What the data for this use case looks like
The investment research data for this use case includes monthly operational briefings, quarterly and annual financial reports, and industry research reports from internal subsidiaries. It also includes external public regulatory policy documents, macroeconomic databases, and peer benchmarking data. Update cadences vary significantly: subsidiary financial reports are updated quarterly or annually, industry research reports are updated weekly or in real time, and regulatory documents are released irregularly. Document structures include structured financial metric data, semi-structured report ratings and industry analyses, and unstructured policy interpretation text. Fields include holding hierarchy, subsidiary unique identifiers, disclosure dates, rating agencies, revenue scale units, and more. Some fields are enumerations, such as investment ratings split into buy, hold, and sell.
Constraints on vector models and indexing workflows
Multi-source heterogeneous data structures require vector models to support semantic encoding of mixed text, to avoid losing semantic information from structured financial fields or enumerated rating fields. Varied update frequencies require indexes to support flexible switching between incremental and full updates, to adapt to different data update cadences. Rich metadata fields require indexes to support multi-dimensional filtering. For example, narrow search scope by subsidiary identifier or disclosure date, to improve the accuracy of investment research retrieval. Wide variation in document length requires chunking strategies adapted to different text lengths. This avoids semantic breaks from over-splitting long documents, or missing valid information in short documents. Data scale grows with the expansion of subsidiary count, requiring indexes with horizontal scaling capabilities, to avoid overloading single shards.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the wide length span of investment research documents for this use case, covering semantic units of most texts such as industry commentaries and in-depth research reports |
chunk_overlap | 100–150 characters | Prevents semantic breaks between adjacent chunks, ensuring contextual coherence for long texts |
retrieval_top_k | Top 10–15 results | Investment research decisions require reference to multi-dimensional information. This value balances result coverage and information redundancy |
similarity_threshold | 0.65–0.75 | Meets the semantic matching accuracy requirements for investment research scenarios, filtering low-relevance retrieval results |
index_shard_count | 1 shard per 1 million vectors | Adapts to the growth trend of data volume as subsidiary count expands, facilitating future horizontal scaling |
embedding_batch_size | 32–64 | Balances processing speed and memory usage for vectorization tasks, avoiding service freezes caused by overly large single batches |
The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on independent samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Only vector data is returned during retrieval, and original document fragments cannot be viewed. Cause: Metadata storage fields for original documents are not configured. Only vector data is written to the vector database, with no association to the original text content.
- Phenomenon: Semantic retrieval similarity scores are generally higher than expected when using the bge-m3 vector model. Cause: No length normalization is applied to chunked text, or similarity threshold calibration parameters are not adjusted, leading to abnormal score ranges.
- Phenomenon: Knowledge base creation gets stuck during the indexing step, with task timeout failure. Cause: A reasonable
embedding_batch_sizeis not set. Too much vector data is processed in a single batch, exceeding service memory limits or task timeout thresholds.
How to Confirm Proper Configuration
- View metadata fields in the vector database, confirm that associated information such as original document fragments, subsidiary identifiers, and disclosure dates are present.
- Input a test query related to investment research, perform a retrieval operation, check that the similarity scores of returned results fall within the preset threshold range, and the number of results matches the configured requirements.
- Add a new test subsidiary financial report document, trigger the incremental indexing workflow, confirm that the new data can be retrieved normally.
- Replace the test embedding model, verify that index creation and retrieval workflows run without errors, and that vector dimensions match correctly.
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.