What Data Looks Like for This Category
Software development investment research data mainly comes from code repository comments and commit records, third-party SDK/API official documentation, industry technical white papers, internal R&D specifications and version change logs. Update frequency fluctuates with R&D progress: multiple new documents are added daily during high-frequency periods, and updates occur several times a week during low-frequency periods. Document structures include code snippets, function parameter tables, dependency relationship descriptions, version number identifiers, error code descriptions, and more. Fields include commit ID, dependency library names, interface addresses, character lengths, version numbers, and more. Units are mostly character counts, version number formats, and timestamps.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
High-frequency updated data sources require retrieval systems to support incremental index updates, to avoid performance loss caused by full reconstruction. The structure of long texts and code snippets requires a segmentation strategy that balances code logic integrity, to avoid splitting function definitions and dependency descriptions. The presence of multiple structured fields requires retrieval to support specified field matching, such as accurately locating code comments for a specific version via commit ID. Cross-document dependency associations require recall results to include documentation for relevant dependency libraries; returning only a single matching entry will reduce investment research accuracy.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Software development documents often contain code blocks and long parameter descriptions. Segmentation that is too long will lose contextual association, while segmentation that is too short will destroy the integrity of function definitions |
recall_top_k | Top 10–15 results | Investment research scenarios need to cover relevant results for multi-version APIs and dependency libraries. Too few results will miss key dependency information |
similarity_threshold | 0.75–0.85 | Semantic similarity for code and technical documents needs to balance precise matching and recall coverage, to avoid missing key function descriptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large technical white papers and dependency library source code packages takes a long time; timeouts will cause file import failures |
enable_field_retrieval | Enabled | Software development data includes structured fields such as version numbers and commit IDs. Specifying retrieval fields can improve matching accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to the import requirements of large technical document packages and source code compressed packages |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require on-site analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The knowledge base search node returns a 403 status code after authentication configuration is applied, and cannot access the specified data source. Cause: The authentication scope is not bound to the exclusive data source path of code repositories and technical documents, and only general knowledge base access permissions are configured.
- Phenomenon: No optional values appear in the variable reference dropdown menu of the knowledge base search node, and the code version number parameter cannot be bound. Cause: The upstream output node for the code version number is not configured in the application workflow, or the variable is not mapped to the field mapping configuration of the retrieval parameter.
- Phenomenon: After importing a CSV-format technical document list, Chinese fields appear garbled, and parsed document content is missing. Cause: The UTF-8 encoding format is not specified during the file import process, or the CSV file is actually encoded in GBK and no conversion is completed.
How to Confirm Proper Configuration
- Upload a test code snippet and technical document, and check if the parsed segment length matches the preset
chunk_sizeconfiguration. - Initiate a retrieval request, and verify that the number of returned results matches the
recall_top_kconfiguration. - Access the authentication configuration page, confirm that the exclusive access path for software development data sources is bound, and simulate an unauthorized request to verify the returned status code.
- Import a CSV-format test document, check that Chinese content displays correctly, and verify that the encoding configuration matches the actual file encoding.
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.