VectorShift is an end-to-end AI automation platform that offers no-code and low-code solutions for building AI applications such as search engines, chatbots, and workflow automations. It features a user-friendly interface, robust SDK, and integration capabilities with various tools. Users can leverage AI across different data formats, automate tasks, and customize applications easily. The platform also provides enterprise solutions, report generation, and personalized email outreach, along with extensive documentation and support.
VectorShift
Platform
Solutions
Resources
The No-Code
An integrated framework of no-code, low-code, and out of the box generative AI solutions to build AI search engines, assistants, chatbots, and automations.
An ecosystem to build, deploy, and manage AI applications
Empowering development with no-code and code SDK
VectorShift combines a user-friendly No-code interface with a robust Code SDK. Effortlessly create applications using drag-and-drop, or dive into coding with seamless IDE integration. Enjoy flexibility and power, all in one platform.
Instruction text
Describe this file to me
File input
JSON, CSV, PDF
OpenAI LLM
Model: gpt-4.0-turbo (processes input)
File loader
Reads the input file
Result
Generates output
pipeline_setup.py
from vectorshift.node import *
from vectorshift.pipeline import *file_node = InputNode(name='file_input', input_type='file')model_text_node = TextNode(text='Describe this file to me.')
fileloader_node = FileLoaderNode (file_input=file_node.output())
llm_node = OpenAI_LLMNode(
model='gpt-4.0-turbo',
system_input=model_text_node.output(),
prompt_input=fileloader_node.output()
)
output_node = OutputNode(
name='my_output',
output_type='text',
input=llm_node.output()
)
Marketplace: Pre-built use cases
Agents: execute complex tasks
Search: use the optimal data retrieval method
Integrations and automations
Live-sync, set up action based triggers (e.g., receive an email), and automate actions (e.g., send a slack message) across your tool stack
Google Drive
OneDrive
SalesForce
Hubspot
Notion
Airtable
Large language models
Access the latest models through the VectorShift platform
OpenAI
Anthropic
Huggingface
LLAMA
AWS
Mistral AI_
Leverage AI throughout yourcompany and products
Assistants
Integrate natural language search and live-sync databases such as Notion and Airtable to automate information retrieval.
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When was this contract started?
20230329-Product-Contract-Acme.pdf
The contract started on January 1, 2023.
When was this contract last modified?
20230329-Product-Contract-Acme.pdf
The contract was last modified by John D. on June 13, 2023. The modifications were done on page 3,4 and 16.
What’s the contract ceiling?
20230329-Product-Contract-Acme.pdf
The contract ceiling is USD$1,000,000.
Chatbot
Prototype, customize, and deploy a customer facing chatbot in minutes. Use cases including customer support, onboarding flow, lead collection, and white-glove advisory.
Workflow Automation
Automate the creation of marketing copy, personalized outbound emails, call summaries, and graphics at scale.
Outbound
Copy
Summaries
Analytics
Website
Tables
PDFs
Videos
Audio
Document
Leverage AI across data of all formats
Summarize and answer questions about documents, videos, audio files, and websites. Analyze and compare documents seamlessly.
How it works
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Start witha template
Leverage dozens of pre-built templates for end use cases - ranging from research report generators to resume screeners.
Connectdata
Allow your AI application to leverage raw data in any format (websites, documents, or CSVs) or directly connect with your database.
Intuitive drag and drop builder
Build and rapidly iterate on your application’s architecture with a large library of drag and drop components. Transfer your work seamlessly between no-code and our python SDK.
Customize and deploy to end users
Export a chatbot or generate an API endpoint instantly. Customize the look and feel of the application.
Enterprise solutions
Report generation
High volume chatbot
Personalized email outbound
RFP and proposal generators
Knowledge search
VectorShift Docs
Unlock advanced features and detailed guides in our extensive documentation.
pipeline_setup.py
from vectorshift.node import *
from vectorshift.pipeline import *file_node = InputNode(name='file_input', input_type='file')model_text_node = TextNode(text='Describe this file to me.')llm_node = OpenAI_LLMNode(
model='gpt-4.0-turbo',
system_input=model_text_node.output(),
prompt_input=fileloader_node.output()
)
output_node = OutputNode(
name='my_output',
output_type='text',
input=llm_node.output()
)