================================================================================ ARTICLE: How to Turn a News Archive into a RAG-Ready Knowledge Base URL: https://omniscient.news/blog/turn-news-archive-rag-knowledge-base Published: 2026-03-15 Updated: 2026-04-01 Category: AI Agents & LLMs Tags: RAG, news archive, knowledge base, AI journalism, retrieval ================================================================================ News archives contain decades of verified reporting that AI tools cannot currently access. Here is how to transform your archive into a RAG-ready resource that powers AI-assisted research. A newsroom with 20 years of archived reporting holds an invaluable corpus of verified, sourced, expert-reviewed content that no external AI tool can access. Converting this archive into a RAG-ready knowledge base transforms it from a passive historical record into an active AI-research asset — one that can answer journalist queries with verified archive citations in seconds. The Four-Step Archive-to-RAG Pipeline Step 1: Export and clean. Export all articles from your CMS as structured JSON or XML. Clean out boilerplate (navigation, ads, cookie notices), standardise metadata (date, author, section, tags). Step 2: Chunk and embed. Split articles into 500–1000 token chunks. Generate vector embeddings for each chunk using an embedding model (OpenAI text-embedding-3-small, Cohere embed-v3). Step 3: Index in a vector database. Store chunks and embeddings in a vector database (Pinecone, Weaviate, Chroma). This enables semantic search: query "What did we report about [Topic] in 2022?" and retrieve the 10 most semantically relevant archive chunks. Step 4: Build the query interface. Connect the vector database to an LLM (GPT-4o, Claude) to generate answers with archive citations. Journalists query the system in natural language; it returns synthesised answers with article-level citations from the archive. Frequently Asked Questions Q: undefined A: undefined Q: undefined A: undefined Q: undefined A: undefined