SuperAGI's latest integration with LlamaIndex can extend the overall agent’s capability of understanding and working with a wide range of data types and sources. With LlamaIndex, AI agents in SuperAGI can now ingest data from: such as, Unstructured Data sources : Like word processing documents or simple text notes (.docx, .txt) Documents and Raw Text Files : Digital documents PDFs : Visual media formats (.jpg, .png, .mp4 etc) Videos and Images as well as like Structured Data sources : Tabulated data where information is presented in rows and columns. Excel and CSV : A database format where data is stored in tables with rows and columns. SQL or even, such as Slack & Notion. Semi-structured Data sources Data Processing in SuperAGI There are various steps involved in fetching, processing, and sending the data to the Vector Database through LlamaIndex in the form of vector node objects. 🔁 Resource Management & Data Conversion Files and documents are uploaded to the SuperAGI Resource Manager where these files are parsed through LlamaIndex and converted into vector node objects, which are subsequently stored in a VectorDB like Redis, Chroma, Pinecone, or Qdrant. 📝 Conversion to Vector Node Objects SuperAGI resource Manager stores the data in the form of vectorized node objects allowing fast and easy accessibility. Alongside vectorized node objects, SuperAGI also stores summaries of each file. A master summary of all files within the Resource Manager is created, which can be utilized by the Agent based on the Agent's goal and instructions. 🆎 Metadata Filtering and Database Support Metadata filtering is primarily used to filter specific resources required for an agent run. Each agent run is associated with a unique identifier, or 'agent id', which is used as a key to filter the resources. This means that the system can identify and select only those resources that are relevant to a particular agent run, improving the accuracy of the data retrieval process. The integration supports databases that inherently support metadata filtering (Redis, Chroma, Pinecone, or Qdrant). 🔍 Running Query using QueryResourceTool Once an agent run is initiated, SuperAGI agents can query these node objects using the 'QueryResource Tool'. It allows agents to work with a large set of data resources and provides the agent with the required information throughout any iteration to accomplish its goals. ✅ Use Cases Users can interact with financial data, extract analysis from it by uploading a CSV, and request the agent to generate an analysis report. Financial Report Analysis: By uploading an EPUB/PDF file of an entire book and instructing the agent to summarize a chapter, a summary of that chapter will be generated and stored in the output folder. Book Chapter Summarization: https://youtu.be/DiOZQH_fRCA?embedable=true Also published . here
