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Merge branch 'zustand/io/migration' into cz/new-tests

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cristhianzl 2024-04-04 15:44:47 -03:00
commit 06fcb61d0f
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2
.dockerignore
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@ -1,6 +1,6 @@
.venv/
**/aws
# node_modules
node_modules
**/node_modules/
dist/
**/build/

66
.github/workflows/pre-release-base.yml vendored Normal file
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@ -0,0 +1,66 @@
name: Langflow Base Pre-release
on:
pull_request:
types:
- closed
branches:
- dev
paths:
- "pyproject.toml"
workflow_dispatch:
env:
POETRY_VERSION: "1.8.2"
jobs:
if_release:
if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v5
with:
python-version: "3.10"
cache: "poetry"
- name: Build project for distribution
run: make build base=true
- name: Check Version
id: check-version
run: |
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
- name: Create Release
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
draft: false
generateReleaseNotes: true
prerelease: true
tag: v${{ steps.check-version.outputs.version }}
commit: dev
- name: Publish to PyPI
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
run: |
poetry publish base=true
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push
uses: docker/build-push-action@v5
with:
context: .
push: true
file: ./build_and_push_base.Dockerfile
tags: |
logspace/langflow:base-${{ steps.check-version.outputs.version }}

13
.github/workflows/pre-release.yml → .github/workflows/pre-release-langflow.yml vendored
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@ -1,4 +1,4 @@
name: pre-release
name: Langflow Pre-release
on:
pull_request:
@ -9,6 +9,10 @@ on:
paths:
- "pyproject.toml"
workflow_dispatch:
workflow_run:
workflows: ["pre-release-base"]
types: [completed]
branches: [dev]
env:
POETRY_VERSION: "1.8.2"
@ -20,14 +24,14 @@ jobs:
steps:
- uses: actions/checkout@v4
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION && poetry self add poetry-monorepo-dependency-plugin
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v5
with:
python-version: "3.10"
cache: "poetry"
- name: Build project for distribution
run: make build
run: make build main=true
- name: Check Version
id: check-version
run: |
@ -46,7 +50,7 @@ jobs:
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
run: |
poetry publish
poetry publish main=true
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
@ -64,4 +68,3 @@ jobs:
file: ./build_and_push.Dockerfile
tags: |
logspace/langflow:${{ steps.check-version.outputs.version }}
logspace/langflow:latest-dev

2
.github/workflows/release.yml vendored
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@ -19,7 +19,7 @@ jobs:
steps:
- uses: actions/checkout@v4
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION && poetry self add poetry-monorepo-dependency-plugin
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v5
with:

33
Makefile
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@ -10,7 +10,6 @@ path = src/backend/base/langflow/frontend
setup_poetry:
pipx install poetry
poetry self add poetry-monorepo-dependency-plugin
add:
@echo 'Adding dependencies'
@ -168,22 +167,27 @@ build_frontend:
build:
@echo 'Building the project'
@make setup_env
make build_langflow_base
make build_langflow
build_langflow_base:
ifdef base
make install_frontendci
make build_frontend
cd src/backend/base && poetry build-rewrite-path-deps --version-pinning-strategy=semver
make build_langflow_base
endif
ifdef main
make build_langflow
endif
build_langflow_base:
cd src/backend/base && poetry build
rm -rf src/backend/base/langflow/frontend
build_langflow_backup:
poetry lock && poetry build-rewrite-path-deps --version-pinning-strategy=semver
poetry lock && poetry build
build_langflow:
cd ./scripts && python update_dependencies.py
cd ./scripts && poetry run python update_dependencies.py
poetry lock
poetry build-rewrite-path-deps --version-pinning-strategy=semver
poetry build
mv pyproject.toml.bak pyproject.toml
mv poetry.lock.bak poetry.lock
@ -208,6 +212,7 @@ lock:
@echo 'Locking dependencies'
cd src/backend/base && poetry lock
poetry lock
publish_base:
make build_langflow_base
cd src/backend/base && poetry publish
@ -217,8 +222,14 @@ publish_langflow:
poetry publish
publish:
make publish_base
make publish_langflow
@echo 'Publishing the project'
ifdef base
-make publish_base
endif
ifdef main
-make publish_langflow
endif
help:
@echo '----'

4
README.md
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@ -15,7 +15,7 @@
[![GitHub fork](https://img.shields.io/github/forks/logspace-ai/langflow?style=social)](https://github.com/logspace-ai/langflow/fork)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langflow_ai.svg?style=social&label=Follow%20%40langflow_ai)](https://twitter.com/langflow_ai)
[![](https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat)](https://discord.com/invite/EqksyE2EX9)
[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true)
[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/logspace-ai/langflow)
<a href="https://github.com/logspace-ai/langflow">
@ -62,7 +62,7 @@ langflow run # or langflow --help
### HuggingFace Spaces
You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true)
You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
# 🖥️ Command Line Interface (CLI)

7
build_and_push.Dockerfile
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@ -65,8 +65,11 @@ COPY src ./src
COPY scripts ./scripts
COPY Makefile ./
COPY README.md ./
RUN make build
RUN --mount=type=cache,target=/root/.cache \
curl -sSL https://install.python-poetry.org | python3 -
RUN python -m pip install requests && cd ./scripts && python update_dependencies.py
RUN $POETRY_HOME/bin/poetry lock
RUN $POETRY_HOME/bin/poetry build
# Final stage for the application
FROM python-base as final

91
build_and_push_base.Dockerfile Normal file
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@ -0,0 +1,91 @@
# 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 \
# npm
npm \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN --mount=type=cache,target=/root/.cache \
curl -sSL https://install.python-poetry.org | python3 -
# Now we need to copy the entire project into the image
COPY pyproject.toml poetry.lock ./
COPY src/frontend/package.json /tmp/package.json
RUN cd /tmp && npm install
WORKDIR /app
COPY src/frontend ./src/frontend
RUN rm -rf src/frontend/node_modules
RUN cp -a /tmp/node_modules /app/src/frontend
COPY scripts ./scripts
COPY Makefile ./
COPY README.md ./
RUN cd src/frontend && npm run build
COPY src/backend ./src/backend
RUN cp -r src/frontend/build src/backend/base/langflow/frontend
RUN rm -rf src/backend/base/dist
RUN cd src/backend/base && $POETRY_HOME/bin/poetry build --format sdist
# Final stage for the application
FROM python-base as final
# Copy virtual environment and built .tar.gz from builder base
COPY --from=builder-base /app/src/backend/base/dist/*.tar.gz ./
# Install the package from the .tar.gz
RUN pip install *.tar.gz
WORKDIR /app
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]

126
docs/docs/components/embeddings.mdx
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@ -2,19 +2,11 @@ import Admonition from "@theme/Admonition";
# Embeddings
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
<p>
We appreciate your understanding as we polish our documentation – it may
contain some rough edges. Share your feedback or report issues to help us
improve! 🛠️📝
</p>
</Admonition>
Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space.
---
### BedrockEmbeddings
### Amazon Bedrock Embeddings
Used to load [Amazon Bedrocks’s](https://aws.amazon.com/bedrock/) embedding models.
@ -30,7 +22,7 @@ Used to load [Amazon Bedrocks’s](https://aws.amazon.com/bedrock/) embedding mo
---
### CohereEmbeddings
### Cohere Embeddings
Used to load [Cohere’s](https://cohere.com/) embedding models.
@ -44,57 +36,93 @@ Used to load [Cohere’s](https://cohere.com/) embedding models.
---
### HuggingFaceEmbeddings
### Azure OpenAI Embeddings
Generate embeddings using Azure OpenAI models.
**Params**
- **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`
- **Deployment Name:** The name of the deployment.
- **API Version:** The API version to use. (Options: 2022-12-01, 2023-03-15-preview, 2023-05-15, 2023-06-01-preview, 2023-07-01-preview, 2023-08-01-preview)
- **API Key:** The API key to access the Azure OpenAI service.
---
### Hugging Face API Embeddings
Generate embeddings using Hugging Face Inference API models.
**Params**
- **API Key:** API key for accessing the Hugging Face Inference API. (Type: str)
- **API URL:** URL of the Hugging Face Inference API. (Default: http://localhost:8080)
- **Model Name:** Name of the model to use. (Default: BAAI/bge-large-en-v1.5)
- **Cache Folder:** Folder path to cache Hugging Face models. (Advanced)
- **Encode Kwargs:** Additional arguments for the encoding process. (Type: dict, Advanced)
- **Model Kwargs:** Additional arguments for the model. (Type: dict, Advanced)
- **Multi Process:** Whether to use multiple processes. (Default: False, Advanced)
---
### Hugging Face Embeddings
Used to load [HuggingFace’s](https://huggingface.co) embedding models.
**Params**
- **cache_folder:** Used to specify the folder where the embeddings will be cached. When embeddings are computed for a text, they can be stored in the cache folder so that they can be reused later without the need to recompute them. This can improve the performance of the application by avoiding redundant computations.
- **encode_kwargs:** Used to pass additional keyword arguments to the encoding method of the underlying HuggingFace model. These keyword arguments can be used to customize the encoding process, such as specifying the maximum length of the input sequence or enabling truncation or padding.
- **model_kwargs:** Used to customize the behavior of the model, such as specifying the model architecture, the tokenizer, or any other model-specific configuration options. By using `model_kwargs`, the user can configure the HuggingFace model according to specific needs and preferences.
- **model_name:** Used to specify the name or identifier of the HuggingFace model that will be used for generating embeddings. It allows users to choose a specific pre-trained model from the Hugging Face model hub — defaults to `sentence-transformers/all-mpnet-base-v2`.
- **Cache Folder:** Folder path to cache HuggingFace models.
- **Encode Kwargs:** Additional arguments for the encoding process. (Type: dict)
- **Model Kwargs:** Additional arguments for the model. (Type: dict)
- **Model Name:** Name of the HuggingFace model to use. (Default: sentence-transformers/all-mpnet-base-v2)
- **Multi Process:** Whether to use multiple processes. (Default: False)
---
### OpenAIEmbeddings
### Ollama Embeddings
Generate embeddings using Ollama models.
**Params**
- **Ollama Model:** Name of the Ollama model to use. (Default: llama2)
- **Ollama Base URL:** Base URL of the Ollama API. (Default: http://localhost:11434)
- **Model Temperature:** Temperature parameter for the model. (Type: float)
---
### OpenAI Embeddings
Used to load [OpenAI’s](https://openai.com/) embedding models.
**Params**
- **chunk_size:** Determines the maximum size of each chunk of text that is processed for embedding. If any of the incoming text chunks exceeds `chunk_size` characters, it will be split into multiple chunks of size `chunk_size` or less before being embedded — defaults to `1000`.
- **deployment:** Used to specify the deployment name or identifier of the text embedding model. It allows the user to choose a specific deployment of the model to use for embedding. When the deployment is provided, this can be useful when the user has multiple deployments of the same model with different configurations or versions — defaults to `text-embedding-ada-002`.
- **embedding_ctx_length:** This parameter determines the maximum context length for the text embedding model. It specifies the number of tokens that the model considers when generating embeddings for a piece of text — defaults to `8191` (this means that the model will consider up to 8191 tokens when generating embeddings).
- **max_retries:** Determines the maximum number of times to retry a request if the model provider returns an error from their API — defaults to `6`.
- **model:** Defines which pre-trained text embedding model to use — defaults to `text-embedding-ada-002`.
- **openai_api_base:** Refers to the base URL for the Azure OpenAI resource. It is used to configure the API to connect to the Azure OpenAI service. The base URL can be found in the Azure portal under the user Azure OpenAI resource.
- **openai_api_key:** Is used to authenticate and authorize access to the OpenAI service.
- **openai_api_type:** Is used to specify the type of OpenAI API being used, either the regular OpenAI API or the Azure OpenAI API. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate API service.
- **openai_api_version:** Is used to specify the version of the OpenAI API being used. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate version of the OpenAI API service.
- **openai_organization:** Is used to specify the organization associated with the OpenAI API key. If not provided, the default organization associated with the API key will be used.
- **openai_proxy:** Proxy enables better budgeting and cost management for making OpenAI API calls, including more transparency into pricing.
- **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text.
- **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name.
- **OpenAI API Key:** The API key to use for accessing the OpenAI API. (Type: str)
- **Default Headers:** Default headers for the HTTP requests. (Type: Dict[str, str], Optional)
- **Default Query:** Default query parameters for the HTTP requests. (Type: NestedDict, Optional)
- **Allowed Special:** Special tokens allowed for processing. (Type: List[str], Default: [])
- **Disallowed Special:** Special tokens disallowed for processing. (Type: List[str], Default: ["all"])
- **Chunk Size:** Chunk size for processing. (Type: int, Default: 1000)
- **Client:** HTTP client for making requests. (Type: Any, Optional)
- **Deployment:** Deployment name for the model. (Type: str, Default: "text-embedding-3-small")
- **Embedding Context Length:** Length of embedding context. (Type: int, Default: 8191)
- **Max Retries:** Maximum number of retries for failed requests. (Type: int, Default: 6)
- **Model:** Name of the model to use. (Type: str, Default: "text-embedding-3-small")
- **Model Kwargs:** Additional keyword arguments for the model. (Type: NestedDict, Optional)
- **OpenAI API Base:** Base URL of the OpenAI API. (Type: str, Optional)
- **OpenAI API Type:** Type of the OpenAI API. (Type: str, Optional)
- **OpenAI API Version:** Version of the OpenAI API. (Type: str, Optional)
- **OpenAI Organization:** Organization associated with the API key. (Type: str, Optional)
- **OpenAI Proxy:** Proxy server for the requests. (Type: str, Optional)
- **Request Timeout:** Timeout for the HTTP requests. (Type: float, Optional)
- **Show Progress Bar:** Whether to show a progress bar for processing. (Type: bool, Default: False)
- **Skip Empty:** Whether to skip empty inputs. (Type: bool, Default: False)
- **TikToken Enable:** Whether to enable TikToken. (Type: bool, Default: True)
- **TikToken Model Name:** Name of the TikToken model. (Type: str, Optional)
---
### VertexAIEmbeddings
### VertexAI Embeddings
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
@ -113,11 +141,3 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
### OllamaEmbeddings
Used to load [Ollama’s](https://ollama.ai/) embedding models. Wrapper around LangChain's [Ollama API](https://python.langchain.com/docs/integrations/text_embedding/ollama).
- **model** The name of the Ollama model to use – defaults to `llama2`.
- **base_url** The base URL for the Ollama API – defaults to `http://localhost:11434`.
- **temperature** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.

134
docs/docs/components/inputs.mdx
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@ -1,4 +1,5 @@
import Admonition from '@theme/Admonition';
import Admonition from "@theme/Admonition";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# Inputs
@ -17,42 +18,147 @@ This component is designed to get user input from the chat.
- **Session ID:** specifies the session ID of the chat history. If provided, the message will be saved in the Message History.
<Admonition type="note" title="Note">
<p>
If _`As Record`_ is _`true`_ and the _`Message`_ is a _`Record`_, the data of the _`Record`_ will be updated with the _`Sender`_, _`Sender Name`_, and _`Session ID`_.
</p>
<p>
If _`As Record`_ is _`true`_ and the _`Message`_ is a _`Record`_, the data
of the _`Record`_ will be updated with the _`Sender`_, _`Sender Name`_, and
_`Session ID`_.
</p>
</Admonition>
When you get it from the sidebar, it will look like the image below but that is because some fields are in the advanced section.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/chat-input.png",
dark: "img/chat-input.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
If you expose all its fields, it will look like the image below.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/chat-input-expanded.png",
dark: "img/chat-input-expanded.png",
}}
style={{ width: "40%", margin: "20px auto" }}
/>
One key capability of the Chat Input component is how it transforms the Interaction Panel into a chat window. This feature is particularly useful for scenarios where user input is required to initiate or influence the flow.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/interaction-panel-with-chat-input.png",
dark: "img/interaction-panel-with-chat-input.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
---
### Prompt
Create a prompt template with dynamic variables.
Create a prompt template with dynamic variables. This is a very useful component for structuring prompts and passing dynamic data to a language model.
**Parameters**
- **Template:** the template for the prompt.
- **Template:** the template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like this: _`"Hello {name}, how are you?"`_, a new field called _`name`_ will be created.
<Admonition type="note" title="Note">
<p>
Prompt variables can be created with any chosen name inside curly brackets, e.g. `{variable_name}`
</p>
<p>
Prompt variables can be created with any chosen name inside curly brackets,
e.g. `{variable_name}`
</p>
</Admonition>
---
Here is how it looks when you get it from the sidebar.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/prompt.png",
dark: "img/prompt.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
And here when you add a Template with the value _`Hello {name}, how are you?`_.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/prompt-with-template.png",
dark: "img/prompt-with-template.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
---
### Text Input
This component is designed for simple text input, allowing users to pass textual data to subsequent components in the workflow. It's particularly useful for scenarios where a brief user input is required to initiate or influence the flow.
**Params**
- **Value:** Specifies the text input value. This is where the user can input the text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string.
- **Record Template:** Specifies how a Record should be converted into Text.
<Admonition type="note" title="Note">
<p>
The `TextInput` component serves as a straightforward means for setting Text input values in the chat window. It ensures that textual data can be seamlessly passed to subsequent components in the flow.
</p>
<p>
The `TextInput` component serves as a straightforward means for setting Text
input values in the chat window. It ensures that textual data can be
seamlessly passed to subsequent components in the flow.
</p>
</Admonition>
It should look like this when dropped directly from the sidebar.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/text-input.png",
dark: "img/text-input.png",
}}
style={{ width: "50%", margin: "20px auto", margin: "20px auto" }}
/>
And when you expose all its fields, it will look like the image below.
The **Record Template** field is used to specify how a Record should be converted into Text. This is particularly useful when you want to extract specific information from a Record and pass it as text to the next component in the sequence.
For example, if you have a Record with the following structure:
```json
{
"name": "John Doe",
"age": 30,
"email": "johndoe@email.com"
}
```
You can use a template like this: _`"Name: {name}, Age: {age}"`_ to convert the Record into a text string like this: _`"Name: John Doe, Age: 30"`_, and if you pass more than one Record, the text will be concatenated with a new line separator.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/text-input-expanded.png",
dark: "img/text-input-expanded.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
The Text Input component gives you the possibility to add an Input field on the Interaction Panel. This is useful because it allows you to define parameters while running and testing your flow.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/interaction-panel-text-input.png",
dark: "img/interaction-panel-text-input.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>

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@ -1,41 +1,42 @@
import Admonition from '@theme/Admonition';
import Admonition from "@theme/Admonition";
# Vector Stores
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
<p>
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
</p>
<p>
We appreciate your understanding as we polish our documentation – it may
contain some rough edges. Share your feedback or report issues to help us
improve! 🛠️📝
</p>
</Admonition>
### Astra DB
### AstraDB
The `AstraDB` is a component for initializing an AstraDB Vector Store from Records. It facilitates the creation of AstraDB-based vector indexes for efficient document storage and retrieval.
The `Astra DB` is a component for initializing an Astra DB Vector Store from Records. It facilitates the creation of Astra DB-based vector indexes for efficient document storage and retrieval.
**Params**
- **Input:** The input documents or records.
- **Embedding:** The embedding model used by AstraDB.
- **Embedding:** The embedding model used by Astra DB.
- **Collection Name:** The name of the collection in AstraDB.
- **Collection Name:** The name of the collection in Astra DB.
- **Token:** The token for AstraDB.
- **Token:** The token for Astra DB.
- **API Endpoint:** The API endpoint for AstraDB.
- **API Endpoint:** The API endpoint for Astra DB.
- **Namespace:** The namespace in AstraDB.
- **Namespace:** The namespace in Astra DB.
- **Metric:** The metric to use in AstraDB.
- **Metric:** The metric to use in Astra DB.
- **Batch Size:** The batch size for AstraDB.
- **Batch Size:** The batch size for Astra DB.
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB.
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB.
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB.
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB.
- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB.
- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB.
- **Setup Mode:** The setup mode for the vector store.
@ -49,16 +50,16 @@ The `AstraDB` is a component for initializing an AstraDB Vector Store from Recor
<Admonition type="note" title="Note">
<p>
Ensure that the required AstraDB token and API endpoint are properly configured.
Ensure that the required Astra DB token and API endpoint are properly configured.
</p>
</Admonition>
---
### AstraDB Search
### Astra DB Search
The `AstraDBSearch` is a component for searching an existing AstraDB Vector Store for similar documents. It extends the functionality of the `AstraDB` component to provide efficient document retrieval based on similarity metrics.
The `Astra DBSearch` is a component for searching an existing Astra DB Vector Store for similar documents. It extends the functionality of the `Astra DB` component to provide efficient document retrieval based on similarity metrics.
**Params**
@ -66,25 +67,25 @@ The `AstraDBSearch` is a component for searching an existing AstraDB Vector Stor
- **Input Value:** The input value to search for.
- **Embedding:** The embedding model used by AstraDB.
- **Embedding:** The embedding model used by Astra DB.
- **Collection Name:** The name of the collection in AstraDB.
- **Collection Name:** The name of the collection in Astra DB.
- **Token:** The token for AstraDB.
- **Token:** The token for Astra DB.
- **API Endpoint:** The API endpoint for AstraDB.
- **API Endpoint:** The API endpoint for Astra DB.
- **Namespace:** The namespace in AstraDB.
- **Namespace:** The namespace in Astra DB.
- **Metric:** The metric to use in AstraDB.
- **Metric:** The metric to use in Astra DB.
- **Batch Size:** The batch size for AstraDB.
- **Batch Size:** The batch size for Astra DB.
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB.
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB.
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB.
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB.
- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB.
- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB.
- **Setup Mode:** The setup mode for the vector store.
@ -118,7 +119,6 @@ The `Chroma` is a component designed for implementing a Vector Store using Chrom
- **Server SSL Enabled (Optional):** Whether to enable SSL for the Chroma server.
- **Input:** Input data for creating the Vector Store.
- **Embedding:** The embeddings to use for the Vector Store.
@ -129,7 +129,6 @@ For detailed documentation and integration guides, please refer to the [Chroma C
### Chroma Search
The `ChromaSearch` is a component designed for searching a Chroma collection for similar documents. This component integrates with Chroma to facilitate efficient document retrieval based on similarity metrics.
**Params**
@ -154,7 +153,6 @@ The `ChromaSearch` is a component designed for searching a Chroma collection for
- **Server SSL Enabled (Optional):** Whether SSL is enabled for the Chroma server.
---
### FAISS
@ -171,7 +169,6 @@ The `FAISS` is a component designed for ingesting documents into a FAISS Vector
- **Index Name:** The name of the FAISS index.
For detailed documentation and integration guides, please refer to the [FAISS Component Documentation](https://faiss.ai/index.html).
---
@ -190,10 +187,8 @@ The `FAISSSearch` is a component for searching a FAISS Vector Store for similar
- **Index Name:** The name of the FAISS index.
---
### MongoDB Atlas
The `MongoDBAtlas` is a component used to construct a MongoDB Atlas Vector Search vector store from Records. It facilitates the creation of MongoDB Atlas-based vector stores for efficient document storage and retrieval.
@ -214,11 +209,8 @@ The `MongoDBAtlas` is a component used to construct a MongoDB Atlas Vector Searc
- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
<Admonition type="note" title="Note">
<p>
Ensure that pymongo is installed to use MongoDB Atlas Vector Store.
</p>
<p>Ensure that pymongo is installed to use MongoDB Atlas Vector Store.</p>
</Admonition>
---
@ -245,7 +237,6 @@ The `MongoDBAtlasSearch` is a component for searching a MongoDB Atlas Vector Sto
- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
---
### PGVector
@ -262,14 +253,13 @@ The `PGVector` is a component for implementing a Vector Store using PostgreSQL.
- **Table:** The name of the table in the PostgreSQL database.
For detailed documentation and integration guides, please refer to the [PGVector Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/pgvector).
<Admonition type="note" title="Note">
<p>
Ensure that the required PostgreSQL server is accessible and properly configured.
</p>
<p>
Ensure that the required PostgreSQL server is accessible and properly
configured.
</p>
</Admonition>
---
@ -290,7 +280,6 @@ The `PGVectorSearch` is a component for searching a PGVector Store for similar d
- **Search Type:** The type of search to perform (e.g., "Similarity", "MMR").
---
### Pinecone
@ -315,11 +304,11 @@ The `Pinecone` is a component used to construct a Pinecone wrapper from Records.
- **Pool Threads:** The number of threads to use for Pinecone.
<Admonition type="note" title="Note">
<p>
Ensure that the required Pinecone API key and environment are properly configured.
</p>
<p>
Ensure that the required Pinecone API key and environment are properly
configured.
</p>
</Admonition>
---
@ -348,7 +337,6 @@ The `PineconeSearch` is a component used to search a Pinecone Vector Store for s
- **Pool Threads:** The number of threads to use for Pinecone.
---
### Qdrant
@ -462,9 +450,11 @@ The `Redis` is a component for implementing a Vector Store using Redis. It provi
For detailed documentation, please refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis).
<Admonition type="note" title="Note">
<p>
Ensure that the required Redis server connection URL and index name are properly configured. If no documents are provided, a schema must be provided.
</p>
<p>
Ensure that the required Redis server connection URL and index name are
properly configured. If no documents are provided, a schema must be
provided.
</p>
</Admonition>
---
@ -512,9 +502,10 @@ The `Supabase` is a component for initializing a Supabase Vector Store from text
- **Table Name:** The name of the table in Supabase (advanced).
<Admonition type="note" title="Note">
<p>
Ensure that the required Supabase service key, Supabase URL, and table name are properly configured.
</p>
<p>
Ensure that the required Supabase service key, Supabase URL, and table name
are properly configured.
</p>
</Admonition>
---
@ -562,9 +553,10 @@ The `Vectara` is a component for implementing a Vector Store using Vectara.
For detailed documentation and integration guides, please refer to the [Vectara Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/vectara).
<Admonition type="note" title="Note">
<p>
If `inputs` are provided, they will be upserted to the corpus. If `files_url` are provided, Vectara will process the files from the URLs.
</p>
<p>
If `inputs` are provided, they will be upserted to the corpus. If
`files_url` are provided, Vectara will process the files from the URLs.
</p>
</Admonition>
---
@ -586,6 +578,7 @@ The `VectaraSearch` is a component for searching a Vectara Vector Store for simi
- **Vectara API Key:** The API key for Vectara.
- **Files Url:** The URL(s) of the file(s) to be used for initializing the Vectara Vector Store (optional).
---
### Weaviate
@ -613,9 +606,14 @@ The `Weaviate` is a component for implementing a Vector Store using Weaviate.
For detailed documentation and integration guides, please refer to the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate).
<Admonition type="note" title="Note">
<p>
Before using the Weaviate Vector Store component, ensure that you have a Weaviate instance running and accessible at the specified URL. Additionally, make sure to provide the correct API key for authentication if required. Adjust the index name, text key, and attributes according to your dataset and indexing requirements. Finally, ensure that the provided embeddings are compatible with Weaviate's requirements.
</p>
<p>
Before using the Weaviate Vector Store component, ensure that you have a
Weaviate instance running and accessible at the specified URL. Additionally,
make sure to provide the correct API key for authentication if required.
Adjust the index name, text key, and attributes according to your dataset
and indexing requirements. Finally, ensure that the provided embeddings are
compatible with Weaviate's requirements.
</p>
</Admonition>
---
@ -642,4 +640,4 @@ The `WeaviateSearch` component facilitates searching a Weaviate Vector Store for
- **Embedding:** The embedding model used by Weaviate.
- **Attributes:** Additional attributes to consider during indexing (optional).
- **Attributes:** Additional attributes to consider during indexing (optional).

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@ -1,20 +0,0 @@
import Admonition from '@theme/Admonition';
# Wrappers
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
<p>
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
</p>
</Admonition>
### TextRequestsWrapper
This component is designed to work with the Python Requests module, which is a popular tool for making web requests. Used to fetch data from a particular website.
**Params**
- **header:** specifies the headers to be included in the HTTP request. Defaults to `{'Authorization': 'Bearer <token>'}`.
Headers are key-value pairs that provide additional information about the request or the client making the request. They can be used to send authentication credentials, specify the content type of the request, set cookies, and more. They allow the client and the server to communicate additional information beyond the basic request.

6
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@ -16,6 +16,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
light: "img/buffer-memory.png",
dark: "img/buffer-memory.png",
}}
style={{
width: "80%",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
/>
#### <a target="\_blank" href="json_files/Buffer_Memory.json" download>Download Flow</a>

7
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@ -22,6 +22,13 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
light: "img/basic-chat.png",
dark: "img/basic-chat.png",
}}
style={{
width: "80%",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
/>
#### <a target="\_blank" href="json_files/Basic_Chat.json" download>Download Flow</a>

6
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@ -34,6 +34,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
light: "img/csv-loader.png",
dark: "img/csv-loader.png",
}}
style={{
width: "80%",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
/>
#### <a target="\_blank" href="json_files/CSV_Loader.json" download>Download Flow</a>

4
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@ -3,7 +3,6 @@ description: Custom Components
hide_table_of_contents: true
---
# FlowRunner Component
The CustomComponent class allows us to create components that interact with Langflow itself. In this example, we will make a component that runs other flows available in "My Collection".
@ -16,7 +15,7 @@ The CustomComponent class allows us to create components that interact with Lang
}}
style={{
width: "30%",
margin: "0 auto",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
@ -367,4 +366,3 @@ Done! This is what our script and custom component looks like:
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";

28
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@ -1,28 +0,0 @@
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# 📚 How to Upload Examples?
We welcome all examples that can help our community learn and explore Langflow's capabilities.
Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/langflow_examples) that contains examples of flows that people can use for inspiration and learning.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/community-examples.png",
dark: "img/community-examples.png",
}}
style={{ width: "100%" }}
/>
To upload examples, please follow these steps:
1. **Create a Flow:** First, create a flow using Langflow. You can use any of the available templates or create a new flow from scratch.
2. **Export the Flow:** Once you have created a flow, export it as a JSON file. Make sure to give your file a descriptive name and include a brief description of what it does.
3. **Submit a Pull Request:** Finally, submit a pull request (PR) to the examples repo. Make sure to include your JSON file in the PR.
If your example uses any third-party libraries or packages, please include them in your PR and make sure that your example follows the [**⛓️ Langflow Code Of Conduct**](https://github.com/logspace-ai/langflow/blob/dev/CODE_OF_CONDUCT.md).

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@ -1,46 +0,0 @@
import Admonition from "@theme/Admonition";
# MidJourney Prompt Chain
The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts.
For example, type something like:
```bash
Dragon
```
And get a response such as:
```text
Imagine a mysterious forest, the trees are tall and ancient, their branches reaching up to the sky. Through the darkness, a dragon emerges from the shadows, its scales shimmering in the moonlight. Its wingspan is immense, and its eyes glow with a fierce intensity. It is a majestic and powerful creature, one that commands both respect and fear.
```
<Admonition type="tip">
Notice that the `ConversationSummaryMemory` stores a summary of the
conversation over time. Try using it to create better prompts as the
conversation goes on.
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/midjourney-prompt-chain.png",
dark: "img/midjourney-prompt-chain.png",
}}
/>
#### <a target="\_blank" href="json_files/MidJourney_Prompt_Chain.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/types/summary)
</Admonition>

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@ -1,58 +0,0 @@
import Admonition from "@theme/Admonition";
# Multiple Vector Stores
The example below shows an agent operating with two vector stores built upon different data sources.
The `TextLoader` loads a TXT file, while the `WebBaseLoader` pulls text from webpages into a document format to accessed downstream. The `Chroma` vector stores are created analogous to what we have demonstrated in our [CSV Loader](/examples/csv-loader.mdx) example. Finally, the `VectorStoreRouterAgent` constructs an agent that routes between the vector stores.
<Admonition type="info">
Get the TXT file used
[here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
</Admonition>
URL used by the `WebBaseLoader`:
```text
https://pt.wikipedia.org/wiki/Harry_Potter
```
<Admonition type="tip">
When you build the flow, request information about one of the sources. The
agent should be able to use the correct source to generate a response.
</Admonition>
<Admonition type="info">
Learn more about Multiple Vector Stores
[here](https://python.langchain.com/docs/modules/data_connection/vectorstores/).
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/multiple-vectorstores.png",
dark: "img/multiple-vectorstores.png",
}}
/>
#### <a target="\_blank" href="json_files/Multiple_Vector_Stores.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`WebBaseLoader`](https://python.langchain.com/docs/integrations/document_loaders/web_base)
- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/)
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
- [`OpenAIEmbedding`](https://python.langchain.com/docs/integrations/text_embedding/openai)
- [`Chroma`](https://python.langchain.com/docs/integrations/vectorstores/chroma)
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`VectorStoreRouterToolkit`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores)
- [`VectorStoreRouterAgent`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores)
</Admonition>

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@ -43,6 +43,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
light: "img/python-function.png",
dark: "img/python-function.png",
}}
style={{
width: "80%",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
/>
#### <a target="\_blank" href="json_files/Python_Function.json" download>Download Flow</a>

6
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@ -37,6 +37,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
light: "img/serp-api-tool.png",
dark: "img/serp-api-tool.png",
}}
style={{
width: "80%",
margin: "20px auto",
display: "flex",
justifyContent: "center",
}}
/>
#### <a target="\_blank" href="json_files/SerpAPI_Tool.json" download>Download Flow</a>

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@ -1,51 +0,0 @@
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# 🎨 Creating Flows
## Compose
Creating flows with Langflow is easy. Drag sidebar components onto the canvas and connect them together to create your pipeline.
Langflow provides a range of Components to choose from, including **Chat Input**, **Chat Output**, **API Request** and **Prompt**.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/langflow_canvas.png",
dark: "img/langflow_canvas.png"
}}
/>
## Starter Flows
Langflow provides a range of starter flows to help you get started. These flows are pre-built and can be used as a starting point for your own flows.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_fork.mp4" />
</div>
## Defining Inputs and Outputs
Each flow can have multiple inputs and outputs. These can be defined by placing **Inputs** and **Outputs** components on the canvas.
The **Inputs** components define the inputs to the flow.
Whenever you place an Input component on the canvas, it will allow you to interactively define change its value
from the Interactive Panel.
The **Text Input** component allows you to define a text input, and the **Chat Input** component allows you to use the chat input from the Interactive Panel.
The **Outputs** components define the outputs of the flow and work similarly to the Inputs components.
Both Inputs and Outputs components can be connected to other components on the canvas and are used to define how the API works too.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_build.mp4" />
</div>

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@ -1,30 +0,0 @@
# 🤗 HuggingFace Spaces
## TLDR;
A fully featured version of Langflow can be accessed via [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) with no installation required. All you gotta do is [duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) and you'll have your own copy to play around with!
---
# 🚀 Getting Started
HuggingFace provides great support for running Langflow in their Spaces environment. This means you can run Langflow without any installation required.
The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true).
You'll be greeted with the following screen:
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/duplicate-space.png",
dark: "img/duplicate-space.png",
}}
style={{ width: "100%" }}
/>
From here, you can rename your Space, define the visibility (Public or Private) and click on the `Duplicate Space` button to start the duplication process.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";

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@ -1,16 +1,15 @@
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import DownloadableJsonFile from "/src/theme/DownloadableJsonFile.js";
import Admonition from "@theme/Admonition";
# 🌟 RAG with AstraDB
# 🌟 RAG with Astra DB
This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **AstraDB** and **Langflow**.
This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**.
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. AstraDB is built on the same technology that powers the largest Cassandra deployments in the world.
[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 AstraDB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses.
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
@ -19,26 +18,23 @@ In this guide, we will use AstraDB as a vector store to store and retrieve the d
TLDR;
- Visit the [Astra](https://astra.datastax.com) website and create a free account
- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true)
- [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**
- <DownloadableJsonFile
title="Download AstraDB RAG Flows"
source="/data/AstraDB-RAG-Flows.json"
/>
- 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 **AstraDB** components
- 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 **AstraDB** component
- 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 AstraDB Database
## Create an Astra DB Database
To get started, you will need to create an AstraDB database. Visit the [Astra](https://astra.datastax.com) website and create a free account.
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 AstraDB dashboard. Click on the **Create Database** button.
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"
@ -46,7 +42,7 @@ Once you have created an account, you will be taken to the AstraDB dashboard. Cl
light: "img/astra-create-database.png",
dark: "img/astra-create-database.png",
}}
style={{ width: "80%" }}
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.
@ -59,7 +55,7 @@ After you have configured your database, click on the **Create Database** button
light: "img/astra-configure-deployment.png",
dark: "img/astra-configure-deployment.png",
}}
style={{ width: "70%" }}
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**.
@ -70,22 +66,18 @@ Once your database is initialized, to the right of the page, you will see the _D
light: "img/astra-generate-token.png",
dark: "img/astra-generate-token.png",
}}
style={{ width: "50%" }}
style={{ width: "50%", margin: "20px auto" }}
/>
Now we are all set to start building our RAG application using AstraDB and Langflow.
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/Logspace/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
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.
You'll still need to get the Project file and import it so, let's get to that.
## Open the Vector Store RAG Project
## Import AstraDB RAG Flows
To get started, you will need to <DownloadableJsonFile title="download the AstraDB RAG Flows project file" source="/data/AstraDB-RAG-Flows.json" />.
Once you have downloaded the project file, you can import it into Langflow by dropping it on the Canvas or My Collection page.
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"
@ -93,10 +85,10 @@ Once you have downloaded the project file, you can import it into Langflow by dr
light: "img/drag-and-drop-flow.png",
dark: "img/drag-and-drop-flow.png",
}}
style={{ width: "90%" }}
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 AstraDB database.
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.
@ -105,7 +97,7 @@ 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
- **AstraDB** component that stores the text chunks in the AstraDB database
- **Astra DB** component that stores the text chunks in the Astra DB database
<ZoomableImage
alt="Docusaurus themed image"
@ -113,10 +105,10 @@ The ingestion flow consists of:
light: "img/astra-ingestion-flow.png",
dark: "img/astra-ingestion-flow.png",
}}
style={{ width: "90%" }}
style={{ width: "80%", margin: "20px auto" }}
/>
Now, let's update the **AstraDB** and **AstraDB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key.
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"
@ -124,10 +116,10 @@ Now, let's update the **AstraDB** and **AstraDB Search** components with the **T
light: "img/astra-ingestion-fields.png",
dark: "img/astra-ingestion-fields.png",
}}
style={{ width: "90%" }}
style={{ width: "80%", margin: "20px auto" }}
/>
And run it! This will ingest the Text data from your file into the AstraDB database.
And run it! This will ingest the Text data from your file into the Astra DB database.
<ZoomableImage
alt="Docusaurus themed image"
@ -135,16 +127,16 @@ And run it! This will ingest the Text data from your file into the AstraDB datab
light: "img/astra-ingestion-run.png",
dark: "img/astra-ingestion-run.png",
}}
style={{ width: "90%" }}
style={{ width: "80%", margin: "20px auto" }}
/>
Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries.
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
- **AstraDB Search** component that retrieves the most relevant Records from the AstraDB database
- **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
@ -157,7 +149,7 @@ The RAG flow is a bit more complex. It consists of:
light: "img/astra-rag-flow.png",
dark: "img/astra-rag-flow.png",
}}
style={{ width: "90%" }}
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.
@ -168,7 +160,7 @@ To run it all we have to do is click on the ⚡ _Run_ button and start interacti
light: "img/astra-rag-flow-run.png",
dark: "img/astra-rag-flow-run.png",
}}
style={{ width: "90%" }}
style={{ width: "80%", margin: "20px auto" }}
/>
This opens the Interaction Panel where you can chat your data.
@ -181,7 +173,7 @@ Because this flow has a **Chat Input** and a **Text Output** component, the Pane
light: "img/astra-rag-flow-interaction-panel.png",
dark: "img/astra-rag-flow-interaction-panel.png",
}}
style={{ width: "80%" }}
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.
@ -192,11 +184,12 @@ Once we interact with it we get a response and the Extracted Chunks section is u
light: "img/astra-rag-flow-interaction-panel-interaction.png",
dark: "img/astra-rag-flow-interaction-panel-interaction.png",
}}
style={{ width: "80%" }}
style={{ width: "80%", margin: "20px auto" }}
/>
And that's it! You have successfully built a RAG application using AstraDB and Langflow.
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 build a RAG application using AstraDB and Langflow. We have seen how to create an AstraDB database, import the AstraDB RAG Flows project into Langflow, and run the ingestion and RAG flows.
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.

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@ -1,5 +1,6 @@
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
# API Keys
@ -7,12 +8,17 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
Langflow offers an API Key functionality that allows users to access their individual components and flows without going through traditional login authentication. The API Key is a user-specific token that can be included in the request's header or query parameter to authenticate API calls. The following documentation outlines how to generate, use, and manage these API Keys in Langflow.
<Admonition type="warning">
This feature requires the `LANGFLOW_AUTO_LOGIN` environment variable to be set
to `False`. The default user and password are set using _`LANGFLOW_SUPERUSER`_
and _`LANGFLOW_SUPERUSER_PASSWORD`_ environment variables. Default values are
_`langflow`_ and _`langflow`_ respectively.
</Admonition>
## Generating an API Key
### Through Langflow UI
{/* add image img/api-key.png */}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
@ -36,7 +42,7 @@ Include the `x-api-key` in the HTTP header when making API requests:
```bash
curl -X POST \
http://localhost:3000/api/v1/process/<your_flow_id> \
http://localhost:3000/api/v1/run/<your_flow_id> \
-H 'Content-Type: application/json'\
-H 'x-api-key: <your api key>'\
-d '{"inputs": {"text":""}, "tweaks": {}}'

73
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@ -1,73 +0,0 @@
import Admonition from "@theme/Admonition";
# Asynchronous Processing
## Introduction
Starting from version 0.5, Langflow introduces a new feature to its API: the _`sync`_ flag. This flag allows users to opt for asynchronous processing of their flows, freeing up resources and enabling better control over long-running tasks.
This feature supports running tasks in a Celery worker queue and AnyIO task groups for now.
<Admonition type="warning" caption="Experimental Feature">
This is an experimental feature. The default behavior of the API is still
synchronous processing. The API may change in the future.
</Admonition>
## The _`sync`_ Flag
The _`sync`_ flag can be included in the payload of your POST request to the _`/api/v1/process/<your_flow_id>`_ endpoint.
When set to _`false`_, the API will initiate an asynchronous task instead of processing the flow synchronously.
### API Request with _`sync`_ flag
```bash
curl -X POST \
http://localhost:3000/api/v1/process/<your_flow_id> \
-H 'Content-Type: application/json' \
-H 'x-api-key: <your_api_key>' \
-d '{"inputs": {"text": ""}, "tweaks": {}, "sync": false}'
```
Response:
```json
{
"result": {
"output": "..."
},
"task": {
"id": "...",
"href": "api/v1/task/<task_id>"
},
"session_id": "...",
"backend": "..." // celery or anyio
}
```
## Checking Task Status
You can check the status of an asynchronous task by making a GET request to the `/task/{task_id}` endpoint.
```bash
curl -X GET \
http://localhost:3000/api/v1/task/<task_id> \
-H 'x-api-key: <your_api_key>'
```
### Response
The endpoint will return the current status of the task and, if completed, the result of the task. Possible statuses include:
- _`PENDING`_: The task is waiting for execution.
- _`SUCCESS`_: The task has completed successfully.
- _`FAILURE`_: The task has failed.
Example response for a completed task:
```json
{
"status": "SUCCESS",
"result": {
"output": "..."
}
}
```

3
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@ -26,13 +26,14 @@ Components are the building blocks of the flows. They are made of inputs, output
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/single-compenent.png"),
dark: useBaseUrl("img/single-compenent.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
style={{ width: "100%", maxWidth: "800px", margin: "20px auto" }}
/>
<div style={{ marginBottom: "20px" }}>

6
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@ -406,8 +406,4 @@ Langflow will attempt to load all of the components found in the specified direc
Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components.
Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!

5
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@ -1,4 +1,3 @@
# Features
<div style={{ marginBottom: "20px" }}>
@ -9,13 +8,14 @@
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/features.png"),
dark: useBaseUrl("img/features.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
style={{ width: "100%", maxWidth: "800px", margin: "20px auto" }}
/>
<div style={{ marginBottom: "20px" }}>
@ -63,7 +63,6 @@ The example below shows a Python script making a POST request to a local API end
<ReactPlayer playing controls url="/videos/langflow_api.mp4" />
</div>
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";

7
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@ -105,7 +105,7 @@ Users can change their profile settings by clicking on the profile icon in the t
light: useBaseUrl("img/my-account.png"),
dark: useBaseUrl("img/my-account.png"),
}}
style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
style={{ width: "50%", maxWidth: "600px", margin: "20px auto" }}
/>
By clicking on **Profile Settings**, the user is taken to the profile settings page, where they can change their password and their profile picture.
@ -116,10 +116,11 @@ By clicking on **Profile Settings**, the user is taken to the profile settings p
light: useBaseUrl("img/profile-settings.png"),
dark: useBaseUrl("img/profile-settings.png"),
}}
style={{ maxWidth: "600px", margin: "0 auto" }}
style={{ maxWidth: "600px", margin: "20px auto" }}
/>
By clicking on **Admin Page**, the superuser is taken to the admin page, where they can manage users and groups.
By clicking on **Admin Page**, the superuser is taken to the admin page, where they
can manage users and groups.
<ZoomableImage
alt="Docusaurus themed image"

44
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@ -1,44 +0,0 @@
import Admonition from "@theme/Admonition";
# Async API
## Introduction
<Admonition type="info" caption="In development">
This implementation is still in development. Contributions are welcome!
</Admonition>
The Async API is an implementation of the Langflow API that uses [Celery](https://docs.celeryproject.org/en/stable/)
to run the tasks asynchronously, using a message broker to send and receive messages, a result backend to store the results and a cache to store the task states and session data.
### Configuration
The folder _`./deploy`_ in the [Github repository](https://github.com/logspace-ai/langflow) contains a _`.env.example`_ file that can be used to configure a Langflow deployment.
The file contains the variables required to configure a Celery worker queue, Redis cache and result backend and a RabbitMQ message broker.
To set it up locally you can copy the file to _`.env`_ and run the following command:
```bash
docker compose up -d
```
This will set up the following containers:
- Langflow API
- Celery worker
- RabbitMQ message broker
- Redis cache
- PostgreSQL database
- PGAdmin
- Flower
- Traefik
- Grafana
- Prometheus
### Testing
To run the tests for the Async API, you can run the following command:
```bash
docker compose -f docker-compose.with_tests.yml up --exit-code-from tests tests result_backend broker celeryworker db --build
```

25
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@ -1,10 +1,12 @@
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# 👋 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.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
{" "}
{" "}
@ -17,7 +19,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
style={{ width: "100%" }}
/>
## 🚀 First steps
## Installation
@ -29,19 +30,21 @@ You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installatio
Pipx can fetch the missing Python version for you, but you can also install it manually.
```bash
pipx install langflow --python python3.10 --fetch-missing-python
# or
pip install langflow -U
# or
pipx install langflow --python python3.10 --fetch-missing-python
```
Or you can install a pre-release version using:
```bash
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre"
pip install langflow --pre --force-reinstall
# or
pip install langflow --pre -U
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
```
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.
@ -64,17 +67,15 @@ Remember to use a Chromium-based browser for the best experience. You'll be pres
light: "img/duplicate-space.png",
dark: "img/duplicate-space.png",
}}
style={{ width: "100%" }}
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.
@ -91,4 +92,4 @@ Find more information about the available options by running:
```bash
langflow --help
```
```

0
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65
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@ -0,0 +1,65 @@
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
# Global Variables
Global Variables are a really useful feature of Langflow.
They allow you to define reusable variables that can be accessed from any Text field in your project.
The first thing you need to do is find a **Text field** in a Component, so let's talk about what a Text field is.
## Text Fields
Text fields are the fields in a Component where you can write text but that does not allow you to open a Text Area.
The easiest way to find fields that are Text fields, though, is to look for fields that have a 🌐 button.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/ollama-gv.png",
dark: "img/ollama-gv.png",
}}
style={{ width: "50%" }}
/>
## Creating a Global Variable
To create a Global Variable, you need to click on the 🌐 button in a Text field and that will open a dropdown showing your currently available variables and at the end of it **+ Add New Variable**.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/add-new-variable.png",
dark: "img/add-new-variable.png",
}}
style={{ width: "60%" }}
/>
Click on **+ Add New Variable** and a window will open where you can define your new Global Variable.
In it, you can define the **Name** of the variable, the optional **Type** of the variable, and the **Value** of the variable.
The **Name** is the name that you will use to refer to the variable in your Text fields.
The **Type** is optional for now but will be used in the future to allow for more advanced features.
The **Value** is the value that the variable will have.
{/* say that all variables are encrypted */}
<Admonition type="warning">
All Global Variables are encrypted and cannot be accessed by anyone but you.
</Admonition>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/create-variable-window.png",
dark: "img/create-variable-window.png",
}}
style={{ width: "60%" }}
/>
After you have defined your variable, click on **Save Variable** and your variable will be created.
After that, once you click on the 🌐 button in a Text field, you will see your new variable in the dropdown.

2
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@ -33,4 +33,4 @@ Outputs are components that are used to define where data comes out of your flow
The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user.
You can find out more about it and the other Outputs [here](../components/outputs).
You can find out more about it and the other Outputs [here](../components/outputs).

14
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@ -27,11 +27,9 @@ This is a big change, but it's also a big improvement.
It allows you to define the structure of your conversation and the data that flows through it.
This makes it easier to understand and control your conversation.
This change comes with a new way of visualizing your projects. Before 1.0 you would connect Components to ultimately build one final Component that was processed behind the scenes.
Now, each step of the process is defined by you, is visible on the canvas, and can be monitored and controlled by you. This makes it so that Composition is now just another way of building in Langflow. **Now data flows through your project more transparently**.
The caveat is existing projects may need some new Components to get them back to their full functionality.
[We've made this as easy as possible](../migration/compatibility), and there will be improvements to it as we get feedback in our Discord server and on GitHub.
@ -40,10 +38,8 @@ The caveat is existing projects may need some new Components to get them back to
The moment we decided to make this change, we saw the potential to make Langflow even more yours.
By having a clear definition of Inputs and Outputs, we could build the experience around that which led us to create the **Interaction Panel**.
When building a project testing and debugging is crucial. The Interaction Panel is a tool that changes dynamically based on the Inputs and Outputs you defined in your project.
For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt and the answer.
Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Interaction Panel.
@ -65,9 +61,11 @@ We wanted to create start projects that would help you learn about new features
For now, we have:
- **Basic Prompting**: A simple project that shows you how to use the Prompt Component.
- **Data Ingestion**: A project that shows you how to ingest files into a Vector Store.
- **RAG**: A project that shows you how to use a Vector Store Search and a Prompt to build a simple RAG application.
- **[Basic Prompting (Ahoy World!)](/getting-started/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate.
- **[Vector Store RAG](/getting-started/rag-with-astradb)**: A flow that shows you how to ingest data into a Vector Store and then use it to run a RAG application.
- **[Memory Chatbot](/getting-started/memory-chatbot)**: This one shows you how to create a simple chatbot that can remember things about the user.
- **[Document QA](/getting-started/document-qa)**: This flow shows you how to build a simple flow that helps you get answers about a document.
- **[Blog Writer](/getting-started/blog-writer)**: Shows you how you can expand on the Prompt variables and be creative about what inputs you add to it.
As always, your feedback is invaluable, so please let us know what you think of the new starter projects and what you would like to see in the future.
@ -95,4 +93,4 @@ We also have some experimental features like a State Management System (so cool!
## Reach out
One last time, we want to thank you for being part of the Langflow community. Your feedback is invaluable, and we want to hear from you.
One last time, we want to thank you for being part of the Langflow community. Your feedback is invaluable, and we want to hear from you.

4
docs/docusaurus.config.js
View file

@ -43,6 +43,10 @@ module.exports = {
path: "docs",
// sidebarPath: 'sidebars.js',
},
gtag: {
trackingID: 'G-XHC7G628ZP',
anonymizeIP: true,
},
theme: {
customCss: [
require.resolve("@code-hike/mdx/styles.css"),

1
docs/package-lock.json generated
View file

@ -11,6 +11,7 @@
"@babel/preset-react": "^7.22.3",
"@code-hike/mdx": "^0.9.0",
"@docusaurus/core": "^3.2.0",
"@docusaurus/plugin-google-gtag": "^3.2.0",
"@docusaurus/plugin-ideal-image": "^3.2.0",
"@docusaurus/preset-classic": "^3.2.0",
"@docusaurus/theme-classic": "^3.2.0",

1
docs/package.json
View file

@ -17,6 +17,7 @@
"@babel/preset-react": "^7.22.3",
"@code-hike/mdx": "^0.9.0",
"@docusaurus/core": "^3.2.0",
"@docusaurus/plugin-google-gtag": "^3.2.0",
"@docusaurus/plugin-ideal-image": "^3.2.0",
"@docusaurus/preset-classic": "^3.2.0",
"@docusaurus/theme-classic": "^3.2.0",

50
docs/sidebars.js
View file

@ -7,8 +7,11 @@ module.exports = {
items: [
"index",
"getting-started/cli",
"getting-started/hugging-face-spaces",
"getting-started/creating-flows",
"getting-started/basic-prompting",
"getting-started/document-qa",
"getting-started/blog-writer",
"getting-started/memory-chatbot",
"getting-started/rag-with-astradb",
],
},
{
@ -20,37 +23,26 @@ module.exports = {
"whats-new/migrating-to-one-point-zero",
],
},
{
type: "category",
label: " Step-by-Step Guides",
collapsed: false,
items: [
"guides/rag-with-astradb",
"guides/async-tasks",
"guides/loading_document",
"guides/chatprompttemplate_guide",
"guides/langfuse_integration",
],
},
{
type: "category",
label: "Migration Guides",
label: " Migration Guides",
collapsed: false,
items: [
// "migration/flow-of-data",
"migration/inputs-and-outputs",
// "migration/supported-frameworks",
// "migration/sidebar-and-interaction-panel",
// "migration/new-categories-and-components",
// "migration/text-and-record",
"migration/sidebar-and-interaction-panel",
"migration/new-categories-and-components",
"migration/text-and-record",
// "migration/custom-component",
"migration/compatibility",
// "migration/multiple-flows",
// "migration/component-status-and-data-passing",
"migration/multiple-flows",
"migration/component-status-and-data-passing",
// "migration/connecting-output-components",
// "migration/renaming-and-editing-components",
"migration/renaming-and-editing-components",
// "migration/passing-tweaks-and-inputs",
// "migration/global-variables",
"migration/global-variables",
// "migration/experimental-components",
// "migration/state-management",
],
@ -62,7 +54,6 @@ module.exports = {
items: [
"guidelines/login",
"guidelines/api",
"guidelines/async-api",
"guidelines/components",
"guidelines/features",
"guidelines/collection",
@ -72,6 +63,12 @@ module.exports = {
"guidelines/custom-component",
],
},
{
type: "category",
label: "Step-by-Step Guides",
collapsed: false,
items: ["guides/langfuse_integration"],
},
{
type: "category",
label: "Core Components",
@ -83,7 +80,7 @@ module.exports = {
"components/models",
"components/helpers",
"components/vector-stores",
"components/embeddings",
"components/embeddings",
],
},
{
@ -102,8 +99,6 @@ module.exports = {
"components/text-splitters",
"components/toolkits",
"components/tools",
"components/wrappers",
// "components/prompts",
],
},
{
@ -114,13 +109,10 @@ module.exports = {
"examples/flow-runner",
"examples/conversation-chain",
"examples/buffer-memory",
"examples/midjourney-prompt-chain",
"examples/csv-loader",
"examples/searchapi-tool",
"examples/serp-api-tool",
"examples/multiple-vectorstores",
"examples/python-function",
"examples/how-upload-examples",
],
},
{

58
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16
lcserve.Dockerfile
View file

@ -1,16 +0,0 @@
# This file is used by `lc-serve` to build the image.
# Don't change the name of this file.
FROM jinawolf/serving-gateway:${version}
RUN apt-get update \
&& apt-get install --no-install-recommends -y build-essential libpq-dev
COPY . /appdir/
RUN pip install poetry==1.4.0 && cd /appdir && pip install . && \
pip uninstall -y poetry && \
apt-get remove --auto-remove -y build-essential libpq-dev && \
apt-get autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* && rm -rf /tmp/*
ENTRYPOINT [ "jina", "gateway", "--uses", "config.yml" ]

522
poetry.lock generated
View file

@ -455,17 +455,17 @@ files = [
[[package]]
name = "boto3"
version = "1.34.75"
version = "1.34.77"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "boto3-1.34.75-py3-none-any.whl", hash = "sha256:ba5d2104bba4370766036d64ad9021eb6289d154265852a2a821ec6a5e816faa"},
{file = "boto3-1.34.75.tar.gz", hash = "sha256:eaec72fda124084105a31bcd67eafa1355b34df6da70cadae0c0f262d8a4294f"},
{file = "boto3-1.34.77-py3-none-any.whl", hash = "sha256:7abd327980258ec2ae980d2ff7fc32ede7448146b14d34c56bf0be074e2a149b"},
{file = "boto3-1.34.77.tar.gz", hash = "sha256:8ebed4fa5a3b84dd4037f28226985af00e00fb860d739fc8b1ed6381caa4b330"},
]
[package.dependencies]
botocore = ">=1.34.75,<1.35.0"
botocore = ">=1.34.77,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@ -474,13 +474,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.34.75"
version = "1.34.77"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
{file = "botocore-1.34.75-py3-none-any.whl", hash = "sha256:1d7f683d99eba65076dfb9af3b42fa967c64f11111d9699b65757420902aa002"},
{file = "botocore-1.34.75.tar.gz", hash = "sha256:06113ee2587e6160211a6bd797e135efa6aa21b5bde97bf455c02f7dff40203c"},
{file = "botocore-1.34.77-py3-none-any.whl", hash = "sha256:6d6a402032ca0b89525212356a865397f8f2839683dd53d41b8cee1aa84b2b4b"},
{file = "botocore-1.34.77.tar.gz", hash = "sha256:6dab60261cdbfb7d0059488ea39408d5522fad419c004ba5db3484e6df854ea8"},
]
[package.dependencies]
@ -1129,13 +1129,13 @@ testing = ["pytest (>=7.2.1)", "pytest-cov (>=4.0.0)", "tox (>=4.4.3)"]
[[package]]
name = "cohere"
version = "5.1.7"
version = "5.2.1"
description = ""
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "cohere-5.1.7-py3-none-any.whl", hash = "sha256:66e149425ba10d9d6ed2980ad869afae2ed79b1f4c375f215ff4953f389cf5f9"},
{file = "cohere-5.1.7.tar.gz", hash = "sha256:5b5ba38e614313d96f4eb362046a3470305e57119e39538afa3220a27614ba15"},
{file = "cohere-5.2.1-py3-none-any.whl", hash = "sha256:c694f9d2cdafd87443f54ea5238b51a0fb807f119673e00b814c2a2993368e38"},
{file = "cohere-5.2.1.tar.gz", hash = "sha256:7cd5522bb162c05c67b2db0b7aba2a103622e17ece9e885f5ef2de66bb67a324"},
]
[package.dependencies]
@ -1143,6 +1143,7 @@ fastavro = ">=1.9.4,<2.0.0"
httpx = ">=0.21.2"
pydantic = ">=1.9.2"
requests = ">=2.31.0,<3.0.0"
tokenizers = ">=0.15.2,<0.16.0"
types-requests = ">=2.31.0.20240311,<3.0.0.0"
typing_extensions = ">=4.0.0"
@ -2560,13 +2561,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
[[package]]
name = "google-api-python-client"
version = "2.124.0"
version = "2.125.0"
description = "Google API Client Library for Python"
optional = false
python-versions = ">=3.7"
files = [
{file = "google-api-python-client-2.124.0.tar.gz", hash = "sha256:f6d3258420f7c76b0f5266b5e402e6f804e30351b018a10083f4a46c3ec33773"},
{file = "google_api_python_client-2.124.0-py2.py3-none-any.whl", hash = "sha256:07dc674449ed353704b1169fdee792f74438d024261dad71b6ce7bb9c683d51f"},
{file = "google-api-python-client-2.125.0.tar.gz", hash = "sha256:51a0385cff65ec135106e8be60ee7112557396dde5f44113ae23912baddda143"},
{file = "google_api_python_client-2.125.0-py2.py3-none-any.whl", hash = "sha256:0a62b60fbd61b61a455f15d925264b3301099b67cafd2d33cf8bf151f1fca4f4"},
]
[package.dependencies]
@ -3772,13 +3773,13 @@ numpy = ">=1,<2"
[[package]]
name = "langchain-cohere"
version = "0.1.0rc1"
version = "0.1.0"
description = "An integration package connecting Cohere and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_cohere-0.1.0rc1-py3-none-any.whl", hash = "sha256:698ee4e889169c1115bc2b0992c152aafd574030e6ea18238dd6b5d034733c64"},
{file = "langchain_cohere-0.1.0rc1.tar.gz", hash = "sha256:cc91b33cc5c6cb8d04c12034366c52b94798313d4503b776de9345e7261e8d15"},
{file = "langchain_cohere-0.1.0-py3-none-any.whl", hash = "sha256:f60e9eb41f7d4ead9659bddb3fae7aa18ddc3fdf2b2867be4bd8a565229f488d"},
{file = "langchain_cohere-0.1.0.tar.gz", hash = "sha256:960551293ea58d170fad37d44657d3ae4587f6b2e8f3f58922c53c59b9e9d85c"},
]
[package.dependencies]
@ -3813,13 +3814,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
[[package]]
name = "langchain-core"
version = "0.1.38"
version = "0.1.40"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_core-0.1.38-py3-none-any.whl", hash = "sha256:d881b2754254cb4bdb0d5bb56e5c138d032b6e75e5cb21f151b01224b322e02b"},
{file = "langchain_core-0.1.38.tar.gz", hash = "sha256:ee8da6d061c06cce7dc22fec224b6ecbc3a8de106d6dd9f409c7fe448ea41861"},
{file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"},
{file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"},
]
[package.dependencies]
@ -3828,7 +3829,6 @@ langsmith = ">=0.1.0,<0.2.0"
packaging = ">=23.2,<24.0"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
requests = ">=2,<3"
tenacity = ">=8.1.0,<9.0.0"
[package.extras]
@ -3920,7 +3920,7 @@ six = "*"
[[package]]
name = "langflow-base"
version = "0.0.16"
version = "0.0.17"
description = "A Python package with a built-in web application"
optional = false
python-versions = ">=3.10,<3.12"
@ -3973,13 +3973,13 @@ url = "src/backend/base"
[[package]]
name = "langfuse"
version = "2.21.1"
version = "2.21.2"
description = "A client library for accessing langfuse"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langfuse-2.21.1-py3-none-any.whl", hash = "sha256:5ef286823a4c9903e2120ad2bf0169a929d41789702535abc713e66a0d270f05"},
{file = "langfuse-2.21.1.tar.gz", hash = "sha256:36494ea016784ac339a1a5375b88c33484e81668433956ead442d7a93c217078"},
{file = "langfuse-2.21.2-py3-none-any.whl", hash = "sha256:bd65858e6326776f65c9b2e414e64fdea0f14402f5c784952af93346dfd489bb"},
{file = "langfuse-2.21.2.tar.gz", hash = "sha256:eb7911aa640f020f097cb56eaa7d67f01d39f9e2bdd6226e0c5d642a87f3663c"},
]
[package.dependencies]
@ -3992,18 +3992,18 @@ wrapt = ">=1.14,<2.0"
[package.extras]
langchain = ["langchain (>=0.0.309)"]
llama-index = ["llama-index (>=0.10.12,<0.11.0)"]
llama-index = ["llama-index (>=0.10.12,<2.0.0)"]
openai = ["openai (>=0.27.8)"]
[[package]]
name = "langsmith"
version = "0.1.38"
version = "0.1.39"
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-0.1.38-py3-none-any.whl", hash = "sha256:f36479f82cf537cf40d129ac2e485e72a3981360c7b6cf2549dad77d98eafd8f"},
{file = "langsmith-0.1.38.tar.gz", hash = "sha256:2c1f98ac0a8c02e43b625650a6e13c65b09523551bfc21a59d20963f46f7d265"},
{file = "langsmith-0.1.39-py3-none-any.whl", hash = "sha256:85c19177162585728001cb7ae91ab48ca4abe39b7bc1ff783212ac426ded222b"},
{file = "langsmith-0.1.39.tar.gz", hash = "sha256:2aec9d2f9cc664042d2121b13da569b0902aff842c86b17b440245d57da84ec5"},
]
[package.dependencies]
@ -4028,14 +4028,40 @@ interegular = ["interegular (>=0.3.1,<0.4.0)"]
nearley = ["js2py"]
regex = ["regex"]
[[package]]
name = "litellm"
version = "1.34.22"
description = "Library to easily interface with LLM API providers"
optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [
{file = "litellm-1.34.22-py3-none-any.whl", hash = "sha256:0e573d56d762f4060c53493da4a08c48034b5bb5ba22e34517065739adfd9154"},
{file = "litellm-1.34.22.tar.gz", hash = "sha256:ca50ede3ca8d3f9dc2765ca13cf2ff5c4e4b9afb4db222f9d7cb9ee838b6180f"},
]
[package.dependencies]
aiohttp = "*"
click = "*"
importlib-metadata = ">=6.8.0"
jinja2 = ">=3.1.2,<4.0.0"
openai = ">=1.0.0"
python-dotenv = ">=0.2.0"
requests = ">=2.31.0,<3.0.0"
tiktoken = ">=0.4.0"
tokenizers = "*"
[package.extras]
extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "resend (>=0.8.0,<0.9.0)"]
proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=42.0.5,<43.0.0)", "fastapi (>=0.109.1,<0.110.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=21.2.0,<22.0.0)", "orjson (>=3.9.7,<4.0.0)", "python-multipart (>=0.0.9,<0.0.10)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"]
[[package]]
name = "llama-cpp-python"
version = "0.2.58"
version = "0.2.59"
description = "Python bindings for the llama.cpp library"
optional = true
python-versions = ">=3.8"
files = [
{file = "llama_cpp_python-0.2.58.tar.gz", hash = "sha256:50d4d16835326b15f5c4ed20dbf2f24508bf29b34531d50612ce215a596dde3f"},
{file = "llama_cpp_python-0.2.59.tar.gz", hash = "sha256:4b19283226ab91c74c6d811d88724a6f32d9dd7d07caf9d8b897dd3372d5d4d2"},
]
[package.dependencies]
@ -4077,18 +4103,19 @@ llama-index-readers-llama-parse = ">=0.1.2,<0.2.0"
[[package]]
name = "llama-index-agent-openai"
version = "0.2.1"
version = "0.2.2"
description = "llama-index agent openai integration"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "llama_index_agent_openai-0.2.1-py3-none-any.whl", hash = "sha256:0127414bd0afcdd2eb5f7f97dc9693653ca435160fd09af83ac67fb3b07bf991"},
{file = "llama_index_agent_openai-0.2.1.tar.gz", hash = "sha256:c9d0a2c43d2f752b80f7d3dd7e56e112c49dddbd06974973153cfdb9374b62b4"},
{file = "llama_index_agent_openai-0.2.2-py3-none-any.whl", hash = "sha256:fa8cbc2c7be5a465848f8d5b432db01c55f07dfa06357edb7fb77fb17d534d1e"},
{file = "llama_index_agent_openai-0.2.2.tar.gz", hash = "sha256:12063dd932c74015796f973986cc52d783f51fda38e4ead72a56d0fd195925ee"},
]
[package.dependencies]
llama-index-core = ">=0.10.1,<0.11.0"
llama-index-llms-openai = ">=0.1.5,<0.2.0"
openai = ">=1.14.0"
[[package]]
name = "llama-index-cli"
@ -4328,13 +4355,13 @@ llama-index-core = ">=0.10.7"
[[package]]
name = "llamaindex-py-client"
version = "0.1.15"
version = "0.1.16"
description = ""
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "llamaindex_py_client-0.1.15-py3-none-any.whl", hash = "sha256:d189f23a8f7f78d0e170f62b531dd6ac030eadcb7dd7d38c1b543c4c98c51e5c"},
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{file = "scipy-1.13.0.tar.gz", hash = "sha256:58569af537ea29d3f78e5abd18398459f195546bb3be23d16677fb26616cc11e"},
]
[package.dependencies]
numpy = ">=1.22.4,<1.29.0"
numpy = ">=1.22.4,<2.3"
[package.extras]
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
[[package]]
name = "sentence-transformers"
@ -9176,13 +9244,13 @@ types-pyOpenSSL = "*"
[[package]]
name = "types-requests"
version = "2.31.0.20240402"
version = "2.31.0.20240403"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-requests-2.31.0.20240402.tar.gz", hash = "sha256:e5c09a202f8ae79cd6ffbbba2203b6c3775a83126283bb2a6abbc129abc02a12"},
{file = "types_requests-2.31.0.20240402-py3-none-any.whl", hash = "sha256:bd7eb7102168d4b5b489f15cdd9842b63ab7fe56aa82a0589fa595b94195acf4"},
{file = "types-requests-2.31.0.20240403.tar.gz", hash = "sha256:e1e0cd0b655334f39d9f872b68a1310f0e343647688bf2cee932ec4c2b04de59"},
{file = "types_requests-2.31.0.20240403-py3-none-any.whl", hash = "sha256:06abf6a68f5c4f2a62f6bb006672dfb26ed50ccbfddb281e1ee6f09a65707d5d"},
]
[package.dependencies]
@ -10249,4 +10317,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.12"
content-hash = "3eb1181a83884c7ba52a7d1c98dcff13a307452eaf8f4a148fc0778f97499dfd"
content-hash = "ed8605b2934fceb591d03d5be7461ed05a8f427512b693ce1baefeaa4fa21500"

5
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow"
version = "1.0.0a0"
version = "1.0.0a4"
description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"]
maintainers = [
@ -23,8 +23,6 @@ documentation = "https://docs.langflow.org"
[tool.poetry.scripts]
langflow = "langflow.__main__:main"
[tool.poetry-monorepo-dependency-plugin]
enable = true
[tool.poetry.dependencies]
python = ">=3.10,<3.12"
@ -80,6 +78,7 @@ dspy-ai = "^2.4.0"
crewai = "^0.22.5"
html2text = "^2024.2.26"
assemblyai = "^0.23.1"
litellm = "^1.34.22"
[tool.poetry.group.dev.dependencies]
types-redis = "^4.6.0.5"

8
scripts/setup/update_poetry.sh
View file

@ -146,11 +146,3 @@ else
echo "Poetry version is $1 or higher. No need to update."
fi
# Check if poetry-monorepo-dependency-plugin is installed
if poetry self show | grep -q "poetry-monorepo-dependency-plugin"; then
echo "poetry-monorepo-dependency-plugin is already installed."
else
echo "Installing poetry-monorepo-dependency-plugin..."
poetry run pip install poetry-monorepo-dependency-plugin
echo "poetry-monorepo-dependency-plugin installed successfully."
fi

11
scripts/update_dependencies.py
View file

@ -11,6 +11,15 @@ def read_version_from_pyproject(file_path):
return None
def get_version_from_pypi(package_name):
import requests
response = requests.get(f"https://pypi.org/pypi/{package_name}/json")
if response.ok:
return response.json()["info"]["version"]
return None
def update_pyproject_dependency(pyproject_path, version):
pattern = re.compile(r'langflow-base = \{ path = "\./src/backend/base", develop = true \}')
replacement = f'langflow-base = "^{version}"'
@ -35,7 +44,7 @@ if __name__ == "__main__":
# Reading version and updating pyproject.toml
langflow_base_path = Path(__file__).resolve().parent / "../src/backend/base/pyproject.toml"
version = read_version_from_pyproject(langflow_base_path)
version = get_version_from_pypi("langflow-base")
if version:
update_pyproject_dependency(pyproject_path, version)
else:

14
src/backend/base/langflow/__main__.py
View file

@ -246,9 +246,17 @@ def get_free_port(port):
def print_banner(host, port):
from langflow.version import __version__
try:
from langflow.version import __version__
version = __version__
word = "Langflow"
except ImportError:
from importlib import metadata
version = metadata.version("langflow-base")
word = "Langflow Base"
word = "Langflow"
colors = ["#6e42f5"]
styled_word = ""
@ -259,7 +267,7 @@ def print_banner(host, port):
# Title with emojis and gradient text
title = (
f"[bold]Welcome to :chains: {styled_word} v{__version__}[/bold]\n"
f"[bold]Welcome to :chains: {styled_word} v{version}[/bold]\n"
f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]"
)
info_text = (

12
src/backend/base/langflow/api/v1/endpoints.py
View file

@ -372,9 +372,17 @@ async def create_upload_file(
# get endpoint to return version of langflow
@router.get("/version")
def get_version():
from langflow.version import __version__ # type: ignore
try:
from langflow.version import __version__
return {"version": __version__}
version = __version__
package = "Langflow"
except ImportError:
from importlib import metadata
version = metadata.version("langflow-base")
package = "Langflow Base"
return {"version": version, "package": package}
@router.post("/custom_component", status_code=HTTPStatus.OK)

4
src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
View file

@ -94,10 +94,10 @@ class OpenAIEmbeddingsComponent(CustomComponent):
disallowed_special: List[str] = ["all"],
chunk_size: int = 1000,
client: Optional[Any] = None,
deployment: str = "text-embedding-3-small",
deployment: str = "text-embedding-ada-002",
embedding_ctx_length: int = 8191,
max_retries: int = 6,
model: str = "text-embedding-3-small",
model: str = "text-embedding-ada-002",
model_kwargs: NestedDict = {},
openai_api_base: Optional[str] = None,
openai_api_type: Optional[str] = None,

23
src/backend/base/langflow/components/helpers/SplitText.py
View file

@ -1,14 +1,11 @@
from typing import Optional
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
CharacterTextSplitter,
)
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from langflow.field_typing import Text
from langflow.interface.custom.custom_component import CustomComponent
from langflow.schema import Record
from langflow.field_typing import Text
from langflow.utils.util import unescape_string
@ -18,10 +15,10 @@ class SplitTextComponent(CustomComponent):
def build_config(self):
return {
"texts": {
"display_name": "Texts",
"inputs": {
"display_name": "Inputs",
"info": "Texts to split.",
"input_types": ["Text"],
"input_types": ["Record", "Text"],
},
"separators": {
"display_name": "Separators",
@ -48,7 +45,7 @@ class SplitTextComponent(CustomComponent):
def build(
self,
texts: list[Text],
inputs: list[Text],
separators: Optional[list[str]] = [" "],
chunk_size: Optional[int] = 1000,
chunk_overlap: Optional[int] = 200,
@ -77,9 +74,11 @@ class SplitTextComponent(CustomComponent):
)
documents = []
for _text in texts:
# documents.append(_input.to_lc_document())
documents.append(Document(page_content=_text))
for _input in inputs:
if isinstance(_input, Record):
documents.append(_input.to_lc_document())
else:
documents.append(Document(page_content=_input))
records = self.to_records(splitter.split_documents(documents))
self.status = records

2
src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py
View file

@ -118,7 +118,7 @@ class ChatLiteLLMComponent(CustomComponent):
max_tokens: int = 256,
max_retries: int = 6,
verbose: bool = False,
) -> Union[BaseLanguageModel, Callable]:
) -> BaseLanguageModel:
try:
import litellm # type: ignore

191
src/backend/base/langflow/components/models/ChatLiteLLMModel.py Normal file
View file

@ -0,0 +1,191 @@
from typing import Any, Dict, Optional
from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
from langflow.base.constants import STREAM_INFO_TEXT
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import BaseLanguageModel, Text
class ChatLiteLLMModelComponent(LCModelComponent):
display_name = "LiteLLM"
description = "`LiteLLM` collection of large language models."
documentation = "https://python.langchain.com/docs/integrations/chat/litellm"
field_order = [
"model",
"api_key",
"provider",
"temperature",
"model_kwargs",
"top_p",
"top_k",
"n",
"max_tokens",
"max_retries",
"verbose",
"stream",
"input_value",
"system_message",
]
def build_config(self):
return {
"model": {
"display_name": "Model name",
"field_type": "str",
"advanced": False,
"required": True,
"info": "The name of the model to use. For example, `gpt-3.5-turbo`.",
},
"api_key": {
"display_name": "API key",
"field_type": "str",
"advanced": False,
"required": False,
"password": True,
},
"provider": {
"display_name": "Provider",
"info": "The provider of the API key.",
"options": [
"OpenAI",
"Azure",
"Anthropic",
"Replicate",
"Cohere",
"OpenRouter",
],
},
"temperature": {
"display_name": "Temperature",
"field_type": "float",
"advanced": False,
"required": False,
"default": 0.7,
},
"model_kwargs": {
"display_name": "Model kwargs",
"field_type": "dict",
"advanced": True,
"required": False,
"default": {},
},
"top_p": {
"display_name": "Top p",
"field_type": "float",
"advanced": True,
"required": False,
},
"top_k": {
"display_name": "Top k",
"field_type": "int",
"advanced": True,
"required": False,
},
"n": {
"display_name": "N",
"field_type": "int",
"advanced": True,
"required": False,
"info": "Number of chat completions to generate for each prompt. "
"Note that the API may not return the full n completions if duplicates are generated.",
"default": 1,
},
"max_tokens": {
"display_name": "Max tokens",
"field_type": "int",
"advanced": False,
"required": False,
"default": 256,
"info": "The maximum number of tokens to generate for each chat completion.",
},
"max_retries": {
"display_name": "Max retries",
"field_type": "int",
"advanced": True,
"required": False,
"default": 6,
},
"verbose": {
"display_name": "Verbose",
"field_type": "bool",
"advanced": True,
"required": False,
"default": False,
},
"input_value": {"display_name": "Input"},
"stream": {
"display_name": "Stream",
"info": STREAM_INFO_TEXT,
"advanced": True,
},
"system_message": {
"display_name": "System Message",
"info": "System message to pass to the model.",
"advanced": True,
},
}
def build(
self,
input_value: Text,
model: str,
provider: str,
api_key: Optional[str] = None,
stream: bool = False,
temperature: Optional[float] = 0.7,
model_kwargs: Optional[Dict[str, Any]] = {},
top_p: Optional[float] = None,
top_k: Optional[int] = None,
n: int = 1,
max_tokens: int = 256,
max_retries: int = 6,
verbose: bool = False,
system_message: Optional[str] = None,
) -> BaseLanguageModel:
try:
import litellm # type: ignore
litellm.drop_params = True
litellm.set_verbose = verbose
except ImportError:
raise ChatLiteLLMException(
"Could not import litellm python package. " "Please install it with `pip install litellm`"
)
provider_map = {
"OpenAI": "openai_api_key",
"Azure": "azure_api_key",
"Anthropic": "anthropic_api_key",
"Replicate": "replicate_api_key",
"Cohere": "cohere_api_key",
"OpenRouter": "openrouter_api_key",
}
# Set the API key based on the provider
api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
if variable_name := provider_map.get(provider):
api_keys[variable_name] = api_key
else:
raise ChatLiteLLMException(
f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
)
output = ChatLiteLLM(
model=model,
client=None,
streaming=stream,
temperature=temperature,
model_kwargs=model_kwargs if model_kwargs is not None else {},
top_p=top_p,
top_k=top_k,
n=n,
max_tokens=max_tokens,
max_retries=max_retries,
openai_api_key=api_keys["openai_api_key"],
azure_api_key=api_keys["azure_api_key"],
anthropic_api_key=api_keys["anthropic_api_key"],
replicate_api_key=api_keys["replicate_api_key"],
cohere_api_key=api_keys["cohere_api_key"],
openrouter_api_key=api_keys["openrouter_api_key"],
)
return self.get_chat_result(output, stream, input_value, system_message)

2
src/backend/base/langflow/components/models/__init__.py
View file

@ -2,6 +2,7 @@ from .AmazonBedrockModel import AmazonBedrockComponent
from .AnthropicModel import AnthropicLLM
from .AzureOpenAIModel import AzureChatOpenAIComponent
from .BaiduQianfanChatModel import QianfanChatEndpointComponent
from .ChatLiteLLMModel import ChatLiteLLMModelComponent
from .CohereModel import CohereComponent
from .GoogleGenerativeAIModel import GoogleGenerativeAIComponent
from .HuggingFaceModel import HuggingFaceEndpointsComponent
@ -10,6 +11,7 @@ from .OpenAIModel import OpenAIModelComponent
from .VertexAiModel import ChatVertexAIComponent
__all__ = [
"ChatLiteLLMModelComponent",
"AmazonBedrockComponent",
"AnthropicLLM",
"AzureChatOpenAIComponent",

12
src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
View file

@ -7,8 +7,8 @@ from langflow.schema import Record
class AstraDBSearchComponent(LCVectorStoreComponent):
display_name = "AstraDB Search"
description = "Searches an existing AstraDB Vector Store."
display_name = "Astra DB Search"
description = "Searches an existing Astra DB Vector Store."
icon = "AstraDB"
field_order = ["token", "api_endpoint", "collection_name", "input_value", "embedding"]
@ -25,20 +25,20 @@ class AstraDBSearchComponent(LCVectorStoreComponent):
"embedding": {"display_name": "Embedding", "info": "Embedding to use"},
"collection_name": {
"display_name": "Collection Name",
"info": "The name of the collection within AstraDB where the vectors will be stored.",
"info": "The name of the collection within Astra DB where the vectors will be stored.",
},
"token": {
"display_name": "Token",
"info": "Authentication token for accessing AstraDB.",
"info": "Authentication token for accessing Astra DB.",
"password": True,
},
"api_endpoint": {
"display_name": "API Endpoint",
"info": "API endpoint URL for the AstraDB service.",
"info": "API endpoint URL for the Astra DB service.",
},
"namespace": {
"display_name": "Namespace",
"info": "Optional namespace within AstraDB to use for the collection.",
"info": "Optional namespace within Astra DB to use for the collection.",
"advanced": True,
},
"metric": {

12
src/backend/base/langflow/components/vectorstores/AstraDB.py
View file

@ -9,8 +9,8 @@ from langflow.schema import Record
class AstraDBVectorStoreComponent(CustomComponent):
display_name = "AstraDB"
description = "Builds or loads an AstraDB Vector Store."
display_name = "Astra DB"
description = "Builds or loads an Astra DB Vector Store."
icon = "AstraDB"
field_order = ["token", "api_endpoint", "collection_name", "inputs", "embedding"]
@ -23,20 +23,20 @@ class AstraDBVectorStoreComponent(CustomComponent):
"embedding": {"display_name": "Embedding", "info": "Embedding to use"},
"collection_name": {
"display_name": "Collection Name",
"info": "The name of the collection within AstraDB where the vectors will be stored.",
"info": "The name of the collection within Astra DB where the vectors will be stored.",
},
"token": {
"display_name": "Token",
"info": "Authentication token for accessing AstraDB.",
"info": "Authentication token for accessing Astra DB.",
"password": True,
},
"api_endpoint": {
"display_name": "API Endpoint",
"info": "API endpoint URL for the AstraDB service.",
"info": "API endpoint URL for the Astra DB service.",
},
"namespace": {
"display_name": "Namespace",
"info": "Optional namespace within AstraDB to use for the collection.",
"info": "Optional namespace within Astra DB to use for the collection.",
"advanced": True,
},
"metric": {

10
src/backend/base/langflow/initial_setup/setup.py
View file

@ -88,7 +88,7 @@ def load_starter_projects():
starter_projects = []
folder = Path(__file__).parent / "starter_projects"
for file in folder.glob("*.json"):
project = orjson.loads(file.read_text())
project = orjson.loads(file.read_text(encoding="utf-8"))
starter_projects.append((file, project))
logger.info(f"Loaded starter project {file}")
return starter_projects
@ -124,7 +124,7 @@ def get_project_data(project):
def update_project_file(project_path, project, updated_project_data):
project["data"] = updated_project_data
with open(project_path, "w") as f:
with open(project_path, "w", encoding="utf-8") as f:
f.write(orjson.dumps(project, option=orjson.OPT_INDENT_2).decode())
logger.info(f"Updated starter project {project['name']} file")
@ -197,7 +197,11 @@ def delete_start_projects(session):
def create_or_update_starter_projects():
components_paths = get_settings_service().settings.COMPONENTS_PATH
all_types_dict = get_all_components(components_paths, as_dict=True)
try:
all_types_dict = get_all_components(components_paths, as_dict=True)
except Exception as e:
logger.exception(f"Error loading components: {e}")
raise e
with session_scope() as session:
starter_projects = load_starter_projects()
delete_start_projects(session)

888
src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Ahoy World!).json Normal file
View file

@ -0,0 +1,888 @@
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"id": "ChatOutput-njtka",
"inputTypes": [
"Text"
],
"type": "str"
},
"sourceHandle": {
"baseClasses": [
"object",
"Text",
"str"
],
"dataType": "OpenAIModel",
"id": "OpenAIModel-k39HS"
}
},
"style": {
"stroke": "#555"
},
"className": "stroke-gray-900 stroke-connection",
"id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
},
{
"source": "Prompt-uxBqP",
"sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}",
"target": "OpenAIModel-k39HS",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "input_value",
"id": "OpenAIModel-k39HS",
"inputTypes": [
"Text"
],
"type": "str"
},
"sourceHandle": {
"baseClasses": [
"object",
"str",
"Text"
],
"dataType": "Prompt",
"id": "Prompt-uxBqP"
}
},
"style": {
"stroke": "#555"
},
"className": "stroke-gray-900 stroke-connection",
"id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
},
{
"source": "ChatInput-P3fgL",
"sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}",
"target": "Prompt-uxBqP",
"targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
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"targetHandle": {
"fieldName": "user_input",
"id": "Prompt-uxBqP",
"inputTypes": [
"Document",
"BaseOutputParser",
"Record",
"Text"
],
"type": "str"
},
"sourceHandle": {
"baseClasses": [
"object",
"Record",
"str",
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],
"dataType": "ChatInput",
"id": "ChatInput-P3fgL"
}
},
"style": {
"stroke": "#555"
},
"className": "stroke-gray-900 stroke-connection",
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}
],
"viewport": {
"x": 260.58251815500563,
"y": 318.2261172111936,
"zoom": 0.43514115784696294
}
},
"description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ",
"name": "Basic Prompting (Ahoy World!)",
"last_tested_version": "1.0.0a4",
"is_component": false
}

1134
src/backend/base/langflow/initial_setup/starter_projects/Langflow Basic Prompting.json
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3403
src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json Normal file
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23
src/backend/base/langflow/interface/custom/directory_reader/directory_reader.py
View file

@ -3,9 +3,10 @@ import os
import zlib
from pathlib import Path
from langflow.interface.custom.custom_component import CustomComponent
from loguru import logger
from langflow.interface.custom.custom_component import CustomComponent
class CustomComponentPathValueError(ValueError):
pass
@ -106,8 +107,15 @@ class DirectoryReader:
"""
if not os.path.isfile(file_path):
return None
with open(file_path, "r") as file:
return file.read()
with open(file_path, "r", encoding="utf-8") as file:
# UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 3069: character maps to <undefined>
try:
return file.read()
except UnicodeDecodeError:
# This is happening in Windows, so we need to open the file in binary mode
# The file is always just a python file, so we can safely read it as utf-8
with open(file_path, "rb") as file:
return file.read().decode("utf-8")
def get_files(self):
"""
@ -198,7 +206,12 @@ class DirectoryReader:
Process a file by validating its content and
returning the result and content/error message.
"""
file_content = self.read_file_content(file_path)
try:
file_content = self.read_file_content(file_path)
except Exception as exc:
logger.exception(exc)
logger.error(f"Error while reading file {file_path}: {str(exc)}")
return False, f"Could not read {file_path}"
if file_content is None:
return False, f"Could not read {file_path}"
@ -233,7 +246,7 @@ class DirectoryReader:
filename = os.path.basename(file_path)
validation_result, result_content = self.process_file(file_path)
if not validation_result:
logger.error(f"Error while processing file {file_path}: {result_content}")
logger.error(f"Error while processing file {file_path}")
menu_result = self.find_menu(response, menu_name) or {
"name": menu_name,

6
src/backend/base/langflow/utils/validate.py
View file

@ -203,10 +203,8 @@ def prepare_global_scope(code, module):
imported_module = importlib.import_module(node.module)
for alias in node.names:
exec_globals[alias.name] = getattr(imported_module, alias.name)
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Module {node.module} not found. Please install it and try again. Error: {repr(e)}"
)
except ModuleNotFoundError:
raise ModuleNotFoundError(f"Module {node.module} not found. Please install it and try again")
return exec_globals

546
src/backend/base/poetry.lock generated
View file

@ -1186,6 +1186,20 @@ six = ">=1.9.0"
gmpy = ["gmpy"]
gmpy2 = ["gmpy2"]
[[package]]
name = "emoji"
version = "2.11.0"
description = "Emoji for Python"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
files = [
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]
[package.extras]
dev = ["coverage", "coveralls", "pytest"]
[[package]]
name = "exceptiongroup"
version = "1.2.0"
@ -2487,13 +2501,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
[[package]]
name = "langchain-core"
version = "0.1.38"
version = "0.1.40"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_core-0.1.38-py3-none-any.whl", hash = "sha256:d881b2754254cb4bdb0d5bb56e5c138d032b6e75e5cb21f151b01224b322e02b"},
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]
[package.dependencies]
@ -2502,7 +2516,6 @@ langsmith = ">=0.1.0,<0.2.0"
packaging = ">=23.2,<24.0"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
requests = ">=2,<3"
tenacity = ">=8.1.0,<9.0.0"
[package.extras]
@ -2526,6 +2539,22 @@ langchain-core = ">=0.1.37,<0.2.0"
[package.extras]
extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "pandas (>=2.0.1,<3.0.0)", "presidio-analyzer (>=2.2.352,<3.0.0)", "presidio-anonymizer (>=2.2.352,<3.0.0)", "sentence-transformers (>=2,<3)", "tabulate (>=0.9.0,<0.10.0)", "vowpal-wabbit-next (==0.6.0)"]
[[package]]
name = "langchain-openai"
version = "0.1.1"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_openai-0.1.1-py3-none-any.whl", hash = "sha256:5cf4df5d2550af673337eafedaeec014ba52f9a25aeb8451206ca254bed01e5c"},
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]
[package.dependencies]
langchain-core = ">=0.1.33,<0.2.0"
openai = ">=1.10.0,<2.0.0"
tiktoken = ">=0.5.2,<1"
[[package]]
name = "langchain-text-splitters"
version = "0.0.1"
@ -2545,13 +2574,13 @@ extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
[[package]]
name = "langsmith"
version = "0.1.38"
version = "0.1.39"
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-0.1.38-py3-none-any.whl", hash = "sha256:f36479f82cf537cf40d129ac2e485e72a3981360c7b6cf2549dad77d98eafd8f"},
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]
[package.dependencies]
@ -2606,124 +2635,165 @@ dev = ["Sphinx (==7.2.5)", "colorama (==0.4.5)", "colorama (==0.4.6)", "exceptio
[[package]]
name = "lxml"
version = "5.2.0"
version = "5.2.1"
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
optional = false
python-versions = ">=3.6"
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[metadata]
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content-hash = "27adc9d6515d9e92ee01a6aae0c9a8162aa403456134ab25a8dd98909ecbe5f2"

6
src/backend/base/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow-base"
version = "0.0.16"
version = "0.0.18"
description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"]
maintainers = [
@ -60,6 +60,10 @@ pypdf = "^4.1.0"
chromadb = "^0.4.24"
langchain-anthropic = "^0.1.4"
langchain-astradb = "^0.1.0"
nest-asyncio = "^1.6.0"
emoji = "^2.11.0"
cryptography = "^42.0.5"
langchain-openai = "^0.1.1"
[tool.poetry.group.dev.dependencies]

2
src/frontend/src/components/cardComponent/index.tsx
View file

@ -130,7 +130,7 @@ export default function CollectionCardComponent({
return (
<Card
className={cn(
"group relative flex flex-col justify-between overflow-hidden transition-all hover:shadow-md",
"group relative flex min-h-[11rem] flex-col justify-between overflow-hidden transition-all hover:shadow-md",
disabled ? "pointer-events-none opacity-50" : ""
)}
>

4
src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx
View file

@ -43,7 +43,7 @@ export default function UndrawCardComponent({
}}
/>
);
case "Basic Prompting":
case "Basic Prompting (Ahoy World!)":
return (
<BasicPrompt
style={{
@ -83,7 +83,7 @@ export default function UndrawCardComponent({
}}
/>
);
case "Prompt Chaining":
case "Vector Store RAG":
return (
<PromptChaining
style={{

18
src/frontend/src/modals/NewFlowModal/index.tsx
View file

@ -34,10 +34,12 @@ export default function NewFlowModal({
{/* {examples.map((example, idx) => {
return <UndrawCardComponent key={idx} flow={example} />;
})} */}
{examples.find((e) => e.name == "Basic Prompting") && (
{examples.find((e) => e.name == "Basic Prompting (Ahoy World!)") && (
<UndrawCardComponent
key={1}
flow={examples.find((e) => e.name == "Basic Prompting")!}
flow={
examples.find((e) => e.name == "Basic Prompting (Ahoy World!)")!
}
/>
)}
{examples.find((e) => e.name == "Memory Chatbot") && (
@ -52,18 +54,18 @@ export default function NewFlowModal({
flow={examples.find((e) => e.name == "Document QA")!}
/>
)}
{examples.find((e) => e.name == "Prompt Chaining") && (
<UndrawCardComponent
key={1}
flow={examples.find((e) => e.name == "Prompt Chaining")!}
/>
)}
{examples.find((e) => e.name == "Blog Writer") && (
<UndrawCardComponent
key={1}
flow={examples.find((e) => e.name == "Blog Writer")!}
/>
)}
{examples.find((e) => e.name == "Vector Store RAG") && (
<UndrawCardComponent
key={1}
flow={examples.find((e) => e.name == "Vector Store RAG")!}
/>
)}
</div>
</BaseModal.Content>
</BaseModal>

9
tests/conftest.py
View file

@ -10,10 +10,6 @@ import orjson
import pytest
from fastapi.testclient import TestClient
from httpx import AsyncClient
from sqlmodel import Session, SQLModel, create_engine, select
from sqlmodel.pool import StaticPool
from typer.testing import CliRunner
from langflow.graph.graph.base import Graph
from langflow.initial_setup.setup import STARTER_FOLDER_NAME
from langflow.services.auth.utils import get_password_hash
@ -22,6 +18,9 @@ from langflow.services.database.models.flow.model import Flow, FlowCreate
from langflow.services.database.models.user.model import User, UserCreate
from langflow.services.database.utils import session_getter
from langflow.services.deps import get_db_service
from sqlmodel import Session, SQLModel, create_engine, select
from sqlmodel.pool import StaticPool
from typer.testing import CliRunner
if TYPE_CHECKING:
from langflow.services.database.service import DatabaseService
@ -381,7 +380,7 @@ def get_starter_project(active_user):
# once the client is created, we can get the starter project
with session_getter(get_db_service()) as session:
flow = session.exec(
select(Flow).where(Flow.folder == STARTER_FOLDER_NAME).where(Flow.name == "Basic Prompting")
select(Flow).where(Flow.folder == STARTER_FOLDER_NAME).where(Flow.name == "Basic Prompting (Ahoy World!)")
).first()
if not flow:
raise ValueError("No starter project found")