What This Category of Data Looks Like
Photovoltaic-related financial report data mainly comes from periodic reports publicly disclosed by domestic and overseas stock exchanges, and industry-specific research documents. The update rhythm is quarterly reports updated every quarter, and annual reports updated annually. Document types include complete PDF financial reports, structured quarterly data table files, and industry analysis PPTs. Fields include photovoltaic module shipment volume, unit production cost, power station installed capacity, gross profit margin, R&D investment ratio, and more. Some documents include multi-page nested detailed tables.
Constraints on Vector Models and Indexing
Photovoltaic financial report data mixes long-text reports and structured tables, and field units vary. This requires the indexing process to support retaining structured metadata to avoid losing semantic associations during splitting. The batch update rhythm of quarterly and annual reports requires indexing to support incremental update logic, reducing resource consumption from full reconstruction. Single financial report content is lengthy, so long-text segmentation rules must be adapted to avoid context truncation during vector embedding. The large volume of data for batch indexing requires optimizing parallel processing capabilities for index construction to reduce overall time spent.
Configuration Settings
This configuration is adapted for FastGPT V4.8.20-FIX2.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SPLIT_CHUNK_SIZE | 800–1200 characters | Photovoltaic financial reports contain long sections of technical indicator descriptions. This range covers complete semantic units and avoids segmentation fragmentation |
SPLIT_OVERLAP_RATE | 10%–15% | Retain overlapping content of adjacent segments to solve context breakage after long-text segmentation |
PARSE_TABLE_ENABLE | Enabled | Adapt to the large amount of structured table data in financial reports, retain row and column structure information to improve vector recall accuracy |
VECTOR_STORE_INDEX_TYPE | HNSW | Adapt to high-dimensional photovoltaic financial report vector data, balance recall accuracy and query speed |
RECALL_TOP_K | Top 10–15 entries | Financial report analysis requires covering multi-dimensional indicators, this recall volume provides sufficient context support |
SIMILARITY_THRESHOLD | 0.75–0.85 | Distinguish similar technical terms and core indicators, filter irrelevant recall content |
The parameter values provided on this page are conventional recommendations used to establish a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Retrieval speed is significantly lower than expected after the knowledge base index is completed, and single retrieval time exceeds normal ranges. Cause: Incremental indexing is not enabled, each retrieval triggers full vector matching, and the HNSW index type is not used, resulting in low query efficiency under high data volume.
- Phenomenon: A large number of irrelevant table fragments appear in retrieval results, and the recall rate of core indicator content is low. Cause: The table parsing function is not enabled, and structured tables are split into unstructured plain text, resulting in loss of semantic information related to row and column associations during vector embedding.
- Phenomenon: Semantic truncation occurs in vector embedding after some long texts are segmented, and complete financial report analysis paragraphs cannot be covered. Cause: Segmentation length is set too small, which does not meet the length requirements of long professional descriptions in photovoltaic financial reports.
How to Confirm Proper Configuration
- Perform a segmentation test on a single photovoltaic financial report, check whether the segmented content covers complete technical indicator descriptions, and adjust the value of
SPLIT_CHUNK_SIZEas needed. - Import a photovoltaic financial report document containing structured tables, check whether the retrieval results retain the row and column association information of the table, and confirm the configuration status of
PARSE_TABLE_ENABLE. - Batch import multiple photovoltaic financial report data, observe index construction time and retrieval response speed, and adjust the configurations of
VECTOR_STORE_INDEX_TYPEandRECALL_TOP_K. - Perform multiple retrievals for the same photovoltaic financial report indicator, check the similarity distribution of returned results, and adjust the value of
SIMILARITY_THRESHOLDto filter irrelevant 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.