What the data for this category looks like
Data sources for game industry investment research include official announcements from game developers, third-party industry research reports, player community discussions, public game license approval information, and competitor development analysis documents. Update frequencies vary by data type: official version announcements update in real time alongside game iterations. Industry reports update monthly or quarterly. License information updates alongside approval cycles. Document structures include long texts such as annual development white papers and quarterly revenue financial reports, and short texts such as single-version update notes and player engagement data. Fields cover game names, development studios, release dates, version numbers, core gameplay tags, user profile metrics, revenue data, and more. Some structured data includes units such as "person-times" and "yuan".
Constraints on vector models and indexing
The data characteristics of game investment research impose multiple constraints on the vector models and indexing workflow. The mixed structure of long and short texts requires chunking parameters to balance semantic completeness and model context limits. Real-time community content and periodically updated industry reports require indexes to support flexible switching between incremental updates and full reindexing. Numerous specialized terms such as gameplay mechanisms and revenue metrics require vector models to have domain semantic alignment capabilities. Mixed storage of multiple field types requires indexes to support hybrid retrieval of structured and unstructured data.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
embedding_model | text-embedding-v3 or text-embedding-ada-002 | Supports long-text embedding, and adapts to semantic alignment for long documents and specialized terms in game investment research |
chunk_size | 800–1200 characters | Balances paragraph integrity of game investment research documents and model context limits, avoiding semantic fragmentation |
chunk_overlap | 100–150 characters | Retains contextual connections between chunks, ensuring continuity of cross-chunk business logic |
recall_top_k | 10–15 results | Filters redundant recall results, focusing on core relevant documents within the game investment research track |
similarity_score_threshold | 0.75–0.85 | Filters low-similarity noise data, retaining retrieval results that meet business requirements |
vector_index_type | HNSW | Balances incremental update efficiency and query speed, adapting to the high-frequency update characteristics of game investment research data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After replacing the
embedding_model, the relevance of knowledge base search results decreases, and the issue persists after reimporting documents. Cause: Vector index reconstruction was not performed. The old index is generated based on the vector space of the original model, which is incompatible with the vector space of the new model. Repair via full or incremental index reconstruction. - Phenomenon: Knowledge base search results return unexpected rankings, not sorted by semantic similarity from highest to lowest. Cause: The reranking module is not enabled, or the
rerank_top_nparameter of the reranking module does not matchrecall_top_k, leading to ineffective sorting logic. - Phenomenon: An
undefined model must match "^(texterror occurs when configuring a vector model other thantext-embedding-ada-002. Cause: API access information for the corresponding model is not configured on the platform, or the model name input format does not comply with platform verification rules.
How to confirm correct configuration
- Perform a vector model switch test: Upload a single game development white paper, check that vectors are normally generated and indexed, with no error logs.
- Input a query containing game-specific terms such as "the core gameplay mechanism of a certain game", verify the sorting logic of retrieval results, and confirm that results conform to semantic relevance after the reranking module takes effect.
- Upload a long document such as a quarterly revenue report, check that segmented content retains complete business logic with no obvious semantic fragmentation.
- View the index monitoring panel, confirm that incremental update tasks trigger normally, with no timeout or failure 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.