What the Data for This Category Looks Like
Data sources for professional chain investment research include store operation reports, supply chain quotation documents, regional consumer research reports, member consumption records, and more. Update frequencies follow tiered differences: daily store operation data is updated daily, weekly supply chain reports are updated weekly, and industry research reports are updated irregularly based on their release schedules. Document structures include structured Excel tables (with fields such as store ID, per-square-meter-day efficiency, inventory turnover rate), semi-structured PDF research reports, and unstructured TXT inspection records. Some documents include specific units: for example, per-square-meter-day efficiency uses yuan/square meter/day, and inventory turnover rate uses times/quarter.
Constraints Imposed on Vector Models and Indexing
Mixed multi-type documents require vector models to support encoding for both structured and unstructured data, to avoid losing semantic associations of structured fields with a single encoding logic. Data with different update frequencies needs to support batch incremental indexing, to avoid resource consumption from full reindexing. The wide range of document lengths requires adaptive adjustment of chunking parameters, to prevent short inspection records from being over-split or long research reports from failing to encode completely. Specific units for structured fields can interfere with general semantic encoding, so targeted field-level indexing rules need to be configured to ensure unit information does not affect business relevance judgments.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3 or text-embedding-v3 | Supports multimodal and long-text encoding, adapts to mixed document types such as store operation data and research reports |
chunk_size | 800–1200 characters | Balances short store inspection records and long regional consumer research reports, reduces semantic segmentation breaks |
chunk_overlap | 100–150 characters | Retains contextual association between adjacent segments, prevents key indicators such as single-store revenue and per-square-meter-day efficiency from being split |
retrieve_top_k | 8–12 entries | Covers multi-store and multi-dimensional investment research data dimensions, meets retrieval requirements for cross-store comparison |
similarity_threshold | 0.72–0.78 | Filters low-relevance supply chain quotations or irrelevant regional research reports, retains accurately matched business data |
rerank_top_k | 3–5 entries | Compresses the reranked result set, balances retrieval speed and effective information density required for investment research decisions |
The parameter values provided on this page are common recommendations for establishing configuration starting points. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: Vector access fails, interface prompts "No available vector model" or API call returns 401 error. Cause: API key or endpoint for the vector model is not configured correctly. For example, when deploying bge-m3 with ollama, the embedding interface address of the local service is not filled in.
- Symptom: Reranked results returned after knowledge base retrieval are slow, single-round retrieval time exceeds conventional thresholds. Cause: Recall or rerank count is set too high, and long documents are not properly segmented, leading to vector retrieval and reranking computation exceeding server load limits.
- Symptom: A large number of irrelevant cross-region store data is mixed in retrieval results, with very low business matching degree. Cause: A reasonable similarity threshold is not set, or separate encoding rules are not applied to structured fields such as store ID and city hierarchy, leading to deviations in semantic similarity calculation.
How to Confirm the Configuration Is Complete
- Enter the vector model configuration module, initiate an embedding test call, verify that the returned vector dimension matches the official standard dimension of the selected model, to confirm the access link is normal.
- Upload a mixed test document containing store operation tables and industry research reports, check the automatic segmentation results, confirm that the segment length matches the preset range, and no key business fields are split.
- Initiate a retrieval request for single-store revenue analysis, check that the number of returned recall entries matches the preset
retrieve_top_kparameter, and the sorting logic of the reranked results meets the business requirements of investment research analysis. - View the vector index refresh logs, confirm that incremental refresh tasks are executed according to the preset cycle, and there are no consecutive failed error records.
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.