What the Data for This Category Looks Like
Baijiu research report data primarily comes from broker research institute industry and company reports, public documents from baijiu industry associations, annual and quarterly reports of listed baijiu enterprises, and third-party beverage circulation monitoring data. Update cadence varies by content type. Broker reports release immediately alongside industry events and corporate earnings reports. Association data updates quarterly. Corporate reports follow fixed quarterly and annual update schedules.
Single documents have wide character count ranges. Content includes industry trends, core distiller financial metrics, channel sales data, and policy impact analysis. Fields cover per-kiloliter ex-factory price, terminal retail price, inventory turnover days, and sales turnover rate. Units are mostly yuan per kiloliter and calendar days.
Constraints Imposed on Vector Models and Indexing Workflows
The long text span, mixed field attributes, and uneven update cadence of baijiu research reports impose multiple constraints on vector model and indexing workflows.
Long texts contain both macro industry analysis and fine-grained financial data. This requires balancing semantic coherence and vector alignment of fine-grained metrics, to avoid splitting critical associated content across chunks. Numeric fields such as per-kiloliter price and inventory turnover days need support for mixed numeric and text indexing, to prevent loss of numeric semantic associations in plain text vectors. Uneven update frequencies require indexes to support incremental updates, reducing resource consumption from full index rebuilds.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the document length of baijiu research reports, covers complete corporate financial analysis units, and avoids splitting critical metrics and associated analysis |
chunk_overlap | 100–150 characters | Balances long text semantic coherence and vector storage redundancy, prevents cross-chunk industry analysis and data associations from being split |
INDEX_TYPE | HNSW | Meets the vector retrieval needs of baijiu research reports, balances recall speed and accuracy for million-scale vector databases |
RECALL_TOP_K | Top 10–15 results | Matches the information density of baijiu research reports, avoids excessive recall causing context redundancy, or insufficient recall missing segmented data |
RERANK_TOP_N | Top 3–5 results | Focuses on core report conclusions and key data, retaining a small number of highly relevant documents after reranking meets question and answer requirements |
EMBEDDING_BATCH_SIZE | 32–64 | Balances local deployment video memory usage and index construction speed, adapts to batch upload scenarios for research reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values depend on material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After enabling
RERANK_MODELand index configuration, online recall test results return without reranking, and the number of returned results matchesRECALL_TOP_K. Cause: The reranking trigger switch was not enabled in the knowledge base retrieval workflow. Only the reranking configuration in the index phase was completed, and the reranking logic of the retrieval process was not linked. - Symptom: When deploying version 4.9.0 locally, the Embedding model calls OneAPI and returns
500 Internal Server Error, while the Chat model calls without exceptions. Cause: OneAPI has not configured a forwarding route for the corresponding Embedding model, or the FastGPT Embedding interface request format does not match the format accepted by OneAPI. - Symptom: When uploading a baijiu research report PDF, terminal price screenshots in the document do not generate vector indexes, only the text portion is processed. Cause: The local deployment did not enable the linked configuration of image OCR and vector embedding, or the corresponding OCR dependency packages were not installed.
How to Confirm Proper Configuration
- Access the knowledge base management interface, view the
chunk_sizeandchunk_overlapconfiguration parameters, compare them with the preset configuration plan, and confirm that the parameters match the document characteristics of the current research reports. - Upload a single baijiu research report document, view the running logs of the upload task, and confirm that there are successful records for text parsing, OCR processing, and vector embedding, with no error fields.
- Initiate an online recall test, enter a query containing baijiu industry terminology and numeric metrics, view the sorting logic of returned results, and confirm that the reranking function is triggered normally.
- Verify the connectivity of the Embedding model, initiate a simulated request, confirm that the vector data format returned by the interface meets system requirements, and there are no connection exception errors.
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.