Data Characteristics for This Category
Clinical trial pre-screening data from bid tendering primarily originates from public resource trading platforms, medical institution official websites, and third-party information aggregation platforms. This data updates frequently, often with new bid announcements or award information released weekly or even daily. Document structures are predominantly unstructured text, such as PDF bid announcements, Word document project specifications, and some structured web forms. Key fields include: project name, sponsor (pharmaceutical company), CRO company, indication, trial phase (I/II/III), recruitment target, number of research centers, budget amount, and bid submission deadline. Units for recruitment numbers are typically "persons," budget amounts are in "CNY 10,000" or "USD," and time fields involve specific "year/month/day" dates.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The high-frequency update nature of bid tendering data requires the tool calling module to have efficient data fetching and processing capabilities to ensure the timeliness of pre-screening information. Unstructured documents are the primary data source, making direct parsing difficult. This necessitates robust text extraction and information structuring tools, such as OCR technology or deep learning-based entity recognition models. The variety of fields and units presents challenges for data cleaning and standardization; for example, "budget amount" may appear in multiple currencies and units, requiring conversion to a comparable format. Additionally, data format differences across various source platforms demand flexible adaptation from plugins to handle diverse HTML structures or API interfaces. These constraints collectively determine that when configuring tool calling and plugins in FastGPT, focus must be placed on data source integration, robust text processing, and data standardization processes.
Configuration Guidelines
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Fetch Frequency | Twice Daily | Ensures timeliness of bidding information, balancing system resource usage with data update needs. |
Concurrent Requests | 5–10 | Handles multi-source data fetching, avoids excessive pressure on target websites, and prevents IP blocking. |
Text Chunk Size (Text Segment Length) | 800–1200 characters (800–1200 characters) | Ensures single text segments contain sufficient context while avoiding excessive length that impacts LLM processing efficiency. |
Similarity threshold (Similarity Threshold) | 0.75 | Accurately matches relevant bidding information, reducing false positives. |
Rerank result count (Rerank Return Count) | Top 5 entries (Top 5) | Focuses on the most relevant results, reduces LLM processing burden, and improves response speed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Addresses parsing needs for large PDFs or complex documents, preventing parsing timeouts. |
Common Pitfalls
- After calling the RAG knowledge base in a workflow, returned bid project information is incomplete or missing fields. This typically occurs because the original unstructured document failed to identify all key fields during information extraction, or field mapping configuration was incorrect.
- When using a database connection plugin, response times are much longer than expected, or even timeout errors occur. This can be related to inefficient database queries, improper database connection pool configuration, or excessive data volume leading to long query times.
- Failing to pass the session ID in a tool call, preventing subsequent steps from obtaining user context. This usually happens because the
Session IDvariable in the workflow is not correctly configured as_chat_idor another agreed-upon global variable, or the passing method does not meet the tool interface requirements.
How to Confirm Correct Configuration
- Run the toolchain on typical bid announcement documents and check the output results. Verify if key fields (e.g., sponsor, indication, recruitment target) are complete and accurate.
- Monitor system logs to confirm that data fetching and parsing tasks are executing at the expected frequency and that there are no significant parsing failures or network request errors.
- Simulate different query scenarios to test the recall and precision of pre-screening results. Compare these against manual screening results to validate the effectiveness of the similarity threshold and reranking strategy.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.