What the documents look like
For product questions in On-demand retail fulfillment, this page examines FastGPT document chunking. Preserve speakers across order notes, product descriptions and quoted retail messages. The examples are hypothetical.
Constraints at the document chunking step
A proposed immediate-retail packet describes a scented product, records a customer wishing for fragrance-free and quotes that wish in a service note while available options remain unverified. The question asks whether the current item is already fragrance-free. Parsing and chunks need the three source identities, including the quoted customer voice, preserved. Repetition in a service note adds no specification evidence, and a wish changes no product. No real ingredient or substitution conditions are supplied. Shared product naming and repeated fragrance-free wording establish no actual sold attribute for the current item.
Configuration and implementation checks
| Material check | Conditions in this test | Implementation evidence |
|---|---|---|
| Product source | Scented original page | Do not merge customer preference. |
| Speaker | Customer and service quotation | Inspect parsed attribution. |
| Quotation qualifiers | Customer says and pending | Retain across chunks. |
| Repeated evidence | Same wish repeated | Do not make independent specification. |
| Update adoption | Current product details | Inspect actual reference. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Original-document check | Compare original files with parsed output. |
Chunk completeness | Keep conditions traceable to their subjects. |
Data and indexes | Retain original-data and index evidence separately. |
Sample retrieval | Retest literal wording and natural questions. |
Post-update check | Inspect training or index state after updates. |
Three common errors
First, a customer note may merge into product description; inspect original speaker, parsed paragraph and block identity. Second, service quotation may lose the customer says and pending clauses; compare quotation boundaries and adjacent text instead of promoting reported speech into confirmed specification. Third, an updated product page may still return an older preference; inspect original version, processing and index state with actual reference content. These concern source attribution, quotation level and update adoption. Several repeated keywords establish no independent product evidence when they trace to one unresolved wish.
How to verify the setup
Ask what the product states, what the customer requests and what the service note still needs, tracing each answer to its paragraph. Repeat fragrance-free, scented and natural questions to inspect speaker retention after chunking. A request note alone leaves specification unresolved without proving acceptance or refusal. Added details need the current item matched, so earlier preference no longer serves as specification support. This is a proposed text test without substitution or ingredient judgment. Successful parsing also establishes no actual changed order; that state requires a separate applicable operational record beyond the quoted classroom material.
| Material context | Scope |
|---|---|
| Document types | Instant Order, Picking List, Out-of-Stock Report and Delivery Exception Order |
| Candidate sources | Douyin Instant Retail, Meituan or JD Daojia Backend, Store Inventory and Rider Tracking |
| Technical baseline | FastGPT v4.17.2 |