What the data for this category looks like
The data primarily comes from government procurement websites at all levels, public resource trading centers, official websites of industry regulatory authorities, and official tender platforms. It is updated in daily batches, with temporary releases for major emergency projects. Document structures typically include fixed fields such as project name, tender number, purchaser information, budget amount, bid submission deadline, qualification requirements, and evaluation method. Some documents include attachment lists and technical parameter requirements, with field formats strictly aligned with official release specifications.
Constraints on Multi-turn Dialogue and Prompt Engineering
The fixed field structure of tender announcements requires multi-turn dialogue to gradually guide users to clarify query dimensions, avoiding result deviations from vague questions. Document lengths vary widely, from hundreds to thousands of words, so the system must adapt to context processing for different lengths. The high-frequency update feature requires the dialogue system to support real-time synchronization of the latest announcement content, or use a scheduled knowledge base refresh cycle. The strict field format requires prompts to clearly specify extraction rules, preventing the model from altering units or omitting key information.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Adapts to the maximum several-thousand-word length of individual tender announcement documents, avoiding context overflow |
prompt_template | Follow the template: "First clarify the category of the target tender announcement, then extract corresponding fields based on user queries" | Tender announcement field formats are fixed, and structured prompts improve extraction accuracy |
chunk_max_tokens | 1000-1500 tokens | Splits long tender announcement documents to ensure each context segment can be fully processed by the model |
recall_top_k | Top 6-8 entries | Knowledge base entries related to tender announcements are mostly similar projects, and a moderate number of recalls covers more reference information |
similarity_score_threshold | 0.75-0.85 | Filters low-match non-tender announcement knowledge base content to avoid interference |
max_input_token | 16000 characters | Adapts to cumulative query and context lengths during multi-turn dialogue, preventing key information from being truncated |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After selecting a locally deployed LLM in the debug preview interface, the actual call returns results from another model. Cause: The forwarding parameters for the LLM service are not correctly configured, or the third-party proxy service is not bound to the specified model.
- Symptom: Uploaded tender announcement JSON file content is accidentally truncated. Cause: The
max_input_tokenparameter is not adjusted to a suitable character length, exceeding the system's default limit. - Symptom: After associating a specified number of knowledge bases, the system prompts that more content cannot be loaded. Cause: The knowledge base association limit or total word count limit parameter is not adjusted, exceeding the system's default configuration.
How to Verify Proper Configuration
- Enter the application debug interface, enter a query that includes multiple tender announcement fields, and check whether the returned results accurately extract the specified fields.
- Upload the longest single tender announcement document, and check that the parsed content is complete with no abnormal truncation.
- Associate multiple target knowledge bases, adjust recall-related parameters, and verify that the number of returned reference entries matches expectations.
- Switch to the locally deployed LLM, initiate a multi-turn dialogue, and confirm that the called model matches the selected one.
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.