LangChain is an advanced artificial intelligence (AI) development platform specifically designed for the development of LLM (Legal Language Modeling) applications. LLM applications are crucial in the legal field as they assist in automating legal processes, improving efficiency, and providing accurate legal language analysis. LangChain offers a comprehensive set of tools and resources to enable developers to create sophisticated LLM applications. With its powerful AI capabilities, LangChain revolutionizes the way legal professionals interact with legal documents, making the development process faster and more efficient.

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Langchain is a powerful and innovative technology that is revolutionising the field of data extraction. It is a software platform that utilises natural language processing and machine learning algorithms to extract valuable information from unstructured data sources such as text documents, websites, and social media. Langchain is designed to understand and interpret human language, allowing it to accurately extract relevant data points and insights. By automating the data extraction process, Langchain eliminates the need for manual data entry and significantly reduces the time and effort required to extract and analyse data.

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Text embeddings have emerged as a powerful tool in the realm of artificial intelligence and language processing. In this comprehensive guide, we delve into the world of text embeddings, exploring their immense potential in AI and Langchain. By understanding and applying text embeddings effectively, we can unlock new possibilities for enhancing language-based applications. Join us on this insightful journey as we uncover the power of text embeddings in AI and Langchain.

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DIN-SQL is a state-of-the-art model that tackles the challenging task of converting natural language queries into SQL statements. It introduces a novel approach by leveraging the power of deep in-context learning to improve accuracy and efficiency in this conversion process. Unlike traditional methods that rely on rule-based or template-based approaches, DIN-SQL utilizes a neural network architecture that can learn from context and generate semantically correct SQL queries.

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Document splitting in Langchain refers to the process of dividing a large document into smaller, more manageable sections or segments. It is a crucial step in organising and analysing language data effectively. By breaking down a document into smaller units, it becomes easier to extract meaningful insights, identify patterns, and perform various language analysis tasks.

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Unlock the Future of Conversational AI with Langchain – The Ultimate Framework for Developers!
Imagine building chatbots and virtual assistants that don’t just respond — they understand. They remember everything you said earlier in the conversation and reply like a human friend who *gets* you. That’s exactly what Langchain delivers.
Created by the pioneers at Anthropic, Langchain is an open-source powerhouse designed to help developers craft conversational agents with unmatched natural-language comprehension and context awareness.

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Artificial intelligence (AI) is transforming businesses and our daily lives. From virtual assistants to self-driving cars, AI is powering innovations that make tasks easier and processes more efficient. However, developing impactful AI systems requires significant data, computing resources, and expertise that are out of reach for most organisations and developers.

This is where Deep.ai comes in.

Deep.ai provides developers with easy access to the tools needed to build AI models and applications. Their platform offers GPU-powered notebooks, curated datasets, and pre-trained models to lower the barriers to AI adoption. With Deep.ai, you can quickly get started with computer vision, natural language processing, speech recognition, and other cutting-edge capabilities to enhance your products and workflows.

I wrote this Letter to the Government a couple of months ago and got a response. I also wrote a small paper on the subject. See my paper below and my response from the Government. This was to the previous Conservative Government.

Appreciate serious feedback to see how Python could be used in all schools across the country, where the language, and development environment (Pycharm and other libraries) are all free. Plus it would enable Data Science, Maths, AI, Machine Learning and Data Science to be learned in one place quickly. Thanks

This is a copy of The Letter sent to The PM:

The PM,10 Downing Street, London SW1A 2AA

Dear Prime Minister Sunak,

CC: Rt Hon Gillian Keegan MP, Rt Hon Paul Scully MP, Rt Hon Michelle Donelan MP

I am writing to you today to outline a proposal for how we can better engage students, teachers, and the general public in learning more a

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