Find similar content

Source channel @githubtrending · Post #15299 · Nov 12

#python#agent#ai#aiagent#awesome#chatgpt#hacktoberfest#hacktoberfest2025#llm#long_short_term_memory#memori_ai#memory#memory_management#python#rag#state_management Memori is an open-source memory engine that gives AI language models human-like memory using standard SQL databases like PostgreSQL, MySQL, or SQLite.[1][2] With just one line of code, you can enable any LLM to remember conversations, learn from interactions, and maintain context across sessions.[1] The key benefits are significant cost savings of 80-90% compared to expensive vector databases, complete data ownership and transparency since memories are stored in SQL databases you control, and zero vendor lock-in allowing you to export and move your data anywhere.[1][3] Memori works with popular frameworks like OpenAI, Anthropic, and LangChain, making it easy to integrate into existing projects without complex setup.[1] https://github.com/GibsonAI/Memori

Hashtags

#python #agent #ai #aiagent #awesome #chatgpt #hacktoberfest #hacktoberfest2025 #llm #long_short_term_memory #memori_ai #memory #memory_management #rag #state_management

Results

1 similar post found

Search: #parallelism

当前筛选 #parallelism清除筛选

djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

Find similar View

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,

Hashtags

#multiprocessing #apis #threading #module #parallelism