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Source channel @githubtrending · Post #14800 · Jun 7

#java#anthropic#chatgpt#chroma#embeddings#gemini#gpt#huggingface#java#langchain#llama#milvus#ollama#onnx#openai#openai_api#pgvector#pinecone#vector_database#weaviate LangChain4j helps you add powerful AI to your Java applications by making it easy to use Large Language Models (LLMs). It provides a simple way to switch between different LLMs and embedding stores without needing to learn each one's specific API. This means you can easily experiment with different models and tools, making your development process faster and more flexible. LangChain4j also offers many examples and tools to help you build complex AI applications quickly, such as chatbots and retrieval systems. This simplifies the integration of AI into your projects, allowing you to focus on creating better applications. https://github.com/langchain4j/langchain4j

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#java #anthropic #chatgpt #chroma #embeddings #gemini #gpt #huggingface #langchain #llama #milvus #ollama #onnx #openai #openai_api #pgvector #pinecone #vector_database #weaviate

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djangoproject

@djangoproject · Post #173 · 09/22/2016, 06:55 PM

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#signal — Set handlers for #asynchronous events This module provides mechanisms to use signal handlers in Python. The signal.signal() function allows to define custom handlers to be executed when a signal is received. A small number of default handlers are installed: #SIGPIPE is ignored (so write errors on pipes and sockets can be reported as ordinary Python exceptions) and #SIGINT is translated into a KeyboardInterrupt exception. #Asyncio https://docs.python.org/3.4/library/signal.html

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#signal #asynchronous #sigpipe #sigint #asyncio