What the documents look like
Marketplace advertising work in this category runs on four recurring document families: delivery plans, keyword reports, audience package descriptions, and conversion attribution tables. The operational data comes from advertising platforms and store transaction records, and it refreshes at daily or hourly cadence. Procurement of inventory and supplier materials adds a second layer to these documents: purchase plans, supplier lists, quotation notes, and delivery terms. The parsing and chunking step has to keep the correspondence between supplier name, material object, quotation validity period, delivery quantity, and authorization boundary intact, because the downstream retrieval result feeds budget allocation and stock replenishment timing.
Two industry facts shape this page. First, advertising spend is repriced against traffic and conversion in near real time, so procurement documents are read under time pressure rather than through a platform campaign review cycle. Second, the same source material can be mapped to more than one vector index, and when a group of original data is recalled through any one of those indexes, the retrieval score takes the highest value. That mapping behavior is what makes chunk boundaries consequential: a chunk that separates a delivery condition from the quantity it constrains will still be retrieved, but the retrieved text will not carry the full condition.
Constraints at the document chunking step
This page addresses only the document parsing and chunking step for inventory procurement and supplier materials. When source material updates daily or per order, a successful upload does not by itself mean retrieval has updated. The actual training and index state has to be checked separately. Chunking should follow the structure of the material itself, and a condition, its explanation, and the content it constrains should not be split apart.
Retrieval indexes and original data are stored separately. One group of original data can correspond to multiple vector indexes, and when vectors resolve to the same original data group, the results merge and the vector score takes the highest value. Semantic retrieval suits natural-language questions; full-text retrieval suits exact queries such as numbers, model designations, and proper nouns. Hybrid retrieval combines both paths through RRF, which is a fusion process for multiple result channels rather than a standalone toggle. The minimum relevance value applies only under semantic retrieval or result reranking, and it filters low-relevance results; it does not perform entity isolation or permission filtering. Reranking requires a model and runs according to its configuration. Image retrieval requires corresponding image input and model capability. These optional paths are not executed on every request.
Hypothetical paper exercise sample: a marketplace advertising team's inventory procurement and supplier materials include a quotation note whose original text reads, "This quotation is valid until 30 June 2025. The supply scope is summer delivery materials for the East China warehouse. A single purchase quantity does not exceed 5,000 units. Supplier A is responsible for delivery." A draft sentence awaiting judgment reads, "Supplier A undertakes to continue supplying at this quotation in July 2025." The check compares whether the supply period in the draft sentence and the quotation validity period in the original belong to the same field. The original states only a quotation validity period and does not state a July supply undertaking, so the draft sentence is not supported by the original.
Configuration and implementation checks
The table below covers the sample material and lists documented settings or implementation checks relevant to this feature and primary task. The values are starting points for this hypothetical exercise, with the reason for each choice and the comparison object stated.
| Configuration item | Hypothetical exercise starting point | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains supplier names and quotation validity periods, so semantic and full-text coverage are both needed; compare against semantic retrieval alone |
| Reference limit | 3,000 tokens | The sample has four segments covering quotation, scope, quantity, and delivery, and needs complete reference; compare against 2,000 tokens to see whether truncation occurs |
| Minimum relevance | 0.3 | The sample question points clearly at the quotation validity period; compare against 0.5 to see whether the supplier delivery segment is missed |
| Question optimization | Enabled | The sample is a single-turn explicit question; compare against disabled to see whether unrelated expansion is introduced |
| Result reranking | Enabled | Candidates include multiple supplier material segments; compare against no reranking to see whether ordering changes |
| Segmentation basis | By natural paragraph of the quotation note | The four sample segments are semantically single; compare against fixed-length splitting to see whether conditions are separated |
Three common errors
- Treating a successful upload as updated retrieval. Input is a retrieval run immediately after the sample material upload; the comparison object is the actual training and index state; the observable result is whether retrieval hits the new segment. Without checking training state, that judgment has no basis.
- Merging quotation validity period and supply period into one field. Input is the sample draft sentence; the comparison object is the quotation validity period field in the original; the observable result is whether the draft sentence is supported by the original. The original does not state a July supply undertaking, so the draft sentence is not supported.
- Using minimum relevance to exclude unrelated suppliers. Input is the sample retrieval request; the comparison object is the result list before and after filtering; the observable result is whether unrelated suppliers are excluded by the threshold. That threshold only filters low-relevance results and does not perform entity isolation.
How to verify the setup
- Input the sample original text and the draft sentence, and compare whether the supply period in the draft sentence and the quotation validity period in the original share the same subject, the same period, and the same field. The original states only a quotation validity period until 30 June 2025 and does not state a July supply undertaking, so the draft sentence is not supported. If the draft sentence instead read, "Supplier A's quotation is valid until 30 June 2025," it would match the original.
- Input the sample material after parsing, and compare whether the segmentation result preserves the complete correspondence of the four segments covering quotation, scope, quantity, and delivery. The observation condition is that each segment stands independently and no condition is separated from what it constrains. If segmentation separates "a single purchase quantity does not exceed 5,000 units" from the supply scope, the condition is not met.
- Input the sample retrieval request, and compare what semantic retrieval and full-text retrieval each return. The observation condition is that both retrieval types hit the quotation validity period field. If full-text retrieval hits and semantic retrieval does not, record the actual observation without inferring a missed recall or a redundant return. An empty result can be checked against downstream design using the paper input [], without claiming that a fictional rule retrieval necessarily returns [].
Authorization state remains outside retrieval. Whether materials, operations, or requests are authorized for handover is a separate fact that a retrieval result or a form submission does not establish.