tl;dr - Cisco Research proudly presents BLAZE, a flexible, standardized, no-code, open-source platform to easily assemble, modify, and deploy various NLP models, datasets, and components

Check out our TechBlog and Homepage for more information and exciting applications!

Further technical documentation can also be found at our Cisco Research GitHub Pages.

Section-specific documentation can be found at the following smaller README.md's:

• Drag-and-Drop YAML Builder - drag/README.md
• Generated YAML Files Folder - yaml/README.md
• Models Usage Instructions - backend/models/README.md

Make sure to use the Table of Contents (the three horizontal lines next to README.md) for easier viewing!

BLAZE is designed to streamline the integration of Natural Language Pipelines into software solutions¹. We offer an open, extensible framework to benchmark existing solutions and compare them with novel ones.

The building blocks of BLAZE are flexible blocks of the NLP pipeline. The functionality of different stages in the pipeline are abstracted out to create blocks that can be combined in various ways. Users can arrange these Lego-like blocks to create new recipes of varying NLP pipelines.

In such, BLAZE will help democratize NLP applications, providing a no-code solution to experiment with SOTA research and serving as a framework to implement NLP pipeline recipes into usable solutions.

BLAZE currently supports the following building blocks and sample applications:

HF* - BLAZE's flexible design enables seamless integration of new components, many of which are underway!

Datasets* - Summarization datasets are currently being stabilized, but will be ready with a few tweaks!

In addition, we have several sample applications, each of which are detailed below:

1. Semantic Search on User-Uploaded Documents - TechBlog: Building WebApps with BLAZE
2. Benchmarking ElasticBERT Q/A on SQUAD 2.0 - TechBlog: Building WebApps with BLAZE
3. Transcript Analysis with WebEx ChatBot - TechBLog: Building ChatBot with BLAZE
4. Embedded WebEx Meeting Assistant Plugin - TechBlog: Building Plugins with BLAZE

Some of our more-specific offerings include:

Feature	Sample
(Tool) Drag-and-Drop Builder Visual builder for configuring and deploying pipelines Allows for building from scratch or uploading existing pipelines (visualize "recipes") Converts recipe into downloadable YAML config file Generates and launches custom NLP pipeline solution
(Interface) Dashboard WebApp Visual representation of generated pipeline Supports semantic search, summarization, file upload, etc.
(Interface) Conversational AI Interact with generated pipeline through natural language Can be purely functional or conversational Powered by Cisco MindMeld
(Function) Model Benchmarking Benchmark selected model on select knowledge base(s) Gives metrics such as latency, accuracy (real-time graphs) Displays incorrect questions and trends in performance
(Function) Model Comparison Benchmark multiple models on selected knowledge base(s) Gives metrics such as latency, accuracy (real-time graphs) Compares trends in performance (ex. areas of strength)

The BLAZE framework operates in three stages: Build, Execute, and Interact

In the Build stage, users can specify the models, data, and processing components of their pipeline using a YAML format. THey can create a fresh recipe via a block-based drag-and-drop UI, modify a pre-existing recipe, or use one directly out of the box. The YAML file contains the specifications of their custom pipeline.

Our drag-and-drop builder tool allows one to create, visualize, upload, and download YAML recipes.

Upon completing the drag-and-drop step, users can examine their generated YAML recipes. For example, here we can examine what the generated YAML recipe looks like for a virtual meeting assistant.

We provide several pre-made YAML files recipes in the yaml folder as well!

In the Execute stage, BLAZE utilizes the YAML file generated or chosen in the preceding stage to establish a server, hosting the appropriate models, datasets, and components as specified. This server servers as the heart of the pipeline, allowing users to interact with their specified configuration of components to run their task.

The following diagram represnts the architecture, illustrating how the server enables pipeline functionality.

YAML files can be executed via the run.py script, which is discussed in Installation below!

In the Interact stage, users can choose to interact with their hosted, active pipelines through a number of pre-build interfaces, or directly access each functionality through REST API services. Our current offering of interfaces include:

• WebApps (both in React and Dash)
• ChatBots (powered by WebEx, MindMeld)
• Plugins (both WebEx bots and WebEx Meeting Apps)
• Postman (or any other REST API Client)

All of these interfaces are automatically generated and are specific to the user's pipeline.

Steps to launch each of the above interfaces are discussed in the Installation below!

Powered by BLAZE's modular design, these varying interfaces were made without a single line of code. All a user has to do is specify their task in either the drag-and-drop builder or in the YAML recipe directly.

BLAZE is currently supported on Linux, Windows, and Mac. Specific instrucutions shown below:

At the very start, please clone this repostiory using

git clone https://github.com/cisco-open/Blaze.git

Next, we must install the necessary packages for BLAZE. For this, we have two options:

Install Dependencies

• For Ubuntu/Debian

sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python-openssl

• For Fedora/CentOS

sudo yum install gcc zlib-devel bzip2 bzip2-devel readline-devel sqlite sqlite-devel openssl-devel xz xz-devel libffi-devel

Install Pyenv

curl https://pyenv.run | bash

Install Python version 3.9.16

pyenv install -v 3.9.16

You can check the installed Python version by using command pyenv versions

Create virtual environment with specific version using below command

pyenv virtualenv 3.9.16 venv

Activate virtual environment using

pyenv local venv

or by running the following

pyenv activate <environment_name>

You can verify this by running pyenv which python

Finally, install packages and modules using

pip install -r requirements.txt

NOTE: The use of Conda may require the use of Anaconda Commercial Edition to comply with Anaconda's Terms of Service if your use is considered commercial according to Anaconda. More information about Anaconda's Terms of Service and what qualifies as commercial usage can be found here: https://www.anaconda.com/blog/anaconda-commercial-edition-faq/

Once you have cloned the BLAZE repository, cd into it

cd Blaze 

Create your conda environment with

conda env create -f blaze_env.yml

Then, activate your conda environment with

conda activate blaze

Run the Drag-and-Drop YAML Builder with:

python build.py

Now, a link should appear (ex. Dash is running on http://127.0.0.1:5000/). Follow this link in your browser to open the builder!

Make sure to check-out the instructions found in BLAZE Drag-and-Drop - README for using the YAML Builder.

Additionally, depending on the model chosen, make sure to check out model-specific instructions found in BLAZE Models - README!

Once you have your YAML file (either generated or pre-existing), note its relative path.

Run the following command to launch the pipeline specified in the YAML file:

python run.py <path-to-your-yaml-file> 

For example, one could run python run.py yaml/01_search_custom.yaml

Now, a link should appear (ex. Dash is running on http://127.0.0.1:5000/). Follow this this link in your browser to open the dashboard!

To verify that the server hosting the pipeline is running correctly, one can do so using POSTMAN.

Additionally, one can use Swagger by simply appending /apidocs/ upon launching the backend. For example, if the backend server is running on https://localhost:3000, then we can simply navigate to https://localhost:3000/apidocs/ in any browser to view the REST server via Swagger.

To interact with one's pipeline using our React WebApp, we need to first install Node.

To install node in Windows/Mac/Linux, we can do so with:

• Visit the NodeJS Homepage and install nodejs and npm
• We can check installation with node -v and npm -v

Next, we will move into the client directory by using

cd client

Then, we install node_modules by using command

npm install

Finally, we can run the frontend with

npm start

To run the frontend built with Python's dash, we can simply run

python run_fr.py <path-to-yaml-file>

Coming soon!

Coming Soon! Detailed instructions are currently being written, and will later be found at: BLAZE Conversational AI - README

Please raise an issue or reach out to our mailing list ([email protected]) with any questions!

Thank you so much for checking us out!