Data Characteristics of This Category
Data for film and theater financial reports comes from box office settlement reports in theater operation systems, daily revenue logs from cinema terminals, upstream film copyright revenue sharing agreement documents, and quarterly and annual officially published operation reports. Update cycles fall into three categories: monthly box office updates are released weekly, quarterly financial reports are updated within 15 days after the end of each quarter, and annual reports are updated by April of the following year. Documents include fields such as individual cinema revenue details, total viewer count, advertising placement costs, and copyright revenue sharing amounts. Units include ten thousand yuan, number of viewers, and number of screenings. A single quarterly financial report has a text length of approximately 5,000 to 10,000 characters, and includes both structured tables and unstructured explanatory content.
Constraints on Vector Models and Indexing
The data includes both structured tables and unstructured text. Vector models must support embedding for structured fields and encoding for long text passages to avoid truncating critical information. Update frequencies vary: monthly box office updates have high real-time requirements, while quarterly and annual reports require batch updates. Indexing must support both incremental refresh and batch import modes. There are many closely related field dimensions: viewer count is directly linked to revenue. Indexing must support joint recall across multiple fields to avoid recall bias from single dimensions. Individual documents have long lengths. When splitting passages, business logic integrity must be maintained, and related content such as revenue sharing clauses and revenue details must not be split apart.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the single passage length of film and theater financial reports, avoids splitting related business content such as revenue sharing clauses and revenue details |
embedding_model | Open-source general long-text embedding model | Adapts to the long text and multi-field structure of financial reports, meets vector generation requirements for multi-dimensional data |
index_refresh_interval | 10 minutes | Matches the real-time update requirement of monthly box office updates, balances computing power consumption for index refreshes and data timeliness |
vector_store_type | Zilliz or similar distributed vector storage | Supports batch import and incremental updates, adapts to the multi-batch, high-frequency index maintenance needs of film financial reports |
rerank_top_n | Top 8–12 results | Filters redundant recall results, focuses on core revenue and revenue sharing related content in financial reports |
similarity_threshold | 0.75–0.85 | Filters low-correlation non-financial report documents, ensures recall results are strongly linked to film and theater business |
The parameter values provided on this page are general recommendations for establishing configuration starting points. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Vector generation tasks return 500 internal service errors, and recall results are empty. The cause is that when deploying version 4.9.6 locally, the environment variable pointing to the interface address of an external free embedding model was not modified correctly, leading to vector generation failure.
- Existing financial report data cannot be recalled normally after vector store migration. The cause is that the automatic refresh mechanism of the original PGSQL index was not disabled, leading to data synchronization deviation between the old and new indexes.
- Rerank model results do not match the keyword matching degree of financial reports. The cause is that the input token limit for the rerank model was not set, leading to truncation of long text passages in financial reports and loss of critical information.
How to Verify Proper Configuration
- Upload a single quarterly film and theater financial report document, check whether valid vector data is generated in the embedding task log, with no error prompts.
- Enter core keywords from film and theater financial reports, perform a recall test, and confirm that the recall results include correct revenue and revenue sharing related fields.
- After switching the vector store type, verify that existing index data can be read normally, with no significant loss of recall results.
- After configuring the rerank model, compare the original recall results with the sorted logic after reranking, and confirm alignment with business expectations.
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.