Vector Models and Indexing for Professional Services

Data Characteristics of This Category

Data sources for professional services investment research include brokerage industry research reports, periodic and interim announcements of listed companies, regulatory policy documents, macroeconomic statistical datasets, industry white papers, and more. Update cycles cover real-time (on the day regulatory documents are released), periodic (earnings disclosure seasons, monthly industry data updates), and irregular (research reports on sudden industry events). Document structures are primarily long texts, mixed with structured data tables, rating fields, and quantitative indicators. Fields include industry classification codes, revenue growth rates, company rating levels, and others. Units cover hundreds of millions of yuan, percentage points, rating symbols, and similar units.

Constraints Imposed on Vector Models and Indexing

Multi-source and heterogeneous data types require vector models to support both unstructured text and structured table parsing, to avoid loss of semantic information. Long documents and lengthy research reports require chunking strategies aligned with content logic, to avoid excessive cutting that causes context breaks. Differentiated update frequencies require indexes to support incremental update mechanisms, reducing resource consumption from full reindexing. The presence of specific fields and units requires indexes to retain metadata associations, preventing confusion during retrieval of similar indicators using different units.

Configuration Settings

Configuration Item	Recommended Values	Rationale
`chunk_size`	1500–2000 characters	Matches the average paragraph length of professional services investment research documents, avoiding semantic cutting breaks
`chunk_overlap`	150–200 characters	Retains contextual connections between adjacent chunks, covering logically connected sections of long documents
`vector_model_provider`	Configured based on actual testing	Adapts to access requirements for multiple types of vector models, covering self-hosted deployment and third-party service scenarios
`top_k`	Top 10–15 results	Matches the multi-dimensional reference needs of investment research decisions, filtering low-relevance retrieval results
`similarity_threshold`	0.75–0.85	Filters document fragments with high semantic matching to the retrieval query, reducing invalid retrievals
`index_refresh_interval`	Hourly incremental update	Adapts to the real-time and periodic update rhythm of investment research data, reducing resource usage for index updates

The parameter values provided on this page are common recommended 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 values.

Three Common Misconfigurations

Incorrect vector model channel configuration: The console returns the vector_model_connection_failed error code. This occurs because access endpoints and key permissions for vector models and large language models are not differentiated. For example, some third-party vector services require separate exclusive configuration applications.
Excessively small chunking parameter settings: Retrieval results show context breaks, failing to fully cover the core analytical logic of research reports. This occurs because the configuration does not match the long paragraph content characteristics of investment research documents.
Configuring index updates as full updates: Newly uploaded investment research documents cannot be retrieved within the expected timeframe. This occurs because the incremental indexing mechanism is not enabled, leading to excessively long full reindexing times.

How to Verify Proper Configuration

After a standard investment research report is uploaded, the parsed chunk length is confirmed to fall within the range specified by the chunk_size configuration.
After a retrieval request is initiated, the score field values of the returned results are verified to meet the requirements of the similarity_threshold configuration.
After a new regulatory policy document is uploaded and the duration specified by the index_refresh_interval configuration elapses, keywords from the document are retrieved to confirm normal result returns.
Vector model connection logs are reviewed to confirm there are no connection_timeout or invalid_api_key error messages.

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.