Knowledge Base Retrieval and Recall for Investment Research

What This Type of Data Looks Like

Investment research data for this scenario primarily comes from internal research departments’ industry and macro research reports, public regulatory documents released by central banks and banking regulators, industry research materials from cooperating third-party institutions, and project due diligence documents from corporate business segments. Data update cycles cover three categories: real-time, weekly, and monthly. Single documents follow fixed structures. Research reports include title, publishing entity, release time, main body, and associated industry tag fields. Regulatory documents include document number, issuing authority, effective date, and clause content fields. Due diligence documents include project entity, research cycle, and core risk point fields.

Constraints Imposed on Knowledge Base Retrieval and Recall

Data formats vary significantly across sources. Regulatory documents use a clause-based structure, research reports use a paragraph-based structure, and due diligence documents mix structured fields with unstructured text. Different segmentation and preprocessing rules must be adapted to avoid chaotic recalled information. Update cycles differ widely across data types. Real-time files require real-time incremental pulling. Weekly research reports need synchronization per their release cycle. Full updates will consume excessive retrieval resources. Some existing documents lack industry tag fields. An automatic tagging process must be added to support targeted recall. Corporate due diligence documents contain sensitive business information. Additional user access permission checks are required during recall to prevent data leaks.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Investment research documents for this scenario have high information density. This segmentation length preserves complete logical units and fits the input limits of most vector models
`recall_top_k`	`Top 10–15 results`	Investment research scenarios require multi-dimensional information support. Too many results increase context overhead, while too few will miss relevant documents
`similarity_threshold`	`0.72–0.80`	In text scenarios with many professional terms, this threshold balances recall precision and coverage, avoiding irrelevant content or filtered valid information
`rerank_top_k`	`Top 3–5 results`	Reranking retains the most relevant core content, reduces context length to improve response efficiency
`incremental_sync_interval`	Configured per data type: `300 seconds` (regulatory documents), `86400 seconds` (research reports)	Matches the update cycles of different data types, avoiding excessive retrieval resource usage from full synchronization

The parameter values provided on this page are all common starting points for configuration. 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 settings.

Three Common Misconfigurations

Issue: Retrieval returns references to local knowledge base documents, but the final response does not use the corresponding content. Cause: The force_retrieve parameter is not enabled. The model prioritizes using pre-trained data instead of recalled local documents.
Issue: Retrieval requests return a 504 status code. Cause: recall_top_k is set to more than 20 results, and reranking is not enabled. A large number of vector calculations consume excessive service resources, causing a timeout.
Issue: Binding a third-party search interface prompts an operation failure. Cause: The search_api_whitelist parameter is not configured, and the interface request does not carry an internal identity verification token, triggering access restrictions.

How to Verify Successful Configuration

Run a retrieval test for a single research report. Check that the returned result segments preserve complete logical units, and verify that the chunk_size configuration matches the document structure.
Send a retrieval request containing industry terminology. Check that the similarity of recalled results meets expectations, and verify that the similarity_threshold configuration fits the professional text scenario.
Configure an incremental sync task, view sync logs, and confirm that data from different sources is pulled per their respective cycles. Verify that the incremental_sync_interval configuration matches the data update cycle.
Send a retrieval request for sensitive business documents. Check that only authorized users can access the corresponding content, and confirm that permission verification configurations are active.

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.