To build RAG (retrieval-augmented generation) with GLM-5 and Milvus, you implement a simple pipeline: index your knowledge as vectors, retrieve relevant chunks at query time, then prompt GLM-5 with only those chunks to generate a grounded answer. Concretely, Milvus stores embeddings for your docs (plus metadata like URL, version, product area). When a user asks a question, you embed the question, search Milvus for top-k similar chunks, and place those chunks into the GLM-5 prompt under a “Context” section. This architecture is popular for developer documentation assistants because it’s faster and more accurate than pasting entire docs into the model prompt, and it’s easy to keep current: re-index docs, don’t retrain the model. GLM-5 is designed for long-context and agent workflows, but the best RAG systems still keep prompts compact and relevant (GLM-5 supports large context/output per the vendor docs, but that doesn’t mean you should spend it unnecessarily). Primary GLM-5 references: GLM-5 overview and Migrate to GLM-5.
A practical Milvus + GLM-5 RAG setup has four stages you can implement incrementally:
Chunking: split documents into chunks (often 300–800 tokens) with overlap.
Embedding: generate a vector for each chunk (choose an embedding model and stick to it).
Indexing: upsert into a Milvus collection with fields like
chunk_id,doc_id,text,url,version,lang,updated_at, plus the vector field.Retrieval + Generation: at query time, search Milvus with
top_k, apply metadata filters (e.g.,version == "v2.5"), then call GLM-5 with a strict prompt.
A minimal “prompt contract” that works well is:
System: “Answer using only the Context. If missing, say you don’t know.”
Context: retrieved chunks (include URL + version in each chunk header)
User: the question
Output rules: “Return Markdown with short sections and bullet points.”
If you want a managed option, Zilliz Cloud is managed Milvus and supports the same core retrieval patterns while reducing operational overhead. On the GLM-5 side, you can start with basic chat calls, then add function calling to let the model explicitly request retrieval via a search_docs tool (see Z.ai’s Function Calling).
To make RAG production-ready (and not just a demo), focus on filters, tracing, and evaluation. Filters prevent “version drift”: always store product/version metadata and filter retrieval accordingly, especially on Milvus.io where docs change frequently. Tracing means logging: query text, embedding model, top-k chunk IDs, similarity scores, and the final answer. Evaluation means building a small test set of representative questions and measuring: retrieval hit rate (did you fetch the right chunk?), groundedness (does the answer match chunk text?), and helpfulness (does it actually solve the developer’s problem?). When you do this, GLM-5 becomes the “answer composer,” while Milvus or Zilliz Cloud becomes the “source of truth.” That separation is what keeps your docs assistant accurate as your content grows.