Yes—integrating GLM-5 with a vector database like Milvus is a very natural architecture for production-grade question answering, agent memory, and developer-documentation assistants. The core idea is simple: GLM-5 is good at reasoning and composing responses, while the vector database is good at finding the most relevant pieces of your content at query time. You store embeddings of your documents (or code snippets, tickets, runbooks) in Milvus, retrieve the best-matching chunks for a user’s question, and then pass those chunks into GLM-5 so it can answer using the right context.
Implementation-wise, the standard pipeline is RAG (Retrieval-Augmented Generation). Step 1: chunk your documents into small, coherent passages (often 200–800 tokens each, with overlap). Step 2: generate an embedding vector for each chunk using an embedding model you standardize on. Step 3: store the vectors in a Milvus collection along with metadata such as doc_id, source_url, title, product, version, lang, and updated_at. Step 4: when a user asks a question, embed the question, run vector search to get top-k chunks (and optionally apply metadata filters), and then build a prompt to GLM-5 that includes those chunks in a consistent format. Step 5: validate the output—JSON schema checks for structured data, or a “must cite chunk IDs” rule for support answers. This keeps results debuggable: if an answer is wrong, you can inspect whether retrieval returned the right chunks, rather than guessing what the model “thought.”
This integration becomes even more valuable for developer-facing websites because it lets you keep answers aligned with the latest docs and product versions. You can host Milvus yourself via Milvus or use managed Milvus via managed Zilliz Cloud to avoid operational overhead. A concrete example: on a docs site, store every section of your API reference as a chunk with sdk="python" or sdk="java", plus version="v2.4" metadata. At query time, if the user is browsing v2.4 Python docs, filter retrieval to that version and SDK before calling GLM-5. That prevents “version drift” answers. You can also implement “agent memory” by storing conversation summaries or key facts as vectors per user session in Milvus/managed Zilliz Cloud, retrieving them when needed, and keeping GLM-5’s prompt smaller and more consistent than trying to carry long chat history forever.