What the data for this category looks like
Game financial report data primarily comes from quarterly and annual financial reports publicly disclosed by listed game companies, plus official periodic operational data announcements. Updates follow a quarterly core cycle, with annual reports serving as full cycle reviews. Temporary announcements release alongside major operational milestones.
Document structures include structured revenue details, user scale metrics, cost composition tables, paired with unstructured text explaining business adjustments. Fields cover game category-specific revenue, monthly active users, R&D investment amounts, and more. Units are mostly currency or user scale units, and some indicators include comparative dimensions.
What constraints do these characteristics impose on the vector model and indexing workflow?
Multi-source heterogeneous formatting, quarterly batch update requirements, and mixed structured and unstructured content in game financial reports create multiple constraints for the vector model and indexing workflow. Mixed public financial report and operational announcement formats require indexing systems to support parsing and semantic chunking for multiple document types. Quarterly update cycles create efficiency needs for batch index construction and incremental synchronization. The content structure of financial reports — structured indicators categorized by game category and region, paired with business descriptions — requires chunking logic to align with business units. This avoids semantic confusion across modules. Multi-dimensional business fields require the indexing workflow to balance semantic recall and lightweight attribute filtering adaptability.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Game financial reports contain structured business tables and long text descriptions. This range preserves complete semantics of a single business module and avoids semantic fragmentation caused by cross-block splitting |
embedding_model | text-embedding-3-large or as determined through actual testing | Game financial reports mix structured business indicators and unstructured business descriptions. text-embedding-3-large better adapts to semantic encoding associated with numerical values and supports multi-dimensional business content |
index_batch_size | 50–100 items per batch | The volume of data after chunking a single financial report is large. This batch size balances index construction speed and system resource usage, avoiding overload from a single indexing operation |
recall_top_k | Top 10–15 results | Business relevance of financial report content is high. Too many recall results will introduce irrelevant business modules, while too few may miss key business information |
enable_field_filter | Enabled | Financial reports include structured fields such as revenue categorized by game category and region. Enabling this feature allows combining semantic recall and attribute filtering to improve precise matching efficiency |
incremental_update_strategy | Triggered by document update time | Financial reports follow a quarterly core update cycle. Triggering based on update time avoids reindexing processed historical documents and reduces invalid computational load |
The parameter values provided on this page are general recommendations used as a starting point for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to conduct testing on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: After calling the knowledge base interface to generate an index, the embedding model automatically switches from
text-embedding-ada-002totext-embedding-3. This causes inconsistent semantic matching results between historical and new indexes. Cause: Theembedding_modelparameter is not fixed in the configuration, and the platform’s default dynamic model update policy is used. - Phenomenon: The QA split file collection created via the OpenAPI interface has a long index build time, and query response delays exceed expected thresholds. Cause: The
index_batch_sizeparameter is not set appropriately. An overly large batch value causes system resource overload, while an overly small batch value increases overhead from multiple network requests. - Phenomenon: Recall results include business content unrelated to the target game category, with insufficient precise matching accuracy. Cause: The
chunk_sizesetting does not align with the financial report’s business module structure. Chunks span revenue data across multiple game categories, leading to semantic blocks containing irrelevant business information.
How to confirm the configuration is properly set
- Review the embedding model configuration to confirm the
embedding_modelparameter is fixed to the target value, and no platform default dynamic options are used. - Submit a single test financial report for index construction, and verify chunking results split along business modules, with no semantic blocks spanning multiple business units.
- Initiate a simulated query, and verify recall results combine field filtering conditions to precisely match target business content.
- Trigger an incremental update task, and confirm only newly updated documents are included in index construction, with no repeated processing of historical documents.
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.