Merge remote-tracking branch 'origin/dev' into node-shortcuts-refactor
This commit is contained in:
Lucas Oliveira
commit
1b3d62c5ca
151 changed files with 6035 additions and 2674 deletions
|
|
@ -3,7 +3,7 @@
|
|||
{
|
||||
"name": "Langflow Dev Container",
|
||||
// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
|
||||
"image": "mcr.microsoft.com/devcontainers/python:1-3.10-bullseye",
|
||||
"image": "mcr.microsoft.com/devcontainers/python:3.10",
|
||||
|
||||
// Features to add to the dev container. More info: https://containers.dev/features.
|
||||
"features": {
|
||||
|
|
|
|||
13
.github/workflows/pre-release-base.yml
vendored
13
.github/workflows/pre-release-base.yml
vendored
|
|
@ -13,9 +13,12 @@ env:
|
|||
POETRY_VERSION: "1.8.2"
|
||||
|
||||
jobs:
|
||||
if_release:
|
||||
release:
|
||||
name: Release Langflow Base
|
||||
if: inputs.release_package == true
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
version: ${{ steps.check-version.outputs.version }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install poetry
|
||||
|
|
@ -49,6 +52,12 @@ jobs:
|
|||
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
make publish base=true
|
||||
docker_build:
|
||||
name: Build Docker Image
|
||||
runs-on: ubuntu-latest
|
||||
needs: release
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
- name: Set up Docker Buildx
|
||||
|
|
@ -65,4 +74,4 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push_base.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:base-${{ steps.check-version.outputs.version }}
|
||||
langflowai/langflow:base-${{ needs.release.outputs.version }}
|
||||
|
|
|
|||
32
.github/workflows/pre-release-langflow.yml
vendored
32
.github/workflows/pre-release-langflow.yml
vendored
|
|
@ -17,9 +17,12 @@ env:
|
|||
POETRY_VERSION: "1.8.2"
|
||||
|
||||
jobs:
|
||||
if_release:
|
||||
release:
|
||||
name: Release Langflow
|
||||
if: inputs.release_package == true
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
version: ${{ steps.check-version.outputs.version }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install poetry
|
||||
|
|
@ -49,6 +52,18 @@ jobs:
|
|||
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
make publish main=true
|
||||
- name: Upload Artifact
|
||||
uses: actions/upload-artifact@v2
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
|
||||
docker_build:
|
||||
name: Build Docker Image
|
||||
runs-on: ubuntu-latest
|
||||
needs: release
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
- name: Set up Docker Buildx
|
||||
|
|
@ -65,9 +80,18 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:${{ steps.check-version.outputs.version }}
|
||||
logspace/langflow:1.0-alpha
|
||||
langflowai/langflow:${{ needs.release.outputs.version }}
|
||||
langflowai/langflow:1.0-alpha
|
||||
|
||||
create_release:
|
||||
name: Create Release
|
||||
runs-on: ubuntu-latest
|
||||
needs: [docker_build, release]
|
||||
steps:
|
||||
- uses: actions/download-artifact@v2
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
- name: Create Release
|
||||
uses: ncipollo/release-action@v1
|
||||
with:
|
||||
|
|
@ -76,5 +100,5 @@ jobs:
|
|||
draft: false
|
||||
generateReleaseNotes: true
|
||||
prerelease: true
|
||||
tag: v${{ steps.check-version.outputs.version }}
|
||||
tag: v${{ needs.release.outputs.version }}
|
||||
commit: dev
|
||||
|
|
|
|||
4
.github/workflows/release.yml
vendored
4
.github/workflows/release.yml
vendored
|
|
@ -52,8 +52,8 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:${{ steps.check-version.outputs.version }}
|
||||
logspace/langflow:latest
|
||||
langflowai/langflow:${{ steps.check-version.outputs.version }}
|
||||
langflowai/langflow:latest
|
||||
- name: Create Release
|
||||
uses: ncipollo/release-action@v1
|
||||
with:
|
||||
|
|
|
|||
51
.github/workflows/typescript_test.yml
vendored
51
.github/workflows/typescript_test.yml
vendored
|
|
@ -19,8 +19,8 @@ jobs:
|
|||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
shardIndex: [1, 2, 3, 4]
|
||||
shardTotal: [4]
|
||||
shardIndex: [1]
|
||||
shardTotal: [1]
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
|
@ -38,27 +38,6 @@ jobs:
|
|||
npm ci
|
||||
if: ${{ steps.setup-node.outputs.cache-hit != 'true' }}
|
||||
|
||||
# Attempt to restore the correct Playwright browser binaries based on the
|
||||
# currently installed version of Playwright (The browser binary versions
|
||||
# may change with Playwright versions).
|
||||
# Note: Playwright's cache directory is hard coded because that's what it
|
||||
# says to do in the docs. There doesn't appear to be a command that prints
|
||||
# it out for us.
|
||||
# - uses: actions/cache@v4
|
||||
# id: playwright-cache
|
||||
# with:
|
||||
# path: ${{ env.PLAYWRIGHT_BROWSERS_PATH }}
|
||||
# key: "${{ runner.os }}-playwright-${{ hashFiles('src/frontend/package-lock.json') }}"
|
||||
# # As a fallback, if the Playwright version has changed, try use the
|
||||
# # most recently cached version. There's a good chance that at least one
|
||||
# # of the browser binary versions haven't been updated, so Playwright can
|
||||
# # skip installing that in the next step.
|
||||
# # Note: When falling back to an old cache, `cache-hit` (used below)
|
||||
# # will be `false`. This allows us to restore the potentially out of
|
||||
# # date cache, but still let Playwright decide if it needs to download
|
||||
# # new binaries or not.
|
||||
# restore-keys: |
|
||||
# ${{ runner.os }}-playwright-
|
||||
- name: Cache playwright binaries
|
||||
uses: actions/cache@v4
|
||||
id: playwright-cache
|
||||
|
|
@ -82,32 +61,26 @@ jobs:
|
|||
npx playwright install-deps
|
||||
if: steps.playwright-cache.outputs.cache-hit != 'true'
|
||||
|
||||
# If the Playwright browser binaries weren't able to be restored, we tell
|
||||
# paywright to install everything for us.
|
||||
# - name: Install Playwright's dependencies
|
||||
# if: steps.playwright-cache.outputs.cache-hit != 'true'
|
||||
# run: npx playwright install --with-deps
|
||||
|
||||
- name: Install Poetry
|
||||
run: pipx install "poetry==${{ env.POETRY_VERSION }}"
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
id: setup-python
|
||||
- name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
|
||||
uses: "./.github/actions/poetry_caching"
|
||||
with:
|
||||
python-version: ${{ env.PYTHON_VERSION }}
|
||||
cache: "poetry"
|
||||
|
||||
poetry-version: ${{ env.POETRY_VERSION }}
|
||||
cache-key: ${{ runner.os }}-poetry-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
poetry env use ${{ env.PYTHON_VERSION }}
|
||||
poetry install
|
||||
if: ${{ steps.setup-python.outputs.cache-hit != 'true' }}
|
||||
|
||||
- name: create .env
|
||||
run: |
|
||||
touch .env
|
||||
echo "${{ secrets.ENV_VARS }}" > .env
|
||||
|
||||
- name: Run Playwright Tests
|
||||
run: |
|
||||
cd src/frontend
|
||||
npx playwright test --shard=${{ matrix.shardIndex }}/${{ matrix.shardTotal }}
|
||||
npx playwright test
|
||||
|
||||
- name: Upload blob report to GitHub Actions Artifacts
|
||||
if: always()
|
||||
|
|
|
|||
|
|
@ -17,23 +17,23 @@ diverse, inclusive, and healthy community.
|
|||
Examples of behavior that contributes to a positive environment for our
|
||||
community include:
|
||||
|
||||
* Demonstrating empathy and kindness toward other people
|
||||
* Being respectful of differing opinions, viewpoints, and experiences
|
||||
* Giving and gracefully accepting constructive feedback
|
||||
* Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
- Demonstrating empathy and kindness toward other people
|
||||
- Being respectful of differing opinions, viewpoints, and experiences
|
||||
- Giving and gracefully accepting constructive feedback
|
||||
- Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
* Focusing on what is best not just for us as individuals, but for the
|
||||
- Focusing on what is best not just for us as individuals, but for the
|
||||
overall community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
* The use of sexualized language or imagery, and sexual attention or
|
||||
- The use of sexualized language or imagery, and sexual attention or
|
||||
advances of any kind
|
||||
* Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or email
|
||||
- Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
- Public or private harassment
|
||||
- Publishing others' private information, such as a physical or email
|
||||
address, without their explicit permission
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
- Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
|
|
@ -60,7 +60,7 @@ representative at an online or offline event.
|
|||
|
||||
Instances of abusive, harassing, or otherwise unacceptable behavior may be
|
||||
reported to the community leaders responsible for enforcement at
|
||||
contact@logspace.ai.
|
||||
contact@langflow.org.
|
||||
All complaints will be reviewed and investigated promptly and fairly.
|
||||
|
||||
All community leaders are obligated to respect the privacy and security of the
|
||||
|
|
@ -106,7 +106,7 @@ Violating these terms may lead to a permanent ban.
|
|||
### 4. Permanent Ban
|
||||
|
||||
**Community Impact**: Demonstrating a pattern of violation of community
|
||||
standards, including sustained inappropriate behavior, harassment of an
|
||||
standards, including sustained inappropriate behavior, harassment of an
|
||||
individual, or aggression toward or disparagement of classes of individuals.
|
||||
|
||||
**Consequence**: A permanent ban from any sort of public interaction within
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
|
|||
|
||||
## Standard VM
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&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.
|
||||
|
||||
|
|
@ -14,7 +14,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
|
|||
|
||||
## Spot/Preemptible Instance
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
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.
|
||||
|
||||
|
|
|
|||
2
LICENSE
2
LICENSE
|
|
@ -1,6 +1,6 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2024 Logspace
|
||||
Copyright (c) 2024 Langflow
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
|
|
|||
|
|
@ -63,8 +63,10 @@ RUN --mount=type=cache,target=/root/.cache \
|
|||
|
||||
# copy project requirement files here to ensure they will be cached.
|
||||
WORKDIR $PYSETUP_PATH
|
||||
# Copy just one file to avoid rebuilding the whole image
|
||||
COPY poetry.lock pyproject.toml ./
|
||||
COPY ./src/backend/langflow/main.py ./src/backend/langflow/main.py
|
||||
COPY ./src/backend/langflow ./src/backend/langflow
|
||||
COPY ./src/backend/base/pyproject.toml ./src/backend/base/pyproject.toml
|
||||
# Copy README.md to the build context
|
||||
COPY README.md .
|
||||
# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
|
||||
|
|
@ -84,7 +86,7 @@ COPY --from=builder-base $POETRY_HOME $POETRY_HOME
|
|||
COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
|
||||
|
||||
# Copy just one file to avoid rebuilding the whole image
|
||||
COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
|
||||
COPY ./src/backend/langflow ./src/backend/langflow
|
||||
# quicker install as runtime deps are already installed
|
||||
RUN --mount=type=cache,target=/root/.cache \
|
||||
poetry install --with=dev --extras deploy
|
||||
|
|
|
|||
|
|
@ -74,10 +74,11 @@ RUN $POETRY_HOME/bin/poetry build
|
|||
FROM python-base as final
|
||||
|
||||
# Copy virtual environment and built .tar.gz from builder base
|
||||
RUN useradd -m -u 1000 user
|
||||
COPY --from=builder-base /app/dist/*.tar.gz ./
|
||||
|
||||
# Install the package from the .tar.gz
|
||||
RUN pip install *.tar.gz
|
||||
RUN python -m pip install *.tar.gz --user
|
||||
|
||||
WORKDIR /app
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
ENTRYPOINT ["python", "-m", "langflow", "run"]
|
||||
CMD ["--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
@ -82,10 +82,11 @@ RUN cd src/backend/base && $POETRY_HOME/bin/poetry build --format sdist
|
|||
FROM python-base as final
|
||||
|
||||
# Copy virtual environment and built .tar.gz from builder base
|
||||
RUN useradd -m -u 1000 user
|
||||
COPY --from=builder-base /app/src/backend/base/dist/*.tar.gz ./
|
||||
|
||||
# Install the package from the .tar.gz
|
||||
RUN pip install *.tar.gz
|
||||
RUN pip install *.tar.gz --user
|
||||
|
||||
WORKDIR /app
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
ENTRYPOINT ["python", "-m", "langflow", "run"]
|
||||
CMD ["--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
export LANGFLOW_DATABASE_URL="mysql+pymysql://${username}:${password}@${host}:3306/${dbname}"
|
||||
# echo $LANGFLOW_DATABASE_URL
|
||||
uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --log-level debug
|
||||
uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --log-level debug --loop asyncio
|
||||
|
||||
# python -m langflow run --host 0.0.0.0 --port 7860
|
||||
|
|
@ -1,92 +0,0 @@
|
|||
|
||||
|
||||
# syntax=docker/dockerfile:1
|
||||
# Keep this syntax directive! It's used to enable Docker BuildKit
|
||||
|
||||
# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
|
||||
# but I try to keep it updated (see history)
|
||||
|
||||
################################
|
||||
# PYTHON-BASE
|
||||
# Sets up all our shared environment variables
|
||||
################################
|
||||
FROM python:3.10-slim as python-base
|
||||
|
||||
# python
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
# prevents python creating .pyc files
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
\
|
||||
# pip
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=on \
|
||||
PIP_DEFAULT_TIMEOUT=100 \
|
||||
\
|
||||
# poetry
|
||||
# https://python-poetry.org/docs/configuration/#using-environment-variables
|
||||
POETRY_VERSION=1.8.2 \
|
||||
# make poetry install to this location
|
||||
POETRY_HOME="/opt/poetry" \
|
||||
# make poetry create the virtual environment in the project's root
|
||||
# it gets named `.venv`
|
||||
POETRY_VIRTUALENVS_IN_PROJECT=true \
|
||||
# do not ask any interactive question
|
||||
POETRY_NO_INTERACTION=1 \
|
||||
\
|
||||
# paths
|
||||
# this is where our requirements + virtual environment will live
|
||||
PYSETUP_PATH="/opt/pysetup" \
|
||||
VENV_PATH="/opt/pysetup/.venv"
|
||||
|
||||
|
||||
# prepend poetry and venv to path
|
||||
ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
|
||||
|
||||
|
||||
################################
|
||||
# BUILDER-BASE
|
||||
# Used to build deps + create our virtual environment
|
||||
################################
|
||||
FROM python-base as builder-base
|
||||
RUN apt-get update \
|
||||
&& apt-get install --no-install-recommends -y \
|
||||
# deps for installing poetry
|
||||
curl \
|
||||
# deps for building python deps
|
||||
build-essential
|
||||
|
||||
# install poetry - respects $POETRY_VERSION & $POETRY_HOME
|
||||
# The --mount will mount the buildx cache directory to where
|
||||
# Poetry and Pip store their cache so that they can reuse it
|
||||
RUN --mount=type=cache,target=/root/.cache \
|
||||
curl -sSL https://install.python-poetry.org | python3 -
|
||||
|
||||
# copy project requirement files here to ensure they will be cached.
|
||||
WORKDIR $PYSETUP_PATH
|
||||
COPY ./poetry.lock ./pyproject.toml ./
|
||||
# Copy README.md to the build context
|
||||
COPY ./README.md ./
|
||||
# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
|
||||
RUN --mount=type=cache,target=/root/.cache \
|
||||
poetry install --without dev --extras deploy
|
||||
|
||||
|
||||
################################
|
||||
# DEVELOPMENT
|
||||
# Image used during development / testing
|
||||
################################
|
||||
FROM python-base as development
|
||||
WORKDIR $PYSETUP_PATH
|
||||
|
||||
# copy in our built poetry + venv
|
||||
COPY --from=builder-base $POETRY_HOME $POETRY_HOME
|
||||
COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
|
||||
|
||||
# Copy just one file to avoid rebuilding the whole image
|
||||
COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
|
||||
# quicker install as runtime deps are already installed
|
||||
RUN --mount=type=cache,target=/root/.cache \
|
||||
poetry install --with=dev --extras deploy
|
||||
|
||||
# copy in our app code
|
||||
COPY ./src/backend ./src/backend
|
||||
COPY ./tests ./tests
|
||||
|
|
@ -69,10 +69,7 @@ services:
|
|||
- traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
|
||||
|
||||
backend: &backend
|
||||
image: "ogabrielluiz/langflow:latest"
|
||||
build:
|
||||
context: ../
|
||||
dockerfile: base.Dockerfile
|
||||
image: "langflowai/langflow:latest"
|
||||
depends_on:
|
||||
- db
|
||||
- broker
|
||||
|
|
@ -143,9 +140,6 @@ services:
|
|||
<<: *backend
|
||||
env_file:
|
||||
- .env
|
||||
build:
|
||||
context: ../
|
||||
dockerfile: base.Dockerfile
|
||||
command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h -P eventlet
|
||||
healthcheck:
|
||||
test: "exit 0"
|
||||
|
|
@ -158,9 +152,6 @@ services:
|
|||
- .env
|
||||
networks:
|
||||
- default
|
||||
build:
|
||||
context: ../
|
||||
dockerfile: base.Dockerfile
|
||||
environment:
|
||||
- FLOWER_PORT=5555
|
||||
|
||||
|
|
|
|||
|
|
@ -15,4 +15,4 @@ COPY ./ ./
|
|||
# Install dependencies
|
||||
RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi
|
||||
|
||||
CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]
|
||||
CMD ["uvicorn", "--factory", "langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug", "--loop", "asyncio"]
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ services:
|
|||
[
|
||||
"sh",
|
||||
"-c",
|
||||
"pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload",
|
||||
"pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio",
|
||||
]
|
||||
ports:
|
||||
- 7860:7860
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ services:
|
|||
- "7860:7860"
|
||||
volumes:
|
||||
- ./:/app
|
||||
command: bash -c "uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
|
||||
command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio",
|
||||
networks:
|
||||
- langflow
|
||||
frontend:
|
||||
|
|
@ -23,7 +23,7 @@ services:
|
|||
args:
|
||||
- BACKEND_URL=http://backend:7860
|
||||
depends_on:
|
||||
- backend
|
||||
- backend
|
||||
environment:
|
||||
- VITE_PROXY_TARGET=http://backend:7860
|
||||
ports:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
FROM logspace/langflow:latest
|
||||
FROM langflowai/langflow:latest
|
||||
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ The Docker Compose configuration spins up two services: `langflow` and `postgres
|
|||
|
||||
### LangFlow Service
|
||||
|
||||
The `langflow` service uses the `logspace/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
|
||||
Environment variables:
|
||||
|
||||
|
|
@ -62,4 +62,4 @@ Volumes:
|
|||
|
||||
## Switching to a Specific LangFlow Version
|
||||
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `logspace/langflow:latest` to `logspace/langflow:1.0-alpha`.
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ version: "3.8"
|
|||
|
||||
services:
|
||||
langflow:
|
||||
image: logspace/langflow:latest
|
||||
image: langflowai/langflow:latest
|
||||
ports:
|
||||
- "7860:7860"
|
||||
depends_on:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
FROM logspace/langflow:1.0-alpha
|
||||
FROM langflowai/langflow:1.0-alpha
|
||||
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ version: "3.8"
|
|||
|
||||
services:
|
||||
langflow:
|
||||
image: logspace/langflow:1.0-alpha
|
||||
image: langflowai/langflow:1.0-alpha
|
||||
ports:
|
||||
- "7860:7860"
|
||||
depends_on:
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
|
|||
|
||||
## Standard VM
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&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.
|
||||
|
||||
|
|
@ -16,7 +16,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
|
|||
|
||||
## Spot/Preemptible Instance
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.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.
|
||||
|
||||
|
|
|
|||
27
docs/docs/getting-started/huggingface-spaces.mdx
Normal file
27
docs/docs/getting-started/huggingface-spaces.mdx
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# 🤗 HuggingFace Spaces
|
||||
|
||||
Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required.
|
||||
|
||||
The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
|
||||
Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen:
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/duplicate-space.png",
|
||||
dark: "img/duplicate-space.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away!
|
||||
|
||||
Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started.
|
||||
|
||||
To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs.
|
||||
77
docs/docs/getting-started/install-langflow.mdx
Normal file
77
docs/docs/getting-started/install-langflow.mdx
Normal file
|
|
@ -0,0 +1,77 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# 📦 Install Langflow
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally.
|
||||
</Admonition>
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Langflow requires the following programs installed on your system.
|
||||
|
||||
* [Python 3.10](https://www.python.org/downloads/release/python-3100/)
|
||||
|
||||
* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
|
||||
|
||||
## Install Langflow
|
||||
|
||||
To install Langflow:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
|
||||
## Install Langflow pre-release
|
||||
|
||||
Use `--force-reinstall` to ensure you have the latest version of Langflow and its dependencies.
|
||||
|
||||
To install a pre-release version of Langflow:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
|
||||
```
|
||||
|
||||
## Having a problem?
|
||||
|
||||
If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues).
|
||||
|
||||
To get help in the Langflow CLI:
|
||||
|
||||
```bash
|
||||
python -m langflow --help
|
||||
```
|
||||
|
||||
## ⛓️ Run Langflow
|
||||
|
||||
1. To run Langflow, enter the following command.
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in your browser.
|
||||
```bash
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
3. Continue on to the [Quickstart](./quickstart.mdx).
|
||||
10
docs/docs/getting-started/new-to-llms.mdx
Normal file
10
docs/docs/getting-started/new-to-llms.mdx
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
# 📚 New to LLMs?
|
||||
|
||||
Large Language Models, or LLMs, are part of an exciting new world in computing.
|
||||
|
||||
We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart.mdx).
|
||||
|
||||
If you want to learn more about LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai), an open-source repository of prompt engineering content maintained by AI experts.
|
||||
PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress.
|
||||
|
||||
Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab.
|
||||
119
docs/docs/getting-started/quickstart.mdx
Normal file
119
docs/docs/getting-started/quickstart.mdx
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# ⚡️ Quickstart
|
||||
|
||||
This quickstart demonstrates how to install Langflow, run it locally, build a basic prompt flow, and modify that prompt for different outcomes.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
* [Python 3.10](https://www.python.org/downloads/release/python-3100/)
|
||||
|
||||
* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
|
||||
|
||||
* [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
## Install Langflow
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally. This quickstart will run there, too.
|
||||
</Admonition>
|
||||
|
||||
1. To install Langflow, enter the following command in pip or pipx:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
3. Go to `http://127.0.0.1:7860` and confirm the Langflow UI is available.
|
||||
|
||||
<Admonition type="info">
|
||||
If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues).
|
||||
</Admonition>
|
||||
|
||||
## Create the basic prompting project
|
||||
|
||||
Now that you have Langflow installed and running, let us formally welcome you to Langflow!👋
|
||||
|
||||
You will use Langflow's prompt tools to issue prompts to the OpenAI LLM.
|
||||
|
||||
Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks.
|
||||
|
||||
By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems.
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Basic Prompting**.
|
||||
3. The **Basic Prompting** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/quickstart.png",
|
||||
dark: "img/quickstart.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow allows you to chat with the **OpenAI** component via a **Prompt** component.
|
||||
Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.`
|
||||
This should be interesting...
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot responds in a markedly piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
|
||||
## Next steps
|
||||
|
||||
Well done! You've built your first prompt in Langflow. 🎉
|
||||
|
||||
By adding Langflow components to this prompt, you can build all sorts of interesting flows.
|
||||
|
||||
* [Memory chatbot](/guides/memory-chatbot.mdx)
|
||||
* [Blog writer](/guides/blog-writer.mdx)
|
||||
* [Document QA](/guides/document-qa.mdx)
|
||||
|
||||
|
||||
|
|
@ -1,195 +0,0 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# 🌟 RAG with Astra DB
|
||||
|
||||
This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**.
|
||||
|
||||
[Astra DB](https://www.datastax.com/products/datastax-astra?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=astradb) is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. Astra DB is built on the same technology that powers the largest Cassandra deployments in the world.
|
||||
|
||||
In this guide, we will use Astra DB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses.
|
||||
|
||||
<Admonition type="tip">
|
||||
This guide assumes that you have Langflow up and running. If you are new to
|
||||
Langflow, you can check out the [Getting Started](/) guide.
|
||||
</Admonition>
|
||||
|
||||
TLDR;
|
||||
|
||||
- [Create a free Astra DB account](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account)
|
||||
- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
- Create a new database, get a **Token** and the **API Endpoint**
|
||||
- Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components
|
||||
- Import the project into Langflow by dropping it on the Canvas or My Collection page
|
||||
- Update the **Token** and **API Endpoint** in the **Astra DB** components
|
||||
- Update the OpenAI API key in the **OpenAI** components
|
||||
- Run the ingestion flow which is the one that uses the **Astra DB** component
|
||||
- Click on the ⚡ _Run_ button and start interacting with your RAG application
|
||||
|
||||
# First things first
|
||||
|
||||
## Create an Astra DB Database
|
||||
|
||||
To get started, you will need to [create an Astra DB database](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-an-astradb-database).
|
||||
|
||||
Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-create-database.png",
|
||||
dark: "img/astra-create-database.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region.
|
||||
|
||||
After you have configured your database, click on the **Create Database** button.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-configure-deployment.png",
|
||||
dark: "img/astra-configure-deployment.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-generate-token.png",
|
||||
dark: "img/astra-generate-token.png",
|
||||
}}
|
||||
style={{ width: "50%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Now we are all set to start building our RAG application using Astra DB and Langflow.
|
||||
|
||||
## (Optional) Duplicate the Langflow 1.0 HuggingFace Space
|
||||
|
||||
If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
|
||||
|
||||
## Open the Vector Store RAG Project
|
||||
|
||||
To get started, click on the **New Project** button and look for the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/drag-and-drop-flow.png",
|
||||
dark: "img/drag-and-drop-flow.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database.
|
||||
|
||||
Your first step should be to understand what each flow does and how they interact with each other.
|
||||
|
||||
The ingestion flow consists of:
|
||||
|
||||
- **Files** component that uploads a text file to Langflow
|
||||
- **Recursive Character Text Splitter** component that splits the text into smaller chunks
|
||||
- **OpenAIEmbeddings** component that generates embeddings for the text chunks
|
||||
- **Astra DB** component that stores the text chunks in the Astra DB database
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-ingestion-flow.png",
|
||||
dark: "img/astra-ingestion-flow.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Now, let's update the **Astra DB** and **Astra DB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-ingestion-fields.png",
|
||||
dark: "img/astra-ingestion-fields.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
And run it! This will ingest the Text data from your file into the Astra DB database.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-ingestion-run.png",
|
||||
dark: "img/astra-ingestion-run.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. It will define all of the steps from getting the User's input to generating a response and displaying it in the Interaction Panel.
|
||||
|
||||
The RAG flow is a bit more complex. It consists of:
|
||||
|
||||
- **Chat Input** component that defines where to put the user input coming from the Interaction Panel
|
||||
- **OpenAI Embeddings** component that generates embeddings from the user input
|
||||
- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database
|
||||
- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel
|
||||
- One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel
|
||||
- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model
|
||||
- **OpenAI** component that generates a response to the prompt
|
||||
- **Chat Output** component that displays the response in the Interaction Panel
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-rag-flow.png",
|
||||
dark: "img/astra-rag-flow.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-rag-flow-run.png",
|
||||
dark: "img/astra-rag-flow-run.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This opens the Interaction Panel where you can chat your data.
|
||||
|
||||
Because this flow has a **Chat Input** and a **Text Output** component, the Panel displays a chat input at the bottom and the Extracted Chunks section on the left.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-rag-flow-interaction-panel.png",
|
||||
dark: "img/astra-rag-flow-interaction-panel.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/astra-rag-flow-interaction-panel-interaction.png",
|
||||
dark: "img/astra-rag-flow-interaction-panel-interaction.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
And that's it! You have successfully ran a RAG application using Astra DB and Langflow.
|
||||
|
||||
# Conclusion
|
||||
|
||||
In this guide, we have learned how to run a RAG application using Astra DB and Langflow.
|
||||
We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Basic prompting
|
||||
|
||||
Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks.
|
||||
|
||||
By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems.
|
||||
|
||||
This article demonstrates how to use Langflow's prompt tools to issue basic prompts to an LLM, and how various prompting strategies can affect your outcomes.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the basic prompting project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Basic Prompting**.
|
||||
3. The **Basic Prompting** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/basic-prompting.png",
|
||||
dark: "img/basic-prompting.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow allows you to chat with the **OpenAI** component via a **Prompt** component.
|
||||
Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.`
|
||||
This should be interesting...
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot responds in a markedly piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Blog writer
|
||||
|
||||
Build a blog writer with OpenAI that uses URLs for reference content.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```bash
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the Blog Writer project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Blog Writer**.
|
||||
3. The **Blog Writer** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/blog-writer.png",
|
||||
dark: "img/blog-writer.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components.
|
||||
|
||||
The **Prompt** component's default **Template** field looks like this:
|
||||
```bash
|
||||
Reference 1:
|
||||
|
||||
{reference_1}
|
||||
|
||||
---
|
||||
|
||||
Reference 2:
|
||||
|
||||
{reference_2}
|
||||
|
||||
---
|
||||
|
||||
{instructions}
|
||||
|
||||
Blog:
|
||||
|
||||
```
|
||||
|
||||
The `{instructions}` value is received from the **Value** field of the **Instructions** component.
|
||||
The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the Blog Writer flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can run your one-shot flow.
|
||||
2. Click the **Lighting Bolt** icon to run your flow.
|
||||
3. The **OpenAI** component constructs a blog post with the **URL** items as context.
|
||||
The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs.
|
||||
|
||||
To write about something different, change the values in the **URL** components, and see what the LLM constructs.
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Document QA
|
||||
|
||||
Build a question-and-answer chatbot with a document loaded from local memory.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the Document QA project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Document QA**.
|
||||
3. The **Document QA** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/document-qa.png",
|
||||
dark: "img/document-qa.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow creates a basic chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components.
|
||||
This chatbot is augmented with the **Files** component, which loads a file from your local machine into the **Prompt** component as `{Document}`.
|
||||
The **Prompt** component is instructed to answer questions based on the contents of `{Document}`.
|
||||
Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
5. To select a document to load, in the **Files** component, click within the **Path** field.
|
||||
1. Select a local file, and then click **Open**.
|
||||
2. The file name appears in the field.
|
||||
<Admonition type="tip">
|
||||
The file must be of an extension type listed [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13).
|
||||
</Admonition>
|
||||
|
||||
## Run the Document QA flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
For this example, we loaded an error log `.txt` file and asked, "What went wrong?"
|
||||
The bot responded:
|
||||
```
|
||||
The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties.
|
||||
```
|
||||
|
||||
This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice!
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Memory chatbot
|
||||
|
||||
This flow extends the [basic prompting flow](./basic-prompting.mdx) to include chat memory for unique SessionIDs.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the memory chatbot project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Memory Chatbot**.
|
||||
3. The **Memory Chatbot** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/memory-chatbot.png",
|
||||
dark: "img/memory-chatbot.png",
|
||||
}}
|
||||
style={{
|
||||
width: "80%",
|
||||
margin: "20px auto",
|
||||
display: "flex",
|
||||
justifyContent: "center",
|
||||
}}
|
||||
/>
|
||||
|
||||
This flow creates a basic chatbot with the **Chat Input**, **Prompt**, and **OpenAI** components.
|
||||
This chatbot is augmented with the **Chat Memory** component, which stores messages submitted via **Chat Input** and prepends them to subsequent prompts to OpenAI via `{context}`.
|
||||
The **Chat History** component gives the **OpenAI** component a memory of previous questions.
|
||||
|
||||
4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
1. In the **Variable Name** field, enter `openai_api_key`.
|
||||
2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
|
||||
3. Click **Save Variable**.
|
||||
|
||||
## Run the memory chatbot flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot will respond according to the template in the **Prompt** component.
|
||||
3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears.
|
||||
|
||||
## Modify the Session ID field to have multiple conversations
|
||||
|
||||
`SessionID` is a unique identifier in Langchain for a conversation session between a chatbot and a client.
|
||||
A `SessionID` is created when a conversation is initiated, and then associated with all subsequent messages during that session.
|
||||
|
||||
In the **Memory Chatbot** flow you created, the **Chat Memory** component references past interactions with **Chat Input** by **Session ID**.
|
||||
You can demonstrate this by modifying the **Session ID** value to switch between conversation histories.
|
||||
|
||||
1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`.
|
||||
2. Click the **Run** button to run your flow.
|
||||
In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button).
|
||||
3. Type a few questions to your bot.
|
||||
4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`.
|
||||
5. Run your flow.
|
||||
The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`.
|
||||
|
||||
## Store Session ID as a Langflow variable
|
||||
|
||||
To store **Session ID** as a Langflow variable, in the **Session ID** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
|
||||
1. In the **Variable Name** field, enter a name like `customer_chat_emea`.
|
||||
2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`.
|
||||
3. Click **Save Variable**.
|
||||
4. Apply this variable to **Chat Input**.
|
||||
|
||||
|
|
@ -5,7 +5,9 @@ import Admonition from "@theme/Admonition";
|
|||
|
||||
# 👋 Welcome to Langflow
|
||||
|
||||
Langflow is an easy way to build from simple to complex AI applications. It is a low-code platform that allows you to integrate AI into everything you do.
|
||||
Langflow is a low-code platform that allows you to integrate AI into everything you do.
|
||||
|
||||
Use Langflow's simple but powerful UI to build any AI application you can dream up, from simple to complex.
|
||||
|
||||
{" "}
|
||||
|
||||
|
|
@ -20,96 +22,17 @@ Langflow is an easy way to build from simple to complex AI applications. It is a
|
|||
|
||||
## 🚀 First steps
|
||||
|
||||
## Installation
|
||||
* [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server.
|
||||
|
||||
Make sure you have **Python 3.10** installed on your system.
|
||||
* [Quickstart](/getting-started/quickstart) - Install Langflow, create a flow, and run it.
|
||||
|
||||
You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installation/) or with pip.
|
||||
* [HuggingFace Spaces](/getting-started/huggingface-spaces) - Duplicate the Langflow preview space and try it out before you install.
|
||||
|
||||
Pipx can fetch the missing Python version for you, but you can also install it manually.
|
||||
* [New to LLMs?](/getting-started/new-to-llms) - Learn more about LLMs, prompting, and more at [promptingguide.ai](https://promptingguide.ai).
|
||||
|
||||
```bash
|
||||
# Remember to check if you have Python 3.10 installed
|
||||
python -m pip install langflow -U
|
||||
# or
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
## Learn more about Langflow 1.0
|
||||
|
||||
Or you can install a pre-release version using:
|
||||
Learn more about the exciting changes in Langflow 1.0, and how to migrate your existing Langflow projects.
|
||||
|
||||
```bash
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
# or
|
||||
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
|
||||
```
|
||||
|
||||
<Admonition type="tip">
|
||||
<p>
|
||||
Please, check out our [Possible Installation Issues
|
||||
section](/migration/possible-installation-issues) if you encounter any
|
||||
problems.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
We recommend using --force-reinstall to ensure you have the latest version of Langflow and its dependencies.
|
||||
|
||||
### ⛓️ Running Langflow
|
||||
|
||||
Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces.
|
||||
|
||||
```bash
|
||||
python -m langflow run # or langflow --help
|
||||
```
|
||||
|
||||
#### 🤗 HuggingFace Spaces
|
||||
|
||||
Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required.
|
||||
|
||||
The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
|
||||
Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen:
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/duplicate-space.png",
|
||||
dark: "img/duplicate-space.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away!
|
||||
|
||||
Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started.
|
||||
|
||||
To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs.
|
||||
|
||||
### 🖥️ Command Line Interface (CLI)
|
||||
|
||||
Langflow provides a command-line interface (CLI) for easy management and configuration.
|
||||
|
||||
#### Usage
|
||||
|
||||
You can run the Langflow using the following command:
|
||||
|
||||
```bash
|
||||
langflow run [OPTIONS]
|
||||
```
|
||||
|
||||
Find more information about the available options by running:
|
||||
|
||||
```bash
|
||||
python -m langflow --help
|
||||
```
|
||||
|
||||
## Find out more about 1.0
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We are currently working on updating the documentation for Langflow 1.0.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
To get you learning more about what's new and why you should be excited about Langflow 1.0,
|
||||
go to [A new chapter for Langflow](/whats-new/a-new-chapter-langflow) and also come back often
|
||||
to check out our [migration guides](/whats-new/migrating-to-one-point-zero) as we release them.
|
||||
* [A new chapter for Langflow](/whats-new/a-new-chapter-langflow)
|
||||
* [Migration guides](/whats-new/migrating-to-one-point-zero)
|
||||
|
|
@ -7,11 +7,11 @@ module.exports = {
|
|||
title: "Langflow Documentation",
|
||||
tagline: "Langflow is a GUI for LangChain, designed with react-flow",
|
||||
favicon: "img/favicon.ico",
|
||||
url: "https://logspace-ai.github.io",
|
||||
url: "https://langflow-ai.github.io",
|
||||
baseUrl: "/",
|
||||
onBrokenLinks: "throw",
|
||||
onBrokenMarkdownLinks: "warn",
|
||||
organizationName: "logspace-ai",
|
||||
organizationName: "langflow-ai",
|
||||
projectName: "langflow",
|
||||
trailingSlash: false,
|
||||
staticDirectories: ["static"],
|
||||
|
|
@ -131,7 +131,7 @@ module.exports = {
|
|||
},
|
||||
footer: {
|
||||
links: [],
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Logspace.`,
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Langflow.`,
|
||||
},
|
||||
zoom: {
|
||||
selector: ".markdown :not(a) > img:not(.no-zoom)",
|
||||
|
|
|
|||
|
|
@ -6,14 +6,26 @@ module.exports = {
|
|||
collapsed: false,
|
||||
items: [
|
||||
"index",
|
||||
"getting-started/cli",
|
||||
// "guides/basic-prompting",
|
||||
// "guides/document-qa",
|
||||
// "guides/blog-writer",
|
||||
// "guides/memory-chatbot",
|
||||
"getting-started/install-langflow",
|
||||
"getting-started/quickstart",
|
||||
"getting-started/huggingface-spaces",
|
||||
"getting-started/new-to-llms",
|
||||
],
|
||||
},
|
||||
|
||||
{
|
||||
type: "category",
|
||||
label: " Starter Projects",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"guides/basic-prompting",
|
||||
"guides/blog-writer",
|
||||
"guides/document-qa",
|
||||
"guides/memory-chatbot",
|
||||
"guides/rag-with-astradb",
|
||||
],
|
||||
},
|
||||
|
||||
{
|
||||
type: "category",
|
||||
label: " What's New",
|
||||
|
|
@ -46,6 +58,7 @@ module.exports = {
|
|||
"migration/global-variables",
|
||||
// "migration/experimental-components",
|
||||
// "migration/state-management",
|
||||
//"guides/rag-with-astradb",
|
||||
],
|
||||
},
|
||||
{
|
||||
|
|
@ -53,6 +66,7 @@ module.exports = {
|
|||
label: "Guidelines",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"getting-started/cli",
|
||||
"guidelines/login",
|
||||
"guidelines/api",
|
||||
"guidelines/components",
|
||||
|
|
|
|||
BIN
docs/static/img/basic-prompting.png
vendored
Normal file
BIN
docs/static/img/basic-prompting.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 271 KiB |
BIN
docs/static/img/blog-writer.png
vendored
Normal file
BIN
docs/static/img/blog-writer.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 524 KiB |
BIN
docs/static/img/document-qa.png
vendored
Normal file
BIN
docs/static/img/document-qa.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 126 KiB |
BIN
docs/static/img/memory-chatbot.png
vendored
Normal file
BIN
docs/static/img/memory-chatbot.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 271 KiB |
BIN
docs/static/img/quickstart.png
vendored
Normal file
BIN
docs/static/img/quickstart.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 486 KiB |
2622
poetry.lock
generated
2622
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,16 +1,16 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a18"
|
||||
version = "1.0.0a28"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
"Carlos Coelho <carlos@logspace.ai>",
|
||||
"Carlos Coelho <carlos@langflow.org>",
|
||||
"Cristhian Zanforlin <cristhian.lousa@gmail.com>",
|
||||
"Gabriel Almeida <gabriel@logspace.ai>",
|
||||
"Gabriel Almeida <gabriel@langflow.org>",
|
||||
"Igor Carvalho <igorr.ackerman@gmail.com>",
|
||||
"Lucas Eduoli <lucaseduoli@gmail.com>",
|
||||
"Otávio Anovazzi <otavio2204@gmail.com>",
|
||||
"Rodrigo Nader <rodrigo@logspace.ai>",
|
||||
"Rodrigo Nader <rodrigo@langflow.org>",
|
||||
]
|
||||
repository = "https://github.com/langflow-ai/langflow"
|
||||
license = "MIT"
|
||||
|
|
@ -81,6 +81,7 @@ chromadb = "^0.4.24"
|
|||
langchain-anthropic = "^0.1.6"
|
||||
langchain-astradb = "^0.1.0"
|
||||
langchain-openai = "^0.1.1"
|
||||
zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
types-redis = "^4.6.0.5"
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ 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,tcp:22,:tcp:3389 --source-ranges 35.235.240.0/20 --direction INGRESS
|
||||
gcloud compute firewall-rules create allow-iap --network $VPC_NAME --allow tcp:80,tcp:443,tcp:22,tcp:3389 --source-ranges 35.235.240.0/20 --direction INGRESS
|
||||
fi
|
||||
|
||||
# Define the startup script as a multiline Bash here-doc
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
FROM logspace/backend_build as backend_build
|
||||
FROM langflowai/backend_build as backend_build
|
||||
|
||||
FROM python:3.10-slim
|
||||
WORKDIR /app
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ Revises: 63b9c451fd30
|
|||
Create Date: 2024-03-25 09:40:02.743453
|
||||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
|
|
|||
|
|
@ -0,0 +1,130 @@
|
|||
"""Fix date times again
|
||||
|
||||
Revision ID: 4e5980a44eaa
|
||||
Revises: 79e675cb6752
|
||||
Create Date: 2024-04-12 18:11:06.454037
|
||||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
from loguru import logger
|
||||
from sqlalchemy.dialects import postgresql
|
||||
from sqlalchemy.engine.reflection import Inspector
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = "4e5980a44eaa"
|
||||
down_revision: Union[str, None] = "79e675cb6752"
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
if "apikey" in table_names:
|
||||
columns = inspector.get_columns("apikey")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=postgresql.TIMESTAMP(),
|
||||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=False,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'apikey'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
|
||||
if "variable" in table_names:
|
||||
columns = inspector.get_columns("variable")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=postgresql.TIMESTAMP(),
|
||||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
|
||||
if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP):
|
||||
batch_op.alter_column(
|
||||
"updated_at",
|
||||
existing_type=postgresql.TIMESTAMP(),
|
||||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if updated_at_column is None:
|
||||
logger.warning("Column 'updated_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
if "variable" in table_names:
|
||||
columns = inspector.get_columns("variable")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if updated_at_column is not None and isinstance(updated_at_column["type"], sa.DateTime):
|
||||
batch_op.alter_column(
|
||||
"updated_at",
|
||||
existing_type=sa.DateTime(timezone=True),
|
||||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if updated_at_column is None:
|
||||
logger.warning("Column 'updated_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.DateTime(timezone=True),
|
||||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
|
||||
|
||||
if "apikey" in table_names:
|
||||
columns = inspector.get_columns("apikey")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.DateTime(timezone=True),
|
||||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=False,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'apikey'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
"""Modify nullable
|
||||
|
||||
Revision ID: 58b28437a398
|
||||
Revises: 4e5980a44eaa
|
||||
Create Date: 2024-04-13 10:57:23.061709
|
||||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
from loguru import logger
|
||||
from sqlalchemy.engine.reflection import Inspector
|
||||
|
||||
down_revision: Union[str, None] = "4e5980a44eaa"
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
# Revision identifiers, used by Alembic.
|
||||
revision = "58b28437a398"
|
||||
down_revision = "4e5980a44eaa"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade():
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn)
|
||||
tables = ["apikey", "variable"] # List of tables to modify
|
||||
|
||||
for table_name in tables:
|
||||
modify_nullable(conn, inspector, table_name, upgrade=True)
|
||||
|
||||
|
||||
def downgrade():
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn)
|
||||
tables = ["apikey", "variable"] # List of tables to revert
|
||||
|
||||
for table_name in tables:
|
||||
modify_nullable(conn, inspector, table_name, upgrade=False)
|
||||
|
||||
|
||||
def modify_nullable(conn, inspector, table_name, upgrade=True):
|
||||
columns = inspector.get_columns(table_name)
|
||||
nullable_changes = {"apikey": {"created_at": False}, "variable": {"created_at": True, "updated_at": True}}
|
||||
|
||||
if table_name in columns:
|
||||
with op.batch_alter_table(table_name, schema=None) as batch_op:
|
||||
for column_name, nullable_setting in nullable_changes.get(table_name, {}).items():
|
||||
column_info = next((col for col in columns if col["name"] == column_name), None)
|
||||
if column_info:
|
||||
current_nullable = column_info["nullable"]
|
||||
target_nullable = nullable_setting if upgrade else not nullable_setting
|
||||
|
||||
if current_nullable != target_nullable:
|
||||
batch_op.alter_column(
|
||||
column_name, existing_type=sa.DateTime(timezone=True), nullable=target_nullable
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
f"Column '{column_name}' in table '{table_name}' already has nullable={target_nullable}"
|
||||
)
|
||||
else:
|
||||
logger.warning(f"Column '{column_name}' not found in table '{table_name}'")
|
||||
|
|
@ -5,13 +5,14 @@ Revises: e3bc869fa272
|
|||
Create Date: 2024-04-11 19:23:10.697335
|
||||
|
||||
"""
|
||||
from calendar import c
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
from sqlalchemy.dialects import postgresql
|
||||
from sqlalchemy.engine.reflection import Inspector
|
||||
from loguru import logger
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = "79e675cb6752"
|
||||
|
|
@ -28,7 +29,7 @@ def upgrade() -> None:
|
|||
if "apikey" in table_names:
|
||||
columns = inspector.get_columns("apikey")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP():
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
|
|
@ -36,25 +37,40 @@ def upgrade() -> None:
|
|||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=False,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'apikey'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
|
||||
if "variable" in table_names:
|
||||
columns = inspector.get_columns("variable")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP():
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=postgresql.TIMESTAMP(),
|
||||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=True,
|
||||
)
|
||||
if updated_at_column is not None and updated_at_column["type"] == postgresql.TIMESTAMP():
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
|
||||
if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP):
|
||||
batch_op.alter_column(
|
||||
"updated_at",
|
||||
existing_type=postgresql.TIMESTAMP(),
|
||||
type_=sa.DateTime(timezone=True),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if updated_at_column is None:
|
||||
logger.warning("Column 'updated_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
|
@ -69,25 +85,35 @@ def downgrade() -> None:
|
|||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if updated_at_column is not None and updated_at_column["type"] == sa.DateTime(timezone=True):
|
||||
if updated_at_column is not None and isinstance(updated_at_column["type"], sa.DateTime):
|
||||
batch_op.alter_column(
|
||||
"updated_at",
|
||||
existing_type=sa.DateTime(timezone=True),
|
||||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=True,
|
||||
)
|
||||
if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True):
|
||||
else:
|
||||
if updated_at_column is None:
|
||||
logger.warning("Column 'updated_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.DateTime(timezone=True),
|
||||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=True,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'variable'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
|
||||
|
||||
if "apikey" in table_names:
|
||||
columns = inspector.get_columns("apikey")
|
||||
created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
|
||||
if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True):
|
||||
if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
|
|
@ -95,5 +121,10 @@ def downgrade() -> None:
|
|||
type_=postgresql.TIMESTAMP(),
|
||||
existing_nullable=False,
|
||||
)
|
||||
else:
|
||||
if created_at_column is None:
|
||||
logger.warning("Column 'created_at' not found in table 'apikey'")
|
||||
else:
|
||||
logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -26,19 +26,21 @@ def upgrade() -> None:
|
|||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
if "variable" not in table_names:
|
||||
return
|
||||
|
||||
column_names = [column["name"] for column in inspector.get_columns("variable")]
|
||||
columns = [column for column in inspector.get_columns("variable")]
|
||||
column_names = [column["name"] for column in columns]
|
||||
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if "created_at" in column_names:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.TIMESTAMP(timezone=True),
|
||||
nullable=True,
|
||||
# existing_server_default expects str | bool | Identity | Computed | None
|
||||
# sa.text("now()") is not a valid value for existing_server_default
|
||||
existing_server_default=False,
|
||||
)
|
||||
created_at_colunmn = next(column for column in columns if column["name"] == "created_at")
|
||||
if created_at_colunmn["nullable"] is False:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.TIMESTAMP(timezone=True),
|
||||
nullable=True,
|
||||
# existing_server_default expects str | bool | Identity | Computed | None
|
||||
# sa.text("now()") is not a valid value for existing_server_default
|
||||
existing_server_default=False,
|
||||
)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
|
@ -50,13 +52,17 @@ def downgrade() -> None:
|
|||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
if "variable" not in table_names:
|
||||
return
|
||||
columns = [column for column in inspector.get_columns("variable")]
|
||||
column_names = [column["name"] for column in columns]
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
if "created_at" in inspector.get_columns("variable"):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.TIMESTAMP(timezone=True),
|
||||
nullable=False,
|
||||
existing_server_default=False,
|
||||
)
|
||||
if "created_at" in column_names:
|
||||
created_at_colunmn = next(column for column in columns if column["name"] == "created_at")
|
||||
if created_at_colunmn["nullable"] is True:
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.TIMESTAMP(timezone=True),
|
||||
nullable=False,
|
||||
existing_server_default=False,
|
||||
)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -109,6 +109,7 @@ async def simplified_run_flow(
|
|||
This endpoint provides a powerful interface for executing flows with enhanced flexibility and efficiency, supporting a wide range of applications by allowing for dynamic input and output configuration along with performance optimizations through session management and caching.
|
||||
"""
|
||||
session_id = input_request.session_id
|
||||
|
||||
try:
|
||||
task_result: List[RunOutputs] = []
|
||||
artifacts = {}
|
||||
|
|
@ -127,8 +128,9 @@ async def simplified_run_flow(
|
|||
if flow.data is None:
|
||||
raise ValueError(f"Flow {flow_id} has no data")
|
||||
graph_data = flow.data
|
||||
graph_data = process_tweaks(graph_data, input_request.tweaks or {})
|
||||
graph = Graph.from_payload(graph_data, flow_id=flow_id)
|
||||
|
||||
graph_data = process_tweaks(graph_data, input_request.tweaks or {}, stream=stream)
|
||||
graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=str(api_key_user.id))
|
||||
inputs = [
|
||||
InputValueRequest(components=[], input_value=input_request.input_value, type=input_request.input_type)
|
||||
]
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class BuildStatus(Enum):
|
|||
|
||||
|
||||
class TweaksRequest(BaseModel):
|
||||
tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict)
|
||||
tweaks: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class UpdateTemplateRequest(BaseModel):
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from typing import Annotated, List, Optional, Union
|
|||
from uuid import UUID
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
from loguru import logger
|
||||
|
||||
from langflow.api.utils import check_langflow_version
|
||||
from langflow.services.auth import utils as auth_utils
|
||||
|
|
@ -27,8 +28,11 @@ def get_user_store_api_key(
|
|||
):
|
||||
if not user.store_api_key:
|
||||
raise HTTPException(status_code=400, detail="You must have a store API key set.")
|
||||
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
|
||||
return decrypted
|
||||
try:
|
||||
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
|
||||
return decrypted
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail="Failed to decrypt API key. Please set a new one.") from e
|
||||
|
||||
|
||||
def get_optional_user_store_api_key(
|
||||
|
|
@ -37,8 +41,12 @@ def get_optional_user_store_api_key(
|
|||
):
|
||||
if not user.store_api_key:
|
||||
return None
|
||||
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
|
||||
return decrypted
|
||||
try:
|
||||
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
|
||||
return decrypted
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to decrypt API key: {e}")
|
||||
return user.store_api_key
|
||||
|
||||
|
||||
@router.get("/check/")
|
||||
|
|
|
|||
|
|
@ -1,13 +1,19 @@
|
|||
from typing import List, Optional, Union, cast
|
||||
|
||||
from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.runnables import Runnable
|
||||
|
||||
from langflow.base.agents.utils import get_agents_list, records_to_messages
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseMemory, Text, Tool
|
||||
from langflow.field_typing import Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class LCAgentComponent(CustomComponent):
|
||||
def get_agents_list(self):
|
||||
return get_agents_list()
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"lc": {
|
||||
|
|
@ -42,9 +48,8 @@ class LCAgentComponent(CustomComponent):
|
|||
self,
|
||||
agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
|
||||
inputs: str,
|
||||
input_variables: list[str],
|
||||
tools: List[Tool],
|
||||
memory: Optional[BaseMemory] = None,
|
||||
message_history: Optional[List[Record]] = None,
|
||||
handle_parsing_errors: bool = True,
|
||||
output_key: str = "output",
|
||||
) -> Text:
|
||||
|
|
@ -55,13 +60,11 @@ class LCAgentComponent(CustomComponent):
|
|||
agent=agent, # type: ignore
|
||||
tools=tools,
|
||||
verbose=True,
|
||||
memory=memory,
|
||||
handle_parsing_errors=handle_parsing_errors,
|
||||
)
|
||||
input_dict = {"input": inputs}
|
||||
for var in input_variables:
|
||||
if var not in ["agent_scratchpad", "input"]:
|
||||
input_dict[var] = ""
|
||||
input_dict: dict[str, str | list[BaseMessage]] = {"input": inputs}
|
||||
if message_history:
|
||||
input_dict["chat_history"] = records_to_messages(message_history)
|
||||
result = await runnable.ainvoke(input_dict)
|
||||
self.status = result
|
||||
if output_key in result:
|
||||
|
|
|
|||
23
src/backend/base/langflow/base/agents/default_prompts.py
Normal file
23
src/backend/base/langflow/base/agents/default_prompts.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
XML_AGENT_PROMPT = """You are a helpful assistant. Help the user answer any questions.
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{tools}
|
||||
|
||||
In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
|
||||
For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
|
||||
|
||||
<tool>search</tool><tool_input>weather in SF</tool_input>
|
||||
<observation>64 degrees</observation>
|
||||
|
||||
When you are done, respond with a final answer between <final_answer></final_answer>. For example:
|
||||
|
||||
<final_answer>The weather in SF is 64 degrees</final_answer>
|
||||
|
||||
Begin!
|
||||
|
||||
Previous Conversation:
|
||||
{chat_history}
|
||||
|
||||
Question: {input}
|
||||
{agent_scratchpad}"""
|
||||
143
src/backend/base/langflow/base/agents/utils.py
Normal file
143
src/backend/base/langflow/base/agents/utils.py
Normal file
|
|
@ -0,0 +1,143 @@
|
|||
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
|
||||
|
||||
from langchain.agents import (
|
||||
create_json_chat_agent,
|
||||
create_openai_tools_agent,
|
||||
create_tool_calling_agent,
|
||||
create_xml_agent,
|
||||
)
|
||||
from langchain.agents.xml.base import render_text_description
|
||||
from langchain_core.language_models import BaseLanguageModel
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
|
||||
from langchain_core.tools import BaseTool
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
from .default_prompts import XML_AGENT_PROMPT
|
||||
|
||||
|
||||
class AgentSpec(BaseModel):
|
||||
func: Callable[
|
||||
[
|
||||
BaseLanguageModel,
|
||||
Sequence[BaseTool],
|
||||
BasePromptTemplate | ChatPromptTemplate,
|
||||
Optional[Callable[[List[BaseTool]], str]],
|
||||
Optional[Union[bool, List[str]]],
|
||||
],
|
||||
Any,
|
||||
]
|
||||
prompt: Optional[Any] = None
|
||||
fields: List[str]
|
||||
hub_repo: Optional[str] = None
|
||||
|
||||
|
||||
def records_to_messages(records: List[Record]) -> List[BaseMessage]:
|
||||
"""
|
||||
Convert a list of records to a list of messages.
|
||||
|
||||
Args:
|
||||
records (List[Record]): The records to convert.
|
||||
|
||||
Returns:
|
||||
List[Message]: The records as messages.
|
||||
"""
|
||||
return [record.to_lc_message() for record in records]
|
||||
|
||||
|
||||
def validate_and_create_xml_agent(
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: BasePromptTemplate,
|
||||
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
|
||||
*,
|
||||
stop_sequence: Union[bool, List[str]] = True,
|
||||
):
|
||||
return create_xml_agent(
|
||||
llm=llm,
|
||||
tools=tools,
|
||||
prompt=prompt,
|
||||
tools_renderer=tools_renderer,
|
||||
stop_sequence=stop_sequence,
|
||||
)
|
||||
|
||||
|
||||
def validate_and_create_openai_tools_agent(
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: ChatPromptTemplate,
|
||||
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
|
||||
*,
|
||||
stop_sequence: Union[bool, List[str]] = True,
|
||||
):
|
||||
return create_openai_tools_agent(
|
||||
llm=llm,
|
||||
tools=tools,
|
||||
prompt=prompt,
|
||||
)
|
||||
|
||||
|
||||
def validate_and_create_tool_calling_agent(
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: ChatPromptTemplate,
|
||||
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
|
||||
*,
|
||||
stop_sequence: Union[bool, List[str]] = True,
|
||||
):
|
||||
return create_tool_calling_agent(
|
||||
llm=llm,
|
||||
tools=tools,
|
||||
prompt=prompt,
|
||||
)
|
||||
|
||||
|
||||
def validate_and_create_json_chat_agent(
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: ChatPromptTemplate,
|
||||
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
|
||||
*,
|
||||
stop_sequence: Union[bool, List[str]] = True,
|
||||
):
|
||||
return create_json_chat_agent(
|
||||
llm=llm,
|
||||
tools=tools,
|
||||
prompt=prompt,
|
||||
tools_renderer=tools_renderer,
|
||||
stop_sequence=stop_sequence,
|
||||
)
|
||||
|
||||
|
||||
AGENTS: Dict[str, AgentSpec] = {
|
||||
"Tool Calling Agent": AgentSpec(
|
||||
func=validate_and_create_tool_calling_agent,
|
||||
prompt=None,
|
||||
fields=["llm", "tools", "prompt"],
|
||||
hub_repo=None,
|
||||
),
|
||||
"XML Agent": AgentSpec(
|
||||
func=validate_and_create_xml_agent,
|
||||
prompt=XML_AGENT_PROMPT, # Ensure XML_AGENT_PROMPT is properly defined and typed.
|
||||
fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
|
||||
hub_repo="hwchase17/xml-agent-convo",
|
||||
),
|
||||
"OpenAI Tools Agent": AgentSpec(
|
||||
func=validate_and_create_openai_tools_agent,
|
||||
prompt=None,
|
||||
fields=["llm", "tools", "prompt"],
|
||||
hub_repo=None,
|
||||
),
|
||||
"JSON Chat Agent": AgentSpec(
|
||||
func=validate_and_create_json_chat_agent,
|
||||
prompt=None,
|
||||
fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
|
||||
hub_repo="hwchase17/react-chat-json",
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def get_agents_list():
|
||||
return list(AGENTS.keys())
|
||||
|
|
@ -6,6 +6,7 @@ Constants:
|
|||
- NODE_FORMAT_ATTRIBUTES: A list of attributes used for formatting nodes.
|
||||
- FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields.
|
||||
"""
|
||||
|
||||
STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
|
||||
|
||||
NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"]
|
||||
|
|
|
|||
51
src/backend/base/langflow/base/memory/memory.py
Normal file
51
src/backend/base/langflow/base/memory/memory.py
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class BaseMemoryComponent(CustomComponent):
|
||||
display_name = "Chat Memory"
|
||||
description = "Retrieves stored chat messages given a specific Session ID."
|
||||
beta: bool = True
|
||||
icon = "history"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User", "Machine and User"],
|
||||
"display_name": "Sender Type",
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name", "advanced": True},
|
||||
"n_messages": {
|
||||
"display_name": "Number of Messages",
|
||||
"info": "Number of messages to retrieve.",
|
||||
},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"order": {
|
||||
"options": ["Ascending", "Descending"],
|
||||
"display_name": "Order",
|
||||
"info": "Order of the messages.",
|
||||
"advanced": True,
|
||||
},
|
||||
"record_template": {
|
||||
"display_name": "Record Template",
|
||||
"multiline": True,
|
||||
"info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
raise NotImplementedError
|
||||
|
||||
def add_message(
|
||||
self, sender: str, sender_name: str, text: str, session_id: str, metadata: Optional[dict] = None, **kwargs
|
||||
):
|
||||
raise NotImplementedError
|
||||
|
|
@ -2,7 +2,7 @@ from typing import Optional, Union
|
|||
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
|
||||
|
|
@ -31,6 +31,47 @@ class LCModelComponent(CustomComponent):
|
|||
self.status = result
|
||||
return result
|
||||
|
||||
def build_status_message(self, message: AIMessage):
|
||||
"""
|
||||
Builds a status message from an AIMessage object.
|
||||
|
||||
Args:
|
||||
message (AIMessage): The AIMessage object to build the status message from.
|
||||
|
||||
Returns:
|
||||
The status message.
|
||||
"""
|
||||
if message.response_metadata:
|
||||
# Build a well formatted status message
|
||||
content = message.content
|
||||
response_metadata = message.response_metadata
|
||||
openai_keys = ["token_usage", "model_name", "finish_reason"]
|
||||
inner_openai_keys = ["completion_tokens", "prompt_tokens", "total_tokens"]
|
||||
anthropic_keys = ["model", "usage", "stop_reason"]
|
||||
inner_anthropic_keys = ["input_tokens", "output_tokens"]
|
||||
if all(key in response_metadata for key in openai_keys) and all(
|
||||
key in response_metadata["token_usage"] for key in inner_openai_keys
|
||||
):
|
||||
token_usage = response_metadata["token_usage"]
|
||||
completion_tokens = token_usage["completion_tokens"]
|
||||
prompt_tokens = token_usage["prompt_tokens"]
|
||||
total_tokens = token_usage["total_tokens"]
|
||||
finish_reason = response_metadata["finish_reason"]
|
||||
status_message = f"Tokens:\n- Input: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}"
|
||||
elif all(key in response_metadata for key in anthropic_keys) and all(
|
||||
key in response_metadata["usage"] for key in inner_anthropic_keys
|
||||
):
|
||||
usage = response_metadata["usage"]
|
||||
input_tokens = usage["input_tokens"]
|
||||
output_tokens = usage["output_tokens"]
|
||||
stop_reason = response_metadata["stop_reason"]
|
||||
status_message = f"Tokens:\n- Input: {input_tokens}\n- Output: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
|
||||
else:
|
||||
status_message = f"Response: {content}"
|
||||
else:
|
||||
status_message = f"Response: {message.content}"
|
||||
return status_message
|
||||
|
||||
def get_chat_result(
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
|
||||
):
|
||||
|
|
@ -46,5 +87,9 @@ class LCModelComponent(CustomComponent):
|
|||
else:
|
||||
message = runnable.invoke(messages)
|
||||
result = message.content
|
||||
self.status = result
|
||||
if isinstance(message, AIMessage):
|
||||
status_message = self.build_status_message(message)
|
||||
self.status = status_message
|
||||
else:
|
||||
self.status = result
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -0,0 +1,64 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class ToolCallingAgentComponent(LCAgentComponent):
|
||||
display_name: str = "Tool Calling Agent"
|
||||
description: str = "Agent that uses tools. Only models that are compatible with function calling are supported."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"tools": {"display_name": "Tools"},
|
||||
"user_prompt": {
|
||||
"display_name": "Prompt",
|
||||
"multiline": True,
|
||||
"info": "This prompt must contain 'input' key.",
|
||||
},
|
||||
"handle_parsing_errors": {
|
||||
"display_name": "Handle Parsing Errors",
|
||||
"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
|
||||
"advanced": True,
|
||||
},
|
||||
"memory": {
|
||||
"display_name": "Memory",
|
||||
"info": "Memory to use for the agent.",
|
||||
},
|
||||
"input_value": {
|
||||
"display_name": "Inputs",
|
||||
"info": "Input text to pass to the agent.",
|
||||
},
|
||||
}
|
||||
|
||||
async def build(
|
||||
self,
|
||||
input_value: str,
|
||||
llm: BaseLanguageModel,
|
||||
tools: List[Tool],
|
||||
user_prompt: str = "{input}",
|
||||
message_history: Optional[List[Record]] = None,
|
||||
system_message: str = "You are a helpful assistant",
|
||||
handle_parsing_errors: bool = True,
|
||||
) -> Text:
|
||||
if "input" not in user_prompt:
|
||||
raise ValueError("Prompt must contain 'input' key.")
|
||||
messages = [
|
||||
("system", system_message),
|
||||
(
|
||||
"placeholder",
|
||||
"{chat_history}",
|
||||
),
|
||||
("human", user_prompt),
|
||||
("placeholder", "{agent_scratchpad}"),
|
||||
]
|
||||
prompt = ChatPromptTemplate.from_messages(messages)
|
||||
agent = create_tool_calling_agent(llm, tools, prompt)
|
||||
result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
|
||||
self.status = result
|
||||
return result
|
||||
|
|
@ -1,10 +1,12 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain.agents import create_xml_agent
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class XMLAgentComponent(LCAgentComponent):
|
||||
|
|
@ -15,7 +17,7 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"tools": {"display_name": "Tools"},
|
||||
"prompt": {
|
||||
"user_prompt": {
|
||||
"display_name": "Prompt",
|
||||
"multiline": True,
|
||||
"info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
|
||||
|
|
@ -43,6 +45,11 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
Question: {input}
|
||||
{agent_scratchpad}""",
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to be passed to the LLM.",
|
||||
"advanced": True,
|
||||
},
|
||||
"tool_template": {
|
||||
"display_name": "Tool Template",
|
||||
"info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
|
||||
|
|
@ -53,9 +60,9 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
|
||||
"advanced": True,
|
||||
},
|
||||
"memory": {
|
||||
"display_name": "Memory",
|
||||
"info": "Memory to use for the agent.",
|
||||
"message_history": {
|
||||
"display_name": "Message History",
|
||||
"info": "Message history to pass to the agent.",
|
||||
},
|
||||
"input_value": {
|
||||
"display_name": "Inputs",
|
||||
|
|
@ -68,12 +75,13 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
input_value: str,
|
||||
llm: BaseLanguageModel,
|
||||
tools: List[Tool],
|
||||
prompt: str,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
user_prompt: str = "{input}",
|
||||
system_message: str = "You are a helpful assistant",
|
||||
message_history: Optional[List[Record]] = None,
|
||||
tool_template: str = "{name}: {description}",
|
||||
handle_parsing_errors: bool = True,
|
||||
) -> Text:
|
||||
if "input" not in prompt:
|
||||
if "input" not in user_prompt:
|
||||
raise ValueError("Prompt must contain 'input' key.")
|
||||
|
||||
def render_tool_description(tools):
|
||||
|
|
@ -81,9 +89,23 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
[tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
|
||||
)
|
||||
|
||||
prompt_template = PromptTemplate.from_template(prompt)
|
||||
input_variables = prompt_template.input_variables
|
||||
agent = create_xml_agent(llm, tools, prompt_template, tools_renderer=render_tool_description)
|
||||
result = await self.run_agent(agent, input_value, input_variables, tools, memory, handle_parsing_errors)
|
||||
messages = [
|
||||
("system", system_message),
|
||||
(
|
||||
"placeholder",
|
||||
"{chat_history}",
|
||||
),
|
||||
("human", user_prompt),
|
||||
("placeholder", "{agent_scratchpad}"),
|
||||
]
|
||||
prompt = ChatPromptTemplate.from_messages(messages)
|
||||
agent = create_xml_agent(llm, tools, prompt, tools_renderer=render_tool_description)
|
||||
result = await self.run_agent(
|
||||
agent=agent,
|
||||
inputs=input_value,
|
||||
tools=tools,
|
||||
message_history=message_history,
|
||||
handle_parsing_errors=handle_parsing_errors,
|
||||
)
|
||||
self.status = result
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -0,0 +1,185 @@
|
|||
from typing import Any, List, Optional, cast
|
||||
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessagePromptTemplate
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
|
||||
from langflow.field_typing import BaseLanguageModel, Text, Tool
|
||||
from langflow.schema.dotdict import dotdict
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class AgentComponent(LCAgentComponent):
|
||||
display_name = "Agent"
|
||||
description = "Run any LangChain agent using a simplified interface."
|
||||
field_order = [
|
||||
"agent_name",
|
||||
"llm",
|
||||
"tools",
|
||||
"prompt",
|
||||
"tool_template",
|
||||
"handle_parsing_errors",
|
||||
"memory",
|
||||
"input_value",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"agent_name": {
|
||||
"display_name": "Agent",
|
||||
"info": "The agent to use.",
|
||||
"refresh_button": True,
|
||||
"real_time_refresh": True,
|
||||
"options": get_agents_list(),
|
||||
},
|
||||
"llm": {"display_name": "LLM"},
|
||||
"tools": {"display_name": "Tools"},
|
||||
"user_prompt": {
|
||||
"display_name": "Prompt",
|
||||
"multiline": True,
|
||||
"info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to be passed to the LLM.",
|
||||
"advanced": True,
|
||||
},
|
||||
"tool_template": {
|
||||
"display_name": "Tool Template",
|
||||
"info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
|
||||
"advanced": True,
|
||||
},
|
||||
"handle_parsing_errors": {
|
||||
"display_name": "Handle Parsing Errors",
|
||||
"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
|
||||
"advanced": True,
|
||||
},
|
||||
"message_history": {
|
||||
"display_name": "Message History",
|
||||
"info": "Message history to pass to the agent.",
|
||||
},
|
||||
"input_value": {
|
||||
"display_name": "Input",
|
||||
"info": "Input text to pass to the agent.",
|
||||
},
|
||||
"langchain_hub_api_key": {
|
||||
"display_name": "LangChain Hub API Key",
|
||||
"info": "API key to use for LangChain Hub. If provided, prompts will be fetched from LangChain Hub.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def get_system_and_user_message_from_prompt(self, prompt: Any):
|
||||
"""
|
||||
Extracts the system message and user prompt from a given prompt object.
|
||||
|
||||
Args:
|
||||
prompt (Any): The prompt object from which to extract the system message and user prompt.
|
||||
|
||||
Returns:
|
||||
Tuple[Optional[str], Optional[str]]: A tuple containing the system message and user prompt.
|
||||
If the prompt object does not have any messages, both values will be None.
|
||||
"""
|
||||
if hasattr(prompt, "messages"):
|
||||
system_message = None
|
||||
user_prompt = None
|
||||
for message in prompt.messages:
|
||||
if isinstance(message, SystemMessagePromptTemplate):
|
||||
s_prompt = message.prompt
|
||||
if isinstance(s_prompt, list):
|
||||
s_template = " ".join([cast(str, s.template) for s in s_prompt if hasattr(s, "template")])
|
||||
elif hasattr(s_prompt, "template"):
|
||||
s_template = s_prompt.template
|
||||
system_message = s_template
|
||||
elif isinstance(message, HumanMessagePromptTemplate):
|
||||
h_prompt = message.prompt
|
||||
if isinstance(h_prompt, list):
|
||||
h_template = " ".join([cast(str, h.template) for h in h_prompt if hasattr(h, "template")])
|
||||
elif hasattr(h_prompt, "template"):
|
||||
h_template = h_prompt.template
|
||||
user_prompt = h_template
|
||||
return system_message, user_prompt
|
||||
return None, None
|
||||
|
||||
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Text | None = None):
|
||||
"""
|
||||
Updates the build configuration based on the provided field value and field name.
|
||||
|
||||
Args:
|
||||
build_config (dotdict): The build configuration to be updated.
|
||||
field_value (Any): The value of the field being updated.
|
||||
field_name (Text | None, optional): The name of the field being updated. Defaults to None.
|
||||
|
||||
Returns:
|
||||
dotdict: The updated build configuration.
|
||||
"""
|
||||
if field_name == "agent":
|
||||
build_config["agent"]["options"] = get_agents_list()
|
||||
if field_value in AGENTS:
|
||||
# if langchain_hub_api_key is provided, fetch the prompt from LangChain Hub
|
||||
if build_config["langchain_hub_api_key"]["value"] and AGENTS[field_value].hub_repo:
|
||||
from langchain import hub
|
||||
|
||||
hub_repo: str | None = AGENTS[field_value].hub_repo
|
||||
if hub_repo:
|
||||
hub_api_key: str = build_config["langchain_hub_api_key"]["value"]
|
||||
prompt = hub.pull(hub_repo, api_key=hub_api_key)
|
||||
system_message, user_prompt = self.get_system_and_user_message_from_prompt(prompt)
|
||||
if system_message:
|
||||
build_config["system_message"]["value"] = system_message
|
||||
if user_prompt:
|
||||
build_config["user_prompt"]["value"] = user_prompt
|
||||
|
||||
if AGENTS[field_value].prompt:
|
||||
build_config["user_prompt"]["value"] = AGENTS[field_value].prompt
|
||||
else:
|
||||
build_config["user_prompt"]["value"] = "{input}"
|
||||
fields = AGENTS[field_value].fields
|
||||
for field in ["llm", "tools", "prompt", "tools_renderer"]:
|
||||
if field not in fields:
|
||||
build_config[field]["show"] = False
|
||||
return build_config
|
||||
|
||||
async def build(
|
||||
self,
|
||||
agent_name: str,
|
||||
input_value: str,
|
||||
llm: BaseLanguageModel,
|
||||
tools: List[Tool],
|
||||
system_message: str = "You are a helpful assistant. Help the user answer any questions.",
|
||||
user_prompt: str = "{input}",
|
||||
message_history: Optional[List[Record]] = None,
|
||||
tool_template: str = "{name}: {description}",
|
||||
handle_parsing_errors: bool = True,
|
||||
) -> Text:
|
||||
agent_spec: Optional[AgentSpec] = AGENTS.get(agent_name)
|
||||
if agent_spec is None:
|
||||
raise ValueError(f"{agent_name} not found.")
|
||||
|
||||
def render_tool_description(tools):
|
||||
return "\n".join(
|
||||
[tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
|
||||
)
|
||||
|
||||
messages = [
|
||||
("system", system_message),
|
||||
(
|
||||
"placeholder",
|
||||
"{chat_history}",
|
||||
),
|
||||
("human", user_prompt),
|
||||
("placeholder", "{agent_scratchpad}"),
|
||||
]
|
||||
prompt = ChatPromptTemplate.from_messages(messages)
|
||||
agent_func = agent_spec.func
|
||||
agent = agent_func(llm, tools, prompt, render_tool_description, True)
|
||||
result = await self.run_agent(
|
||||
agent=agent,
|
||||
inputs=input_value,
|
||||
tools=tools,
|
||||
message_history=message_history,
|
||||
handle_parsing_errors=handle_parsing_errors,
|
||||
)
|
||||
self.status = result
|
||||
return result
|
||||
|
|
@ -10,8 +10,10 @@ from .RunFlow import RunFlowComponent
|
|||
from .RunnableExecutor import RunnableExecComponent
|
||||
from .SQLExecutor import SQLExecutorComponent
|
||||
from .SubFlow import SubFlowComponent
|
||||
from .AgentComponent import AgentComponent
|
||||
|
||||
__all__ = [
|
||||
"AgentComponent",
|
||||
"ClearMessageHistoryComponent",
|
||||
"ExtractKeyFromRecordComponent",
|
||||
"FlowToolComponent",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,25 @@
|
|||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CombineTextsUnsortedComponent(CustomComponent):
|
||||
display_name = "Combine Texts (Unsorted)"
|
||||
description = "Concatenate text sources into a single text chunk using a specified delimiter."
|
||||
icon = "merge"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"texts": {
|
||||
"display_name": "Texts",
|
||||
"info": "The first text input to concatenate.",
|
||||
},
|
||||
"delimiter": {
|
||||
"display_name": "Delimiter",
|
||||
"info": "A string used to separate the two text inputs. Defaults to a whitespace.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(self, texts: list[str], delimiter: str = " ") -> Text:
|
||||
combined = delimiter.join(texts)
|
||||
self.status = combined
|
||||
return combined
|
||||
|
|
@ -1,12 +1,13 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.memory import get_messages
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class MemoryComponent(CustomComponent):
|
||||
class MemoryComponent(BaseMemoryComponent):
|
||||
display_name = "Chat Memory"
|
||||
description = "Retrieves stored chat messages given a specific Session ID."
|
||||
beta: bool = True
|
||||
|
|
@ -42,6 +43,24 @@ class MemoryComponent(CustomComponent):
|
|||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
# Validate kwargs by checking if it contains the correct keys
|
||||
if "sender" not in kwargs:
|
||||
kwargs["sender"] = None
|
||||
if "sender_name" not in kwargs:
|
||||
kwargs["sender_name"] = None
|
||||
if "session_id" not in kwargs:
|
||||
kwargs["session_id"] = None
|
||||
if "limit" not in kwargs:
|
||||
kwargs["limit"] = 5
|
||||
if "order" not in kwargs:
|
||||
kwargs["order"] = "Descending"
|
||||
|
||||
kwargs["order"] = "DESC" if kwargs["order"] == "Descending" else "ASC"
|
||||
if kwargs["sender"] == "Machine and User":
|
||||
kwargs["sender"] = None
|
||||
return get_messages(**kwargs)
|
||||
|
||||
def build(
|
||||
self,
|
||||
sender: Optional[str] = "Machine and User",
|
||||
|
|
@ -51,10 +70,7 @@ class MemoryComponent(CustomComponent):
|
|||
order: Optional[str] = "Descending",
|
||||
record_template: Optional[str] = "{sender_name}: {text}",
|
||||
) -> Text:
|
||||
order = "DESC" if order == "Descending" else "ASC"
|
||||
if sender == "Machine and User":
|
||||
sender = None
|
||||
messages = get_messages(
|
||||
messages = self.get_messages(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
session_id=session_id,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,137 @@
|
|||
from typing import Optional, cast
|
||||
|
||||
from langchain_community.chat_message_histories.zep import SearchScope, SearchType, ZepChatMessageHistory
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class ZepMessageReaderComponent(BaseMemoryComponent):
|
||||
display_name = "Zep Message Reader"
|
||||
description = "Retrieves stored chat messages from Zep."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"url": {
|
||||
"display_name": "Zep URL",
|
||||
"info": "URL of the Zep instance.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"api_key": {
|
||||
"display_name": "Zep API Key",
|
||||
"info": "API Key for the Zep instance.",
|
||||
"password": True,
|
||||
},
|
||||
"query": {
|
||||
"display_name": "Query",
|
||||
"info": "Query to search for in the chat history.",
|
||||
},
|
||||
"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"info": "Optional metadata to attach to the message.",
|
||||
"advanced": True,
|
||||
},
|
||||
"search_scope": {
|
||||
"options": ["Messages", "Summary"],
|
||||
"display_name": "Search Scope",
|
||||
"info": "Scope of the search.",
|
||||
"advanced": True,
|
||||
},
|
||||
"search_type": {
|
||||
"options": ["Similarity", "MMR"],
|
||||
"display_name": "Search Type",
|
||||
"info": "Type of search.",
|
||||
"advanced": True,
|
||||
},
|
||||
"limit": {
|
||||
"display_name": "Limit",
|
||||
"info": "Limit of search results.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
"""
|
||||
Retrieves messages from the ZepChatMessageHistory memory.
|
||||
|
||||
If a query is provided, the search method is used to search for messages in the memory, otherwise all messages are returned.
|
||||
|
||||
Args:
|
||||
memory (ZepChatMessageHistory): The ZepChatMessageHistory instance to retrieve messages from.
|
||||
query (str, optional): The query string to search for messages. Defaults to None.
|
||||
metadata (dict, optional): Additional metadata to filter the search results. Defaults to None.
|
||||
search_scope (str, optional): The scope of the search. Can be 'messages' or 'summary'. Defaults to 'messages'.
|
||||
search_type (str, optional): The type of search. Can be 'similarity' or 'exact'. Defaults to 'similarity'.
|
||||
limit (int, optional): The maximum number of search results to return. Defaults to None.
|
||||
|
||||
Returns:
|
||||
list[Record]: A list of Record objects representing the search results.
|
||||
"""
|
||||
memory: ZepChatMessageHistory = cast(ZepChatMessageHistory, kwargs.get("memory"))
|
||||
if not memory:
|
||||
raise ValueError("ZepChatMessageHistory instance is required.")
|
||||
query = kwargs.get("query")
|
||||
search_scope = kwargs.get("search_scope", SearchScope.messages).lower()
|
||||
search_type = kwargs.get("search_type", SearchType.similarity).lower()
|
||||
limit = kwargs.get("limit")
|
||||
|
||||
if query:
|
||||
memory_search_results = memory.search(
|
||||
query,
|
||||
search_scope=search_scope,
|
||||
search_type=search_type,
|
||||
limit=limit,
|
||||
)
|
||||
# Get the messages from the search results if the search scope is messages
|
||||
result_dicts = []
|
||||
for result in memory_search_results:
|
||||
result_dict = {}
|
||||
if search_scope == SearchScope.messages:
|
||||
result_dict["text"] = result.message
|
||||
else:
|
||||
result_dict["text"] = result.summary
|
||||
result_dict["metadata"] = result.metadata
|
||||
result_dict["score"] = result.score
|
||||
result_dicts.append(result_dict)
|
||||
results = [Record(data=result_dict) for result_dict in result_dicts]
|
||||
else:
|
||||
messages = memory.messages
|
||||
results = [Record.from_lc_message(message) for message in messages]
|
||||
return results
|
||||
|
||||
def build(
|
||||
self,
|
||||
session_id: Text,
|
||||
url: Optional[Text] = None,
|
||||
api_key: Optional[Text] = None,
|
||||
query: Optional[Text] = None,
|
||||
search_scope: SearchScope = SearchScope.messages,
|
||||
search_type: SearchType = SearchType.similarity,
|
||||
limit: Optional[int] = None,
|
||||
) -> list[Record]:
|
||||
try:
|
||||
from zep_python import ZepClient
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import zep-python package. " "Please install it with `pip install zep-python`."
|
||||
)
|
||||
if url == "":
|
||||
url = None
|
||||
zep_client = ZepClient(api_url=url, api_key=api_key)
|
||||
memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
|
||||
records = self.get_messages(
|
||||
memory=memory,
|
||||
query=query,
|
||||
search_scope=search_scope,
|
||||
search_type=search_type,
|
||||
limit=limit,
|
||||
)
|
||||
self.status = records
|
||||
return records
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
from typing import Optional, TYPE_CHECKING
|
||||
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
|
||||
|
||||
class ZepMessageWriterComponent(BaseMemoryComponent):
|
||||
display_name = "Zep Message Writer"
|
||||
description = "Writes a message to Zep."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"url": {
|
||||
"display_name": "Zep URL",
|
||||
"info": "URL of the Zep instance.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"api_key": {
|
||||
"display_name": "Zep API Key",
|
||||
"info": "API Key for the Zep instance.",
|
||||
"password": True,
|
||||
},
|
||||
"limit": {
|
||||
"display_name": "Limit",
|
||||
"info": "Limit of search results.",
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {
|
||||
"display_name": "Input Record",
|
||||
"info": "Record to write to Zep.",
|
||||
},
|
||||
}
|
||||
|
||||
def add_message(
|
||||
self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs
|
||||
):
|
||||
"""
|
||||
Adds a message to the ZepChatMessageHistory memory.
|
||||
|
||||
Args:
|
||||
sender (Text): The type of the message sender. Valid values are "Machine" or "User".
|
||||
sender_name (Text): The name of the message sender.
|
||||
text (Text): The content of the message.
|
||||
session_id (Text): The session ID associated with the message.
|
||||
metadata (dict | None, optional): Additional metadata for the message. Defaults to None.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Raises:
|
||||
ValueError: If the ZepChatMessageHistory instance is not provided.
|
||||
|
||||
"""
|
||||
memory: ZepChatMessageHistory | None = kwargs.pop("memory", None)
|
||||
if memory is None:
|
||||
raise ValueError("ZepChatMessageHistory instance is required.")
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
metadata["sender_name"] = sender_name
|
||||
metadata.update(kwargs)
|
||||
if sender == "Machine":
|
||||
memory.add_ai_message(text, metadata=metadata)
|
||||
elif sender == "User":
|
||||
memory.add_user_message(text, metadata=metadata)
|
||||
else:
|
||||
raise ValueError(f"Invalid sender type: {sender}")
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Record,
|
||||
session_id: Text,
|
||||
url: Optional[Text] = None,
|
||||
api_key: Optional[Text] = None,
|
||||
) -> Record:
|
||||
try:
|
||||
from zep_python import ZepClient
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import zep-python package. " "Please install it with `pip install zep-python`."
|
||||
)
|
||||
if url == "":
|
||||
url = None
|
||||
zep_client = ZepClient(api_url=url, api_key=api_key)
|
||||
memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
|
||||
self.add_message(**input_value.data, memory=memory)
|
||||
self.status = f"Added message to Zep memory for session {session_id}"
|
||||
return input_value
|
||||
|
|
@ -105,7 +105,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
|
|||
system_message: Optional[str] = None,
|
||||
max_tokens: Optional[int] = 1000,
|
||||
stream: bool = False,
|
||||
) -> BaseLanguageModel:
|
||||
) -> Text:
|
||||
if api_key:
|
||||
secret_api_key = SecretStr(api_key)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -142,7 +142,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
|
|||
max_retries: int = 6,
|
||||
verbose: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
) -> BaseLanguageModel:
|
||||
) -> Text:
|
||||
try:
|
||||
import litellm # type: ignore
|
||||
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
"display_name": "Model Name",
|
||||
"advanced": False,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
|
|
@ -61,10 +61,10 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
|
|||
input_value: Text,
|
||||
search_type: str,
|
||||
url: str,
|
||||
index_name: str,
|
||||
number_of_results: int = 4,
|
||||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
index_name: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
attributes: Optional[list] = None,
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from typing import List, Optional
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_astradb import AstraDBVectorStore
|
||||
from langchain_astradb.utils.astradb import SetupMode
|
||||
|
||||
|
|
@ -110,7 +111,7 @@ class AstraDBVectorStoreComponent(CustomComponent):
|
|||
metadata_indexing_include: Optional[List[str]] = None,
|
||||
metadata_indexing_exclude: Optional[List[str]] = None,
|
||||
collection_indexing_policy: Optional[dict] = None,
|
||||
) -> VectorStore:
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
try:
|
||||
setup_mode_value = SetupMode[setup_mode.upper()]
|
||||
except KeyError:
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import weaviate # type: ignore
|
|||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore, Weaviate
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
|
@ -50,9 +51,9 @@ class WeaviateVectorStoreComponent(CustomComponent):
|
|||
def build(
|
||||
self,
|
||||
url: str,
|
||||
index_name: str,
|
||||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
index_name: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
inputs: Optional[Record] = None,
|
||||
|
|
@ -78,11 +79,13 @@ class WeaviateVectorStoreComponent(CustomComponent):
|
|||
return pascal_case_word
|
||||
|
||||
index_name = _to_pascal_case(index_name) if index_name else None
|
||||
documents = []
|
||||
if not index_name:
|
||||
raise ValueError("Index name is required")
|
||||
documents: list[Document] = []
|
||||
for _input in inputs or []:
|
||||
if isinstance(_input, Record):
|
||||
documents.append(_input.to_lc_document())
|
||||
else:
|
||||
elif isinstance(_input, Document):
|
||||
documents.append(_input)
|
||||
|
||||
if documents and embedding is not None:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
import asyncio
|
||||
import uuid
|
||||
from collections import defaultdict, deque
|
||||
from functools import partial
|
||||
from itertools import chain
|
||||
from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Type, Union
|
||||
|
||||
|
|
@ -16,6 +18,7 @@ from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, R
|
|||
from langflow.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.schema import INPUT_FIELD_NAME, InputType
|
||||
from langflow.services.deps import get_chat_service
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.schema import ResultData
|
||||
|
|
@ -29,6 +32,7 @@ class Graph:
|
|||
nodes: List[Dict],
|
||||
edges: List[Dict[str, str]],
|
||||
flow_id: Optional[str] = None,
|
||||
user_id: Optional[str] = None,
|
||||
) -> None:
|
||||
"""
|
||||
Initializes a new instance of the Graph class.
|
||||
|
|
@ -44,6 +48,7 @@ class Graph:
|
|||
self._runs = 0
|
||||
self._updates = 0
|
||||
self.flow_id = flow_id
|
||||
self.user_id = user_id
|
||||
self._is_input_vertices: List[str] = []
|
||||
self._is_output_vertices: List[str] = []
|
||||
self._is_state_vertices: List[str] = []
|
||||
|
|
@ -164,13 +169,14 @@ class Graph:
|
|||
raise ValueError("Run ID not set")
|
||||
return self._run_id
|
||||
|
||||
def set_run_id(self, run_id: str):
|
||||
def set_run_id(self, run_id: str | uuid.UUID):
|
||||
"""
|
||||
Sets the ID of the current run.
|
||||
|
||||
Args:
|
||||
run_id (str): The run ID.
|
||||
"""
|
||||
run_id = str(run_id)
|
||||
for vertex in self.vertices:
|
||||
self.state_manager.subscribe(run_id, vertex.update_graph_state)
|
||||
self._run_id = run_id
|
||||
|
|
@ -446,7 +452,7 @@ class Graph:
|
|||
self.__init__(**state)
|
||||
|
||||
@classmethod
|
||||
def from_payload(cls, payload: Dict, flow_id: Optional[str] = None) -> "Graph":
|
||||
def from_payload(cls, payload: Dict, flow_id: Optional[str] = None, user_id: Optional[str] = None) -> "Graph":
|
||||
"""
|
||||
Creates a graph from a payload.
|
||||
|
||||
|
|
@ -461,7 +467,7 @@ class Graph:
|
|||
try:
|
||||
vertices = payload["nodes"]
|
||||
edges = payload["edges"]
|
||||
return cls(vertices, edges, flow_id)
|
||||
return cls(vertices, edges, flow_id, user_id)
|
||||
except KeyError as exc:
|
||||
logger.exception(exc)
|
||||
if "nodes" not in payload and "edges" not in payload:
|
||||
|
|
@ -748,31 +754,53 @@ class Graph:
|
|||
async def process(self, start_component_id: Optional[str] = None) -> "Graph":
|
||||
"""Processes the graph with vertices in each layer run in parallel."""
|
||||
|
||||
self.sort_vertices(start_component_id=start_component_id)
|
||||
vertices_layers = self.sorted_vertices_layers
|
||||
first_layer = self.sort_vertices(start_component_id=start_component_id)
|
||||
vertex_task_run_count: Dict[str, int] = {}
|
||||
for layer_index, layer in enumerate(vertices_layers):
|
||||
to_process = deque(first_layer)
|
||||
layer_index = 0
|
||||
chat_service = get_chat_service()
|
||||
run_id = uuid.uuid4()
|
||||
self.set_run_id(run_id)
|
||||
while to_process:
|
||||
current_batch = list(to_process) # Copy current deque items to a list
|
||||
to_process.clear() # Clear the deque for new items
|
||||
tasks = []
|
||||
for vertex_id in layer:
|
||||
for vertex_id in current_batch:
|
||||
vertex = self.get_vertex(vertex_id)
|
||||
lock = chat_service._cache_locks[self.run_id]
|
||||
set_cache_coro = partial(chat_service.set_cache, flow_id=self.run_id)
|
||||
task = asyncio.create_task(
|
||||
vertex.build(),
|
||||
self.build_vertex(
|
||||
lock=lock,
|
||||
set_cache_coro=set_cache_coro,
|
||||
vertex_id=vertex_id,
|
||||
user_id=self.user_id,
|
||||
inputs_dict={},
|
||||
),
|
||||
name=f"{vertex.display_name} Run {vertex_task_run_count.get(vertex_id, 0)}",
|
||||
)
|
||||
tasks.append(task)
|
||||
vertex_task_run_count[vertex_id] = vertex_task_run_count.get(vertex_id, 0) + 1
|
||||
|
||||
logger.debug(f"Running layer {layer_index} with {len(tasks)} tasks")
|
||||
await self._execute_tasks(tasks)
|
||||
next_runnable_vertices = await self._execute_tasks(tasks)
|
||||
to_process.extend(next_runnable_vertices)
|
||||
|
||||
logger.debug("Graph processing complete")
|
||||
return self
|
||||
|
||||
async def _execute_tasks(self, tasks):
|
||||
async def _execute_tasks(self, tasks: List[asyncio.Task]) -> List[str]:
|
||||
"""Executes tasks in parallel, handling exceptions for each task."""
|
||||
results = []
|
||||
for i, task in enumerate(asyncio.as_completed(tasks)):
|
||||
try:
|
||||
result = await task
|
||||
results.append(result)
|
||||
if isinstance(result, tuple) and len(result) == 7:
|
||||
# Get the next runnable vertices
|
||||
next_runnable_vertices = result[0]
|
||||
results.extend(next_runnable_vertices)
|
||||
else:
|
||||
raise ValueError(f"Invalid result: {result}")
|
||||
except Exception as e:
|
||||
# Log the exception along with the task name for easier debugging
|
||||
# task_name = task.get_name()
|
||||
|
|
|
|||
|
|
@ -161,7 +161,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -222,6 +222,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
|
|
@ -494,7 +494,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -555,6 +555,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
|
|
@ -651,7 +651,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -712,6 +712,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
|
|
@ -377,7 +377,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.memory import get_messages\n\n\nclass MemoryComponent(CustomComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n order = \"DESC\" if order == \"Descending\" else \"ASC\"\n if sender == \"Machine and User\":\n sender = None\n messages = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n",
|
||||
"value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -751,7 +751,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -812,6 +812,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
|
|
@ -884,7 +884,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -945,6 +945,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
@ -1270,7 +1271,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1331,6 +1332,7 @@
|
|||
"file_path": "",
|
||||
"password": false,
|
||||
"options": [
|
||||
"gpt-4-turbo-2024-04-09",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-4-0125-preview",
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -227,34 +227,11 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict):
|
|||
return class_object.from_documents(**params)
|
||||
|
||||
|
||||
def initialize_elasticsearch(class_object: Type[ElasticsearchStore], params: dict):
|
||||
"""Initialize elastic and return the class object"""
|
||||
if "index_name" not in params:
|
||||
raise ValueError("Elasticsearch Index must be provided in the params")
|
||||
if "es_url" not in params:
|
||||
raise ValueError("Elasticsearch URL must be provided in the params")
|
||||
if not docs_in_params(params):
|
||||
existing_index_params = {
|
||||
"embedding": params.pop("embedding"),
|
||||
}
|
||||
if "index_name" in params:
|
||||
existing_index_params["index_name"] = params.pop("index_name")
|
||||
if "es_url" in params:
|
||||
existing_index_params["es_url"] = params.pop("es_url")
|
||||
|
||||
return class_object.from_existing_index(**existing_index_params)
|
||||
# If there are docs in the params, create a new index
|
||||
if "texts" in params:
|
||||
params["documents"] = params.pop("texts")
|
||||
return class_object.from_documents(**params)
|
||||
|
||||
|
||||
vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
|
||||
"Pinecone": initialize_pinecone,
|
||||
"Chroma": initialize_chroma,
|
||||
"Qdrant": initialize_qdrant,
|
||||
"Weaviate": initialize_weaviate,
|
||||
"ElasticsearchStore": initialize_elasticsearch,
|
||||
"FAISS": initialize_faiss,
|
||||
"SupabaseVectorStore": initialize_supabase,
|
||||
"MongoDBAtlasVectorSearch": initialize_mongodb,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,5 @@
|
|||
from typing import Dict, Tuple
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph import Graph
|
||||
|
||||
|
||||
def get_memory_key(langchain_object):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ from fastapi.responses import FileResponse
|
|||
from fastapi.staticfiles import StaticFiles
|
||||
from loguru import logger
|
||||
from rich import print as rprint
|
||||
from starlette.middleware.base import BaseHTTPMiddleware
|
||||
|
||||
from langflow.api import router
|
||||
from langflow.initial_setup.setup import create_or_update_starter_projects
|
||||
|
|
@ -20,15 +21,38 @@ from langflow.services.utils import initialize_services, teardown_services
|
|||
from langflow.utils.logger import configure
|
||||
|
||||
|
||||
class JavaScriptMIMETypeMiddleware(BaseHTTPMiddleware):
|
||||
async def dispatch(self, request: Request, call_next):
|
||||
try:
|
||||
response = await call_next(request)
|
||||
except Exception as exc:
|
||||
logger.error(exc)
|
||||
raise exc
|
||||
if "files/" not in request.url.path and request.url.path.endswith(".js") and response.status_code == 200:
|
||||
response.headers["Content-Type"] = "text/javascript"
|
||||
return response
|
||||
|
||||
|
||||
def get_lifespan(fix_migration=False, socketio_server=None):
|
||||
from langflow.version import __version__ # type: ignore
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
nest_asyncio.apply()
|
||||
initialize_services(fix_migration=fix_migration, socketio_server=socketio_server)
|
||||
setup_llm_caching()
|
||||
LangfuseInstance.update()
|
||||
create_or_update_starter_projects()
|
||||
yield
|
||||
# Startup message
|
||||
if __version__:
|
||||
rprint(f"[bold green]Starting Langflow v{__version__}...[/bold green]")
|
||||
else:
|
||||
rprint("[bold green]Starting Langflow...[/bold green]")
|
||||
try:
|
||||
initialize_services(fix_migration=fix_migration, socketio_server=socketio_server)
|
||||
setup_llm_caching()
|
||||
LangfuseInstance.update()
|
||||
create_or_update_starter_projects()
|
||||
yield
|
||||
except Exception as exc:
|
||||
if "langflow migration --fix" not in str(exc):
|
||||
logger.error(exc)
|
||||
# Shutdown message
|
||||
rprint("[bold red]Shutting down Langflow...[/bold red]")
|
||||
teardown_services()
|
||||
|
|
@ -52,6 +76,7 @@ def create_app():
|
|||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
app.add_middleware(JavaScriptMIMETypeMiddleware)
|
||||
|
||||
@app.middleware("http")
|
||||
async def flatten_query_string_lists(request: Request, call_next):
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ from pathlib import Path
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph import Graph
|
||||
from langflow.graph.schema import RunOutputs
|
||||
from langflow.processing.process import process_tweaks, run_graph
|
||||
|
|
@ -101,6 +103,12 @@ def run_flow_from_json(
|
|||
List[RunOutputs]: A list of RunOutputs objects representing the results of running the flow.
|
||||
"""
|
||||
# Set all streaming to false
|
||||
try:
|
||||
import nest_asyncio # type: ignore
|
||||
|
||||
nest_asyncio.apply()
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not apply nest_asyncio: {e}")
|
||||
if tweaks is None:
|
||||
tweaks = {}
|
||||
tweaks["stream"] = False
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
|
||||
from langchain.agents import AgentExecutor
|
||||
from langchain.schema import AgentAction
|
||||
from loguru import logger
|
||||
|
|
@ -13,6 +14,7 @@ from langflow.schema.graph import InputValue, Tweaks
|
|||
from langflow.schema.schema import INPUT_FIELD_NAME
|
||||
from langflow.services.session.service import SessionService
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.api.v1.schemas import InputValueRequest
|
||||
|
||||
|
|
@ -243,7 +245,6 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None:
|
|||
|
||||
for tweak_name, tweak_value in node_tweaks.items():
|
||||
if tweak_name not in template_data:
|
||||
logger.warning(f"Node {node.get('id')} does not have a tweak named {tweak_name}")
|
||||
continue
|
||||
if tweak_name in template_data:
|
||||
key = "file_path" if template_data[tweak_name]["type"] == "file" else "value"
|
||||
|
|
@ -256,27 +257,33 @@ def apply_tweaks_on_vertex(vertex: Vertex, node_tweaks: Dict[str, Any]) -> None:
|
|||
vertex.params[tweak_name] = tweak_value
|
||||
|
||||
|
||||
def process_tweaks(graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]]) -> Dict[str, Any]:
|
||||
def process_tweaks(
|
||||
graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]], stream: bool = False
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
This function is used to tweak the graph data using the node id and the tweaks dict.
|
||||
|
||||
:param graph_data: The dictionary containing the graph data. It must contain a 'data' key with
|
||||
'nodes' as its child or directly contain 'nodes' key. Each node should have an 'id' and 'data'.
|
||||
:param tweaks: The dictionary containing the tweaks. The keys can be the node id or the name of the tweak.
|
||||
The values can be a dictionary containing the tweaks for the node or the value of the tweak.
|
||||
The values can be a dictionary containing the tweaks for the node or the value of the tweak.
|
||||
:param stream: A boolean flag indicating whether streaming should be deactivated across all components or not. Default is False.
|
||||
:return: The modified graph_data dictionary.
|
||||
|
||||
:raises ValueError: If the input is not in the expected format.
|
||||
"""
|
||||
tweaks_dict = {}
|
||||
if not isinstance(tweaks, dict):
|
||||
tweaks = tweaks.model_dump()
|
||||
|
||||
nodes = validate_input(graph_data, tweaks)
|
||||
tweaks_dict = tweaks.model_dump()
|
||||
else:
|
||||
tweaks_dict = tweaks
|
||||
if "stream" not in tweaks_dict:
|
||||
tweaks_dict["stream"] = stream
|
||||
nodes = validate_input(graph_data, tweaks_dict)
|
||||
nodes_map = {node.get("id"): node for node in nodes}
|
||||
nodes_display_name_map = {node.get("data", {}).get("node", {}).get("display_name"): node for node in nodes}
|
||||
|
||||
all_nodes_tweaks = {}
|
||||
for key, value in tweaks.items():
|
||||
for key, value in tweaks_dict.items():
|
||||
if isinstance(value, dict):
|
||||
if node := nodes_map.get(key):
|
||||
apply_tweaks(node, value)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
from typing import List, Optional, Union
|
||||
from typing import Any, List, Optional, Union
|
||||
|
||||
from pydantic import BaseModel, Field, RootModel
|
||||
|
||||
from langflow.schema.schema import InputType
|
||||
from pydantic import BaseModel, Field, RootModel
|
||||
|
||||
|
||||
class InputValue(BaseModel):
|
||||
|
|
@ -14,7 +15,7 @@ class InputValue(BaseModel):
|
|||
|
||||
|
||||
class Tweaks(RootModel):
|
||||
root: dict[str, Union[str, dict[str, str]]] = Field(
|
||||
root: dict[str, Union[str, dict[str, Any]]] = Field(
|
||||
description="A dictionary of tweaks to adjust the flow's execution. Allows customizing flow behavior dynamically. All tweaks are overridden by the input values.",
|
||||
)
|
||||
model_config = {
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
import copy
|
||||
from typing import Literal, Optional
|
||||
from typing import Literal, Optional, cast
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from pydantic import BaseModel, model_validator
|
||||
|
||||
|
||||
|
|
@ -54,6 +55,21 @@ class Record(BaseModel):
|
|||
data["text"] = document.page_content
|
||||
return cls(data=data, text_key="text")
|
||||
|
||||
@classmethod
|
||||
def from_lc_message(cls, message: BaseMessage) -> "Record":
|
||||
"""
|
||||
Converts a BaseMessage to a Record.
|
||||
|
||||
Args:
|
||||
message (BaseMessage): The BaseMessage to convert.
|
||||
|
||||
Returns:
|
||||
Record: The converted Record.
|
||||
"""
|
||||
data: dict = {"text": message.content}
|
||||
data["metadata"] = cast(dict, message.to_json())
|
||||
return cls(data=data, text_key="text")
|
||||
|
||||
def __add__(self, other: "Record") -> "Record":
|
||||
"""
|
||||
Combines the data of two records by attempting to add values for overlapping keys
|
||||
|
|
@ -85,6 +101,26 @@ class Record(BaseModel):
|
|||
text = self.data.pop(self.text_key, self.default_value)
|
||||
return Document(page_content=text, metadata=self.data)
|
||||
|
||||
def to_lc_message(self) -> BaseMessage:
|
||||
"""
|
||||
Converts the Record to a BaseMessage.
|
||||
|
||||
Returns:
|
||||
BaseMessage: The converted BaseMessage.
|
||||
"""
|
||||
# The idea of this function is to be a helper to convert a Record to a BaseMessage
|
||||
# It will use the "sender" key to determine if the message is Human or AI
|
||||
# If the key is not present, it will default to AI
|
||||
# But first we check if all required keys are present in the data dictionary
|
||||
# they are: "text", "sender"
|
||||
if not all(key in self.data for key in ["text", "sender"]):
|
||||
raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
|
||||
sender = self.data.get("sender", "Machine")
|
||||
text = self.data.get("text", "")
|
||||
if sender == "User":
|
||||
return HumanMessage(content=text)
|
||||
return AIMessage(content=text)
|
||||
|
||||
def __getattr__(self, key):
|
||||
"""
|
||||
Allows attribute-like access to the data dictionary.
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID
|
|||
api_key=generated_api_key,
|
||||
name=api_key_create.name,
|
||||
user_id=user_id,
|
||||
created_at=api_key_create.created_at or datetime.datetime.now(datetime.timezone.utc),
|
||||
)
|
||||
|
||||
session.add(api_key)
|
||||
|
|
|
|||
|
|
@ -3,17 +3,18 @@ from typing import TYPE_CHECKING, Optional
|
|||
from uuid import UUID, uuid4
|
||||
|
||||
from pydantic import field_validator, validator
|
||||
from sqlmodel import Field, Relationship, SQLModel, Column, func, DateTime
|
||||
from sqlmodel import Column, DateTime, Field, Relationship, SQLModel, func
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.services.database.models.user import User
|
||||
|
||||
|
||||
def utc_now():
|
||||
return datetime.now(timezone.utc)
|
||||
|
||||
|
||||
class ApiKeyBase(SQLModel):
|
||||
name: Optional[str] = Field(index=True, nullable=True, default=None)
|
||||
created_at: datetime = Field(
|
||||
default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
|
||||
)
|
||||
last_used_at: Optional[datetime] = Field(default=None, nullable=True)
|
||||
total_uses: int = Field(default=0)
|
||||
is_active: bool = Field(default=True)
|
||||
|
|
@ -21,7 +22,9 @@ class ApiKeyBase(SQLModel):
|
|||
|
||||
class ApiKey(ApiKeyBase, table=True):
|
||||
id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
|
||||
|
||||
created_at: Optional[datetime] = Field(
|
||||
default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
|
||||
)
|
||||
api_key: str = Field(index=True, unique=True)
|
||||
# User relationship
|
||||
# Delete API keys when user is deleted
|
||||
|
|
@ -34,10 +37,11 @@ class ApiKey(ApiKeyBase, table=True):
|
|||
class ApiKeyCreate(ApiKeyBase):
|
||||
api_key: Optional[str] = None
|
||||
user_id: Optional[UUID] = None
|
||||
created_at: Optional[datetime] = Field(default_factory=utc_now)
|
||||
|
||||
@field_validator("created_at", mode="before")
|
||||
def set_created_at(cls, v):
|
||||
return v or datetime.now(timezone.utc)
|
||||
return v or utc_now()
|
||||
|
||||
|
||||
class UnmaskedApiKeyRead(ApiKeyBase):
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ class Variable(VariableBase, table=True):
|
|||
description="Unique ID for the variable",
|
||||
)
|
||||
# name is unique per user
|
||||
created_at: datetime = Field(
|
||||
created_at: Optional[datetime] = Field(
|
||||
default=None,
|
||||
sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=True),
|
||||
description="Creation time of the variable",
|
||||
|
|
|
|||
|
|
@ -133,7 +133,7 @@ class DatabaseService(Service):
|
|||
alembic_cfg = Config(stdout=buffer)
|
||||
# alembic_cfg.attributes["connection"] = session
|
||||
alembic_cfg.set_main_option("script_location", str(self.script_location))
|
||||
alembic_cfg.set_main_option("sqlalchemy.url", self.database_url)
|
||||
alembic_cfg.set_main_option("sqlalchemy.url", self.database_url.replace('%', '%%'))
|
||||
|
||||
should_initialize_alembic = False
|
||||
with Session(self.engine) as session:
|
||||
|
|
|
|||
|
|
@ -101,10 +101,16 @@ def add_row_to_table(
|
|||
conn.execute(insert_sql, values)
|
||||
except Exception as e:
|
||||
# Log values types
|
||||
column_error_message = ""
|
||||
for key, value in validated_dict.items():
|
||||
logger.error(f"{key}: {type(value)}")
|
||||
if value in str(e):
|
||||
column_error_message = f"Column: {key} Value: {value} Error: {e}"
|
||||
|
||||
logger.error(f"Error adding row to table: {e}")
|
||||
if column_error_message:
|
||||
logger.error(f"Error adding row to {table_name}: {column_error_message}")
|
||||
else:
|
||||
logger.error(f"Error adding row to {table_name}: {e}")
|
||||
|
||||
|
||||
async def log_message(
|
||||
|
|
|
|||
|
|
@ -121,7 +121,7 @@ class Settings(BaseSettings):
|
|||
|
||||
# Define the app name and author
|
||||
app_name = "langflow"
|
||||
app_author = "logspace"
|
||||
app_author = "langflow"
|
||||
|
||||
# Get the cache directory for the application
|
||||
cache_dir = user_cache_dir(app_name, app_author)
|
||||
|
|
|
|||
|
|
@ -163,7 +163,6 @@ def initialize_services(fix_migration: bool = False, socketio_server=None):
|
|||
try:
|
||||
initialize_database(fix_migration=fix_migration)
|
||||
except Exception as exc:
|
||||
logger.error(exc)
|
||||
raise exc
|
||||
setup_superuser(get_service(ServiceType.SETTINGS_SERVICE), next(get_session()))
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -54,22 +54,24 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None,
|
|||
|
||||
if not log_file:
|
||||
cache_dir = Path(user_cache_dir("langflow"))
|
||||
logger.debug(f"Cache directory: {cache_dir}")
|
||||
log_file = cache_dir / "langflow.log"
|
||||
logger.debug(f"Log file: {log_file}")
|
||||
try:
|
||||
log_file = Path(log_file)
|
||||
log_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
log_file = Path(log_file)
|
||||
log_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logger.add(
|
||||
sink=str(log_file),
|
||||
level=log_level.upper(),
|
||||
format=log_format,
|
||||
rotation="10 MB", # Log rotation based on file size
|
||||
serialize=True,
|
||||
)
|
||||
logger.add(
|
||||
sink=str(log_file),
|
||||
level=log_level.upper(),
|
||||
format=log_format,
|
||||
rotation="10 MB", # Log rotation based on file size
|
||||
serialize=True,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.error(f"Error setting up log file: {exc}")
|
||||
|
||||
logger.debug(f"Logger set up with log level: {log_level}")
|
||||
if log_file:
|
||||
logger.debug(f"Log file: {log_file}")
|
||||
|
||||
setup_uvicorn_logger()
|
||||
setup_gunicorn_logger()
|
||||
|
|
|
|||
419
src/backend/base/poetry.lock
generated
419
src/backend/base/poetry.lock
generated
|
|
@ -2,87 +2,87 @@
|
|||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.9.4"
|
||||
version = "3.9.5"
|
||||
description = "Async http client/server framework (asyncio)"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:76d32588ef7e4a3f3adff1956a0ba96faabbdee58f2407c122dd45aa6e34f372"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:56181093c10dbc6ceb8a29dfeea1e815e1dfdc020169203d87fd8d37616f73f9"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7a5b676d3c65e88b3aca41816bf72831898fcd73f0cbb2680e9d88e819d1e4d"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1df528a85fb404899d4207a8d9934cfd6be626e30e5d3a5544a83dbae6d8a7e"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f595db1bceabd71c82e92df212dd9525a8a2c6947d39e3c994c4f27d2fe15b11"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c0b09d76e5a4caac3d27752027fbd43dc987b95f3748fad2b924a03fe8632ad"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:689eb4356649ec9535b3686200b231876fb4cab4aca54e3bece71d37f50c1d13"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a3666cf4182efdb44d73602379a66f5fdfd5da0db5e4520f0ac0dcca644a3497"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b65b0f8747b013570eea2f75726046fa54fa8e0c5db60f3b98dd5d161052004a"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:a1885d2470955f70dfdd33a02e1749613c5a9c5ab855f6db38e0b9389453dce7"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:0593822dcdb9483d41f12041ff7c90d4d1033ec0e880bcfaf102919b715f47f1"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:47f6eb74e1ecb5e19a78f4a4228aa24df7fbab3b62d4a625d3f41194a08bd54f"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c8b04a3dbd54de6ccb7604242fe3ad67f2f3ca558f2d33fe19d4b08d90701a89"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-win32.whl", hash = "sha256:8a78dfb198a328bfb38e4308ca8167028920fb747ddcf086ce706fbdd23b2926"},
|
||||
{file = "aiohttp-3.9.4-cp310-cp310-win_amd64.whl", hash = "sha256:e78da6b55275987cbc89141a1d8e75f5070e577c482dd48bd9123a76a96f0bbb"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:c111b3c69060d2bafc446917534150fd049e7aedd6cbf21ba526a5a97b4402a5"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:efbdd51872cf170093998c87ccdf3cb5993add3559341a8e5708bcb311934c94"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7bfdb41dc6e85d8535b00d73947548a748e9534e8e4fddd2638109ff3fb081df"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2bd9d334412961125e9f68d5b73c1d0ab9ea3f74a58a475e6b119f5293eee7ba"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:35d78076736f4a668d57ade00c65d30a8ce28719d8a42471b2a06ccd1a2e3063"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:824dff4f9f4d0f59d0fa3577932ee9a20e09edec8a2f813e1d6b9f89ced8293f"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52b8b4e06fc15519019e128abedaeb56412b106ab88b3c452188ca47a25c4093"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eae569fb1e7559d4f3919965617bb39f9e753967fae55ce13454bec2d1c54f09"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:69b97aa5792428f321f72aeb2f118e56893371f27e0b7d05750bcad06fc42ca1"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4d79aad0ad4b980663316f26d9a492e8fab2af77c69c0f33780a56843ad2f89e"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:d6577140cd7db19e430661e4b2653680194ea8c22c994bc65b7a19d8ec834403"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:9860d455847cd98eb67897f5957b7cd69fbcb436dd3f06099230f16a66e66f79"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:69ff36d3f8f5652994e08bd22f093e11cfd0444cea310f92e01b45a4e46b624e"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-win32.whl", hash = "sha256:e27d3b5ed2c2013bce66ad67ee57cbf614288bda8cdf426c8d8fe548316f1b5f"},
|
||||
{file = "aiohttp-3.9.4-cp311-cp311-win_amd64.whl", hash = "sha256:d6a67e26daa686a6fbdb600a9af8619c80a332556245fa8e86c747d226ab1a1e"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:c5ff8ff44825736a4065d8544b43b43ee4c6dd1530f3a08e6c0578a813b0aa35"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d12a244627eba4e9dc52cbf924edef905ddd6cafc6513849b4876076a6f38b0e"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:dcad56c8d8348e7e468899d2fb3b309b9bc59d94e6db08710555f7436156097f"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f7e69a7fd4b5ce419238388e55abd220336bd32212c673ceabc57ccf3d05b55"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c4870cb049f10d7680c239b55428916d84158798eb8f353e74fa2c98980dcc0b"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3b2feaf1b7031ede1bc0880cec4b0776fd347259a723d625357bb4b82f62687b"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:939393e8c3f0a5bcd33ef7ace67680c318dc2ae406f15e381c0054dd658397de"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d2334e387b2adcc944680bebcf412743f2caf4eeebd550f67249c1c3696be04"},
|
||||
{file = "aiohttp-3.9.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e0198ea897680e480845ec0ffc5a14e8b694e25b3f104f63676d55bf76a82f1a"},
|
||||
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|
||||
{file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3916c8692dbd9d55c523374a3b8213e628424d19116ac4308e434dbf6d95bbdd"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:8d1964eb7617907c792ca00b341b5ec3e01ae8c280825deadbbd678447b127e1"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:d5ab8e1f6bee051a4bf6195e38a5c13e5e161cb7bad83d8854524798bd9fcd6e"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:52c27110f3862a1afbcb2af4281fc9fdc40327fa286c4625dfee247c3ba90156"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:7f64cbd44443e80094309875d4f9c71d0401e966d191c3d469cde4642bc2e031"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8b4f72fbb66279624bfe83fd5eb6aea0022dad8eec62b71e7bf63ee1caadeafe"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-win32.whl", hash = "sha256:6380c039ec52866c06d69b5c7aad5478b24ed11696f0e72f6b807cfb261453da"},
|
||||
{file = "aiohttp-3.9.5-cp38-cp38-win_amd64.whl", hash = "sha256:da22dab31d7180f8c3ac7c7635f3bcd53808f374f6aa333fe0b0b9e14b01f91a"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:1732102949ff6087589408d76cd6dea656b93c896b011ecafff418c9661dc4ed"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c6021d296318cb6f9414b48e6a439a7f5d1f665464da507e8ff640848ee2a58a"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:239f975589a944eeb1bad26b8b140a59a3a320067fb3cd10b75c3092405a1372"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b7b30258348082826d274504fbc7c849959f1989d86c29bc355107accec6cfb"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cd2adf5c87ff6d8b277814a28a535b59e20bfea40a101db6b3bdca7e9926bc24"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9a3d838441bebcf5cf442700e3963f58b5c33f015341f9ea86dcd7d503c07e2"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3a1ae66e3d0c17cf65c08968a5ee3180c5a95920ec2731f53343fac9bad106"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9c69e77370cce2d6df5d12b4e12bdcca60c47ba13d1cbbc8645dd005a20b738b"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0cbf56238f4bbf49dab8c2dc2e6b1b68502b1e88d335bea59b3f5b9f4c001475"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:d1469f228cd9ffddd396d9948b8c9cd8022b6d1bf1e40c6f25b0fb90b4f893ed"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:45731330e754f5811c314901cebdf19dd776a44b31927fa4b4dbecab9e457b0c"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:3fcb4046d2904378e3aeea1df51f697b0467f2aac55d232c87ba162709478c46"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8cf142aa6c1a751fcb364158fd710b8a9be874b81889c2bd13aa8893197455e2"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-win32.whl", hash = "sha256:7b179eea70833c8dee51ec42f3b4097bd6370892fa93f510f76762105568cf09"},
|
||||
{file = "aiohttp-3.9.5-cp39-cp39-win_amd64.whl", hash = "sha256:38d80498e2e169bc61418ff36170e0aad0cd268da8b38a17c4cf29d254a8b3f1"},
|
||||
{file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -517,13 +517,13 @@ test-randomorder = ["pytest-randomly"]
|
|||
|
||||
[[package]]
|
||||
name = "dataclasses-json"
|
||||
version = "0.6.4"
|
||||
version = "0.6.5"
|
||||
description = "Easily serialize dataclasses to and from JSON."
|
||||
optional = false
|
||||
python-versions = ">=3.7,<4.0"
|
||||
python-versions = "<4.0,>=3.7"
|
||||
files = [
|
||||
{file = "dataclasses_json-0.6.4-py3-none-any.whl", hash = "sha256:f90578b8a3177f7552f4e1a6e535e84293cd5da421fcce0642d49c0d7bdf8df2"},
|
||||
{file = "dataclasses_json-0.6.4.tar.gz", hash = "sha256:73696ebf24936560cca79a2430cbc4f3dd23ac7bf46ed17f38e5e5e7657a6377"},
|
||||
{file = "dataclasses_json-0.6.5-py3-none-any.whl", hash = "sha256:f49c77aa3a85cac5bf5b7f65f4790ca0d2be8ef4d92c75e91ba0103072788a39"},
|
||||
{file = "dataclasses_json-0.6.5.tar.gz", hash = "sha256:1c287594d9fcea72dc42d6d3836cf14848c2dc5ce88f65ed61b36b57f515fe26"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -624,13 +624,13 @@ gmpy2 = ["gmpy2"]
|
|||
|
||||
[[package]]
|
||||
name = "emoji"
|
||||
version = "2.11.0"
|
||||
version = "2.11.1"
|
||||
description = "Emoji for Python"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
|
||||
files = [
|
||||
{file = "emoji-2.11.0-py2.py3-none-any.whl", hash = "sha256:63fc9107f06c6c2e48e5078ce9575cef98518f5ac09474f6148a43e989989582"},
|
||||
{file = "emoji-2.11.0.tar.gz", hash = "sha256:772eaa30f4e0b1ce95148a092df4c7dc97644532c03225326b0fd05e8a9f72a3"},
|
||||
{file = "emoji-2.11.1-py2.py3-none-any.whl", hash = "sha256:b7ba25299bbf520cc8727848ae66b986da32aee27dc2887eaea2bff07226ce49"},
|
||||
{file = "emoji-2.11.1.tar.gz", hash = "sha256:062ff0b3154b6219143f8b9f4b3e5c64c35bc2b146e6e2349ab5f29e218ce1ee"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -638,13 +638,13 @@ dev = ["coverage", "coveralls", "pytest"]
|
|||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.2.0"
|
||||
version = "1.2.1"
|
||||
description = "Backport of PEP 654 (exception groups)"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
|
||||
{file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
|
||||
{file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
|
||||
{file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -652,13 +652,13 @@ test = ["pytest (>=6)"]
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.110.1"
|
||||
version = "0.110.2"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fastapi-0.110.1-py3-none-any.whl", hash = "sha256:5df913203c482f820d31f48e635e022f8cbfe7350e4830ef05a3163925b1addc"},
|
||||
{file = "fastapi-0.110.1.tar.gz", hash = "sha256:6feac43ec359dfe4f45b2c18ec8c94edb8dc2dfc461d417d9e626590c071baad"},
|
||||
{file = "fastapi-0.110.2-py3-none-any.whl", hash = "sha256:239403f2c0a3dda07a9420f95157a7f014ddb2b770acdbc984f9bdf3ead7afdb"},
|
||||
{file = "fastapi-0.110.2.tar.gz", hash = "sha256:b53d673652da3b65e8cd787ad214ec0fe303cad00d2b529b86ce7db13f17518d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -828,22 +828,23 @@ test = ["objgraph", "psutil"]
|
|||
|
||||
[[package]]
|
||||
name = "gunicorn"
|
||||
version = "21.2.0"
|
||||
version = "22.0.0"
|
||||
description = "WSGI HTTP Server for UNIX"
|
||||
optional = false
|
||||
python-versions = ">=3.5"
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "gunicorn-21.2.0-py3-none-any.whl", hash = "sha256:3213aa5e8c24949e792bcacfc176fef362e7aac80b76c56f6b5122bf350722f0"},
|
||||
{file = "gunicorn-21.2.0.tar.gz", hash = "sha256:88ec8bff1d634f98e61b9f65bc4bf3cd918a90806c6f5c48bc5603849ec81033"},
|
||||
{file = "gunicorn-22.0.0-py3-none-any.whl", hash = "sha256:350679f91b24062c86e386e198a15438d53a7a8207235a78ba1b53df4c4378d9"},
|
||||
{file = "gunicorn-22.0.0.tar.gz", hash = "sha256:4a0b436239ff76fb33f11c07a16482c521a7e09c1ce3cc293c2330afe01bec63"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
packaging = "*"
|
||||
|
||||
[package.extras]
|
||||
eventlet = ["eventlet (>=0.24.1)"]
|
||||
eventlet = ["eventlet (>=0.24.1,!=0.36.0)"]
|
||||
gevent = ["gevent (>=1.4.0)"]
|
||||
setproctitle = ["setproctitle"]
|
||||
testing = ["coverage", "eventlet", "gevent", "pytest", "pytest-cov"]
|
||||
tornado = ["tornado (>=0.2)"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1063,19 +1064,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.0.32"
|
||||
version = "0.0.34"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_community-0.0.32-py3-none-any.whl", hash = "sha256:406977009999952d0705de3806de2b4867e9bb8eda8ca154a59c7a8ed58da38d"},
|
||||
{file = "langchain_community-0.0.32.tar.gz", hash = "sha256:1510217d646c8380f54e9850351f6d2a0b0dd73c501b666c6f4b40baa8160b29"},
|
||||
{file = "langchain_community-0.0.34-py3-none-any.whl", hash = "sha256:bc13b21a44bbfca01bff8b35c10a26d71485b57c1d284f499b577ba6e1a5d84a"},
|
||||
{file = "langchain_community-0.0.34.tar.gz", hash = "sha256:96e9a807d9b4777820df5a970996f6bf3ad5632137bf0f4d863bd832bdeb2b0f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.8.3,<4.0.0"
|
||||
dataclasses-json = ">=0.5.7,<0.7"
|
||||
langchain-core = ">=0.1.41,<0.2.0"
|
||||
langchain-core = ">=0.1.45,<0.2.0"
|
||||
langsmith = ">=0.1.0,<0.2.0"
|
||||
numpy = ">=1,<2"
|
||||
PyYAML = ">=5.3"
|
||||
|
|
@ -1089,13 +1090,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
|
|||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.1.42"
|
||||
version = "0.1.46"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_core-0.1.42-py3-none-any.whl", hash = "sha256:c5653ffa08a44f740295c157a24c0def4a753333f6a2c41f76bf431cd00be8b5"},
|
||||
{file = "langchain_core-0.1.42.tar.gz", hash = "sha256:40751bf60ea5d8e2b2efe65290db434717ee3834870c002e40e2811f09d814e6"},
|
||||
{file = "langchain_core-0.1.46-py3-none-any.whl", hash = "sha256:1c0befcd2665dd4aa153318aa9bf729071644b4c179e491769b8e583b4bf7441"},
|
||||
{file = "langchain_core-0.1.46.tar.gz", hash = "sha256:17c416349f5c7a9808e70e3725749a3a2df5088f1ecca045c883871aa95f9c9e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1144,15 +1145,30 @@ langchain-core = ">=0.1.28,<0.2.0"
|
|||
[package.extras]
|
||||
extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchainhub"
|
||||
version = "0.1.15"
|
||||
description = "The LangChain Hub API client"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langchainhub-0.1.15-py3-none-any.whl", hash = "sha256:89a0951abd1db255e91c6d545d092a598fc255aa865d1ffc3ce8f93bbeae60e7"},
|
||||
{file = "langchainhub-0.1.15.tar.gz", hash = "sha256:fa3ff81a31946860f84c119f1e2f6b7c7707e2bd7ed2394a7313b286d59f3bda"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
requests = ">=2,<3"
|
||||
types-requests = ">=2.31.0.2,<3.0.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.45"
|
||||
version = "0.1.51"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langsmith-0.1.45-py3-none-any.whl", hash = "sha256:5a5b7fafe767fa28826c925f175875c09bf5368bfdb141286381a94bf737e6ef"},
|
||||
{file = "langsmith-0.1.45.tar.gz", hash = "sha256:713206107df636db1edf30867d64b92495afb1f09d2fee0857a77b7a8ee083d5"},
|
||||
{file = "langsmith-0.1.51-py3-none-any.whl", hash = "sha256:1e7363a3f472ecf02a1d91f6dbacde25519554b98c490be71716fcffaab0ca6b"},
|
||||
{file = "langsmith-0.1.51.tar.gz", hash = "sha256:b99b40a8c00e66174540865caa61412622fa1dc4f02602965364919c90528f97"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1927,18 +1943,19 @@ xmp = ["defusedxml"]
|
|||
|
||||
[[package]]
|
||||
name = "platformdirs"
|
||||
version = "4.2.0"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
|
||||
version = "4.2.1"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
|
||||
{file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
|
||||
{file = "platformdirs-4.2.1-py3-none-any.whl", hash = "sha256:17d5a1161b3fd67b390023cb2d3b026bbd40abde6fdb052dfbd3a29c3ba22ee1"},
|
||||
{file = "platformdirs-4.2.1.tar.gz", hash = "sha256:031cd18d4ec63ec53e82dceaac0417d218a6863f7745dfcc9efe7793b7039bdf"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
|
||||
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
|
||||
type = ["mypy (>=1.8)"]
|
||||
|
||||
[[package]]
|
||||
name = "pyasn1"
|
||||
|
|
@ -1964,18 +1981,18 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.7.0"
|
||||
version = "2.7.1"
|
||||
description = "Data validation using Python type hints"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
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[package.dependencies]
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|
@ -2568,6 +2585,20 @@ rich = ">=10.11.0"
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shellingham = ">=1.3.0"
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typing-extensions = ">=3.7.4.3"
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[[package]]
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description = "Typing stubs for requests"
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optional = false
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python-versions = ">=3.8"
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[package.dependencies]
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[[package]]
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name = "typing-extensions"
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version = "4.11.0"
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|
@ -2861,4 +2892,4 @@ local = []
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[metadata]
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|
|
@ -1,16 +1,16 @@
|
|||
[tool.poetry]
|
||||
name = "langflow-base"
|
||||
version = "0.0.30"
|
||||
version = "0.0.39"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
"Carlos Coelho <carlos@logspace.ai>",
|
||||
"Carlos Coelho <carlos@langflow.org>",
|
||||
"Cristhian Zanforlin <cristhian.lousa@gmail.com>",
|
||||
"Gabriel Almeida <gabriel@logspace.ai>",
|
||||
"Gabriel Almeida <gabriel@langflow.org>",
|
||||
"Igor Carvalho <igorr.ackerman@gmail.com>",
|
||||
"Lucas Eduoli <lucaseduoli@gmail.com>",
|
||||
"Otávio Anovazzi <otavio2204@gmail.com>",
|
||||
"Rodrigo Nader <rodrigo@logspace.ai>",
|
||||
"Rodrigo Nader <rodrigo@langflow.org>",
|
||||
]
|
||||
repository = "https://github.com/langflow-ai/langflow"
|
||||
license = "MIT"
|
||||
|
|
@ -29,8 +29,9 @@ python = ">=3.10,<3.12"
|
|||
fastapi = "^0.110.1"
|
||||
httpx = "*"
|
||||
uvicorn = "^0.29.0"
|
||||
gunicorn = "^21.2.0"
|
||||
langchain = "~0.1.14"
|
||||
gunicorn = "^22.0.0"
|
||||
langchain = "~0.1.16"
|
||||
langchainhub = "~0.1.15"
|
||||
sqlmodel = "^0.0.16"
|
||||
loguru = "^0.7.1"
|
||||
rich = "^13.7.0"
|
||||
|
|
|
|||
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