What the Category Data Looks Like
Heating industry financing daily report data comes from three primary sources: internal financing ledgers of heating enterprises, public financing announcements for local public utility projects, and publicly available credit approval information from financial institutions. Updates occur daily. Each document uses a semi-structured format. It includes 8 core fields: heating project name, project type (such as centralized heating pipe network renovation, new heat source plant construction), financing amount (unit: ten thousand yuan), financing subject, funding provider, financing term, announcement date, and project location. Some documents include snippets of the original announcement text.
Constraints for Vector Models and Indexing
The daily update requirement means the index must support low-latency incremental writes. This avoids resource consumption from full index rebuilding. The semi-structured field set includes categorical fields (project type, location), numerical fields (financing amount), and text fields (project descriptions). A single vector index cannot fully preserve the relational structure of structured information. Documents from multiple sources may have duplicate announcements for the same project. Additional deduplication logic is required. Some fields have close semantic connections, such as location and project type. The vector model must accurately capture these associated features.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model_name | bge-m3 | Supports semantic understanding for Chinese financial and public utility scenarios, allows local deployment, and has high recognition accuracy for heating project and financing terminology |
chunk_size | 800–1200 characters | Most individual entries for heating industry financing daily reports range from 500 to 1000 characters. This setting preserves core information completely and avoids semantic truncation |
vector_db_type | pgvector | Supports local deployment, is compatible with FastGPT's incremental indexing workflow, and adapts to common server hardware configurations |
vector_index_type | HNSW | Meets low-latency retrieval requirements for daily incremental updates. Has higher write efficiency than Flat indexes, with retrieval precision loss within a controllable range |
recall_top_k | Top 10–15 results | Relevant information for heating industry financing is mostly concentrated in projects of the same region and type. This setting balances retrieval coverage and contextual redundancy |
enable_duplicate_removal | Enabled | Daily reports from multiple sources may contain duplicate announcements for the same project. This configuration automatically filters duplicate entries based on vector similarity |
embedding_batch_size | 32–64 | Adapts to hardware configurations with 32GB of memory, preventing out-of-memory errors during batch embedding |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, data volume, and business rules. Each scenario requires targeted analysis. It is recommended to test with your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: OOM errors occur during vector database writes. Cause:
embedding_batch_sizewas not adjusted based on hardware configuration, leading to excessive memory usage during batch embedding. - Issue: A large number of off-target regional heating financing projects appear in retrieval results. Cause: Semantic weights for structured fields were not configured. Only global vector similarity was relied on, without strengthening associations for key dimensions such as region and project type.
- Issue: Duplicate records of the same heating financing project appear multiple times in retrieval results. Cause: The
enable_duplicate_removalconfiguration was not enabled, or a reasonable similarity deduplication threshold was not set.
How to Verify Correct Configuration
- Upload a single test entry from the heating industry financing daily report. Confirm that the vector dimension after embedding matches the official output dimension of the selected model. This verifies proper model loading.
- Perform an incremental import of 10 entries from the same day’s financing daily report data. Confirm that the write latency returned by the system has no significant timeouts, and the vector database disk usage matches expectations.
- Run a retrieval for heating financing projects in a specified region. Verify that the region field of the returned results matches the retrieval condition. This confirms that structured information relational retrieval is working.
- Import two entries with completely identical heating financing records. Confirm that the number of deduplicated data entries is 1. This verifies that the deduplication configuration is functioning properly.
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.