Our users need a fast, forgiving search across the knowledge base—process models, analysis reports, and documentation—so they can type imperfect queries, filter by type or author, and see relevant snippets in under a second. The current MongoDB text search isn’t delivering that experience.

Please add a modern search layer with MeiliSearch and connect it to our Django/MongoDB stack. What we’re aiming for:
- Index content from reports, process_models, and documents with fields like title/body/tags/author/created_at; support typo tolerance, facets, sorting, and highlighted snippets.
- Keep the index fresh without slowing requests: do an initial bulk index, then background updates whenever data changes.
- Provide a single REST endpoint for federated search:
  GET /v1/search (JWT)
  q: user query
  filter: "key:value;key2:value2" (e.g., document_type, author_id)
  sort: "created_at:desc"

Return hits with id/type/title/snippet, facet counts, processingTimeMs, and the original query. Respond with 400 for invalid filter syntax and 401 for auth failures.