Vector Models and Indexing for Game Financing Daily Reports

What the Data for This Category Looks Like

Data for game financing daily reports comes from domestic game industry investment and financing disclosure platforms, listed game company announcements, and financing updates from vertical industry media. Updates run daily, covering publicly disclosed game sector investment and financing information from the same day. Each entry’s document structure includes fields such as financing subject, financing amount, financing round, investor list, financing date, core track and product direction. Financing amount is measured in ten thousand or hundred million RMB. Financing dates use the YYYY-MM-DD format. Some entries include supplementary text such as investor background and financing purpose.

Constraints on Vector Models and Indexing

The daily update requirement means indexes must support incremental synchronization. This avoids resource consumption and delays caused by full index reconstruction. Multiple structured fields and nested content require vector models to adapt to embedding text, numerical, and time fields. This prevents different data types from being confused in the vector space. Large variation in entry text length requires flexible adjustment of chunk splitting strategies. This avoids over-splitting key information or semantic overload in a single chunk. Strong business correlation between time and amount fields requires indexes to support multi-field joint filtering. This improves retrieval accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`embedding_model`	`bce-embedding-v1`	Supports long text embedding, adapts to longer text fields such as investor background and product direction in game financing daily reports, and meets actual testing needs of community users
`chunk_size`	`800–1200 characters`	Text length of single entries in game financing daily reports varies widely. This range balances information density and recall accuracy for a single vector, avoiding overly short fragmented information and overly long semantic confusion
`chunk_overlap`	`100–150 characters`	Retains semantic association between adjacent chunks, adapts to contextual connections between investors and product directions in financing entries, and prevents key information from being split across different chunks
`index_refresh_interval`	`5 minutes`	Adapts to the daily update rhythm of financing daily reports, supports incremental index updates, and avoids excessive resource occupation caused by full index
`recall_top_k`	`Top 8–12 entries`	Number of valid recall entries for game financing daily reports is moderate. This range covers most users' retrieval needs while reducing unnecessary vector computing overhead
`structured_field_embedding`	`Enabled`	Adapts to structured fields such as `financing amount` and `financing date` in financing daily reports, converts numerical and time information into vectors, and improves cross-field retrieval accuracy

The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Configuration Mistakes

After using chunk mode to call the pushdata API to upload data, the interface displays a "Indexing" status for a long time with no progress updates. No reasonable incremental update cycle is configured for index_refresh_interval, causing index tasks to accumulate without being triggered for execution.
The final chunk after question-and-answer splitting fails to complete indexing, returning a 400 Bad Request error. Chunk length exceeds the maximum token limit supported by the embedding_model, and the matching relationship between chunk_size and the model's token limit is not verified in advance.
Calling the embedding model returns an invalid_api_key error, and one API transit is not used. embedding_api_base is not configured as the model's official service address, and the default third-party transit address is used directly, causing key verification failure.

How to Confirm Configuration Is Correct

Check the embedding_model configuration item to confirm it matches the actual called model version. Supported field types may be verified via the model's official documentation.
Upload a single test data entry, confirm that the number of split chunks matches the chunk_size and chunk_overlap configuration. Chunk splitting results may be viewed via system logs.
Trigger an incremental indexing task, confirm that the index progress bar completes within a reasonable time frame with no long-term stagnation.
Retrieve keywords that include structured fields, confirm that recall results include matching content for the corresponding fields, and verify that the structured embedding configuration is active.

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.