Chroma, Weaviate & Production RAG Deployment equips developers and ML engineers with end‑to‑end skills to deploy and manage vector databases for advanced search and retrieval‑augmented generation. You’ll start by launching a local Chroma instance via its Python SDK, configuring collections and ingesting thousands of documents. You’ll build automated pipelines that link embedding models (OpenAI, HuggingFace) to Chroma and troubleshoot dimension mismatches. Next, you’ll design a RAG pipeline with Chroma and LangChain to ground LLM responses in verifiable data and assess its impact. Through courses on Weaviate you’ll model complex data with multi‑class schemas, import interconnected objects, benchmark query latency and write semantic, vector and hybrid queries. You’ll spin up Weaviate with Docker Compose, define a schema and perform your first semantic search. Additional modules teach you to build a semantic search API with Chroma and Flask, manage metadata and multi‑collections via an ETL pipeline, and implement advanced RAG patterns (Corrective, Self‑RAG and Agentic). You’ll enable Weaviate’s automatic vectorization and evaluate the tradeoffs, tune index parameters to reduce latency and script migrations from Chroma to Weaviate, and deploy vector databases securely with TLS, RBAC and Grafana monitoring. By the end you’ll be ready to build, tune and maintain production‑ready vector search and RAG systems.

应用的学习项目

Hands‑on projects reinforce your learning. You’ll install Chroma locally, ingest documents and run similarity searches; connect embedding models and fix vector dimension mismatches; build RAG pipelines with Chroma and LangChain and compare grounded vs. ungrounded outputs; design multi‑class schemas in Weaviate, import data and benchmark queries; expose a Chroma API with Flask and evaluate relevance; write advanced Weaviate queries, analyze trace logs and tune for performance; build ETL pipelines to organize metadata and collections; test advanced RAG patterns (Corrective, Self, Agentic); enable automatic vectorization in Weaviate and analyze costs; tune index parameters, migrate vectors, spin up Weaviate with Docker Compose, run semantic queries and containerize and secure vector databases with TLS, RBAC, Grafana and autoscaling. You’ll end with a portfolio of notebooks, APIs, ETL scripts and dashboards demonstrating production deployment skills.