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What are Files and Stores?

  • Definition: Files and Stores in Wispera allow users to upload their documents and assign them to vector stores, thus enabling AI to utilize external context from these documents during its operations.
  • Purpose: This feature expands the AI’s knowledge base and contextual understanding by incorporating external data, enhancing the accuracy and relevance of AI executions.

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Why is it Important?

  1. Expanded Contextual Awareness: Incorporates additional data sources into AI processing, enriching the contextual accuracy of its outputs.
  2. Diverse Application Potential: Supports a wide variety of use cases — from detailed data analysis to comprehensive content creation.
  3. Efficiency and Scalability: Streamlines integration of large datasets into AI tasks, facilitating scalable and efficient use of external data.

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Components of Files and Stores

Key elements within the Files and Stores feature:
  • Document Uploads: Functionality for importing a variety of document types into the Wispera environment.
  • Vector Stores: Databases that store vectorized representations of documents for efficient retrieval and context application during AI executions.
  • Context Integration: Mechanisms to integrate vectorized document data seamlessly into AI processes.

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Example Scenarios

1. Documentation Assistant:

Create a Comprehensive Product Documentation Page

  • AI Generated Product Docs: Upload previously written docs as files, then associate them with a vector store that can be assigned to assistants, prompts, and threads.
2. Theme based creative writing assistant:

Satire Startup Blog Series

  • AI based story scripting: Uses previously written blog posts as context to help you write a new post in an ongoing series.

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Summary

Files and Stores empower AI within Wispera to leverage external content, significantly enhancing its contextual intelligence and versatility in prompt executions. Users can harness this feature to integrate detailed data into AI tasks swiftly and effectively, optimizing responses across diverse applications.