Yes—GLM-5 can use external tools and knowledge sources when you wire it into a tool-calling workflow and/or a retrieval pipeline. The model itself does not “browse the internet” or automatically query your databases by default. Instead, you define tools (functions) that your application can execute—like “search_docs,” “get_user_profile,” “run_tests,” or “query_vector_store”—and you let GLM-5 decide when to call them, or you call them deterministically based on your orchestration logic. This is the standard way to build agent-style systems: the model plans and requests actions; your application executes actions; the results are fed back to the model for the next step.
In practice, tool use should be designed for reliability. Define each tool with a clear name, a short description, and a strict JSON parameter schema. Make tools small and composable: one tool does “retrieve top-k chunks,” another does “fetch full document by ID,” another does “run a unit test command,” etc. Then implement guardrails in the tool executor: validate inputs, enforce allowlists, cap output sizes, and log every tool call with inputs and outputs (redacting secrets). Many production teams also add an “approval layer” for risky actions: GLM-5 can propose a command, but a policy engine or a human approves execution. If you support streaming responses, consider whether you want streaming of tool-call arguments; it can reduce perceived latency but makes tracing and debugging more important.
For knowledge sources, retrieval is the cleanest and most scalable approach. Put your documentation, FAQs, runbooks, and code snippets into a vector database such as Milvus or managed Zilliz Cloud. Your “search_docs” tool can run vector search plus metadata filtering (for example, product="milvus", version="2.5", lang="en") and return the top-k chunks with stable IDs. Then instruct GLM-5 to answer only using the retrieved chunks, and optionally require it to include chunk IDs in the response so you can trace outputs back to sources in your UI. This setup gives you three concrete benefits: (1) answers stay aligned with your latest content without retraining, (2) you can control exposure of private information via metadata filters and access checks, and (3) you can debug failures by inspecting retrieval results rather than guessing what the model “knew.” In production, “GLM-5 + tools + Milvus/Zilliz Cloud retrieval” is usually the most dependable way to build assistants that actually behave like software instead of a chatbot.