Vector Models and Indexing for Financial Report Analysis

What the use case data looks like

Data is primarily sourced from listed company periodic reports, consolidated financial statement notes, related party transaction disclosures, and equity structure documents published by domestic and overseas regulatory authorities. Update schedules follow regulatory requirements: annual reports are released once per year, quarterly reports are released each quarter, and temporary announcements are published in real time alongside major events. Individual document lengths vary widely, ranging from short consolidated statement summaries to tens of thousands of words of detailed business notes. Document fields include consolidated revenue, attributable parent net profit, segment business proportion, related party transaction amount, and more. Most units are RMB yuan or ten thousand yuan, and some disclosure documents will note foreign currency converted amounts.

Constraints for vector models and indexing

The wide range of document lengths requires vector segmentation strategies to adapt to different length content fragments. This avoids semantic breaks from over-cutting long texts, or loss of independent information from merging short texts. The large number of closely related fields requires indexes to support targeted recall based on metadata dimensions such as business segments and related parties. This prevents irrelevant content from interfering with retrieval results. The mixed update schedule of periodic and temporary reports requires indexes to support incremental update mechanisms. Only newly added or modified files generate vectors, reducing resource consumption of full indexing. The coexistence of multiple units requires the vector generation process to retain unit information. This avoids semantic deviation caused by separating numerical values and their associated units.

Configuration settings

Configuration Item	Recommended Value	Rationale
`embedding_model`	`ali-emb3`	This model meets accuracy requirements for semantic understanding of financial text, and supports mixed structured and unstructured financial report content
`chunk_size`	`800–1200 characters`	Matches the paragraph length of financial report notes, balancing semantic completeness and retrieval granularity
`chunk_overlap`	`100–150 characters`	Prevents semantic breaks between adjacent segments after splitting, ensuring context coherence
`metadata_filter_enabled`	`Enabled`	Financial reports include structured metadata such as segments and related parties. Metadata filtering enables targeted recall of target segment content, improving retrieval accuracy
`top_k`	`Top 8–12 results`	Financial reports cover multiple business segments, requiring sufficient coverage of segment-specific information
`similarity_threshold`	`0.72–0.78`	Filters low-relevance financial report fragments while retaining cross-segment related content

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. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: Retrieval returns fragments containing target financial report content, but the LLM generates a reply stating no relevant information was found. Cause: The chunk_overlap setting is too small, causing key context for related party transactions and segment reports to be split across different fragments, preventing the LLM from integrating retrieval results.
Phenomenon: The vector generation process returns a 401 Unauthorized error. Cause: The API key for the ali-emb3 model is not configured correctly, or the model identifier for open-source and commercial versions is confused.
Phenomenon: After enabling metadata filtering, retrieval results do not cover financial report content for the target segment. Cause: Metadata fields such as segments and related parties are not correctly labeled when uploading files, causing filtering rules to fail to match target content.

How to confirm configuration is complete

View vector model call logs to confirm that ali-emb3 model call requests and responses are normal, with no error codes returned.
Upload a test segment of segment-based financial report content, then verify that the number of returned top_k results matches the preset range and the similarity score falls within a reasonable range after retrieval.
Submit a newly added temporary announcement file, and check whether the indexing system automatically triggers an incremental update without waiting for the full update cycle.
Manually upload a financial report segment with clear segment identifiers, and check whether metadata fields are correctly identified and applied to filtering rules.

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.