What the data for this category looks like
Data sources for communication equipment investment research include Ministry of Industry and Information Technology communication equipment certification announcements, official technical white papers from equipment manufacturers, centralized procurement bidding documents from carriers, documents from industry standard working groups, and third-party test institution reports. Update frequency varies by document type: technical white papers are updated with product iterations, centralized procurement announcements are released monthly, and certification announcements are updated alongside new device model network access approvals. Most documents include structured parameter tables, test process records, and compliance descriptions. Fields cover device model, operating frequency band, transmit power, certification number, delivery lead time. Units include MHz, dBm, units/sets, working days, and more.
Constraints on vector models and indexing from these data characteristics
Communication equipment investment research data contains large volumes of structured technical parameters and long-text test reports. Vector models must support encoding for both numeric fields and natural language text, to avoid losing parameter precision with single-text vectors. Frequently updated centralized procurement and certification announcements require indexes to support incremental synchronization, to avoid excessive time spent on full index reconstruction. Multi-field structures require clear target fields for vector encoding, to avoid interference from irrelevant fields on recall precision. Hundreds of thousands of data volumes require a reasonable index sharding strategy to ensure retrieval response speed.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | text-embedding-ada-002 or a locally deployed structured-adapted model | Supports encoding of general text and structured parameters, matching the mixed content characteristics of communication equipment investment research data |
embedding_batch_size | 32-64 items/batch | Adapts to the batch indexing process for hundreds of thousands of communication equipment data, balancing single-batch memory usage and indexing completion speed |
vector_index_type | IVFFlat (PGSQL vector database compatible) | Efficient retrieval configuration for hundreds of thousands of vector datasets, balancing recall precision and response speed |
chunk_size | 800-1200 characters | Adapts to long test paragraphs and short parameter entries in communication equipment documents, balancing context completeness and retrieval granularity |
incremental_index | Enabled | Adapts to the high-frequency update rhythm of centralized procurement announcements and certification announcements, avoiding time overhead from full index reconstruction |
vector_field_weight | technical parameter fields:1.2, text description fields:1.0 | Matches the decision priority of structured parameters in investment research scenarios, improving the relevance of recall results |
The parameter values provided on this page are general recommendations for establishing configuration starting points. 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 misconfigurations
- Phenomenon: Retrieval speed is too slow, and the interface shows retrieval time exceeding expected values. Cause: Retrieval speed issues are mistakenly attributed to the vector model, without troubleshooting the language model context window configuration or time consumption in the reranking step. This leads to incorrect optimization directions.
- Phenomenon: A
400 Bad Requesterror is returned when calling the vector model, or a prompt reading "no available embedding model" is displayed. Cause: The target vector model has not been added to the platform's third-party API channel configuration, or a locally deployed model has not been correctly bound to the corresponding field in the configuration file. - Phenomenon: After full index completion, the proportion of structured parameter retrieval results is too low. Cause: The
vector_field_weightparameter is not configured, and reasonable weight is not assigned to technical parameter fields. This leads to higher recall priority for natural language text over core investment research parameters.
How to confirm proper configuration
- View vector index creation logs to confirm the
embedding_batch_sizeparameter matches the configured value, there are no single-batch memory overflow errors, and the setup adapts to the local deployment memory limit of version V4.8.20-FIX2. - Initiate a retrieval test that includes structured technical parameters, and verify that the field weights of returned results match the preset configuration.
- Simulate a single incremental indexing operation, confirm that only newly added centralized procurement or certification announcement data is synchronized to the index library, and no full index reconstruction is triggered.
- Call the vector model interface, confirm that the dimensions of returned vector data match the output dimensions of the 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.