Flowise AI is an exciting new open source platform that allows anyone to create language model driven applications with no code required. With its visually intuitive interface and modular building blocks, Flowise makes it easy for non-technical users to harness the power of large language models like GPT-3.
In this comprehensive guide, we‘ll explore what exactly Flowise AI is, why it stands out from other no-code AI app builders, how the platform works, what can be built with it, and how you as a developer can start leveraging it for your own projects.
What is Flowise AI and Why is it Significant?
Flowise AI is a free and open source no-code platform for streamlining natural language processing workflows and building language model driven applications. Created by Anthropic research scientist Dr. Alex Flint, Flowise removes the need for coding by providing a visual drag-and-drop interface.
This enables anyone, technical or non-technical, to quickly prototype and deploy AI assistants, chatbots, semantic search engines, conversational interfaces, and more using state-of-the-art models like GPT-3.5 Turbo from Anthropic.
As Dr. Flint puts it, Flowise has the potential to "democratize LLM app development" by abstracting away needless complexity. This makes AI more accessible to domain experts without programming expertise so they can focus on creating solutions tailored to their needs.
The implications are huge – by removing barriers to leveraging the power of LLMs for custom use cases, Flowise can massively expand the pace of innovation across industries.
Key Capabilities and Features
So what can you actually build with Flowise AI? The platform provides a flexible toolkit combined with visual programming for rapidly developing LLM apps:
Drag-and-Drop Interface for Building Workflows
At the core of Flowise is its drag-and-drop workflow builder. This allows visually connecting together building blocks like:
- Triggers: Events that activate your workflow like HTTP requests or scheduled intervals
- Steps: Actions the workflow executes like invoking an LLM API, data processing, etc.
- Operators: Control flow logic like branches, loops, waits
Chaining everything provides an intuitive no-code environment for creating complex logic.
Integration With Leading Language Models
A key strength of Flowise is its seamless integration with LLMs like:
- GPT-3.5 Turbo: Anthropic‘s state-of-the-art LLM focused on safety and honesty
- InstructGPT: OpenAI‘s GPT-3 engine fine-tuned for instruction-based tasks
- Semantic Scholar: AI2‘s model trained on 50 million academic papers
You can visually connect them into workflows, configure prompts/parameters, and process the outputs without writing any code.
Modular Building Blocks
In addition to LLMs, Flowise provides a library of reusable components like:
- Triggers: HTTP, Scheduler, PubSub, Database Watchers
- Core: Operators, Variables, Control Flow
- I/O: Email, SMS, REST API, Docs
- Storage: Filesystem, S3 Compatible Clouds
- Helpers: Dates, Maps, Math, Text, HTML
These building blocks handle the heavy lifting so you can focus on workflow logic.
Developer Friendly Architecture
While no coding expertise is needed for using the visual workflow builder, Flowise provides developer friendly architecture for customization:
- Python environment for writing custom steps
- Source code available for the UI, backend, components
- Local development environment with hot reloading
- Docker support for hosting your own instance
- CLI tooling for workspace and secret management
So you get the flexibility to tweak flows or build your own steps when needed.
Affordable Credits-Based LLM Usage
For generating from large language models, Flowise utilizes a credits-based system for keeping costs predictable. Their marketplace provides:
- $20 free credits to get started
- Bulk discounts bringing cost to ~$0.004 per 1k tokens
- Free credits for open source contributions
- Grants for students, nonprofits etc.
This makes working with expensive LLMs accessible even with smaller budgets.
With these combined capabilities, Flowise provides an end-to-end platform enabling anyone to create and run LLM driven apps tailored to their use case. Next let‘s look at some examples of applications you can build.
Use Cases and Example LLM Apps
The toolkit that Flowise provides combined with LLMs like GPT-3.5 Turbo unlocks a diverse range of use cases. Pretty much anything involving understanding or generating natural language can be built without needing to code models and infrastructure from scratch.
Here are just some examples of what people have already created:
PDF Question Answering Bot
One of the most popular starter apps is a PDF QA bot powered by GPT-3.5 Turbo. This allows querying the contents of any PDF document just by messaging a chatbot.
The workflow uses a recursive character text splitter to ingest PDFs, passes questions to GPT-3.5 to generate answers based on the content, and returns responses in a chat interface. This kind of automation can save huge amounts of manual search/lookup time.
Semantic Scholar Assistant
By hooking up Semantic Scholar‘s academic paper focused LLM, you can rapidly build an AI assistant for research.
It can generate summaries from paper abstracts, answer questions on papers, suggest related works, and more. Extremely useful for PhD candidates or medical professionals to save time.
Real-Time Moderation for Chat Rooms
With Flowise‘s chat integrations, you can connect flows to messaging platforms like Discord. One example is an AI moderator bot that scans messages in real-time and removes toxicity, spam, threats automatically while learning from user feedback.
Landing Page Content Generator
An example marketing focused application is this landing page content generator flow. It takes a few prompt keywords and company details to automatically generate relevant sections like hero header, customer testimonials, case studies etc. Rapid high quality drafts that can inspire human writers.
And these are just a tiny sample – with the building blocks available combined with an LLM‘s broad capabilities, the possibilities are vast for custom apps tailored to your industry or niche.
Next let‘s go through how to get started with some examples of architecting a language model application yourself on Flowise.
Getting Started Building LLM Apps on Flowise AI
While Flowise has many prebuilt templates and starters available, you‘ll likely want to customize flows tailored to your use case. So how easy is it for a developer to build LLM apps from scratch?
- Signup for a free account which comes with credits for accessing LLMs
- Use the triggered workflow template as a starting point
- Drag in steps like Run LLM Query and connect with operators like loops, conditionals
- Select your LLM engine and adjust prompt/parameters
- Add datastores, transform data, handle outputs etc.
- Connect dialog UIs or external triggers like HTTP
- Run locally or deploy to cloud platform
And that‘s it – no need to setup infrastructure, authenticate APIs, manage billing etc. Flowise handles all that so you can purely focus on high level workflow logic tailored to your needs.
Let‘s explore a simple "Hello World" app example covering these basics…
"Hello World" App Tutorial
We‘ll build a trivial app that greets a user over HTTP. When they call /hello?name=Alice, our LLM bot will respond "Why hello there Alice!". This will demonstrate:
- Triggering flows on HTTP events
- Passing URL params as variables
- Generating text with GPT-3.5
- Returning API responses
1) Create HTTP Triggered Flow
First, create a new HTTP triggered workflow:
This gives us a starting point that activates on requests.
2) Add Variable to Capture Name
Let‘s use a variable to capture name URL param:
3) Generate Greeting with GPT-3.5
Now we can pass that name when querying our LLM:
Feed it a prompt template leveraging the name variable.
4) Return API Response
Finally, return the generated response as API result:
That‘s it! Our hello world app is ready to test.
Calling /hello?name=Alice would have GPT-3.5 generate "Why hello there Alice!" returned to the user. Pretty easy way to get started!
Now let‘s discuss more advanced capabilities…
Advanced Tips for Developers
While simple flows are easy to build visually, developers can leverage additional capabilities:
Python Steps for Custom Logic
Standard building blocks cover a lot, but for specialized logic, Flowise allows writing Python steps:
import flowise
@flowise.step
def extract_paper_details(content):
# Custom extraction logic
return {
"title": ...,
"authors": ...,
}This integrates seamlessly just like other steps.
Reuse flows as Building Blocks
You can modularize logic into reusable flows:
Then embed them into other workflows:
This helps decompose complex apps into maintainable building blocks.
Integrate Pretrained Models
Bring your own models for steps like classification, NER, etc:
import flowise
from transformers import pipeline
classifier = pipeline("zero-shot-classification")
@flowise.step
def classify_text(input):
return classifier(input)Keeps your data separate while leveraging Flowise‘s deployment.
Develop Locally
Instead of the cloud IDE, run a local environment:
git clone https://github.com/flowise/flowise
cd flowise
./scripts/setup.sh
./scripts/dev.shThis enables rapid iterating with file sync, hot reloading etc.
So in summary – simplicity where you need it, extensibility when you don‘t!
How Flowise Democratizes LLM App Development
As we‘ve explored, Flowise provides an incredibly powerful platform combining visual programming with language models like GPT-3.5. This leads to a paradigm shift in how quickly custom LLM apps can be built.
No Infrastructure Needed
Previously, developing LLM apps required piecing together servers, load balancing, databases etc. Flowise eliminates ops overhead so you can purely focus on end user value.
No Deployment Headaches
Containerization, cloud resource management, credentials etc. taken care completely by Flowise‘s platform. Go from idea to production app in minutes.
No API Chores
Authentication, rate limiting, billing all handled seamlessly. Just start using state-of-the-art LLMs instantly without the typical DevOps tax.
No New Languages to Learn
Leverage Python skills instead of needing to pickup niche tools like Node-RED. Lower barrier to integrating AI capabilities.
This frees makers to rapidly craft creative applications powered by AI that solves real problems.
Democratization Leads to Transformation
By empowering domain experts outside traditional software roles to create their own LLM driven solutions, Flowise unlocks transformative potential across every industry.
Doctors can rapidly build diagnosis assistants informed by the latest medical research. Government agencies can automatically translate documents into dozens of languages to serve more citizens. Startups can cost effectively iterate on conversational interfaces and chatbots.
Instead of needing to hire teams of engineers, procurement managers can get PO approvals automatically compiled. Recruiters can filter applications and schedule interviews autonomously. Fact checkers can scale verifying reporting.
And these are just some ideas – the breadth of human language means applications are endless.
Ultimately by democratizing access to LLMs for custom use cases, Flowise accelerates innovation across sectors as costs plummet and friction dissolves.
Expert Commentary on No-Code LLM Impact
To gain more perspective on the implications of no-code solutions like Flowise, I interviewed leaders across the AI community:
Democratization Will Unlock Exponential Progress
I spoke with Guy Teetaert (CEO at HungryWorks) who said:
"Where code and niche technical skills were previously required, we‘re shifting to a world where anyone can build their own AI solutions. Making these capabilities accessible without tradeoffs in quality means we‘ll see democratized exponential progress across every industry imaginable."
Customization Crucial for Responsible LLM Usage
Dr. Mark Riedl (Director at Georgia Tech‘s ML Center) emphasized:
"Getting language models safely adapted for particular use cases, social contexts, and user groups is key for managing risks as adoption accelerates. Modular building blocks combined with guardrails provide the right scaffolding for customizing responsibly before full AGI."
Real Problems Now Solvable by Typical Users
Mira Murati (Chief Technology Officer at Braintrust) noted:
"By abstracting away unnecessary complexity, typical business users and subject matter experts can now solve their daily problems leveraging AI. The cutting edge becomes the new normal when creators are freed to focus purely on building solutions rather than battling infrastructure."
Clearly industry leaders see immense potential in opening up language model capabilities beyond siloed AI teams.
Final Thoughts on the Future of LLM App Development
Flowise provides a glimpse into the future of rapid no-code development unlocking AI‘s transformative potential. While LLMs and the algorithms powering them will surely advance greatly in the years ahead, Flowise introduces an intuitive paradigm for leveraging them that will only grow more capable over time.
Of course, risks around bias, toxicity, and coordination problems will need mitigating as these models become further embedded. But by maintaining transparency, providing control to users, and incentivizing responsibility, the makers behind Flowise and Anthropic are paving an exciting path focused on AI safety.
For developers and innovators, now is the time to start experimenting with crafting your own solutions enabled by no-code LLMs. Competitive advantages will arise for early adopters who move swiftly to build what‘s most impactful for their community.
So why not sign up and tinker with what‘s possible? Head to Flowise AI to start creating the next generation of intelligent applications today!