Vector Models and Indexing for Multi-Financial Research

What this type of data looks like

Data sources for multi-financial research reports include public research report databases, official disclosure documents of non-bank financial institutions, and public reports from industry research institutions. Updates are synchronized to the repository immediately after a single report is published. Routine daily updates cover the latest stock of research reports. Single documents are mostly structured. They include a title page, abstract, business analysis section, financial data section, and risk disclosure page. Fields include the unique report identifier, full name of the publishing institution, release timestamp, core argument paragraphs, original text of financial data tables, and original text of policy citations. Financial data fields mostly use ten thousand yuan or ten thousand households as units, and do not include percentage-based statistical content.

What constraints do these characteristics impose on the vector models and indexing link

Single research report text lengths vary widely. Short abstracts are fewer than 500 characters, while long analysis sections can reach tens of thousands of characters. This creates risks of context window overflow or encoding bias during vector encoding. Documents contain mixed structured financial data tables and unstructured analysis text. This requires differentiated segmentation and encoding strategies for different content types. Research reports are updated incrementally on a daily basis. Indexes need to support low-latency incremental writes to avoid performance loss from full reconstruction. Additionally, non-bank financial fields have high density of professional terminology. Vector models must adapt to semantic features of specialized industry segments, otherwise semantic retrieval deviation will occur.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Research reports contain long-text analysis and structured tables. This range balances semantic completeness and vector encoding efficiency
`embedding_model`	`bge-large-zh-v1.5` or `text-embedding-3-large`	Adapts to professional terminology in non-bank financial fields, and delivers stable performance for long-text semantic encoding
`retrieval_top_k`	`Top 10–15 results`	Multi-financial research reports cover many specialized segments. Sufficient recall volume is needed to cover relevant arguments
`similarity_threshold`	`0.72–0.78`	Filters low-correlation cross-segment reports, and retains highly matched professional analysis
`index_update_mode`	`Incremental update`	Research reports are released incrementally daily. Full reconstruction incurs extra storage and computing overhead
`parse_table_enable`	`Enabled`	Financial data tables in research reports must be parsed into encodable text to improve vector encoding accuracy

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.

Three common misconfigurations

Phenomenon: Vector retrieval returns similarity scores generally higher than 0.9, with very low relevance. Cause: The chunk_size configuration is not adjusted for long texts, and context redundancy occurs when using the bge-m3 model, which amplifies similarity calculation results.
Phenomenon: After index construction is completed, no matching results appear during retrieval, and the background log shows the INDEX_EMPTY error code. Cause: The parse_table_enable configuration is not enabled. Structured tables in research reports are not parsed into encodable text, resulting in no valid vector data in the index.
Phenomenon: Normal question answering flows respond normally, but relevant research reports cannot be obtained when using the retrieval-augmented template. Cause: The retrieved document context is not spliced to the specified position in the prompt_template, so the model cannot access the retrieved research report content.

How to verify correct configuration

Upload a single research report of 5000 characters, check the vector encoding log to confirm that the segmentation results fall within the value range specified by the chunk_size configuration.
Trigger an incremental index update, check the background monitoring panel to confirm that only newly uploaded research reports are included in the index, and no full reconstruction process is triggered.
Input professional terminology from non-bank financial fields for retrieval, verify whether the returned document types and business segments match.
Adjust the similarity_threshold parameter, verify that the number of returned results changes as expected with the threshold.

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.