What Data for This Category Looks Like
This category’s data originates from software development marketing-related documents across finance, insurance, and wealth management scenarios. This includes front-end development specifications for marketing campaigns, back-end interface documentation, landing page code examples, version update logs, customer onboarding development guides, and more. Updates are released irregularly alongside marketing project iterations and version launches, with individual update document sizes ranging from single pages to dozens of pages. Document structures typically include fields such as version number, applicable scenarios, interface paths, request parameters, return formats, code snippets, and update dates. Core content includes code snippets and structured parameters, with technical units including requests per minute, milliseconds, character count, and others.
Constraints on Knowledge Base Retrieval and Recall Workflows
Scattered data sources and frequent updates require retrieval systems to support incremental updates and multi-data source aggregation, to avoid missing the latest marketing development specifications. Documents contain code snippets and structured parameters, so retrieval systems must preserve context during chunking, and must not compromise the integrity of code blocks or interface definitions. Most fields are technically precise matching items, so retrieval should prioritize matching structured fields such as interface names and parameter names, rather than relying solely on semantic similarity. Document lengths vary widely, from hundreds to thousands of characters, so chunking and recall logic must adapt to text of different lengths.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Software development documents include code snippets and structured parameters. This length balances semantic completeness and chunk granularity, avoiding disruption to the context of function definitions or interface descriptions |
chunk_overlap | 150–200 characters | Preserves the contextual association between code blocks and interface parameters, preventing chunking from breaking continuous technical explanatory content |
recall_top_k | 5–8 results | Adapts to the precise matching requirements of software development documents. Too many results will introduce irrelevant interface documentation, while too few will miss critical parameter explanations |
similarity_threshold | 0.75–0.85 | Distinguishes core development specifications from peripheral reference content, avoiding recall of documents from non-target projects or general development content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing time required for large development documents, preventing document upload and parsing processes from being interrupted by timeouts |
json_schema_validation | Enabled | Aligns with the common presence of JSON-formatted interface examples in software development documents, ensuring retrieval results return structured data that complies with specifications |
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, and it is recommended to test against their own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Retrieval results return an abnormal format that cannot be recognized as valid JSON by the model. Cause: The
json_schema_validationconfiguration is not enabled, or a valid retrieval result return format is not specified, resulting in failure to correctly extract fields from structured documents. - Symptom: The number of recalled documents does not match the preset value, consistently exceeding or falling below the range set by
recall_top_k. Cause: Thesimilarity_thresholdis not configured correctly. A threshold that is too high leads to insufficient recall, while a threshold that is too low leads to excessive recall, or irrelevant test documents are not filtered out. - Symptom: Chunked documents contain a large amount of duplicate content, leading to redundant retrieval results. Cause: The
chunk_overlapvalue is not set correctly, with an excessively high overlap ratio, or the automatic duplicate content filtering function is not enabled.
How to Verify Correct Configuration
- Upload a test document that includes code snippets and JSON interface examples, review the parsed chunked content, and confirm that the chunk length and overlap ratio match the preset configuration.
- Submit a retrieval request targeting a specific interface name, check that the number of returned recall results matches the value set for
recall_top_k. - After enabling the format validation function, submit a retrieval request, confirm that the returned results comply with the preset JSON format requirements.
- After disabling the external retrieval switch, submit a retrieval request, confirm that results only come from uploaded software development marketing content documents.
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.