Release 0.6.4 (#1268)
This PR: - Updates Vectara component by @JAtharva22 - Adds Ollama components by @yamonkjd - Fixes bugs in the canvas by @Cristhianzl - adds Docker image builds on release.
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
57c6dad343
17 changed files with 1288 additions and 793 deletions
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@ -1,2 +1,7 @@
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.venv/
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**/aws
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**/aws
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# node_modules
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**/node_modules/
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dist/
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**/build/
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src/backend/langflow/frontend
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16
.github/workflows/pre-release.yml
vendored
16
.github/workflows/pre-release.yml
vendored
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@ -47,3 +47,19 @@ jobs:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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poetry publish
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Login to Docker Hub
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uses: docker/login-action@v3
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Build and push
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uses: docker/build-push-action@v5
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with:
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context: .
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push: true
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file: ./build_and_push.Dockerfile
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tags: logspace/langflow:${{ steps.check-version.outputs.version }}
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18
.github/workflows/release.yml
vendored
18
.github/workflows/release.yml
vendored
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@ -45,3 +45,21 @@ jobs:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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poetry publish
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Login to Docker Hub
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uses: docker/login-action@v3
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Build and push
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uses: docker/build-push-action@v5
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with:
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context: .
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push: true
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file: ./build_and_push.Dockerfile
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tags: |
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logspace/langflow:${{ steps.check-version.outputs.version }}
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logspace/langflow:latest
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79
build_and_push.Dockerfile
Normal file
79
build_and_push.Dockerfile
Normal file
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@ -0,0 +1,79 @@
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# syntax=docker/dockerfile:1
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# Keep this syntax directive! It's used to enable Docker BuildKit
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# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
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# but I try to keep it updated (see history)
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################################
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# PYTHON-BASE
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# Sets up all our shared environment variables
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################################
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FROM python:3.10-slim as python-base
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# python
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ENV PYTHONUNBUFFERED=1 \
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# prevents python creating .pyc files
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PYTHONDONTWRITEBYTECODE=1 \
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\
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# pip
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PIP_DISABLE_PIP_VERSION_CHECK=on \
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PIP_DEFAULT_TIMEOUT=100 \
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\
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# poetry
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# https://python-poetry.org/docs/configuration/#using-environment-variables
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POETRY_VERSION=1.7.1 \
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# make poetry install to this location
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POETRY_HOME="/opt/poetry" \
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# make poetry create the virtual environment in the project's root
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# it gets named `.venv`
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POETRY_VIRTUALENVS_IN_PROJECT=true \
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# do not ask any interactive question
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POETRY_NO_INTERACTION=1 \
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\
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# paths
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# this is where our requirements + virtual environment will live
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PYSETUP_PATH="/opt/pysetup" \
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VENV_PATH="/opt/pysetup/.venv"
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# prepend poetry and venv to path
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ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
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################################
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# BUILDER-BASE
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# Used to build deps + create our virtual environment
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################################
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FROM python-base as builder-base
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RUN
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RUN apt-get update \
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&& apt-get install --no-install-recommends -y \
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# deps for installing poetry
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curl \
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# deps for building python deps
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build-essential \
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# npm
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npm
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# Now we need to copy the entire project into the image
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WORKDIR /app
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COPY pyproject.toml poetry.lock ./
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COPY src ./src
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COPY Makefile ./
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COPY README.md ./
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RUN curl -sSL https://install.python-poetry.org | python3 - && make build
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# Final stage for the application
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FROM python-base as final
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# Copy virtual environment and built .tar.gz from builder base
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COPY --from=builder-base /app/dist/*.tar.gz ./
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# Install the package from the .tar.gz
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RUN pip install *.tar.gz
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WORKDIR /app
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CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
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991
poetry.lock
generated
991
poetry.lock
generated
File diff suppressed because it is too large
Load diff
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "langflow"
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version = "0.6.3"
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version = "0.6.4"
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description = "A Python package with a built-in web application"
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authors = ["Logspace <contact@logspace.ai>"]
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maintainers = [
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@ -28,15 +28,15 @@ langflow = "langflow.__main__:main"
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python = ">=3.9,<3.11"
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fastapi = "^0.104.0"
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uvicorn = "^0.23.0"
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fastapi = "^0.108.0"
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uvicorn = "^0.25.0"
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beautifulsoup4 = "^4.12.2"
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google-search-results = "^2.4.1"
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google-api-python-client = "^2.79.0"
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typer = "^0.9.0"
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gunicorn = "^21.2.0"
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langchain = "~0.0.345"
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openai = "^1.3.6"
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openai = "^1.6.1"
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pandas = "2.0.3"
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chromadb = "^0.4.0"
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huggingface-hub = { version = "^0.19.0", extras = ["inference"] }
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@ -47,31 +47,31 @@ unstructured = "^0.11.0"
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pypdf = "^3.17.0"
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lxml = "^4.9.2"
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pysrt = "^1.1.2"
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fake-useragent = "^1.3.0"
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fake-useragent = "^1.4.0"
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docstring-parser = "^0.15"
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psycopg2-binary = "^2.9.6"
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pyarrow = "^14.0.0"
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tiktoken = "~0.5.0"
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wikipedia = "^1.4.0"
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qdrant-client = "^1.4.0"
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qdrant-client = "^1.7.0"
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websockets = "^10.3"
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weaviate-client = "^3.23.0"
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weaviate-client = "^3.26.0"
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jina = "*"
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sentence-transformers = { version = "^2.2.2", optional = true }
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ctransformers = { version = "^0.2.10", optional = true }
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cohere = "^4.37.0"
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cohere = "^4.39.0"
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python-multipart = "^0.0.6"
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sqlmodel = "^0.0.14"
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faiss-cpu = "^1.7.4"
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anthropic = "^0.7.0"
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anthropic = "^0.8.0"
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orjson = "3.9.3"
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multiprocess = "^0.70.14"
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cachetools = "^5.3.1"
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types-cachetools = "^5.3.0.5"
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platformdirs = "^4.1.0"
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pinecone-client = "^2.2.2"
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pymongo = "^4.5.0"
|
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supabase = "^2.0.3"
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||||
pymongo = "^4.6.0"
|
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supabase = "^2.3.0"
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certifi = "^2023.11.17"
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google-cloud-aiplatform = "^1.36.0"
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psycopg = "^3.1.9"
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@ -81,13 +81,13 @@ langchain-experimental = "*"
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celery = { extras = ["redis"], version = "^5.3.6", optional = true }
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redis = { version = "^4.6.0", optional = true }
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flower = { version = "^2.0.0", optional = true }
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alembic = "^1.12.0"
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alembic = "^1.13.0"
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passlib = "^1.7.4"
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bcrypt = "4.0.1"
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python-jose = "^3.3.0"
|
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metaphor-python = "^0.1.11"
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pydantic = "^2.0.0"
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pydantic-settings = "^2.0.3"
|
||||
pydantic = "^2.5.0"
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pydantic-settings = "^2.1.0"
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zep-python = "*"
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pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
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loguru = "^0.7.1"
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@ -100,10 +100,11 @@ extract-msg = "^0.45.0"
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jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
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boto3 = "^1.28.63"
|
||||
numexpr = "^2.8.6"
|
||||
qianfan = "0.0.5"
|
||||
qianfan = "0.2.0"
|
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pgvector = "^0.2.3"
|
||||
pyautogen = "^0.2.0"
|
||||
langchain-google-genai = "^0.0.2"
|
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pytube = "^15.0.0"
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|
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[tool.poetry.group.dev.dependencies]
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pytest-asyncio = "^0.23.1"
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|
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252
src/backend/langflow/components/llms/ChatOllamaEndpoint.py
Normal file
252
src/backend/langflow/components/llms/ChatOllamaEndpoint.py
Normal file
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@ -0,0 +1,252 @@
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from typing import Any, Dict, List, Optional
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# from langchain_community.chat_models import ChatOllama
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from langchain.chat_models import ChatOllama
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from langchain.chat_models.base import BaseChatModel
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|
||||
# from langchain.chat_models import ChatOllama
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from langflow import CustomComponent
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# whe When a callback component is added to Langflow, the comment must be uncommented.
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# from langchain.callbacks.manager import CallbackManager
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class ChatOllamaComponent(CustomComponent):
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display_name = "ChatOllama"
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description = "Local LLM for chat with Ollama."
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def build_config(self) -> dict:
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return {
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"base_url": {
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"display_name": "Base URL",
|
||||
"info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
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||||
},
|
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"model": {
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"display_name": "Model Name",
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"value": "llama2",
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"info": "Refer to https://ollama.ai/library for more models.",
|
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},
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"temperature": {
|
||||
"display_name": "Temperature",
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||||
"field_type": "float",
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"value": 0.8,
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"info": "Controls the creativity of model responses.",
|
||||
},
|
||||
"cache": {
|
||||
"display_name": "Cache",
|
||||
"field_type": "bool",
|
||||
"info": "Enable or disable caching.",
|
||||
"advanced": True,
|
||||
"value": False,
|
||||
},
|
||||
### When a callback component is added to Langflow, the comment must be uncommented. ###
|
||||
# "callback_manager": {
|
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# "display_name": "Callback Manager",
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# "info": "Optional callback manager for additional functionality.",
|
||||
# "advanced": True,
|
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# },
|
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# "callbacks": {
|
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# "display_name": "Callbacks",
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# "info": "Callbacks to execute during model runtime.",
|
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# "advanced": True,
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# },
|
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########################################################################################
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"format": {
|
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"display_name": "Format",
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"field_type": "str",
|
||||
"info": "Specify the format of the output (e.g., json).",
|
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"advanced": True,
|
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},
|
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"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"info": "Metadata to add to the run trace.",
|
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"advanced": True,
|
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},
|
||||
"mirostat": {
|
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"display_name": "Mirostat",
|
||||
"options": ["Disabled", "Mirostat", "Mirostat 2.0"],
|
||||
"info": "Enable/disable Mirostat sampling for controlling perplexity.",
|
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"value": "Disabled",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_eta": {
|
||||
"display_name": "Mirostat Eta",
|
||||
"field_type": "float",
|
||||
"info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_tau": {
|
||||
"display_name": "Mirostat Tau",
|
||||
"field_type": "float",
|
||||
"info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_ctx": {
|
||||
"display_name": "Context Window Size",
|
||||
"field_type": "int",
|
||||
"info": "Size of the context window for generating tokens. (Default: 2048)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_gpu": {
|
||||
"display_name": "Number of GPUs",
|
||||
"field_type": "int",
|
||||
"info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_thread": {
|
||||
"display_name": "Number of Threads",
|
||||
"field_type": "int",
|
||||
"info": "Number of threads to use during computation. (Default: detected for optimal performance)",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_last_n": {
|
||||
"display_name": "Repeat Last N",
|
||||
"field_type": "int",
|
||||
"info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_penalty": {
|
||||
"display_name": "Repeat Penalty",
|
||||
"field_type": "float",
|
||||
"info": "Penalty for repetitions in generated text. (Default: 1.1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"tfs_z": {
|
||||
"display_name": "TFS Z",
|
||||
"field_type": "float",
|
||||
"info": "Tail free sampling value. (Default: 1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"timeout": {
|
||||
"display_name": "Timeout",
|
||||
"field_type": "int",
|
||||
"info": "Timeout for the request stream.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"field_type": "int",
|
||||
"info": "Limits token selection to top K. (Default: 40)",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "float",
|
||||
"info": "Works together with top-k. (Default: 0.9)",
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"info": "Whether to print out response text.",
|
||||
},
|
||||
"tags": {
|
||||
"display_name": "Tags",
|
||||
"field_type": "list",
|
||||
"info": "Tags to add to the run trace.",
|
||||
"advanced": True,
|
||||
},
|
||||
"stop": {
|
||||
"display_name": "Stop Tokens",
|
||||
"field_type": "list",
|
||||
"info": "List of tokens to signal the model to stop generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"system": {
|
||||
"display_name": "System",
|
||||
"field_type": "str",
|
||||
"info": "System to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"template": {
|
||||
"display_name": "Template",
|
||||
"field_type": "str",
|
||||
"info": "Template to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
base_url: Optional[str],
|
||||
model: str,
|
||||
mirostat: Optional[str],
|
||||
mirostat_eta: Optional[float] = None,
|
||||
mirostat_tau: Optional[float] = None,
|
||||
### When a callback component is added to Langflow, the comment must be uncommented.###
|
||||
# callback_manager: Optional[CallbackManager] = None,
|
||||
# callbacks: Optional[List[Callbacks]] = None,
|
||||
#######################################################################################
|
||||
repeat_last_n: Optional[int] = None,
|
||||
verbose: Optional[bool] = None,
|
||||
cache: Optional[bool] = None,
|
||||
num_ctx: Optional[int] = None,
|
||||
num_gpu: Optional[int] = None,
|
||||
format: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
num_thread: Optional[int] = None,
|
||||
repeat_penalty: Optional[float] = None,
|
||||
stop: Optional[List[str]] = None,
|
||||
system: Optional[str] = None,
|
||||
tags: Optional[List[str]] = None,
|
||||
temperature: Optional[float] = None,
|
||||
template: Optional[str] = None,
|
||||
tfs_z: Optional[float] = None,
|
||||
timeout: Optional[int] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[int] = None,
|
||||
) -> BaseChatModel:
|
||||
if not base_url:
|
||||
base_url = "http://localhost:11434"
|
||||
|
||||
# Mapping mirostat settings to their corresponding values
|
||||
mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
|
||||
|
||||
# Default to 0 for 'Disabled'
|
||||
mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore
|
||||
|
||||
# Set mirostat_eta and mirostat_tau to None if mirostat is disabled
|
||||
if mirostat_value == 0:
|
||||
mirostat_eta = None
|
||||
mirostat_tau = None
|
||||
|
||||
# Mapping system settings to their corresponding values
|
||||
llm_params = {
|
||||
"base_url": base_url,
|
||||
"cache": cache,
|
||||
"model": model,
|
||||
"mirostat": mirostat_value,
|
||||
"format": format,
|
||||
"metadata": metadata,
|
||||
"tags": tags,
|
||||
## When a callback component is added to Langflow, the comment must be uncommented.##
|
||||
# "callback_manager": callback_manager,
|
||||
# "callbacks": callbacks,
|
||||
#####################################################################################
|
||||
"mirostat_eta": mirostat_eta,
|
||||
"mirostat_tau": mirostat_tau,
|
||||
"num_ctx": num_ctx,
|
||||
"num_gpu": num_gpu,
|
||||
"num_thread": num_thread,
|
||||
"repeat_last_n": repeat_last_n,
|
||||
"repeat_penalty": repeat_penalty,
|
||||
"temperature": temperature,
|
||||
"stop": stop,
|
||||
"system": system,
|
||||
"template": template,
|
||||
"tfs_z": tfs_z,
|
||||
"timeout": timeout,
|
||||
"top_k": top_k,
|
||||
"top_p": top_p,
|
||||
"verbose": verbose,
|
||||
}
|
||||
|
||||
# None Value remove
|
||||
llm_params = {k: v for k, v in llm_params.items() if v is not None}
|
||||
|
||||
try:
|
||||
output = ChatOllama(**llm_params) # type: ignore
|
||||
except Exception as e:
|
||||
raise ValueError("Could not initialize Ollama LLM.") from e
|
||||
|
||||
return output # type: ignore
|
||||
163
src/backend/langflow/components/llms/OllamaLLM.py
Normal file
163
src/backend/langflow/components/llms/OllamaLLM.py
Normal file
|
|
@ -0,0 +1,163 @@
|
|||
from typing import Optional, List
|
||||
|
||||
from langchain.llms import Ollama
|
||||
from langchain.llms.base import BaseLLM
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class OllamaLLM(CustomComponent):
|
||||
display_name = "Ollama"
|
||||
description = "Local LLM with Ollama."
|
||||
|
||||
def build_config(self) -> dict:
|
||||
return {
|
||||
"base_url": {
|
||||
"display_name": "Base URL",
|
||||
"info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
|
||||
},
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"value": "llama2",
|
||||
"info": "Refer to https://ollama.ai/library for more models.",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.8,
|
||||
"info": "Controls the creativity of model responses.",
|
||||
},
|
||||
"mirostat": {
|
||||
"display_name": "Mirostat",
|
||||
"options": ["Disabled", "Mirostat", "Mirostat 2.0"],
|
||||
"info": "Enable/disable Mirostat sampling for controlling perplexity.",
|
||||
"value": "Disabled",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_eta": {
|
||||
"display_name": "Mirostat Eta",
|
||||
"field_type": "float",
|
||||
"info": "Learning rate influencing the algorithm's response to feedback.",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_tau": {
|
||||
"display_name": "Mirostat Tau",
|
||||
"field_type": "float",
|
||||
"info": "Controls balance between coherence and diversity.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_ctx": {
|
||||
"display_name": "Context Window Size",
|
||||
"field_type": "int",
|
||||
"info": "Size of the context window for generating the next token.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_gpu": {
|
||||
"display_name": "Number of GPUs",
|
||||
"field_type": "int",
|
||||
"info": "Number of GPUs to use for computation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_thread": {
|
||||
"display_name": "Number of Threads",
|
||||
"field_type": "int",
|
||||
"info": "Number of threads to use during computation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_last_n": {
|
||||
"display_name": "Repeat Last N",
|
||||
"field_type": "int",
|
||||
"info": "Sets how far back the model looks to prevent repetition.",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_penalty": {
|
||||
"display_name": "Repeat Penalty",
|
||||
"field_type": "float",
|
||||
"info": "Penalty for repetitions in generated text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"stop": {
|
||||
"display_name": "Stop Tokens",
|
||||
"info": "List of tokens to signal the model to stop generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"tfs_z": {
|
||||
"display_name": "TFS Z",
|
||||
"field_type": "float",
|
||||
"info": "Tail free sampling to reduce impact of less probable tokens.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"field_type": "int",
|
||||
"info": "Limits token selection to top K for reducing nonsense generation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "int",
|
||||
"info": "Works with top-k to control diversity of generated text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
base_url: Optional[str],
|
||||
model: str,
|
||||
temperature: Optional[float],
|
||||
mirostat: Optional[str],
|
||||
mirostat_eta: Optional[float] = None,
|
||||
mirostat_tau: Optional[float] = None,
|
||||
num_ctx: Optional[int] = None,
|
||||
num_gpu: Optional[int] = None,
|
||||
num_thread: Optional[int] = None,
|
||||
repeat_last_n: Optional[int] = None,
|
||||
repeat_penalty: Optional[float] = None,
|
||||
stop: Optional[List[str]] = None,
|
||||
tfs_z: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[int] = None,
|
||||
) -> BaseLLM:
|
||||
if not base_url:
|
||||
base_url = "http://localhost:11434"
|
||||
|
||||
# Mapping mirostat settings to their corresponding values
|
||||
mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
|
||||
|
||||
# Default to 0 for 'Disabled'
|
||||
mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore
|
||||
|
||||
# Set mirostat_eta and mirostat_tau to None if mirostat is disabled
|
||||
if mirostat_value == 0:
|
||||
mirostat_eta = None
|
||||
mirostat_tau = None
|
||||
|
||||
llm_params = {
|
||||
"base_url": base_url,
|
||||
"model": model,
|
||||
"mirostat": mirostat_value,
|
||||
"mirostat_eta": mirostat_eta,
|
||||
"mirostat_tau": mirostat_tau,
|
||||
"num_ctx": num_ctx,
|
||||
"num_gpu": num_gpu,
|
||||
"num_thread": num_thread,
|
||||
"repeat_last_n": repeat_last_n,
|
||||
"repeat_penalty": repeat_penalty,
|
||||
"temperature": temperature,
|
||||
"stop": stop,
|
||||
"tfs_z": tfs_z,
|
||||
"top_k": top_k,
|
||||
"top_p": top_p,
|
||||
}
|
||||
|
||||
# None Value remove
|
||||
llm_params = {k: v for k, v in llm_params.items() if v is not None}
|
||||
|
||||
|
||||
try:
|
||||
llm = Ollama(**llm_params)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to Ollama.") from e
|
||||
|
||||
return llm
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
from typing import List
|
||||
from langflow import CustomComponent
|
||||
import json
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain.schema.vectorstore import VectorStore
|
||||
from langchain.base_language import BaseLanguageModel
|
||||
from langchain.retrievers.self_query.base import SelfQueryRetriever
|
||||
from langchain.chains.query_constructor.base import AttributeInfo
|
||||
|
||||
|
||||
class VectaraSelfQueryRetriverComponent(CustomComponent):
|
||||
"""
|
||||
A custom component for implementing Vectara Self Query Retriever using a vector store.
|
||||
"""
|
||||
|
||||
display_name: str = "Vectara Self Query Retriever for Vectara Vector Store"
|
||||
description: str = "Implementation of Vectara Self Query Retriever"
|
||||
documentation = (
|
||||
"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query"
|
||||
)
|
||||
beta = True
|
||||
|
||||
field_config = {
|
||||
"code": {"show": True},
|
||||
"vectorstore": {
|
||||
"display_name": "Vector Store",
|
||||
"info": "Input Vectara Vectore Store"
|
||||
},
|
||||
"llm": {
|
||||
"display_name": "LLM",
|
||||
"info": "For self query retriever"
|
||||
},
|
||||
"document_content_description":{
|
||||
"display_name": "Document Content Description",
|
||||
"info": "For self query retriever",
|
||||
},
|
||||
"metadata_field_info": {
|
||||
"display_name": "Metadata Field Info",
|
||||
"info": "Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\nExample input: {\"name\":\"speech\",\"description\":\"what name of the speech\",\"type\":\"string or list[string]\"}.\nThe keys should remain constant(name, description, type)",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
vectorstore: VectorStore,
|
||||
document_content_description: str,
|
||||
llm: BaseLanguageModel,
|
||||
metadata_field_info: List[str],
|
||||
) -> BaseRetriever:
|
||||
|
||||
metadata_field_obj = []
|
||||
|
||||
for meta in metadata_field_info:
|
||||
meta_obj = json.loads(meta)
|
||||
if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
|
||||
raise Exception('Incorrect metadata field info format.')
|
||||
attribute_info = AttributeInfo(
|
||||
name = meta_obj['name'],
|
||||
description = meta_obj['description'],
|
||||
type = meta_obj['type'],
|
||||
)
|
||||
metadata_field_obj.append(attribute_info)
|
||||
|
||||
return SelfQueryRetriever.from_llm(
|
||||
llm,
|
||||
vectorstore,
|
||||
document_content_description,
|
||||
metadata_field_obj,
|
||||
verbose=True
|
||||
)
|
||||
|
|
@ -1,10 +1,14 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langchain.schema import BaseRetriever, Document
|
||||
from langchain.vectorstores import Vectara
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
|
||||
from typing import Optional, Union, List
|
||||
from langflow import CustomComponent
|
||||
import tempfile
|
||||
import urllib.request
|
||||
import urllib
|
||||
|
||||
from langchain.vectorstores import Vectara
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain.embeddings import FakeEmbeddings
|
||||
|
||||
|
||||
class VectaraComponent(CustomComponent):
|
||||
|
|
@ -12,13 +16,29 @@ class VectaraComponent(CustomComponent):
|
|||
description: str = "Implementation of Vector Store using Vectara"
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
|
||||
beta = True
|
||||
# api key should be password = True
|
||||
field_config = {
|
||||
"vectara_customer_id": {"display_name": "Vectara Customer ID"},
|
||||
"vectara_corpus_id": {"display_name": "Vectara Corpus ID"},
|
||||
"vectara_api_key": {"display_name": "Vectara API Key", "password": True},
|
||||
"vectara_customer_id": {
|
||||
"display_name": "Vectara Customer ID",
|
||||
"required": True,
|
||||
},
|
||||
"vectara_corpus_id": {
|
||||
"display_name": "Vectara Corpus ID",
|
||||
"required": True,
|
||||
},
|
||||
"vectara_api_key": {
|
||||
"display_name": "Vectara API Key",
|
||||
"password": True,
|
||||
"required": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"documents": {"display_name": "Documents"},
|
||||
"documents": {
|
||||
"display_name": "Documents",
|
||||
"info": "Pass in either for Self Query Retriever or for making a Vectara Object",
|
||||
},
|
||||
"files_url": {
|
||||
"display_name": "Files Url",
|
||||
"info": "Make vectara object using url of files(documents not needed)",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
|
|
@ -26,21 +46,35 @@ class VectaraComponent(CustomComponent):
|
|||
vectara_customer_id: str,
|
||||
vectara_corpus_id: str,
|
||||
vectara_api_key: str,
|
||||
files_url: Optional[List[str]] = None,
|
||||
documents: Optional[Document] = None,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
# If documents, then we need to create a Vectara instance using .from_documents
|
||||
if documents is not None:
|
||||
return Vectara.from_documents(
|
||||
documents=documents, # type: ignore
|
||||
documents=documents,
|
||||
embedding=FakeEmbeddings(size=768),
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
)
|
||||
|
||||
if files_url is not None:
|
||||
files_list = []
|
||||
for url in files_url:
|
||||
name = tempfile.NamedTemporaryFile().name
|
||||
urllib.request.urlretrieve(url, name)
|
||||
files_list.append(name)
|
||||
|
||||
return Vectara.from_files(
|
||||
files=files_list,
|
||||
embedding=FakeEmbeddings(size=768),
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source="langflow",
|
||||
)
|
||||
|
||||
return Vectara(
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source="langflow",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -3,13 +3,12 @@ import inspect
|
|||
import types
|
||||
from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph.utils import UnbuiltObject
|
||||
from langflow.interface.initialize import loading
|
||||
from langflow.interface.listing import lazy_load_dict
|
||||
from langflow.utils.constants import DIRECT_TYPES
|
||||
from langflow.utils.util import sync_to_async
|
||||
from loguru import logger
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.edge.base import Edge
|
||||
|
|
@ -140,7 +139,11 @@ class Vertex:
|
|||
if not hasattr(edge, "target_param"):
|
||||
continue
|
||||
param_key = edge.target_param
|
||||
if param_key in template_dict:
|
||||
|
||||
# If the param_key is in the template_dict and the edge.target_id is the current node
|
||||
# We check this to make sure params with the same name but different target_id
|
||||
# don't get overwritten
|
||||
if param_key in template_dict and edge.target_id == self.id:
|
||||
if template_dict[param_key]["list"]:
|
||||
if param_key not in params:
|
||||
params[param_key] = []
|
||||
|
|
|
|||
|
|
@ -23,107 +23,119 @@ export default function Dropdown({
|
|||
|
||||
return (
|
||||
<>
|
||||
<Listbox
|
||||
value={internalValue}
|
||||
onChange={(value) => {
|
||||
setInternalValue(value);
|
||||
onSelect(value);
|
||||
}}
|
||||
>
|
||||
{({ open }) => (
|
||||
<>
|
||||
<div className={"relative mt-1"}>
|
||||
<Listbox.Button
|
||||
data-test={`${id ?? ""}`}
|
||||
className={
|
||||
editNode
|
||||
? "dropdown-component-outline"
|
||||
: "dropdown-component-false-outline"
|
||||
}
|
||||
>
|
||||
<span
|
||||
className="dropdown-component-display"
|
||||
data-testid={`${id ?? ""}-display`}
|
||||
>
|
||||
{internalValue}
|
||||
</span>
|
||||
<span className={"dropdown-component-arrow"}>
|
||||
<IconComponent
|
||||
name="ChevronsUpDown"
|
||||
className="dropdown-component-arrow-color"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
</Listbox.Button>
|
||||
|
||||
<Transition
|
||||
show={open}
|
||||
as={Fragment}
|
||||
leave="transition ease-in duration-100"
|
||||
leaveFrom="opacity-100"
|
||||
leaveTo="opacity-0"
|
||||
>
|
||||
<Listbox.Options
|
||||
className={classNames(
|
||||
editNode
|
||||
? "dropdown-component-true-options nowheel custom-scroll"
|
||||
: "dropdown-component-false-options nowheel custom-scroll",
|
||||
apiModal ? "mb-2 w-[250px]" : "absolute w-full"
|
||||
)}
|
||||
>
|
||||
{options.map((option, id) => (
|
||||
<Listbox.Option
|
||||
key={id}
|
||||
className={({ active }) =>
|
||||
classNames(
|
||||
active ? " bg-accent" : "",
|
||||
editNode
|
||||
? "dropdown-component-false-option"
|
||||
: "dropdown-component-true-option"
|
||||
)
|
||||
}
|
||||
value={option}
|
||||
{Object.keys(options)?.length > 0 ? (
|
||||
<>
|
||||
<Listbox
|
||||
value={internalValue}
|
||||
onChange={(value) => {
|
||||
setInternalValue(value);
|
||||
onSelect(value);
|
||||
}}
|
||||
>
|
||||
{({ open }) => (
|
||||
<>
|
||||
<div className={"relative mt-1"}>
|
||||
<Listbox.Button
|
||||
data-test={`${id ?? ""}`}
|
||||
className={
|
||||
editNode
|
||||
? "dropdown-component-outline"
|
||||
: "dropdown-component-false-outline"
|
||||
}
|
||||
>
|
||||
<span
|
||||
className="dropdown-component-display"
|
||||
data-testid={`${id ?? ""}-display`}
|
||||
>
|
||||
{({ selected, active }) => (
|
||||
<>
|
||||
<span
|
||||
className={classNames(
|
||||
selected ? "font-semibold" : "font-normal",
|
||||
"block truncate "
|
||||
)}
|
||||
data-testid={`${option}-${id ?? ""}-option`}
|
||||
>
|
||||
{option}
|
||||
</span>
|
||||
{internalValue}
|
||||
</span>
|
||||
<span className={"dropdown-component-arrow"}>
|
||||
<IconComponent
|
||||
name="ChevronsUpDown"
|
||||
className="dropdown-component-arrow-color"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
</Listbox.Button>
|
||||
|
||||
{selected ? (
|
||||
<span
|
||||
className={classNames(
|
||||
active ? "text-background " : "",
|
||||
"dropdown-component-choosal"
|
||||
)}
|
||||
>
|
||||
<IconComponent
|
||||
name="Check"
|
||||
className={
|
||||
active
|
||||
? "dropdown-component-check-icon"
|
||||
: "dropdown-component-check-icon"
|
||||
}
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
) : null}
|
||||
</>
|
||||
<Transition
|
||||
show={open}
|
||||
as={Fragment}
|
||||
leave="transition ease-in duration-100"
|
||||
leaveFrom="opacity-100"
|
||||
leaveTo="opacity-0"
|
||||
>
|
||||
<Listbox.Options
|
||||
className={classNames(
|
||||
editNode
|
||||
? "dropdown-component-true-options nowheel custom-scroll"
|
||||
: "dropdown-component-false-options nowheel custom-scroll",
|
||||
apiModal ? "mb-2 w-[250px]" : "absolute w-full"
|
||||
)}
|
||||
</Listbox.Option>
|
||||
))}
|
||||
</Listbox.Options>
|
||||
</Transition>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</Listbox>
|
||||
>
|
||||
{options?.map((option, id) => (
|
||||
<Listbox.Option
|
||||
key={id}
|
||||
className={({ active }) =>
|
||||
classNames(
|
||||
active ? " bg-accent" : "",
|
||||
editNode
|
||||
? "dropdown-component-false-option"
|
||||
: "dropdown-component-true-option"
|
||||
)
|
||||
}
|
||||
value={option}
|
||||
>
|
||||
{({ selected, active }) => (
|
||||
<>
|
||||
<span
|
||||
className={classNames(
|
||||
selected ? "font-semibold" : "font-normal",
|
||||
"block truncate "
|
||||
)}
|
||||
data-testid={`${option}-${id ?? ""}-option`}
|
||||
>
|
||||
{option}
|
||||
</span>
|
||||
|
||||
{selected ? (
|
||||
<span
|
||||
className={classNames(
|
||||
active ? "text-background " : "",
|
||||
"dropdown-component-choosal"
|
||||
)}
|
||||
>
|
||||
<IconComponent
|
||||
name="Check"
|
||||
className={
|
||||
active
|
||||
? "dropdown-component-check-icon"
|
||||
: "dropdown-component-check-icon"
|
||||
}
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
) : null}
|
||||
</>
|
||||
)}
|
||||
</Listbox.Option>
|
||||
))}
|
||||
</Listbox.Options>
|
||||
</Transition>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</Listbox>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<div>
|
||||
<span className="text-sm italic">
|
||||
No parameters are available for display.
|
||||
</span>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -46,7 +46,14 @@ export default function ChatInput({
|
|||
: "hidden"
|
||||
}`,
|
||||
}}
|
||||
value={lockChat ? "Thinking..." : chatValue}
|
||||
value={
|
||||
lockChat
|
||||
? "Thinking..."
|
||||
: typeof chatValue === "object" &&
|
||||
Object.keys(chatValue)?.length === 0
|
||||
? "No chat input variables found. Click to run your flow."
|
||||
: chatValue
|
||||
}
|
||||
onChange={(event): void => {
|
||||
setChatValue(event.target.value);
|
||||
}}
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import Convert from "ansi-to-html";
|
||||
import { useMemo, useState } from "react";
|
||||
import { useState } from "react";
|
||||
import ReactMarkdown from "react-markdown";
|
||||
import rehypeMathjax from "rehype-mathjax";
|
||||
import remarkGfm from "remark-gfm";
|
||||
|
|
@ -74,77 +74,71 @@ export default function ChatMessage({
|
|||
<div className="w-full">
|
||||
<div className="w-full dark:text-white">
|
||||
<div className="w-full">
|
||||
{useMemo(
|
||||
() =>
|
||||
chat.message.toString() === "" && lockChat ? (
|
||||
<IconComponent
|
||||
name="MoreHorizontal"
|
||||
className="h-8 w-8 animate-pulse"
|
||||
/>
|
||||
) : (
|
||||
<ReactMarkdown
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeMathjax]}
|
||||
className="markdown prose min-w-full text-primary word-break-break-word
|
||||
{chat.message.toString() === "" && lockChat ? (
|
||||
<IconComponent
|
||||
name="MoreHorizontal"
|
||||
className="h-8 w-8 animate-pulse"
|
||||
/>
|
||||
) : (
|
||||
<ReactMarkdown
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeMathjax]}
|
||||
className="markdown prose min-w-full text-primary word-break-break-word
|
||||
dark:prose-invert"
|
||||
components={{
|
||||
pre({ node, ...props }) {
|
||||
return <>{props.children}</>;
|
||||
},
|
||||
code: ({
|
||||
node,
|
||||
inline,
|
||||
className,
|
||||
children,
|
||||
...props
|
||||
}) => {
|
||||
if (children.length) {
|
||||
if (children[0] === "▍") {
|
||||
return (
|
||||
<span className="form-modal-markdown-span">
|
||||
▍
|
||||
</span>
|
||||
);
|
||||
}
|
||||
|
||||
children[0] = (children[0] as string).replace(
|
||||
"`▍`",
|
||||
"▍"
|
||||
);
|
||||
}
|
||||
|
||||
const match = /language-(\w+)/.exec(
|
||||
className || ""
|
||||
components={{
|
||||
pre({ node, ...props }) {
|
||||
return <>{props.children}</>;
|
||||
},
|
||||
code: ({
|
||||
node,
|
||||
inline,
|
||||
className,
|
||||
children,
|
||||
...props
|
||||
}) => {
|
||||
if (children.length) {
|
||||
if (children[0] === "▍") {
|
||||
return (
|
||||
<span className="form-modal-markdown-span">
|
||||
▍
|
||||
</span>
|
||||
);
|
||||
}
|
||||
|
||||
return !inline ? (
|
||||
<CodeTabsComponent
|
||||
isMessage
|
||||
tabs={[
|
||||
{
|
||||
name: (match && match[1]) || "",
|
||||
mode: (match && match[1]) || "",
|
||||
image:
|
||||
"https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: (match && match[1]) || "",
|
||||
code: String(children).replace(/\n$/, ""),
|
||||
},
|
||||
]}
|
||||
activeTab={"0"}
|
||||
setActiveTab={() => {}}
|
||||
/>
|
||||
) : (
|
||||
<code className={className} {...props}>
|
||||
{children}
|
||||
</code>
|
||||
);
|
||||
},
|
||||
}}
|
||||
>
|
||||
{chat.message.toString()}
|
||||
</ReactMarkdown>
|
||||
),
|
||||
[chat.message, chat.message.toString()]
|
||||
children[0] = (children[0] as string).replace(
|
||||
"`▍`",
|
||||
"▍"
|
||||
);
|
||||
}
|
||||
|
||||
const match = /language-(\w+)/.exec(className || "");
|
||||
|
||||
return !inline ? (
|
||||
<CodeTabsComponent
|
||||
isMessage
|
||||
tabs={[
|
||||
{
|
||||
name: (match && match[1]) || "",
|
||||
mode: (match && match[1]) || "",
|
||||
image:
|
||||
"https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: (match && match[1]) || "",
|
||||
code: String(children).replace(/\n$/, ""),
|
||||
},
|
||||
]}
|
||||
activeTab={"0"}
|
||||
setActiveTab={() => {}}
|
||||
/>
|
||||
) : (
|
||||
<code className={className} {...props}>
|
||||
{children}
|
||||
</code>
|
||||
);
|
||||
},
|
||||
}}
|
||||
>
|
||||
{chat.message.toString()}
|
||||
</ReactMarkdown>
|
||||
)}
|
||||
</div>
|
||||
{chat.files && (
|
||||
|
|
|
|||
|
|
@ -146,13 +146,14 @@ export default function FormModal({
|
|||
newChat[newChat.length - 1].message + str;
|
||||
}
|
||||
}
|
||||
if (thought) {
|
||||
|
||||
if (thought && newChat[newChat.length - 1]?.thought) {
|
||||
newChat[newChat.length - 1].thought = thought;
|
||||
}
|
||||
if (files) {
|
||||
if (files && newChat[newChat.length - 1]?.files) {
|
||||
newChat[newChat.length - 1].files = files;
|
||||
}
|
||||
if (prompt) {
|
||||
if (prompt && newChat[newChat.length - 2]?.template) {
|
||||
newChat[newChat.length - 2].template = prompt;
|
||||
}
|
||||
return newChat;
|
||||
|
|
|
|||
|
|
@ -122,24 +122,21 @@ export default function GenericModal({
|
|||
}
|
||||
if (apiReturn.data) {
|
||||
let inputVariables = apiReturn.data.input_variables ?? [];
|
||||
if (
|
||||
JSON.stringify(apiReturn.data?.frontend_node) !== JSON.stringify({})
|
||||
) {
|
||||
setNodeClass!(apiReturn.data?.frontend_node, inputValue);
|
||||
setModalOpen(closeModal);
|
||||
setIsEdit(false);
|
||||
}
|
||||
if (!inputVariables || inputVariables.length === 0) {
|
||||
setIsEdit(true);
|
||||
setNoticeData({
|
||||
title: "Your template does not have any variables.",
|
||||
});
|
||||
setModalOpen(false);
|
||||
} else {
|
||||
if (
|
||||
JSON.stringify(apiReturn.data?.frontend_node) !==
|
||||
JSON.stringify({})
|
||||
) {
|
||||
setNodeClass!(apiReturn.data?.frontend_node, inputValue);
|
||||
setModalOpen(closeModal);
|
||||
setIsEdit(false);
|
||||
setSuccessData({
|
||||
title: "Prompt is ready",
|
||||
});
|
||||
}
|
||||
setSuccessData({
|
||||
title: "Prompt is ready",
|
||||
});
|
||||
}
|
||||
} else {
|
||||
setIsEdit(true);
|
||||
|
|
|
|||
|
|
@ -578,4 +578,4 @@ def test_async_task_processing_vector_store(client, added_vector_store, created_
|
|||
# Validate that the task completed successfully and the result is as expected
|
||||
assert "result" in task_status_json, task_status_json
|
||||
assert "output" in task_status_json["result"], task_status_json["result"]
|
||||
assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
|
||||
assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
|
||||
Loading…
Add table
Add a link
Reference in a new issue