What this category’s data looks like
Film theater investment research data comes from theater scheduling systems, box office statistics platforms, film record public announcement websites, promotion material libraries, audience comment communities, and industry research report institutions. Update rhythms vary widely: scheduling and box office data updates daily, film record information updates periodically, and industry research reports and audience comments update irregularly alongside promotion cycles. Single documents include fields such as film ID, title, release date, theater slot count, box office revenue, promotion keywords, and audience ratings. Units include screenings, visitor counts, ten thousand yuan, and more. Text lengths range from hundreds of characters for short reviews to ten-thousand-word industry research reports.
What constraints these characteristics impose on vector models and indexing
The high-frequency updates, mixed data types, and wide range of text lengths in film theater investment research data create clear constraints for the vector model and indexing workflow. Daily updated box office and scheduling data require indexes to support low-latency incremental writes. This avoids resource consumption and time costs from full index rebuilding. Mixed multi-field document structures require vector models to balance semantic understanding of film industry terminology and structured attribute association. This improves the accuracy of investment research retrieval. The wide range of text lengths requires flexible chunking strategies. These strategies adapt to different splitting needs from short comments to long research reports, and prevent semantic breaks or reduced vector accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Adapts text splitting for film theater research reports and long reviews. Prevents semantic breaks from being too short, and avoids reduced vector generation accuracy from being too long |
RECALL_TOP_N | Top 10–15 results | Matches the need for multi-source data recall in investment research scenarios. Balances recall coverage and query latency |
SIMILARITY_THRESHOLD | 0.72–0.78 | Distinguishes semantic associations between similar films and promotion activities. Prevents irrelevant content from being included in recall results |
ENABLE_INCREMENTAL_INDEX | Enabled | Adapts daily updated box office and scheduling data. Completes incremental data ingestion without full index rebuilding |
VECTOR_MODEL_NAME | bge-large-zh-v1.5 | Adapts semantic understanding of Chinese film industry terminology. Meets text vector generation needs for investment research scenarios |
INDEX_SHARD_COUNT | Calibrated per cluster node count | Balances index query performance and storage resource usage. Adapts to the scale of internally deployed clusters |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: The exported knowledge base dataset.csv only includes the index field, with no content field. Cause: Full field configuration for knowledge base content export was not enabled. Only index metadata was synchronized, without associating original text content.
- Phenomenon: Calling the indexing model returns a 503 Service Unavailable error after Docker deployment of FastGPT. Cause: No vector model service port mapping was configured in docker-compose.yml, or no independent vector model container was started. This prevents FastGPT from connecting to the indexing model service.
- Phenomenon: Large semantic deviation or segmentation breaks appear in recall results after importing long text. Cause: The
CHUNK_SIZEparameter was not adjusted to adapt to the text lengths of film theater long research reports and reviews. Using default segmentation values leads to incomplete semantics.
How to confirm correct configuration
- Upload a single ten-thousand-word industry research report or short audience comment. Check that segmented text blocks have complete semantics with no obvious breaks.
- Import a batch of test data, run retrieval queries, and verify that the similarity of recall results matches business scenario requirements.
- Add a real-time box office data entry. Check that the index completes updates within the expected time frame with no noticeable delay.
- Export the knowledge base dataset. Check that both index and content fields are included, with complete field content.
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.