 pt-3 px-5 cursor-pointer" dangerouslySetInnerHTML={{ - __html: chat.thought.replace(/\n/g, "
"), + __html: convert.toHtml(chat.thought) }} >
)} diff --git a/src/frontend/src/components/chatComponent/index.tsx b/src/frontend/src/components/chatComponent/index.tsx index 037c9cdaf..11516f004 100644 --- a/src/frontend/src/components/chatComponent/index.tsx +++ b/src/frontend/src/components/chatComponent/index.tsx @@ -5,34 +5,42 @@ import { PaperAirplaneIcon, XMarkIcon, } from "@heroicons/react/24/outline"; -import { MouseEventHandler, useContext, useEffect, useRef, useState } from "react"; -import { sendAll } from "../../controllers/NodesServices"; +import { + MouseEventHandler, + useContext, + useEffect, + useRef, + useState, +} from "react"; +import { sendAll } from "../../controllers/API"; import { alertContext } from "../../contexts/alertContext"; -import { classNames, nodeColors } from "../../utils"; +import { classNames, nodeColors, snakeToNormalCase } from "../../utils"; import { TabsContext } from "../../contexts/tabsContext"; import { ChatType } from "../../types/chat"; import ChatMessage from "./chatMessage"; +import { NodeType } from "../../types/flow"; const _ = require("lodash"); export default function Chat({ flow, reactFlowInstance }: ChatType) { - const { updateFlow,lockChat,setLockChat,flows,tabIndex } = useContext(TabsContext); + const { updateFlow, lockChat, setLockChat, flows, tabIndex } = + useContext(TabsContext); const [saveChat, setSaveChat] = useState(false); const [open, setOpen] = useState(true); const [chatValue, setChatValue] = useState(""); const [chatHistory, setChatHistory] = useState(flow.chat); - const { setErrorData } = useContext(alertContext); + const { setErrorData, setNoticeData } = useContext(alertContext); const addChatHistory = ( message: string, isSend: boolean, - thought?: string, + thought?: string ) => { let tabsChange = false; setChatHistory((old) => { let newChat = _.cloneDeep(old); - if(JSON.stringify(flow.chat) !==JSON.stringify(old)){ - tabsChange = true - return old + if (JSON.stringify(flow.chat) !== JSON.stringify(old)) { + tabsChange = true; + return old; } if (thought) { newChat.push({ message, isSend, thought }); @@ -41,12 +49,17 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { } return newChat; }); - if(tabsChange){ - if(thought){ - updateFlow({..._.cloneDeep(flow),chat:[...flow.chat,{isSend,message,thought}]}) - } - else{ - updateFlow({..._.cloneDeep(flow),chat:[...flow.chat,{isSend,message}]}) + if (tabsChange) { + if (thought) { + updateFlow({ + ..._.cloneDeep(flow), + chat: [...flow.chat, { isSend, message, thought }], + }); + } else { + updateFlow({ + ..._.cloneDeep(flow), + chat: [...flow.chat, { isSend, message }], + }); } } setSaveChat((chat) => !chat); @@ -61,55 +74,93 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { useEffect(() => { if (ref.current) ref.current.scrollIntoView({ behavior: "smooth" }); }, [chatHistory]); - function validateNodes() { - if ( - reactFlowInstance - .getNodes() - .some( - (n) => - n.data.node && - Object.keys(n.data.node.template).some( - (t: any) => - n.data.node.template[t].required && - n.data.node.template[t].value === "" && - n.data.node.template[t].required && - !reactFlowInstance - .getEdges() - .some( - (e) => - e.sourceHandle.split("|")[1] === t && - e.sourceHandle.split("|")[2] === n.id - ) - ) - ) - ) { - return false; + + function validateNode(n: NodeType): Array { + if (!n.data?.node?.template || !Object.keys(n.data.node.template)) { + setNoticeData({ + title: + "We've noticed a potential issue with a node in the flow. Please review it and, if necessary, submit a bug report with your exported flow file. Thank you for your help!", + }); + return []; } - return true; + + const { + type, + node: { template }, + } = n.data; + + return Object.keys(template).reduce( + (errors: Array, t) => + errors.concat( + (template[t].required && template[t].show) && + (!template[t].value || template[t].value === "") && + !reactFlowInstance + .getEdges() + .some( + (e) => + e.targetHandle.split("|")[1] === t && + e.targetHandle.split("|")[2] === n.id + ) + ? [ + `${type} is missing ${ + template.display_name + ? template.display_name + : snakeToNormalCase(template[t].name) + }.`, + ] + : [] + ), + [] as string[] + ); } + + function validateNodes() { + return reactFlowInstance + .getNodes() + .flatMap((n: NodeType) => validateNode(n)); + } + const ref = useRef(null); function sendMessage() { if (chatValue !== "") { - if (validateNodes()) { + let nodeValidationErrors = validateNodes(); + if (nodeValidationErrors.length === 0) { setLockChat(true); let message = chatValue; setChatValue(""); addChatHistory(message, true); - sendAll({ ...reactFlowInstance.toObject(), message, chatHistory,name:flow.name,description:flow.description}) + sendAll({ + ...reactFlowInstance.toObject(), + message, + chatHistory, + name: flow.name, + description: flow.description, + }) .then((r) => { addChatHistory(r.data.result, false, r.data.thought); setLockChat(false); }) .catch((error) => { - setErrorData({ title: error.message ?? "unknow error" }); + setErrorData({ + title: error.message ?? "Unknown Error", + list: [error.response.data.detail], + }); setLockChat(false); + let lastMessage; + setChatHistory((chatHistory) => { + let newChat = chatHistory; + + lastMessage = newChat.pop().message; + return newChat; + }); + setChatValue(lastMessage); }); } else { setErrorData({ - title: "Error sending message", - list: [ "Oops! Looks like you missed some required information. Please fill in all the required fields before continuing."], + title: "Oops! Looks like you missed some required information:", + list: nodeValidationErrors, }); } } else { @@ -120,8 +171,8 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { } } function clearChat() { - setChatHistory([]) - updateFlow({ ..._.cloneDeep(flow), chat: []}); + setChatHistory([]); + updateFlow({ ..._.cloneDeep(flow), chat: [] }); } return ( @@ -151,9 +202,10 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { /> Chat
- + + + ); +} diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx index d1a0f47b8..1bd163310 100644 --- a/src/frontend/src/components/dropdownComponent/index.tsx +++ b/src/frontend/src/components/dropdownComponent/index.tsx @@ -5,7 +5,7 @@ import { DropDownComponentType } from "../../types/components"; import { classNames } from "../../utils"; export default function Dropdown({value, options, onSelect}:DropDownComponentType) { - let [internalValue,setInternalValue] = useState(value??"choose an option") + let [internalValue,setInternalValue] = useState(value===""||!value?"Choose an option":value) return ( <> { diff --git a/src/frontend/src/components/inputComponent/index.tsx b/src/frontend/src/components/inputComponent/index.tsx index 66ac9b862..5858d4bf6 100644 --- a/src/frontend/src/components/inputComponent/index.tsx +++ b/src/frontend/src/components/inputComponent/index.tsx @@ -23,7 +23,7 @@ export default function InputComponent({ className={classNames( "block w-full form-input dark:bg-gray-900 dark:border-gray-600 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm", disabled ? " bg-gray-200 dark:bg-gray-700" : "", - password?"password":"" + password && myValue.length>0?"password":"" )} placeholder="Type a text" onChange={(e) => { diff --git a/src/frontend/src/components/inputFileComponent/index.tsx b/src/frontend/src/components/inputFileComponent/index.tsx new file mode 100644 index 000000000..36d13e918 --- /dev/null +++ b/src/frontend/src/components/inputFileComponent/index.tsx @@ -0,0 +1,85 @@ +import { DocumentMagnifyingGlassIcon } from "@heroicons/react/24/outline"; +import { useContext, useEffect, useState } from "react"; +import { alertContext } from "../../contexts/alertContext"; +import { FileComponentType } from "../../types/components"; + +export default function InputFileComponent({ + value, + onChange, + disabled, + suffixes, + fileTypes, + onFileChange +}: FileComponentType) { + const [myValue, setMyValue] = useState(value); + const { setErrorData } = useContext(alertContext); + useEffect(() => { + if (disabled) { + setMyValue(""); + onChange(""); + onFileChange("") + } + }, [disabled, onChange]); + + function attachFile(fileReadEvent: ProgressEvent) { + fileReadEvent.preventDefault(); + const file = fileReadEvent.target.result; + onFileChange(file as string) + } + + function checkFileType(fileName:string):boolean{ + for (let index = 0; index < suffixes.length; index++) { + if(fileName.endsWith(suffixes[index])){ + return true + } + } + return false + } + + const handleButtonClick = () => { + const input = document.createElement("input"); + input.type = "file"; + input.accept = suffixes.join(","); + input.style.display = "none"; + input.multiple = false; + input.onchange = (e: Event) => { + const file = (e.target as HTMLInputElement).files?.[0]; + const fileData = new FileReader(); + fileData.onload = attachFile; + if (file && checkFileType(file.name)) { + fileData.readAsDataURL(file); + setMyValue(file.name); + onChange(file.name); + } else { + setErrorData({ + title: + "Please select a valid file. Only files this files are allowed:", + list: fileTypes, + }); + } + }; + input.click(); + }; + + return ( +
+
+ + {myValue !== "" ? myValue : "No file"} + + +
+
+ ); +} diff --git a/src/frontend/src/components/intComponent/index.tsx b/src/frontend/src/components/intComponent/index.tsx new file mode 100644 index 000000000..520a78c0d --- /dev/null +++ b/src/frontend/src/components/intComponent/index.tsx @@ -0,0 +1,38 @@ +import { useEffect, useState } from "react"; +import { FloatComponentType } from "../../types/components"; + +export default function IntComponent({ + value, + onChange, + disabled, +}: FloatComponentType) { + const [myValue, setMyValue] = useState(value ?? ""); + useEffect(() => { + if (disabled) { + setMyValue(""); + onChange(""); + } + }, [disabled, onChange]); + return ( +
+ { + if (event.key !== 'Backspace' && event.key !== 'Enter' && event.key !== 'Delete' && event.key !== 'ArrowLeft' && event.key !== 'ArrowRight' && !/^[-]?\d*$/.test(event.key)) { + event.preventDefault(); + } + }} + type="number" + value={myValue} + className={ + "block w-full form-input dark:bg-gray-900 arrow-hide dark:border-gray-600 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm" + + (disabled ? " bg-gray-200 dark:bg-gray-700" : "") + } + placeholder="Type a integer number" + onChange={(e) => { + setMyValue(e.target.value); + onChange(e.target.value); + }} + /> +
+ ); +} diff --git a/src/frontend/src/components/loadingComponent/index.tsx b/src/frontend/src/components/loadingComponent/index.tsx index 668c1c5a8..6181fd7cc 100644 --- a/src/frontend/src/components/loadingComponent/index.tsx +++ b/src/frontend/src/components/loadingComponent/index.tsx @@ -10,6 +10,7 @@ export default function LoadingComponent({remSize}:LoadingComponentProps){ +

Loading... ) diff --git a/src/frontend/src/components/promptComponent/index.tsx b/src/frontend/src/components/promptComponent/index.tsx new file mode 100644 index 000000000..6ad51f87b --- /dev/null +++ b/src/frontend/src/components/promptComponent/index.tsx @@ -0,0 +1,35 @@ +import { ArrowTopRightOnSquareIcon } from "@heroicons/react/24/outline"; +import { useContext, useEffect, useState } from "react"; +import { PopUpContext } from "../../contexts/popUpContext"; +import CodeAreaModal from "../../modals/codeAreaModal"; +import TextAreaModal from "../../modals/textAreaModal"; +import { TextAreaComponentType } from "../../types/components"; +import PromptAreaModal from "../../modals/promptModal"; + +export default function PromptAreaComponent({ value, onChange, disabled }:TextAreaComponentType) { + const [myValue, setMyValue] = useState(value); + const { openPopUp } = useContext(PopUpContext); + useEffect(() => { + if (disabled) { + setMyValue(""); + onChange(""); + } + }, [disabled, onChange]); + return ( +
+
+ + {myValue !== "" ? myValue : 'Text empty'} + + +
+
+ ); +} diff --git a/src/frontend/src/components/textAreaComponent/index.tsx b/src/frontend/src/components/textAreaComponent/index.tsx index 144c0c66a..df61e1ae1 100644 --- a/src/frontend/src/components/textAreaComponent/index.tsx +++ b/src/frontend/src/components/textAreaComponent/index.tsx @@ -1,6 +1,7 @@ import { ArrowTopRightOnSquareIcon } from "@heroicons/react/24/outline"; import { useContext, useEffect, useState } from "react"; import { PopUpContext } from "../../contexts/popUpContext"; +import CodeAreaModal from "../../modals/codeAreaModal"; import TextAreaModal from "../../modals/textAreaModal"; import { TextAreaComponentType } from "../../types/components"; @@ -9,8 +10,8 @@ export default function TextAreaComponent({ value, onChange, disabled }:TextArea const { openPopUp } = useContext(PopUpContext); useEffect(() => { if (disabled) { - setMyValue([""]); - onChange([""]); + setMyValue(""); + onChange(""); } }, [disabled, onChange]); return ( diff --git a/src/frontend/src/contexts/tabsContext.tsx b/src/frontend/src/contexts/tabsContext.tsx index 10c0b943e..97e62e5e2 100644 --- a/src/frontend/src/contexts/tabsContext.tsx +++ b/src/frontend/src/contexts/tabsContext.tsx @@ -5,6 +5,7 @@ import { normalCaseToSnakeCase } from "../utils"; import { alertContext } from "./alertContext"; const TabsContextInitialValue: TabsContextType = { + save:()=>{}, tabIndex: 0, setTabIndex: (index: number) => {}, flows: [], @@ -16,7 +17,7 @@ const TabsContextInitialValue: TabsContextType = { uploadFlow: () => {}, lockChat: false, setLockChat:(prevState:boolean)=>{}, - hardReset:()=>{} + hardReset:()=>{}, }; export const TabsContext = createContext( @@ -35,15 +36,20 @@ export function TabsProvider({ children }: { children: ReactNode }) { newNodeId.current = newNodeId.current + 1; return newNodeId.current; } + function save(){ + if (flows.length !== 0) + window.localStorage.setItem( + "tabsData", + JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current }) + ); + } useEffect(() => { //save tabs locally - if (flows.length !== 0) - window.localStorage.setItem( - "tabsData", - JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current }) - ); + save() }, [flows, id, tabIndex, newNodeId]); + + useEffect(() => { //get tabs locally saved let cookie = window.localStorage.getItem("tabsData"); @@ -138,7 +144,7 @@ export function TabsProvider({ children }: { children: ReactNode }) { // Create a new flow with a default name if no flow is provided. let newFlow: FlowType = { description, - name: "New Flow", + name: flow?.name??"New Flow", id: id.toString(), data, chat: flow ? flow.chat : [], @@ -177,6 +183,7 @@ export function TabsProvider({ children }: { children: ReactNode }) { return ( > { + return await axios.get(`/all`); +} + +export async function sendAll(data:sendAllProps) { + return await axios.post(`/predict`, data); +} + +export async function checkCode(code:string):Promise>{ + + return await axios.post('/validate/code',{code}) +} + +export async function checkPrompt(template:string):Promise>{ + + return await axios.post('/validate/prompt',{template}) +} + +export async function getExamples(): Promise { + const url = 'https://api.github.com/repos/logspace-ai/langflow_examples/contents/examples'; + const response = await axios.get(url); + + const jsonFiles = response.data.filter((file: any) => { + return file.name.endsWith('.json'); + }); + + const contentsPromises = jsonFiles.map(async (file: any) => { + const contentResponse = await axios.get(file.download_url); + return contentResponse.data; + }); + + const contents = await Promise.all(contentsPromises); + + return contents; + } \ No newline at end of file diff --git a/src/frontend/src/controllers/NodesServices/index.ts b/src/frontend/src/controllers/NodesServices/index.ts deleted file mode 100644 index f0257ea00..000000000 --- a/src/frontend/src/controllers/NodesServices/index.ts +++ /dev/null @@ -1,10 +0,0 @@ -import { APIObjectType, sendAllProps } from '../../types/api/index'; -import axios, { AxiosResponse } from "axios"; - -export async function getAll():Promise> { - return await axios.get(`/all`); -} - -export async function sendAll(data:sendAllProps) { - return await axios.post(`/predict`, data); -} \ No newline at end of file diff --git a/src/frontend/src/modals/codeAreaModal/index.tsx b/src/frontend/src/modals/codeAreaModal/index.tsx new file mode 100644 index 000000000..db4de60f4 --- /dev/null +++ b/src/frontend/src/modals/codeAreaModal/index.tsx @@ -0,0 +1,177 @@ +import { Dialog, Transition } from "@headlessui/react"; +import { XMarkIcon, CommandLineIcon } from "@heroicons/react/24/outline"; +import { Fragment, useContext, useRef, useState } from "react"; +import { PopUpContext } from "../../contexts/popUpContext"; +import AceEditor from "react-ace"; +import "ace-builds/src-noconflict/mode-python"; +import "ace-builds/src-noconflict/theme-github"; +import "ace-builds/src-noconflict/theme-twilight"; +import "ace-builds/src-noconflict/ext-language_tools"; +import "ace-builds/webpack-resolver"; +import { darkContext } from "../../contexts/darkContext"; +import { checkCode } from "../../controllers/API"; +import { alertContext } from "../../contexts/alertContext"; +export default function CodeAreaModal({ + value, + setValue, +}: { + setValue: (value: string) => void; + value: string; +}) { + const [open, setOpen] = useState(true); + const [code, setCode] = useState(value); + const { dark } = useContext(darkContext); + const { setErrorData, setSuccessData } = useContext(alertContext); + const { closePopUp } = useContext(PopUpContext); + const ref = useRef(); + function setModalOpen(x: boolean) { + setOpen(x); + if (x === false) { + setTimeout(() => { + closePopUp(); + }, 300); + } + } + return ( + + + +
+ + +
+
+ + +
+ +
+
+
+
+
+
+ + Edit Code + +
+
+
+
+
+ { + setCode(value); + }} + className="h-full w-full rounded-lg" + /> +
+
+
+
+ +
+
+ + +
+
+
+ + ); +} diff --git a/src/frontend/src/modals/exportModal/index.tsx b/src/frontend/src/modals/exportModal/index.tsx index c1da32946..aa48f6ce1 100644 --- a/src/frontend/src/modals/exportModal/index.tsx +++ b/src/frontend/src/modals/exportModal/index.tsx @@ -26,6 +26,7 @@ export default function ExportModal() { } } const [checked,setChecked] = useState(true) + const [name,setName] = useState(flows[tabIndex].name) return ( void; title: string; @@ -17,31 +19,103 @@ export default function ButtonBox({ icon: ReactNode; bgColor: string; textColor: string; - deactivate?:boolean; + deactivate?: boolean; + size: "small" | "medium" | "big"; }) { + let bigCircle: string; + let smallCircle: string; + let titleFontSize: string; + let descriptionFontSize: string; + let padding: string; + let marginTop: string; + let height: string; + let widht: string; + switch (size) { + case "small": + bigCircle = "h-12 w-12"; + smallCircle = "h-8 w-8"; + titleFontSize = "text-sm"; + descriptionFontSize = "text-xs"; + padding = "p-2"; + marginTop = "mt-2"; + height = "h-36"; + widht = "w-32"; + break; + case "medium": + bigCircle = "h-16 w-16"; + smallCircle = "h-12 w-12"; + titleFontSize = "text-base"; + descriptionFontSize = "text-sm"; + padding = "p-4"; + marginTop = "mt-3"; + height = "h-44"; + widht = "w-36"; + break; + case "big": + bigCircle = "h-20 w-20"; + smallCircle = "h-16 w-16"; + titleFontSize = "text-lg"; + descriptionFontSize = "text-sm"; + padding = "p-8"; + marginTop = "mt-6"; + height = "h-56"; + widht = "w-44"; + break; + default: + bigCircle = "h-20 w-20"; + smallCircle = "h-16 w-16"; + titleFontSize = "text-lg"; + descriptionFontSize = "text-sm"; + padding = "p-8"; + marginTop = "mt-6"; + height = "h-56"; + widht = "w-44"; + break; + } return ( ); } diff --git a/src/frontend/src/modals/importModal/index.tsx b/src/frontend/src/modals/importModal/index.tsx index c471f3955..38b06ae5a 100644 --- a/src/frontend/src/modals/importModal/index.tsx +++ b/src/frontend/src/modals/importModal/index.tsx @@ -1,122 +1,232 @@ import { Dialog, Transition } from "@headlessui/react"; import { - XMarkIcon, - ArrowDownTrayIcon, - DocumentDuplicateIcon, - ComputerDesktopIcon, - ArrowUpTrayIcon, + XMarkIcon, + ArrowDownTrayIcon, + DocumentDuplicateIcon, + ComputerDesktopIcon, + ArrowUpTrayIcon, + ArrowLeftIcon, } from "@heroicons/react/24/outline"; import { Fragment, useContext, useRef, useState } from "react"; import { PopUpContext } from "../../contexts/popUpContext"; import { TabsContext } from "../../contexts/tabsContext"; import ButtonBox from "./buttonBox"; +import { getExamples } from "../../controllers/API"; +import { error } from "console"; +import { alertContext } from "../../contexts/alertContext"; +import LoadingComponent from "../../components/loadingComponent"; +import { FlowType } from "../../types/flow"; +import { classNames } from "../../utils"; export default function ImportModal() { - const [open, setOpen] = useState(true); - const { closePopUp } = useContext(PopUpContext); - const ref = useRef(); - const {uploadFlow} = useContext(TabsContext) - function setModalOpen(x: boolean) { - setOpen(x); - if (x === false) { - setTimeout(() => { - closePopUp(); - }, 300); - } - } - return ( - - - -
- + const [open, setOpen] = useState(true); + const { setErrorData } = useContext(alertContext); + const { closePopUp } = useContext(PopUpContext); + const ref = useRef(); + const [showExamples, setShowExamples] = useState(false); + const [loadingExamples, setLoadingExamples] = useState(false); + const [examples, setExamples] = useState([]); + const { uploadFlow, addFlow } = useContext(TabsContext); + function setModalOpen(x: boolean) { + setOpen(x); + if (x === false) { + setTimeout(() => { + closePopUp(); + }, 300); + } + } -
-
- - -
- -
-
-
-
-
-
- - Import from - -
-
-
-
- - } - onClick={() => console.log("sdsds")} - textColor="text-slate-400" - title="Examples" - > - - } - onClick={() => {uploadFlow();setModalOpen(false)}} - textColor="text-blue-500" - title="Local file" - > -
-
+ function handleExamples() { + setLoadingExamples(true); + getExamples() + .then((result) => { + setLoadingExamples(false); + setExamples(result); + }) + .catch((error) => + setErrorData({ + title: "there was an error loading examples, please try again", + list: [error.message], + }) + ); + } -
- - -
-
-
- - ); + return ( + + + +
+ + +
+
+ + +
+ +
+ {showExamples && ( + <> +
+ +
+ + + )} +
+
+
+
+
+ + {showExamples ? "Select an example" : "Import from"} + +
+
+
+ {!showExamples && ( +
+ + } + onClick={() => { + setShowExamples(true); + handleExamples(); + }} + textColor="text-emerald-400" + title="Examples" + > + + } + onClick={() => { + uploadFlow(); + setModalOpen(false); + }} + textColor="text-blue-500" + title="Local file" + > +
+ )} + {showExamples && loadingExamples && ( +
+ +
+ )} + {showExamples && + !loadingExamples && + examples.map((example, index) => { + return ( +
+ {" "} + + } + onClick={() => { + addFlow(example); + setModalOpen(false); + }} + textColor="text-emerald-400" + title={example.name} + > +
+ ); + })} +
+
+ + +
+
+
+ + ); } diff --git a/src/frontend/src/modals/promptModal/index.tsx b/src/frontend/src/modals/promptModal/index.tsx new file mode 100644 index 000000000..10dab2c2c --- /dev/null +++ b/src/frontend/src/modals/promptModal/index.tsx @@ -0,0 +1,153 @@ +import { Dialog, Transition } from "@headlessui/react"; +import { XMarkIcon, DocumentTextIcon } from "@heroicons/react/24/outline"; +import { Fragment, useContext, useRef, useState } from "react"; +import { PopUpContext } from "../../contexts/popUpContext"; +import { darkContext } from "../../contexts/darkContext"; +import { checkPrompt } from "../../controllers/API"; +import { alertContext } from "../../contexts/alertContext"; +export default function PromptAreaModal({ + value, + setValue, +}: { + setValue: (value: string) => void; + value: string; +}) { + const [open, setOpen] = useState(true); + const [myValue, setMyValue] = useState(value); + const { dark } = useContext(darkContext); + const { setErrorData, setSuccessData } = useContext(alertContext); + const { closePopUp } = useContext(PopUpContext); + const ref = useRef(); + function setModalOpen(x: boolean) { + setOpen(x); + if (x === false) { + setTimeout(() => { + closePopUp(); + }, 300); + } + } + return ( + + + +
+ + +
+
+ + +
+ +
+
+
+
+
+
+ + Edit Prompt + +
+
+
+
+
+