What this type of data looks like
Data for water treatment financing daily reports comes primarily from environmental industry regulatory announcement platforms, local public resource trading centers, official announcements from water utilities, and information disclosed by industry associations. Updates occur once daily. The core data carrier is structured tables, paired with unstructured project detail text. Fields include project name, financing subject, financing amount (unit: ten thousand yuan or hundred million yuan), financing method, signing date, project location, water treatment process type, project treatment scale (unit: ten thousand tons/day). Some entries also include details of the project’s concession terms.
What constraints do these characteristics impose on vector models and indexing
The mixed structured and unstructured data format requires the vector model to support both field-level embedding and full-text embedding. This prevents loss of semantic information from structured fields when using a single embedding method. The daily incremental update rhythm requires the indexing system to support incremental construction. This avoids wasted computing resources from full reindexing. Multi-dimensional professional fields such as process type and treatment scale require vector embedding to retain field semantics and unit associations. This prevents incorrect matching of similar data with different units. The feature that a single data entry contains multiple sets of related content requires the index to support binding multiple vectors to a single data source. This improves retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The text length of individual project detail entries for water treatment financing daily reports mostly ranges from 500 to 1500 characters. Segmentation preserves contextual relevance and prevents truncation of professional terms such as "MBR membrane bioreactor". |
chunk_overlap | 100–150 characters | Process descriptions and financing terms of financing projects have semantic connections across segments. Overlapping segments preserve contextual coherence. |
embedding_model | bge-large-zh-1.5 | This model has better semantic understanding adaptation to professional terms in the water treatment industry, and can accurately identify keywords such as process type and financing model. |
required_embedding_dim | 1024 | Matches the embedding dimension of bge-large-zh-1.5, to avoid incompatibility between vector storage and model output dimensions. |
retrieval_top_k | 5–8 entries | Valid related information for water treatment financing daily reports is mostly concentrated in similar projects and regional financing cases. Too many recall results will introduce irrelevant noise. |
enable_incremental_index | Enabled | Financing daily reports are updated incrementally daily. Full reindexing will consume significant computing resources, and incremental updates improve processing efficiency. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: In FastGPT v4.8.7, after importing water treatment financing daily report data, retrieval results fail to accurately match the associated information of process type and financing amount, and the interface only supports selecting a single vector model. Cause: Multi-field embedding or multi-segment vector generation rules are not configured, and only a single vector is generated for the entire document, which cannot cover the semantic requirements of multiple sets of related data.
- Phenomenon: Retrieval results do not include associated information of auxiliary fields such as project treatment scale and process type, and retrieval hits only cover the main text. Cause: Auxiliary fields are not included in the vector embedding scope, and vectors are only generated for the main text content, so semantic associations of auxiliary dimensions cannot be retrieved.
- Phenomenon: After splitting documents with a custom vector model locally and uploading to the server using the official embedding model, retrieval similarity results have large deviations, and some hit results are semantically irrelevant. Cause: The vector model embedding dimensions used locally and on the server are inconsistent, leading to deviations in vector space mapping and incorrect matching of semantic similarity.
How to confirm the configuration is correct
- Check the storage records of the vector database, confirm that the number of vectors generated for each financing project data matches the configured segmentation and field rules, and verify that the embedding dimension matches the output dimension of the selected model.
- Manually enter industry-related search terms such as "MBR membrane treatment project financing", check the number of recalled results, and confirm that they match the configured recall quantity rules.
- Upload a single test data entry, check whether the retrieval results cover the semantic associations of auxiliary fields such as project treatment scale and process type, and confirm that the multi-field embedding configuration is effective.
- Import incrementally updated daily report data, check the index update log, and confirm that the incremental index construction process is executed normally, with no records triggered by full reindexing.
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.