At their demo day, Open AI reported a series of RAG experiments for a customer that they worked with. While evaluation metics will depend on your specific application, it’s interesting to see what worked and what didn’t for them. Below, we expand on each method mention and show how you can implement each one for yourself. The ability to understand and these methods on your application is critical: from talking to many partners and users, there is no “one-size-fits-all” solution because different problems require different retrieval techniques.
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Story Generation
Eden AI x LangChain: Harnessing LLMs, Embeddings, and AI
The agent can produce detailed, factual and unbiased research reports, with customization options for focusing on relevant resources, outlines, and lessons. Inspired by the recent Plan-and-Solve and RAG papers, GPT Researcher addresses issues of speed, determinism and reliability, offering a more stable performance and increased speed through parallelized agent work, as opposed to synchronous operations.
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Building a Tavily Data Agent
research-assistan
Use routes to remind agents of particular information or routes (we will do this in this notebook). Use routes to act as protective guardrails against specific types of queries.
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Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation.
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LangChain Templates are the easiest and fastest way to build a production-ready LLM application. These templates serve as a set of reference architectures for a wide variety of popular LLM use cases. They are all in a standard format which make it easy to deploy them with LangServe.
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GitHub
