What the data for this use case looks like
The data for this use case comes primarily from industry analysis and macroeconomic research documents produced by joint-stock bank in-house investment research teams, plus third-party public research reports acquired externally. Updates follow the release cycle of research reports: standard industry reports are updated quarterly, while thematic reports are released on an as-needed basis.
Each individual report follows a standard structure: title, issuing entity, release date, core opinion section, accompanying industry data tables, and risk disclosure chapter. Document fields include a unique report identifier, issuing institution, industry classification, core conclusion summary, and appendix data entries. Most appendix data values use standard financial statistical units such as hundreds of millions of yuan and ten thousands of yuan.
What constraints do these characteristics impose on the vector models and indexing workflow
In-house investment research documents are mostly Word or PDF files with structured tables. External research reports are mostly standardized PDF files. This requires vector models to support content parsing and vectorization across multiple document formats. Differences in update cycles across multiple data sources require index systems to support both incremental updates and on-demand full refresh modes.
Individual research reports are lengthy, and contain large volumes of specialized terminology and industry-specific data. This requires vector models to have context windows adapted for long text input. Index chunking strategies must balance the integrity of specialized terminology and retrieval accuracy. Additionally, structured data within documents must be vectorized alongside text content, to avoid losing data associations after chunking.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the specialized terminology and long sentence structure of joint-stock bank research reports, avoiding loss of semantic integrity after chunking |
embeddingBatchSize | 16–32 | Balances vectorization processing speed and system memory usage, meeting batch vectorization requirements for individual research reports |
refreshInterval | 3600 seconds | Matches the on-demand release update cycle of research reports, balancing real-time performance and system resource consumption |
topK | Top 10–15 results | Covers multi-dimensional industry analysis content, ensuring comprehensiveness of retrieval results |
similarityThreshold | Calibrated via actual testing | Adapts to scenarios with large volumes of research report specialized terminology, avoiding false recalls or insufficient recalls |
enableMultiModal | Enabled | Adapts to research report content containing chart data, extracting structured information from charts for vectorization |
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 specific analysis. It is recommended to test on your own samples before finalizing values.
Three Common Configuration Mistakes
- Phenomenon: A single vectorization request processes only one text chunk, resulting in longer-than-expected vectorization time, or the number of vectors returned by the API does not match the number of submitted chunks. Cause: The
embeddingBatchSizeparameter is not configured correctly, or the parameter value is set to 1, and batch vectorization mode is not enabled. - Phenomenon: When calling the chunked index query interface via API, the returned results are empty or contain invalid fields. Cause: The chunked index storage function is not enabled in FastGPT 4.8.10, or the called interface path and parameter format do not meet system requirements.
- Phenomenon: Research reports containing charts cannot be retrieved correctly, and retrieval results do not match industry data within the charts. Cause: Multimodal vectorization configuration is not enabled, or API access information for the
multimodal-embedding-v1model is not configured correctly, resulting in chart content not being included in the vectorization scope.
How to Confirm Configuration is Complete
- Upload a single test research report, view the chunking results in the FastGPT document parsing module, and confirm that the chunk length falls within the configured range of
chunkSize. - Call the custom vectorization test interface, submit 3 or more text chunks, and verify that the length of the returned vector array matches the number of submitted chunks, confirming that the batch processing function is working properly.
- Manually trigger an index refresh, view the refresh records in the system log, and confirm that the execution time of the refresh operation meets the configuration requirements of
refreshInterval. - Enter keywords related to charts within the test research report, and the retrieval results will return document blocks containing these keywords, confirming that the multimodal vectorization configuration has taken effect.
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.