What the data for this category looks like
Data sources for hotel and catering intelligent due diligence reports include store operation licenses, food traceability records, daily passenger flow reports, franchise cooperation agreements, public public opinion information, and more. Update rhythms vary significantly: operation licenses are updated quarterly, passenger flow reports are generated daily, public opinion information is updated in real time, and franchise agreements are only updated when cooperation changes.
Document structures cover structured tables, semi-structured reports, and unstructured text. Fields include business area (square meters), average daily passenger flow (person-times), food traceability batch number, franchise term (years), and other information with clear units. Single document lengths range from hundreds of characters of transcribed license text to tens of thousands of characters of comprehensive due diligence reports.
What constraints these characteristics impose on the vector models and indexing link
Data sources with multiple update rhythms require indexes to support incremental updates and real-time queries, to avoid computing resource consumption from full index rebuilding. Mixed document structures of structured fields and unstructured text require vector models to adapt to vectorization needs for both short and long texts. This prevents over-segmentation of short fields or loss of key associated information in long texts.
Fields with fixed units affect semantic consistency in vector similarity calculations. Vector models must correctly recognize semantic features related to units. High-frequency updated passenger flow and public opinion data increase concurrent pressure on the vectorization link. Parameters must be adjusted to match service carrying capacity.
How to Set Configuration Values
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed text length of hotel and catering due diligence reports, balances semantic integrity of split blocks and the number of blocks |
chunk_overlap | 100–150 characters | Retains cross-block associated information, prevents association fields between stores and food traceability from breaking after segmentation |
embedding_batch_size | 16–32 | Reduces vectorization concurrency, mitigates errors caused by exceeded embedding rate limits, and adapts to high-frequency updated data sources |
vector_index_type | HNSW | Supports high-concurrency real-time recall, adapts to the fast query requirements of due diligence reports |
recall_top_k | Top 10 entries | Covers multi-dimensional associated information of stores, meets the comprehensive retrieval requirements of due diligence reports |
embedding_api | Calibrated based on actual deployment | Supports direct configuration of local deployment of m3e models or custom proxies, no mandatory use of OneAPI |
The parameter values provided on this page are common starting points for configuration work. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Knowledge base upload status remains in indexing for an extended period: The
embedding_batch_sizeis not adjusted to match high-frequency updated catering data, causing vectorization requests to trigger service rate limits. - Chunk loss occurs when segment length is set to 3000 characters: The
chunk_overlapis not adjusted based on the semantic features of short structured fields, so cross-chunk associated field information cannot be fully recalled after long text segmentation. - Vectorization jobs report rate limit exceeded errors: The
embedding_batch_sizevalue is not reduced, and concurrent vectorization requests exceed the carrying capacity of the model service.
How to Confirm the Configuration Is Correct
- Upload a single sample hotel and catering due diligence report, check that the number of split text blocks matches expectations, with no obvious content missing.
- View vectorization run logs, confirm there are no embedding rate limit exceeded error messages, and request frequency matches the configured parameters.
- Launch a vector recall test, confirm that the recall results include core fields such as store licenses, passenger flow data, and public opinion information, with semantic matching meeting expected standards.
- Upload incrementally updated passenger flow report data, confirm that the index can complete incremental updates normally, with no duplicate or missing vector data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.