The video introduces the Pinecone Assistant, a new API service that enables the creation of agents with advanced retrieval-augmented generation capabilities, focusing on providing accurate responses grounded in verifiable sources. It showcases features like custom instructions, structured outputs, and the ability to interact with documents through chat and context APIs, exemplified by a custom “Yorkshire Assistant” that communicates in a regional dialect while explaining complex topics.
The video introduces the newly released Pinecone Assistant, an API service designed to create agents with advanced retrieval-augmented generation (RAG) capabilities. The focus of Pinecone Assistant is to provide accurate and trustworthy responses by grounding its outputs in verifiable sources. The presenter highlights the new features available with the general release, including custom instructions for building agents, new input and output formats (such as Markdown and JSON), and enhanced regional control for compliance with regulations like GDPR.
One of the standout features of Pinecone Assistant is its ability to produce structured outputs, which is particularly beneficial for developers who require reliable data formatting. The video demonstrates how to create a custom assistant, dubbed the “Yorkshire Assistant,” which is designed to respond in a regional dialect while providing helpful information. The presenter walks through the process of setting up the assistant, including obtaining an API key and defining custom instructions that incorporate local slang and metaphors.
The video then transitions to a practical example where the Yorkshire Assistant is tasked with explaining a research paper on reasoning language models (RLMs). The presenter uploads the paper and utilizes the chat API to interact with the assistant. The assistant provides accurate explanations of RLMs while incorporating Yorkshire dialect, showcasing its ability to blend technical information with regional flair. The presenter emphasizes the importance of grounding responses with citations, which enhances user trust in the information provided.
In addition to the chat API, the video explores the context API, which allows users to retrieve document snippets without the generative capabilities of the assistant. This feature is useful for scenarios where users only need to access specific information from uploaded documents. The presenter demonstrates how to query the context API and retrieve relevant snippets, highlighting the flexibility of Pinecone Assistant in various use cases.
Finally, the video discusses the chat completions API, which mimics the OpenAI chat completion format, allowing developers to easily switch between different language model providers. The presenter concludes by encouraging viewers to explore the Pinecone blog for more detailed information about the assistant’s features and capabilities. Overall, the video provides a comprehensive overview of Pinecone Assistant, showcasing its potential for creating reliable and contextually aware AI agents.