Find similar content

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

Hashtags

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

Results

1 similar post found

Search: #sockets

当前筛选 #sockets清除筛选

djangoproject

@djangoproject · Post #261 · 02/16/2017, 06:56 AM

Find similar View

http://www.giantflyingsaucer.com/blog/?p=5557 In spring 2014 Python 3.4 shipped a provisional package (#asyncio) which according to the docs “provides infrastructure for writing single-threaded #concurrent code using #coroutines, #multiplexing I/O access over #sockets and other resources, running network clients and servers, and other related primitives“. I can’t possibly cover everything in this article but I can introduce some of the things you can do with it. As per my New’s Years resolution I’ll be building these #examples using Python 3.4.2 (Asyncio has been ported back to Python 3.3 now as well).

Hashtags

#asyncio #concurrent #coroutines #multiplexing #sockets #examples