What this category of data looks like
Data for warehousing and logistics inquiries mainly comes from internal operation manuals, logistics timeliness rule documents, warehouse location descriptions, historical customer service tickets, and express sorting standards. There are three update cycles for this data:
- Operation process documents are updated quarterly
- Timeliness rule documents are updated monthly
- Real-time ticket data is synced to the knowledge base in real time
Most document structures include fields such as operation steps, warehouse area numbers, timeliness thresholds, and response script templates. Units include specific logistics measurement units like meters, hours, pieces, and vehicle trips. There is no universal unified format.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The multiple update cycles of warehousing and logistics data require the retrieval system to support switching between incremental sync and full updates. This prevents expired timeliness rules from being retrieved. Long-text operation documents and number fields that require exact matching require the retrieval system to balance semantic relevance and exact matching. It cannot rely solely on a single retrieval mode. The high-frequency updates of real-time ticket data require the retrieval pipeline to support low-latency real-time data calls. This avoids returning outdated response content. Differences in fields across documents require the retrieval system to automatically adapt matching rules based on document type. This prevents invalid cross-category retrieval.
How to set the configurations
| Configuration Item | Suggested Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Rule text for warehousing and logistics has high precision requirements. A threshold that is too low will introduce irrelevant general logistics knowledge. A threshold that is too high will miss valid timeliness and operation information. The reasonable range is calibrated based on actual testing. |
limit | Top 6–10 results | Responses to inquiries need to cover core operation nodes. Too many retrieved results will consume excessive tokens. Too few results will fail to cover complete operation guidance. |
searchMode | vector+keyword | Warehousing data includes both semantic operation process descriptions and number fields that require exact matching, such as warehouse area numbers and express waybill rules. Hybrid mode balances comprehensiveness and accuracy. |
usingReRank | Enabled | Mixed long and short text exists in retrieved results. Re-ranking sorts results by semantic relevance, preventing low-relevance long documents from appearing at the top. |
datasetSearchUsingExtensionQuery | Disabled | Most inquiry questions are specific queries about logistics nodes. Extended queries will introduce irrelevant general knowledge and interfere with exact matching. |
collectionIds | Bind dedicated warehousing and logistics knowledge base collections | Isolate knowledge base data from other categories to avoid retrieving irrelevant general logistics documents and ensure targeted retrieval scope. |
Three common mistakes
- Phenomenon: The total character count of documents returned in a single retrieval exceeds the token limit, causing response truncation or errors. Cause: The
limitparameter is not set reasonably, too many long-text warehousing operation documents are retrieved, and total length filtering is not applied to retrieved results. - Phenomenon: Irrelevant casual chat content is included in retrieval results and cannot be filtered using the empty knowledge base search judgment. Cause: Only empty result judgment is used, semantic relevance is not filtered using the
similaritythreshold, and reverse matching rules are not set for general casual chat terms. - Phenomenon: Knowledge base documents after chunking cannot generate valid vectors, or vector retrieval results are abnormal. Cause: Content is not split according to the length characteristics of warehousing and logistics documents, and the maximum input length supported by the vector model is not matched, leading to non-compliant chunking logic.
How to confirm the configuration is correct
- Upload one typical warehousing and logistics document, such as a warehouse area operation manual, and run a retrieval test in FastGPT v4.17.0. Confirm that the
similarityscores of returned results fall within the preset range. - Enter an unrelated casual chat question and confirm that retrieval results are empty or have a relevance level lower than the preset threshold, and will not be included in the final response.
- Upload one warehousing operation document with a length exceeding 1000 characters and confirm that the length of individual chunks after splitting falls within the maximum input range supported by the vector model. No vector generation failure prompts appear.
- Check the number of retrieved results and confirm that it does not exceed the preset return limit, avoiding token consumption exceeding model limits.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.