What the Data for This Category Looks Like
Data for optical module intelligent due diligence reports in financial due diligence scenarios mainly comes from official specification documents of communication equipment manufacturers, technical specifications of industry standardization organizations, performance test reports of third-party testing institutions, and material ledgers from upstream and downstream supply chains. Updates are triggered by new product mass production, industry standard revisions, or supply chain changes, with no fixed cycle. Each report has a fixed document structure, including fields such as model identifier, transmission rate, rated power, operating temperature range, interface type, and compliance certification number. Units uniformly use Gbps, W, ℃, standard interface codes, and similar standard identifiers.
What Constraints These Characteristics Impose on the "Vector Models and Indexing" Link
Multi-source heterogeneous data sources create format differences, so unified cleaning must be completed before vector generation, increasing the complexity of the preprocessing link. No fixed update rhythm requires the indexing system to support incremental synchronization logic, avoiding full reconstruction for each update to reduce computing resource consumption. Fixed field structures and dedicated units require the vector model to adapt to the encoding logic of structured numerical fields, otherwise the precision association between fields will be lost. Single report content is focused but has clear fields, so targeted segmentation rules need to be configured to avoid semantic confusion across fields.
How to Set the Configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length of single-field content in optical module reports, avoiding semantic confusion across fields |
chunk_overlap | 10–15% | Retains contextual association between segments, adapting to semantic integrity of long specification fields |
embedding_model | text-embedding-ada-002 / local open-source vector model | Supports structured field encoding, adapting to numerical parameters of optical modules |
index_type | IVFFlat (Zilliz) / GIN (PGSQL) | Adapts to medium-scale vector datasets of optical module reports, balancing recall speed and accuracy |
recall_topk | Top 10–15 results | Covers recall requirements across multiple fields, avoiding omission of key specification parameters |
similarity_threshold | 0.75–0.85 | Filters low-correlation historical model data, retaining valid information with high matching degree |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, 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 Mistakes
- Phenomenon: After migrating vector storage from PGSQL to Zilliz, vector data for some optical module models cannot be retrieved normally, and the log returns a
400 Bad Requesterror. Cause: The vector dimension of the FastGPT embedding model and the dimension configuration of the Zilliz collection are not aligned, resulting in incompatible data writing. - Phenomenon: After accessing a private reranking model, compliance certification information for some optical modules is not recalled first. Cause: The input format of the reranking model is not adjusted for the structured fields of optical module reports, leading to semantic matching deviation.
- Phenomenon: The number of search return results is far lower than expected, with only a small number of field contents recalled. Cause:
recall_topkis set to an excessively low value, failing to cover the recall requirements of multiple fields in optical module reports.
How to Confirm the Configuration Is Correct
- Perform an upload test for a single optical module report, check the vector generation log, and confirm that the parameters of the segmentation configuration have taken effect.
- Switch the vector database provider, perform a batch data import, and verify that data is written without errors and dimensions match.
- Initiate a targeted search, input an optical module model or specific parameters, and check the field integrity and matching degree of the recall results.
- Trigger an incremental update task, check the index synchronization log, and confirm that new data has been included in the retrieval scope.
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.