What the Data for This Category Looks Like
Water treatment financing daily report data comes from public bidding platforms for the environmental protection industry, project filing systems of local housing and urban-rural development departments, and monthly financing disclosures from water utility operators. Data is updated daily, generating full or incremental project records from the previous day each day. Each daily report document uses a structured table format, with 7 core fields: project name, financing subject, financing amount, project type (e.g., sewage treatment, pipeline network renovation), administrative region, signing date, and funding source. The amount field uses ten thousand yuan as the unit, and the date field uses the YYYY-MM-DD format.
Constraints Imposed on Model Access and Configuration
The daily update feature requires configuring scheduled incremental pull tasks to avoid excessive resource usage from full pulls. The structured table format requires adapting structured data parsing during model access, and explicitly specifying field extraction rules. The need for precise multi-field matching requires setting weighting rules for fields such as region and project type in the recall and ranking stages. The wide fluctuation in the number of entries per document requires adapting context splicing for different lengths, while controlling token consumption per data entry to avoid exceeding model limits.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Water treatment financing daily reports typically contain dozens of structured project records per file. The default timeout duration is insufficient to complete full parsing. |
chunk_size | 800–1200 characters | Descriptive information for a single financing project is approximately 500 characters. Splitting into chunks preserves complete project context and avoids semantic fragmentation. |
recall_count | Top 8 entries | Financing daily reports have high effective information density. Too many recalled entries introduce irrelevant project data and reduce query accuracy. |
similarity_threshold | 0.75–0.85 | Precise matching of fields such as project region and financing type is required. A threshold that is too low will include unrelated entries. |
embedding_model | Doubao-embedding | Adapts to semantic understanding of structured industry data, and is compatible with mainstream large model invocation pipelines. |
max_context | 16000 characters | Context splicing for financing daily reports must include multiple project entries, to avoid exceeding model context limits. |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Incorrect API address when configuring
embedding_model, resulting in404 page not foundduring testing. The cause is failure to correctly enter the official API gateway address for Doubao embedding models, leading to requests that cannot match the corresponding service. - Setting
similarity_thresholdbelow 0.6, causing the model to include a large number of unrelated project entries when identifying financing daily reports. The cause is a threshold that is too low, which matches financing information that is semantically similar but not part of the water treatment field. - Failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter, resulting in request failure with a timeout error when uploading daily report files containing 100 or more project entries. The cause is that the default timeout duration is insufficient to complete parsing and splitting of large volumes of structured data.
How to Verify Successful Configuration
- Manually upload a single water treatment financing daily report file. Verify that the parsed text fully retains core fields such as project name and financing amount, with no truncation or garbled characters.
- Initiate a test query, enter "XX regional water treatment project financing status", and verify that all returned recalled entries belong to the water treatment field and meet the query conditions.
- View model invocation logs, confirm that token consumption for each request falls within the range specified by the preset
max_contextparameter, with no context overflow errors. - Initiate three identical queries consecutively. Verify that the number of returned entries and sorting logic are consistent, with no random fluctuations.
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.