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Welcome to VectorShift

VectorShift is a no-code, drag-and-drop interface to build, design, prototype, and deploy generative AI workflows. Example use cases:
  • Chatbots: Build a fully functional chatbot and embed it into your website in minutes. Chatbot is able to respond to user queries and answer questions based on a knowledge base (e.g., product documentation, support articles)
  • Document Search: Summarize and answer questions about documents, videos, audio files, and websites instantly.
  • Content Creation: Create marketing copy, personalized outbound emails, call summaries, proposals, and graphics at scale and in a predetermined format and style.
  • Analyst: Replicate the logical thinking of an analyst to generate synthesized output.
Our site is structured into the following tabs:
  1. 1.
    Overview: the landing page. Jump either to our pipeline builder or marketplace.
  2. 2.
    Marketplace: a library of pre-built pipelines. Contribute to the marketplace by sharing your pipelines with other users! Within the marketplace, you can search for pipelines and “import” them into your workspace.
  3. 3.
    Pipelines: where you can find pipelines you have built. The “build” button opens up the pipeline builder, our drag-and-drop tool to build generative AI workflows. Additionally, you can utilize the pull-down menu on the top right to find your “imported pipelines” from the marketplace. Once selected, you can select a pipeline and click “add to my pipeline” to add to your pipeline workspace.
  4. 4.
    Files: you can find your stored files. You can upload files here and utilize them later in the pipeline builder.
  5. 5.
    Chatbots: turn pipelines you have built into Chatbots. Test the Chatbots in our interface before deploying to your end users via API.
  6. 6.
At its core, a pipeline will have the following components:
  1. 1.
    Input / Output Node: Information (e.g., written text, video, audio, files) provided in the input node is used as an “input” to connecting nodes. The output node represents the result of the pipeline.
  2. 2.
    Large Language Model (LLM): an LLM is a trained deep-learning model that can generate output in a human-like fashion. You can connect other nodes (e.g., the input node) to the LLM to use as inputs into the model to instruct the model on what to do.
  3. 3.
    Data Loaders: nodes that allow you to import data into a pipeline. We currently offer the following data loaders: files, URL (data scraping), PDF, Wikipedia, YouTube video, and Arxiv.
  4. 4.
    VectorDB: vector databases are a type of database that stores unstructured data in vector embeddings (e.g., vector representation of the data) to facilitate retrieval (e.g., allow users to query to identify relevant data).
Last modified 3d ago