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  2. LangChain - Wikipedia

    en.wikipedia.org/wiki/LangChain

    LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.

  3. List of programming languages for artificial intelligence

    en.wikipedia.org/wiki/List_of_programming...

    Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.

  4. GraphQL - Wikipedia

    en.wikipedia.org/wiki/GraphQL

    GraphQL is a data query and manipulation language for APIs that allows a client to specify what data it needs ("declarative data fetching"). A GraphQL server can fetch data from separate sources for a single client query and present the results in a unified graph . [ 2 ]

  5. Cypher (query language) - Wikipedia

    en.wikipedia.org/wiki/Cypher_(query_language)

    Cypher is a declarative graph query language that allows for expressive and efficient data querying in a property graph. [1]Cypher was largely an invention of Andrés Taylor while working for Neo4j, Inc. (formerly Neo Technology) in 2011. [2]

  6. Gremlin (query language) - Wikipedia

    en.wikipedia.org/wiki/Gremlin_(query_language)

    Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors.

  7. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    The below code demonstrates the jit function's optimization through fusion. # imports from jax import jit import jax.numpy as jnp # define the cube function def cube ( x ): return x * x * x # generate data x = jnp . ones (( 10000 , 10000 )) # create the jit version of the cube function jit_cube = jit ( cube ) # apply the cube and jit_cube ...

  8. GitHub Copilot - Wikipedia

    en.wikipedia.org/wiki/GitHub_Copilot

    GitHub Copilot was initially powered by the OpenAI Codex, [13] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. [14]

  9. Graph Query Language - Wikipedia

    en.wikipedia.org/wiki/Graph_Query_Language

    In September 2019 a proposal for a project to create a new standard graph query language (ISO/IEC 39075 Information Technology — Database Languages — GQL) [3] was approved by a vote of national standards bodies which are members of ISO/IEC Joint Technical Committee 1(ISO/IEC JTC 1).