UX Interface: Last but not least, you will ideally want a ChatGPT-like interface with the ability to track history, have threaded conversations, and display tables and charts. 12/13
CODE
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Flexible LLM Options: Closed-Source and Custom Models
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LLMs: You can use closed-source (e.g. GPT-4) or your own custom LLM (Abacus-Giraffe or Llama2). You can compare and contrast whichever LLM works for you and pick the best one 11/13
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SQL Query Execution and LLM Result Summarization Process
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The orchestrator then routes the SQL query to the data source where it gets executed. Once the results are returned, you will have to make one more call to the LLM to summarize these results. Sometimes, multiple SQL queries are required for harder more nuanced questions 10/13
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Orchestrator Retrieves Tables and Metadata for SQL Query Generation
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When the query comes in the orchestrator will ask the doc retriever for the relevant tables and meta-data that map to the query. It will then send the query along with the meta-data to the LLM will will generate the SQL query. 9/13
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Document Retriever Integration with LLM for SQL Query Construction
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We have to use information from the doc retriever to feed to the LLM to construct the SQL queries. Orchestration layer: This is the layer that talks to the LLM, doc retriever, and your database. 8/13
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LangChain Templates Hub Launches 60+ Community-Contributed LLM Templates
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LangChain Templates Hub With 60+ templates contributed by the community and our partners, LangChain templates are the easiest way to start building with LLMs But with so many, it can often be disorienting to know where to start Today were launching LangChain
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Bedrock Support Now Available in LangSmith Playground
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🛌 🪨 Bedrock support in LangSmith Playground
— LangChain (@LangChain) 22 novembre 2023
Swapping out foundational models is part of the experimentation done when developing context-aware reasoning LLM applications. With #LangSmith we're making swapping providers as easy as possible!
Now you can run the latest models… pic.twitter.com/h5Ajwzc8mqBedrock support in LangSmith Playground Swapping out foundational models is part of the experimentation done when developing context-aware reasoning LLM applications. With #LangSmith we're making swapping providers as easy as possible! Now you can run the latest models
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Building RAG-Powered Chatbots with Chat Embed Rerank
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The Chat endpoint with RAG is easy to use, but it's also customizable. In document mode, the endpoint is highly modular. In this LLM University chapter, learn how to build a RAG-powered chatbot with the Chat, Embed, and Rerank endpoints.
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Léon Bottou’s Contributions to DjVu Technology
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Much of the credit for DjVu goes to Léon Bottou.
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Returning to OpenAI and Getting Back to Coding
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Returning to OpenAI & getting back to coding tonight.