Unlocking RAG Efficiency: Mistro API and Advanced Embedding Techniques
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Today, we delve into the realm of Mistro API for RAG, featuring the cutting-edge Mistro embed model and the formidable Misto large LM. Misto, a trailblazing LM AI company, takes a unique approach by open-sourcing their models and providing top-notch API services for seamless accessibility. Their models, exemplified by the game-changing Mixture of Experts, offer a level of versatility and functionality unparalleled by other open-source counterparts. The introduction of the API further streamlines the utilization of these exceptional models, making the entire process a breeze.
In this exhilarating demonstration using Pine Cone examples, the setup process kicks off with the installation of essential datasets, the Misto AI client, and Pine Cone for efficient storage and retrieval of embeddings. Data restructuring is undertaken to ensure compatibility with Pine Cone, involving the inclusion of ID and metadata fields for optimal organization. The connection to Misto is established to initiate the generation of embeddings utilizing the powerful Misto embed model. Subsequently, the setup for Pine Cone necessitates the acquisition of an API key and the initialization of an index with precise model specifications.
The journey continues with the implementation of an embedding function that adeptly handles token limits, dynamically adjusting batch sizes as needed to avoid any hiccups during the processing phase. The embedding loop then swings into action, systematically embedding data and integrating it into Pine Cone for efficient storage. By incorporating both title and content in the embeddings, a richer context is achieved, enhancing the search capabilities and overall effectiveness of the system. The testing phase involves querying the Misto LM to retrieve pertinent metadata, setting the stage for the impressive generation component utilizing the Mixture large model for crafting insightful responses.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Viewer Reactions for RAG with Mistral AI!
Code for the demo is available on GitHub
Reminder to use region="us-east-1" for free tier usage of Pinecone
Request for more resources on adding metadata to embeddings for recommendations
Question about whether to include metadata like title, dates, author in embeddings or use traditional index
Concern about the promotion of Pinecone in the video and the need to purchase it for replication
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