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FastMemory is a topological representation of text data using concepts as the primary input. It helps in improving the RAG(by replacing embedding and vectors entirely), AI memory and LLM queries by upto 100% as in the huggingface benchmarks(22+ SOTA)
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26
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feat: organic NLTK POS parser via embedded PyO3 python execution
5734089View on GitHub🧠 FastMemory: Add SOTA FinanceBench & LangChain Grounding Notebooks, Update README Quickstart
9f32d2eView on GitHubbuild(core): architectural rename of cbfdae ontology internal serialization keys to topology
64cb29bView on GitHubdocs: rename cbfdae_json_graph to topology_json_graph
a74c0d6View on GitHubfeat: Rename CBFDAE → Topology, raise community license to $20M revenue
e1dab49View on GitHubv0.3.0: Full IP stack capturing and community edition telemetry fallback
8ab63d3View on GitHubfix(cli): drop obsolete await from telemetry ping
6845befView on GitHubchore(release): bump version to 0.2.2 for PyPI and Crates.io deployment
df5521dView on GitHubfix(pyo3): Convert asynchronous Tokio fastmemory License Key enforcement to native synchronous std::thread to preserve stability along the PyO3 macro ingestion boundary
501199fView on GitHub