What the data for this category looks like
Data sources for specialized chain intelligent due diligence reports include chain brand industrial and commercial registration records, single store operation reports, supply chain ledgers, franchisee cooperation agreements, and regional revenue summary tables, among others. Data updates occur monthly or quarterly. Real-time operation data for some core stores can be synchronized weekly. Most documents are structured tables, with supplementary text such as store location descriptions and compliance notes. Included fields include store number, registered address, monthly revenue, employee count, franchise cooperation term, and number of SKU categories. Supported units include RMB yuan, person, day, and unit, among others.
Constraints on vector models and indexing workflows
Multi-dimensional data characteristics of specialized chain due diligence reports create multiple constraints for vector models and indexing workflows. Many structured fields contain mixed numeric and text content. This requires vector models to balance semantic matching and feature extraction for structured information. A single report may cover data from dozens of stores, with wide variation in text length. Segmentation processes must avoid splitting cross-store associated logic. Data updates follow a fixed schedule and are synchronized in batches. Real-time index refresh is not needed, but batch vector import must be supported. Some fields follow repeated patterns, such as revenue data across multiple stores. Index configurations must prevent repeated vector generation and storage.
Configuration settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Specialized chain due diligence reports contain multi-store structured data and text descriptions. This segmentation length balances field integrity and semantic coherence, and prevents splitting that would break single-store business logic |
chunk_overlap | 50–80 characters | Retains overlapping content between segments. This ensures cross-segment store association information is not truncated, and improves recall accuracy |
recall_top_k | 10–15 entries | Professional due diligence requires coverage of cross-store associated information. This recall count balances query efficiency and information completeness |
similarity_threshold | 0.75–0.85 | Professional scenarios require high matching accuracy. This threshold filters low-relevance store data, and prevents unrelated information from being included in due diligence results |
vector_db_batch_size | 32–64 entries per batch | Balances vector import speed and server memory usage. This matches the batch synchronization requirements for due diligence report data updates |
milvus_collection_shards | 2–4 | When the number of vectors in a single collection exceeds 1 million, this shard count improves write and query performance. This supports indexing requirements for multi-store batch data |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Milvus deployment fails when the compose file includes PostgreSQL-related configurations. This occurs when the minimal Milvus deployment template adapted for FastGPT is not used, and the official full compose file is imported directly.
- Connection timeouts occur when connecting to the Qwen3-Embedding-8B model deployed via VLLM. This happens when the correct model interface address and port are not configured in FastGPT, or firewall outbound rules for the server hosting the model service are not opened.
- Knowledge base disk usage statistics incorrectly include log and cache files. This occurs when the FastGPT knowledge base storage directory structure is not clarified, and non-exclusive knowledge base file directories are not excluded.
How to confirm successful configuration
- Run the built-in vector testing tool in FastGPT. Input typical text fragments from specialized chain due diligence reports, and verify that returned recall results include matching store information and associated data.
- Log in to the vector database management panel. Confirm that the number of vectors in the collection matches the number of document chunks in the FastGPT knowledge base, to verify the vector import workflow is functioning normally.
- Run a disk usage statistics script. Specify scanning only the original file directory, chunk storage directory, and vector data directory under the FastGPT knowledge base, to confirm the statistical scope is accurate.
- Upload a new set of store due diligence data. Wait for index refresh, then run a query to confirm the new data can be recalled normally.
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.