Insights, guides, and updates on building AI-powered knowledge solutions
AI Files, is a free app designed to simplify file management for OpenAI’s Files and Vector Store APIs. Quickly bulk upload, delete, manage vector stores, and securely store API keys. Streamline your workflow—no scripting required!
Read the full articleThe OpenAI Responses API simplifies conversation state management by handling it server-side. Developers can maintain context across interactions by including the `previous_response_id` parameter in their requests, referencing the last response's ID. This approach eliminates the need to manually track conversation history, as the API retrieves and incorporates the entire conversation chain automatically. However, it's important to note that all previous input tokens in the conversation chain are billed as input tokens.
Warning: using the OpenAI Assistants API can result in unexpectedly high costs because of inherent flaws in its architecture. Ragwalla Assistants addresses these flaws head-on.
The OpenAI Assistants API does not natively support hybrid search or metadata indexing, but Ragwalla's implementation does. Hybrid search allows developers to build smarter, faster, and more precise data retrieval systems. Whether manually setting keywords or automating the process with LLM Auto mode, this approach enhances the effectiveness and efficiency of vector-based applications.
Understanding cosine similarity, dot product, and Euclidean distance can be much easier with real-world analogies. These measures each capture “similarity” or “distance” in different ways — direction vs. magnitude vs. straight-line distance . We explore two narrative-style scenarios for each measure, showing when to use them and why.
Choosing the right similarity measure depends on your use case. If you’re dealing with text embeddings and want to isolate direction over magnitude, go with Cosine Similarity. When overall vector size or weighting matters, Dot Product shines. And if your model benefits from real geometric distances like clustering points in space — Euclidean Distance is the way to go.
Use multiple vector stores in an OpenAI Assistants API service to enable parallel querying across each store that each handle 5M vectors—500x OpenAI's current limit.
The OpenAI Assistant was a major leap forward for developers, but pitfalls can sink your application and team. The Ragwalla Assistant addresses the challenges related to scale, transparency, support and cost.