What this category of data looks like
Game industry research report data comes from four main sources: securities firm industry research reports, public financial reports of game companies, public content from third-party game data platforms, and information disclosed at industry conferences. Update frequency aligns with industry events. Concentrated updates happen during license issuance periods, new game launches, and quarterly financial report cycles. A single document typically includes an industry market overview, sub-segment breakdown, analysis of key products, competitive landscape, and trend forecasts. Fields include release date, issuing institution, covered category, core product name, user scale, and revenue scale. Common units are person-times and ten thousand yuan.
What constraints these characteristics impose on retrieval and recall
The multi-source, heterogeneous nature of game research reports creates inconsistent parsing formats. These formats include PDF documents, structured financial report tables, and web content. Precise multi-format parsing and field mapping must be supported. Non-fixed update rhythms require incremental recall configuration. This supports rapid data synchronization after sudden industry events. Documents contain large volumes of structured table data and professional game terminology. Table content retrieval and entity association recall logic must be enabled to avoid missing core business data. Indicator naming varies across different research reports. Unified field normalization rules must be configured to ensure consistent metrics during retrieval.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Game research reports contain a large amount of structured table data, and table content needs to be parsed for precise retrieval |
RECALL_TOP_K | Top 8-12 results | Single game research report content is lengthy, so enough candidate segments need to be recalled to cover core business information |
SIMILARITY_THRESHOLD | 0.72-0.80 | There are many professional game terms, so low-similarity irrelevant recall results need to be filtered |
INCREMENTAL_SYNC_INTERVAL | 15 minutes | Sudden industry events occur frequently in the game industry, so rapid synchronization of new research report data is required |
FIELD_MAPPING_RULES | Configure unified indicator mapping rules, such as unifying "revenue/turnover" to "business revenue" | Indicator naming varies across research reports from different sources, so retrieval dimensions need to be normalized |
TRAIN_ORDER_AUTO_TRIGGER | Trigger based on new data volume threshold, set the threshold to 5 research reports | Game research report updates have no fixed cycle, trigger training orders on demand to avoid resource waste |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Only a single result is returned after retrieval, and the top 2 content cannot be passed to the large model for processing. Cause:
RECALL_TOP_Kis not configured to a value greater than or equal to 2, or the large model call link does not specify splicing the first N recall results. - Phenomenon: Parameter exceptions occur after calling the
create_train_orderinterface, or the functional difference between this interface and collection data addition is not distinguished. Cause: The training order is not clearly defined for batch index reconstruction, while collection data addition only supports single or small-scale incremental synchronization, and batch updates need to be triggered via the training order. - Phenomenon: Structured table data is missing from parsed research report content. Cause: The
PARSE_TABLE_ENABLEconfiguration is not enabled, or parsing times out without fully extracting business indicators in the table.
How to confirm the configuration is correct
- Upload a single game research report, check whether the table content in the parsing result is fully extracted, to confirm that the
PARSE_TABLE_ENABLEconfiguration takes effect. - Trigger an incremental synchronization task, check whether new research reports complete index updates within the preset cycle, to confirm that the
INCREMENTAL_SYNC_INTERVALconfiguration meets expectations. - Initiate a retrieval request containing professional game terms, check whether the similarity of recall results falls within the preset range, to confirm that the
SIMILARITY_THRESHOLDconfiguration is reasonable. - Upload a batch of research reports, check whether the system automatically triggers a training order, to confirm that the threshold configuration of
TRAIN_ORDER_AUTO_TRIGGERis correct.
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.