What data looks like for this category
Data for coke financial report analysis comes from public regular reports of listed coke production and trading enterprises, and monthly supply and demand monitoring reports released by industry associations. Quarterly reports are released at the end of each quarter. Semi-annual and annual reports are published within two months after the end of the half-year and year-end respectively.
Each financial report document contains two types of content: structured financial tables and business operation analysis paragraphs. Fields cover coke production and sales volume, per-ton coke production cost, port coke inventory, and other metrics. Industry reports additionally include segmented data such as regional price spreads and transportation costs.
What constraints do these characteristics impose on the vector models and indexing workflow
The mixed data structure and layered update rhythm of coke financial reports impose multiple constraints on the vector models and indexing workflow. First, structured financial values and unstructured analysis text require separate adaptation to vectorization logic. Convert numerical values into natural language descriptions before processing, to avoid misjudgment of numerical features by generic vector models. Second, data updates follow a layered rhythm of quarterly, semi-annual and annual cycles. The indexing system must support trigger rules for incremental refresh and full refresh configured by cycle, to reduce invalid computations. Third, individual documents have large differences in length. Long analysis paragraphs must be split to fit the model input limit, to avoid truncation that loses key semantic information.
Configuration settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
TEXT_SPLITTER_CHUNK_SIZE | 800–1200 characters | Adapts to the average length of coke financial report analysis paragraphs, balances semantic integrity and vector recall accuracy |
CHUNK_OVERLAP_RATE | 10–15% | Prevents loss of contextual association after long paragraph splitting, adapts to the long logical dependencies of financial report analysis |
RECALL_TOP_K | 10–15 entries | Covers multi-dimensional metrics and analysis content of coke financial reports, ensures completeness of retrieval results |
SIMILARITY_THRESHOLD | 0.72–0.80 | Filters low-relevance recall results, adapts to the semantic similarity characteristics of coke industry terminology |
INDEX_INCREMENTAL_UPDATE_ENABLED | Enabled | Adapts to the periodic update rhythm of financial reports, reduces computational overhead of repeated indexing |
VECTOR_MODEL_API_TIMEOUT | 15 seconds | Reserves sufficient time for remote API responses, avoids timeout interruptions during index construction |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. 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 the settings.
Three common mistakes
- The symptom is that the collection is created successfully with a prompt, but the page never displays the established index, or the index construction progress stays at the last group. The cause is that the incremental index update switch is not enabled, or the vector model API timeout is set too short, resulting in a timeout interruption during index construction and no valid index file being generated.
- The symptom is that vector retrieval scores meet expectations in a local environment, but all text vector scores are completely identical after packaging into a Docker image for operation. The cause is that the access permissions of the vector model are not properly configured inside the Docker container, causing all requests to call the default empty vector generation logic.
- The symptom is that the response time of hybrid retrieval is significantly higher than that of pure vector retrieval, with a single retrieval taking more than 10 seconds. The cause is that the recall top K value is set too high, or unnecessary structured field indexing is not turned off, resulting in the need to load and match too many redundant data fragments during hybrid retrieval.
How to confirm the configuration is correct
- Upload a single coke financial report document, check the matching degree between the number of vector shards and the document structure, and confirm that the segment length matches the preset configuration.
- Trigger a full index build, check the task logs for timeout or field parsing failure errors, and confirm that the API timeout and update switch configurations are correct.
- Initiate a retrieval request, check the similarity distribution of returned results, and adjust the value range of the similarity threshold based on business requirements.
- Compare the response times of pure vector retrieval and hybrid retrieval, and confirm that the hybrid retrieval configuration meets the performance requirements of the scenario.
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.