{"slug": "knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management", "title": "Knowledge-and-Memory-Management: v0.0.2 — Knowledge Collection & Memory Management", "summary": "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 $AGENT_HOME for portability.", "body_md": "Version 0.0.2 of Knowledge-and-Memory-Management delivers exactly what experienced developers expect: a clean release with portable path support and streamlined knowledge ingestion. All personal paths have been replaced with the `$AGENT_HOME`\n\nenvironment 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.\n\n**Architecture and Portability**\n\nThe system is divided into two packages: `collector`\n\nand `memory`\n\n. Every configurable path now defaults to `$AGENT_HOME`\n\n. This means no more hardcoded `/home/user/data/`\n\nor `/var/lib/agent/`\n\nentries. When `AGENT_HOME`\n\nis not set, the system falls back to a platform-specific default, but the expectation is that you define it explicitly in containerized or orchestrated deployments. The knowledge entry model is standardized across all sources, containing `source_url`\n\n, `media_type`\n\n, `raw_content`\n\n, `summary`\n\n, `embedding`\n\n, and `timestamp`\n\n.\n\n**Knowledge Collection**\n\nAll collectors adhere to a consistent interface: `collect(source: str, **kwargs) -> KnowledgeEntry`\n\n. This makes adding new sources straightforward.\n\n**Memory Management**\n\nMemory is stored in a vector database (FAISS or Chroma) under `$AGENT_HOME/memory`\n\n. The clean release retroactively updates all previous path references. The `Memory`\n\nclass supports `save`\n\n, `delete`\n\n, `update`\n\n, and `search`\n\noperations. Search uses cosine similarity on stored embeddings for semantic retrieval. Batching and optional GPU acceleration are configurable for embedding generation.\n\n**Code Example**\n\nBelow is a short, self‑contained example showing how to leverage the portable path for collection and memory storage:\n\n``` python\nimport os\nfrom knowledge_and_memory import Collector, Memory\n\n# Initialize with $AGENT_HOME\nagent_home = os.getenv(\"AGENT_HOME\", \"/opt/agent\")\ncollector = Collector(base_dir=agent_home)\nmemory = Memory()\n\n# Collect from sources\nweb_entry = collector.collect_web(\"https://example.com/tech-article\")\nvideo_entry = collector.collect_video(\"https://youtube.com/watch?v=abc\")\narticle_entry = collector.collect_article(\"file:///docs/research.pdf\")\n\n# Store in memory\nmemory.save(web_entry)\nmemory.save(video_entry)\nmemory.save(article_entry)\n\n# Semantic query\nresults = memory.search(\"state-of-the-art in NLP\", top_k=3)\nfor r in results:\n    print(r.summary)\n```\n\nThe `base_dir`\n\nparameter ties everything to `$AGENT_HOME`\n\n, making deployment portable. The collectors normalize content, so the memory layer receives consistent objects.\n\n**Migration and Performance**\n\nExisting users must update configuration files to reference `$AGENT_HOME`\n\nand 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.\n\nv0.0.2 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.", "url": "https://wpnews.pro/news/knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management", "canonical_source": "https://dev.to/mage0535/knowledge-and-memory-management-v002-knowledge-collection-memory-management-5dah", "published_at": "2026-07-07 12:02:14+00:00", "updated_at": "2026-07-07 12:28:24.418576+00:00", "lang": "en", "topics": ["developer-tools", "machine-learning", "natural-language-processing", "ai-infrastructure"], "entities": ["Knowledge-and-Memory-Management", "FAISS", "Chroma"], "alternates": {"html": "https://wpnews.pro/news/knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management", "markdown": "https://wpnews.pro/news/knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management.md", "text": "https://wpnews.pro/news/knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management.txt", "jsonld": "https://wpnews.pro/news/knowledge-and-memory-management-v0-0-2-knowledge-collection-memory-management.jsonld"}}