What the data for this use case looks like
Data sources for game industry intelligent due diligence reports include game developer-submitted game license approval documents, monthly revenue ledgers, version iteration logs, and third-party industry monitoring data. Data update cadence varies: license qualification data is updated quarterly, monthly revenue data is synced monthly, and version logs are updated with game versions (typically every 1–3 months). Document formats include PDF administrative approvals, Excel structured ledgers, Word business reports, and JSON performance reports. Fields include license number, license validity period, monthly revenue (10,000 yuan), peak concurrent users, version iteration date, and other fields with clear numerical, date, and unit identifiers.
What constraints these characteristics impose on vector models and indexing
Mixed-format data requires adapted vector encoding logic. General-purpose text embedding models cannot effectively handle numerical and unit associations in structured tables, so additional metadata tags must be retained. Uneven update cadence requires support for incremental indexing to avoid full index reconstruction. This prevents full index builds from triggering when monthly revenue data is updated, reducing resource consumption. Fields include clear units and time ranges, so metadata must be associated during vector encoding to avoid confusion between different dimensional values during retrieval. Some documents (such as version logs) have coherent content, so segmentation strategies must be adjusted to avoid key information being split, which harms retrieval accuracy.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 or text-embedding-3-large | Adapts to mixed-format data for game due diligence, effectively encodes semantic associations between structured numerical values and unstructured text, and aligns with the embedding logic of FastGPT V4.8.20-FIX2 |
chunk_size | 800–1200 characters | Balances contextual completeness for long-text project approval reports and indexing density for short-entry revenue ledgers, adapting to segmentation needs across different document formats |
chunk_overlap | 100–150 characters | Prevents key information from being split across segments in long documents, adapting to content that requires coherent context such as license approvals and version logs |
index_batch_size | 50–100 items/batch | Adapts to structured table row data for game due diligence, prevents PGSQL vector database connection timeouts caused by overly large batches, and balances indexing efficiency and stability |
recall_top_k | Top 8–12 results | Covers multi-dimensional information required for game due diligence (revenue, license, version), balancing recall comprehensiveness and result relevance |
vector_store_index_type | PGSQL HNSW | In local deployment scenarios, this index type balances retrieval speed and accuracy, adapting to retrieval needs for hundreds of thousands of game due diligence data entries |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. Testing on available samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Search returns far fewer results than expected, with a
no_matchstatus code shown in logs. Cause: Metadata retention configuration was not enabled for mixed-format game due diligence data. General-purpose embedding encoding discards associated information for fields such as monthly revenue and license validity period, leading to failure to match precise retrieval conditions. - Symptom: An
out of memoryerror occurs during index construction. Cause: Theindex_batch_sizeparameter was not adjusted. Loading vector embeddings for hundreds of thousands of game due diligence data entries at once causes PGSQL vector database memory overflow, failing to adapt to batch splitting requirements for structured table data. - Symptom: New index structure is incompatible with existing old data. Cause: Configuration consistency for
vector_store_index_typewas not maintained. Switching fromPGSQL IVFFlattoPGSQL HNSWwithout rebuilding existing indexes leads to inability for old and new indexes to interoperate, and failure to retrieve old data.
How to Confirm Configuration is Correct
- Randomly select 5 game due diligence data entries in different formats (license approval PDF, revenue ledger Excel, project approval report Word). An embedding task is initiated, and verification confirms that the metadata fields of the embedding result include document source, field identifier, and other relevant information.
- A mixed retrieval query is initiated that includes numerical conditions (such as "monthly revenue exceeding 5 million yuan") and text keywords (such as "2024 Q3 version"). It is verified that the recalled results match both numerical filtering criteria and text semantics.
- An incremental indexing operation is performed, and 1 game version log not included in the initial index is added. After waiting for index update to complete, a retrieval query is initiated, and it is confirmed that the newly added content is returned normally.
- The vector database management interface is accessed, and it is checked that the
vector_store_index_typeconfiguration matches the current index structure, with no sharding anomalies or error logs present.
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.