Customer-service knowledge: conversation logs for operating reviews

What the documents look like

Knowledge-base maintenance for customer-service enquiries prepares answer material before a conversation happens. It does not approve refunds or returns. The work draws on the customer-service system, product detail pages, after-sales issue statistics and quality-check sampling. The documents handled day to day are FAQ entries, script templates, product parameter tables and escalation tickets. This page deals with the conversation-log and audit step inside operating reports and exception reviews.

The chat interface determines what a run detail contains through the detail parameter. With detail=false, the response holds choices and usage. With detail=true, responseData carries module status and the complete result; in stream mode these arrive through event values, and in non-stream mode they sit inside responseData. An audit check has to separate behaviour a node already performs, fields that can actually be configured, manual comparison work, and business actions that need a separate system implementation. A stored log does not by itself complete a compliance audit.

Conversation identity is checked against the target conversation and the business record through chatId. When chatId is not passed, FastGPT context is not used and the context is built entirely from the supplied messages. When chatId is a non-empty string, the conversation is taken automatically from the database and the last item in the messages array is used as the user question, with the remaining messages ignored. Conversations from different entry points cannot be counted together.

Constraints at the conversation logs step

Run details require detail to be set according to the official contract and responseData to be checked. Field meaning has to be compared against what the log and the interface actually return; business data that is not returned cannot be assumed to be fully retained. Identity fields in test mode may differ, so input, history and returned content need separate checks.

Hypothetical paper exercise (paper material, not a real record):

Material text one: A knowledge-base operator submitted a handover form on 10 March 2026 stating "conversation-log check scope: appId=A-100, chatId=C-200, dataId=D-300, source=api, period 1 March to 7 March 2026."

Material text two: The same handover form noted "this request covers reading only the session list and single conversation run detail within the stated scope; no permission to delete, clear or modify feedback is requested."

Draft sentence to be judged: "It is confirmed that this operator holds delete permission for all conversations, and the audit scope covers every entry point."

Judgment: Material text two states that delete permission was not requested, and material text one limits the scope to source=api with the named chatId and dataId. The draft sentence goes beyond the authorization boundary and the stated scope and is not supported. A pending request does not show that operational permission is absent; what is recorded here is the requested scope, and the actual authorization state remains to be verified.

Configuration and implementation checks

The table below lists check items for this hypothetical exercise. These are not official default values.

Three common errors

1. Treating retrieval similarity as a permission or entity filter. A similarity score does not carry out authorization, and retrieval is not authorization. The check compares datasetId values in quoteList against the target dataset identifier, excludes non-matching entries, and records the actual observation.
2. Treating a detail=false response as a complete run detail. With detail=false, responseData is absent. The check compares whether the response fields include responseData and records a gap when it is missing.
3. Treating the requested scope on a handover form as approved authorization. Material text two records only what was requested. The check compares the actual authorization record, and keeps the state as pending verification when that record is not provided.

How to verify the setup

1. Input appId=A-100, chatId=C-200 and source=api from material text one, then call getHistories. Compare each chatId in the returned list against C-200, and compare the returned total against the number of records inside the period stated in the material. If list is empty or a chatId does not match, record this as not matched and do not infer that the data does not exist.
2. Input dataId=D-300 from material text one, then call getResData. Compare moduleType and moduleName across the returned data array and check whether a chatNode run detail is present. If responseData is missing, record this as unsupported.
3. Input the authorization boundary "read only" from material text two and compare it against the permission the delete interface requires. If the actual authorization record is not provided, record this as pending verification and do not judge it as rejected or approved. An empty result can be carried forward by checking downstream design against the paper input []; this does not claim that a fictional rule search necessarily returns [].

Datasets built on different vector models cannot be associated together. Check the identifiers first and exclude non-matching ones, and do not run cross-model retrieval to compare missed recall. Image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled according to its settings; an available path does not mean every request executes all of it.

Related reading

• openapi/chat
• FastGPT conversation logs