What this category of data looks like
Account issue data primarily comes from internal institutional account management rule documents, structured transaction ledger Excel files, archived user account support tickets, and account exception handling manuals. Update cadences vary significantly: transaction ledgers are generated in real time, account rule documents are updated quarterly or annually, and ticket archives are aggregated daily. Document formats cover three types: structured Excel tables with headers including fields such as account ID, transaction type, transaction amount, and operation time; semi-structured user support ticket text containing question and response content; unstructured PDF account operation guides. Fields mostly include standardized values and identifiers, with units such as yuan, count, and calendar day.
What constraints do these characteristics impose on the document parsing and chunking process?
The multi-form nature of account issue data creates multiple constraints for document parsing and chunking. Structured transaction ledger Excel files contain tens of thousands of independent transaction records. Default chunking parameters will merge multiple rows into a single chunk, resulting in chunks that are too long to fit within the context window of vector models, and also prevent precise matching of user inquiries corresponding to individual transactions. Semi-structured ticket text requires retaining the contextual association between questions and responses; splitting must not separate user questions from their corresponding solutions. Long-form account operation guides must be split by chapter, not by fixed character count, to avoid splitting complete account rules into disjointed fragments. Additionally, the field uniqueness requirement for account data means that key identifiers such as account ID and transaction amount must be retained during chunking, to prevent confusion during matching.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the context window limits of most vector models, and adapts to the content length of single transactions or single inquiries |
chunk_overlap | 50–100 characters | Retains contextual association between adjacent segments, preventing loss of context for key identifiers such as account ID after splitting |
parse_excel_row_mode | Split by single row | Each row of structured transaction ledger corresponds to an independent account operation; splitting by row ensures each chunk accurately corresponds to a single transaction, meeting the precise matching requirements of account issues |
max_context_per_query | Top 3–5 segments | Account issue inquiries typically relate to single or a small number of consecutive transaction records; recalling too many segments introduces irrelevant information and reduces matching accuracy |
parse_pdf_header_footer_skip | Enabled | Account documents often include institutional identifiers and page numbers in headers and footers; skipping these avoids including invalid content in chunks, improving subsequent matching efficiency |
vector_store_chunk_token_limit | Calibrated via actual testing | Token limits vary across vector models; adjust chunk length based on the actual model in use to ensure single chunks can be properly vectorized |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Scenario: After uploading an Excel transaction ledger with 15,000 rows, the knowledge base chunking results generate fewer than 10 chunks, with individual chunks exceeding 2,000 characters. Cause: The
parse_excel_row_modeparameter was not adjusted, and the default setting chunks the entire table as a single unit, resulting in chunks that exceed the context window limits of vector models. - Scenario: When parsing an account operation guide with 100,000 Chinese characters, the system returns a
PARSE_FILE_TIMEOUTerror. Cause: Chapter-based splitting configuration was not enabled, and the default setting splits by fixed character count; long document parsing exceeds the default threshold ofPARSE_FILE_TIMEOUT_SECONDS. - Scenario: When a user asks for the balance of a specified account, the recalled segments include irrelevant wealth product introductions, and the
account_idfield is empty. Cause: Key identifiers such as account ID were not retained during chunking, and no reasonable recall filtering rules were set, resulting in recall of unrelated document fragments.
How to Verify Correct Configuration
- Upload a single Excel transaction ledger with 1,000 rows, check the character count of individual chunks in the results, and adjust the
chunk_sizeparameter to fit the context window limits of the current vector model. - Import a long-form account operation guide, check if timeout errors appear in the parsing logs, and adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter to match the time required for document parsing. - Submit a test inquiry containing a known account ID, verify that the recalled segments include this account ID, and confirm that key fields were not omitted.
- Test multi-turn account support conversations, check if overlapping content between adjacent segments retains the contextual association of account operations, and adjust the
chunk_overlapparameter to a reasonable range.
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.