Vector Models and Indexing for Steel Trade Financial Report

Data Profile for This Category

Data related to steel trade financial reports comes from three sources: internal enterprise operation ledgers, regularly disclosed financial report documents, and upstream and downstream purchase and sales vouchers. Update cycles fall into two categories: monthly operation data is updated in real time according to business progress, while quarterly and annual financial reports are updated on fixed cycles per regulatory requirements.

Document structures include structured inventory, purchase and sales detail tables, semi-structured financial report notes, and unstructured industry analysis descriptions. Fields and units include steel grade, transaction quantity (tons), transaction unit price (yuan/ton), gross profit amount (ten thousand yuan), inventory turnover days, and a large number of specialized category terms.

Constraints on Vector Models and Indexing

Structured data accounts for a large share of the dataset, with many duplicate values across fields. Vector models must adapt to semantic encoding of structured text, and avoid redundant encoding of duplicate fields.

Update cycles are layered: batch-updated financial report data and real-time incremental operation data coexist. Indexes must support parallel configuration of incremental writes and batch rebuilding.

The dataset includes specialized category terms and clear unit fields. Vector models must have semantic alignment capabilities for domain terms. Indexes must support recall filtering based on field attributes, to reduce the volume of irrelevant recalled results.

The length of structured paragraphs in single documents varies widely. Text splitting granularity must be controlled appropriately, to avoid damaging the integrity of business logic.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`embedding_model`	Recommended to use `bge-large-zh-v1.5` (or domain-adapted models of similar scale)	Adapts to semantic encoding of Chinese specialized terms, and delivers stable encoding performance for financial report text related to steel trade
`chunk_size`	Recommended to use `800–1200 characters`	Structured table paragraphs in financial reports are usually around 800 characters. This avoids splitting that damages the semantic integrity of tables
`index_batch_size`	Recommended to use `500–1000 items/batch`	The single-batch data volume of steel trade financial reports is moderate. This batch size balances index construction speed and memory usage
`recall_top_k`	Recommended to use `Top 10–15 results`	Financial report analysis needs to cover multi-dimensional operation data. Too many recalled results increase context redundancy, while too few may miss key information
`similarity_threshold`	Recommended to use `0.72–0.78`	Specialized terms in steel trade have high semantic similarity. This threshold filters out low-correlation recalled results
`filter_fields`	Recommended to use `["trade_type", "unit"]`	Financial reports contain many entries of the same type but different units. Field filtering enables precise recall of operation data for corresponding categories

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

Three Common Misconfigurations

Symptom: After calling the file upload API, index construction progress cannot be obtained via the interface or API, and direct calls to the knowledge base interface return empty results. Cause: The index_status_callback parameter is not configured, or the status callback event for asynchronous index construction is not monitored.
Symptom: Knowledge base recall results include a large number of irrelevant non-steel trade entries, with similarity scores that are abnormally high or low. Cause: A reasonable threshold for the similarity_threshold parameter is not set, or filter_fields are not configured for category field filtering.
Symptom: When batch uploading multiple financial report files, the vector encoding process throws an out-of-memory error. Cause: The index_batch_size parameter is not adjusted, and an overly large batch write value is used, exceeding the server's memory capacity.

How to Verify Proper Configuration

Call the get_embedding_model_list API, confirm that the currently used vector model matches the value set in the embedding_model configuration item.
Upload a test fragment of a steel trade financial report, query the index construction progress via the get_task_status API, and confirm that the progress transitions normally from pending to completed.
Initiate a knowledge base recall request with category filtering parameters, and verify that the returned result fields match the rules configured in filter_fields.
Check the vector encoding logs, confirm that the dimension of the encoded vector matches the output dimension of the currently configured model.

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.