Knowledge-and-Memory-Management: v0.0.2 — Knowledge Collection & Memory Management

Version 0.0.2 of Knowledge-and-Memory-Management introduces portable path support via the $AGENT_HOME environment variable and streamlined knowledge ingestion from web, video, and articles. The system features a persistent memory layer using vector databases for semantic retrieval, with all configuration paths now defaulting to $AGENT_HOME for portability.

Version 0.0.2 of Knowledge-and-Memory-Management delivers exactly what experienced developers expect: a clean release with portable path support and streamlined knowledge ingestion. All personal paths have been replaced with the $AGENT HOME environment variable, eliminating environment-specific configuration. The focus is on robust knowledge collection from web, video, and articles, paired with a persistent memory layer for retrieval. Architecture and Portability The system is divided into two packages: collector and memory . Every configurable path now defaults to $AGENT HOME . This means no more hardcoded /home/user/data/ or /var/lib/agent/ entries. When AGENT HOME is not set, the system falls back to a platform-specific default, but the expectation is that you define it explicitly in containerized or orchestrated deployments. The knowledge entry model is standardized across all sources, containing source url , media type , raw content , summary , embedding , and timestamp . Knowledge Collection All collectors adhere to a consistent interface: collect source: str, kwargs - KnowledgeEntry . This makes adding new sources straightforward. Memory Management Memory is stored in a vector database FAISS or Chroma under $AGENT HOME/memory . The clean release retroactively updates all previous path references. The Memory class supports save , delete , update , and search operations. Search uses cosine similarity on stored embeddings for semantic retrieval. Batching and optional GPU acceleration are configurable for embedding generation. Code Example Below is a short, self‑contained example showing how to leverage the portable path for collection and memory storage: python import os from knowledge and memory import Collector, Memory Initialize with $AGENT HOME agent home = os.getenv "AGENT HOME", "/opt/agent" collector = Collector base dir=agent home memory = Memory Collect from sources web entry = collector.collect web "https://example.com/tech-article" video entry = collector.collect video "https://youtube.com/watch?v=abc" article entry = collector.collect article "file:///docs/research.pdf" Store in memory memory.save web entry memory.save video entry memory.save article entry Semantic query results = memory.search "state-of-the-art in NLP", top k=3 for r in results: print r.summary The base dir parameter ties everything to $AGENT HOME , making deployment portable. The collectors normalize content, so the memory layer receives consistent objects. Migration and Performance Existing users must update configuration files to reference $AGENT HOME and remove absolute paths. A migration script is included in the release to automate this. Performance-wise, collection pipelines are asynchronous, and memory operations are batched to reduce overhead. The vector database can operate in‑memory for low latency or persist to disk for durability. v0.0.2 is a practical release for agent developers who need reliable memory without path headaches. Future versions will target additional source types and advanced consolidation strategies, but this clean foundation already delivers on the core promises of knowledge collection and memory management.