Vector Models and Indexing for Investment Platform Research

What the Data for This Category Looks Like

Research report data for investment platforms comes primarily from licensed securities firm research departments, white papers released by industry associations, and periodic reports and temporary announcements of listed companies. Update cycles vary by publishing entity. Securities firm research reports are mostly updated around market open on workdays. Industry white papers have no fixed release schedule. Listed company announcements are released when relevant events occur. Individual documents include fields such as title, publishing institution, release time, core rating, target price, industry analysis framework, and investment logic summary. Target price units are Renminbi yuan. Rating fields use standardized text labels.

What Constraints Do These Characteristics Impose on Vector Models and Indexing

Structured fields including release time, target price, and rating coexist with unstructured analysis text in research reports. This requires vector models to support both semantic matching and structured attribute association. Individual documents have significant length and include industry data tables. Segmentation processing must retain chapter logic to avoid splitting core analysis units. Multi-source data has widely varying update cycles. This requires support for a strategy combining incremental indexing and scheduled full updates. Release time is a core user filtering dimension. Indexes must include built-in filtering and sorting rules for time dimensions, ensuring retrieved results can be adjusted based on timeliness.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Matches the average length of single chapters in research reports, avoids splitting core analysis paragraphs, and maintains semantic completeness of individual vectors
`chunk_overlap`	100–150 characters	Retains contextual association between adjacent segments, prevents logical breaks caused by chapter splitting
`vector_model`	Determined via actual testing	Adapts to financial domain semantics of research reports, prioritize vector models trained on financial text
`recall_top_k`	Top 20 results	Covers user needs for comparing multiple research reports, avoids insufficient information caused by too few retrieved results
`index_update_strategy`	Incremental update + daily full synchronization	Matches the workday update cycle of securities firm research reports, balances real-time performance and indexing construction efficiency
`filter_by_time`	Enabled, default to last 30 days	Adapts to core user demand for research report timeliness, time window can be adjusted via application configuration

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis. Testing on local samples is recommended before finalizing configuration settings.

Three Common Mistakes

Phenomenon: An empty result is returned when calling the retrieval interface after uploading a research report. Cause: The file_index_status field is not monitored, and file upload completion is mistakenly treated as indexing completion.
Phenomenon: Retrieval results do not change after switching the vector model. Cause: Full index construction is not re-executed, and the new model is not applied to existing knowledge base data.
Phenomenon: pgvector index recall takes too long. Cause: No sorting index is created for the publish_time field, or the lists parameter value of the vector index does not match the data scale.

How to Confirm Proper Configuration

Run a single research report upload test. Wait for the file_index_status field to return completed before sending a retrieval request.
View the current value of the vector_model parameter on the knowledge base configuration page, confirm it matches the intended model.
Send a retrieval request with a time filtering condition, verify that results are sorted by the publish_time field.
Call the token statistics interface, check if token consumption data by application dimension matches actual retrieval times.

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.