What the Data for This Category Looks Like
Telecommunications equipment financing daily report data sources mainly include operator centralized procurement winning bid announcements, financial report disclosures of telecommunications equipment suppliers, public notices from industry bidding platforms, and financing support public notices from local industry and information technology departments. The data update rhythm is daily. Each daily report covers newly added winning bid projects and financing record information from the current day. The core carrier is structured tables, including fields such as telecommunications equipment model, winning bid entity, winning bid amount, winning bid date, supplier name, project location, financing party credit limit, financing interest rate, financing arrival time, etc. The amount unit is ten thousand yuan, the time format is YYYY-MM-DD, and the interest rate is marked as a percentage.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The large number of specialized structured fields requires that multi-turn dialogue prompts clearly specify field distinction rules, to avoid the model confusing easily mixed items such as winning bid amount and financing limit. The daily update feature requires that dialogue context be limited to a reasonable time window, to prevent invalid cross-date data from interfering with current queries. The need for integrating multiple data sources requires that multi-turn dialogue actively prompt to confirm the query data source range, avoiding misplaced association of cross-platform data. The compact nature of single data entries requires that retrieved knowledge base fragments maintain field integrity, preventing split fragments from losing key associated information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | First 10 rounds of dialogue context | Financing daily reports have many data fields; excessive context will interfere with the field matching accuracy of current queries. 10 rounds can cover the context requirements of conventional multi-turn interactions |
chunk_size | 800–1200 characters | A single financing daily report record contains multiple associated fields. 800–1200 characters can ensure the field integrity of a single record and avoid destroying the table structure during splitting |
recall_top_k | Top 8 entries | The number of newly added daily data entries is stable. Retrieving 8 entries balances token consumption and information coverage, avoiding excessive redundant data increasing the model's inference burden |
similarity_threshold | 0.75–0.85 | Fields such as telecommunications equipment model and winning bid amount have clear matching rules. A threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss some valid entries with approximate matches |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single monthly financing daily report file has a large volume and contains multiple days of data entries. 600 seconds can ensure complete data parsing and field extraction |
WORKFLOW_KNOWLEDGE_RECALL_MODE | Field matching priority | The precise matching requirements of structured data are higher than semantic matching. Prioritizing field matching can improve the retrieval accuracy of professional fields |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to conduct actual tests on respective samples before finalizing the settings.
Three Common Misconfigurations
- Phenomenon: After local deployment, a financing daily report file is uploaded, and an empty result is returned during the data processing stage. Cause: The
chunk_sizeparameter is not configured to adapt to structured table fields, and the row structure of the table is destroyed during splitting, causing the parsing engine to fail to recognize valid data fields. - Phenomenon: Knowledge base search debugging works normally in the workflow, but no knowledge base reference content is returned during the AI dialogue stage. Cause: The
WORKFLOW_KNOWLEDGE_RECALL_MODEparameter is not set to field matching priority; the default semantic matching cannot accurately match structured fields such as telecommunications equipment model and winning bid amount. - Phenomenon: The dialogue interface returns results normally, but no corresponding dialogue history records exist in MongoDB. Cause: The
ENABLE_CONVERSATION_HISTORYparameter is not enabled, or the configured MongoDB connection string has insufficient permissions to write dialogue data.
How to Verify Correct Configuration
- A single standard financing daily report file is uploaded, the data processing log is reviewed, and all preset fields are confirmed to be correctly identified and extracted.
- Multi-turn dialogue is initiated, financing information of telecommunications equipment with different dates and different models is queried in sequence, and the accuracy of context association is verified.
- The HTTP dialogue interface is called, and it is checked whether the returned results contain the source data fragments referenced by the knowledge base.
- The corresponding MongoDB collection is reviewed, and newly initiated dialogue records are confirmed to have been correctly written.
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.