What this category’s data looks like
Intelligent due diligence reports for chemical pharmaceuticals draw data primarily from publicly available materials from the national drug regulatory center’s evaluation department, official clinical trial reports disclosed by pharmaceutical companies, compound patent documents, pharmacopoeia standards, and production process records. Data update timelines adjust based on new compound approvals, clinical trial phase progress, or compliance updates, with no fixed cycle. A single report document typically includes modules such as basic compound information, activity detection data, clinical trial phase records, and production compliance clauses. Fields include IC50 values, administered doses, patent numbers, production batch numbers, and more. Units often involve molar concentration, milligrams per kilogram, and dates follow the YYYY-MM-DD format.
What constraints do these characteristics impose on model access and configuration?
Chemical pharmaceutical data contains a large number of professional terms, structured numerical values, and long text passages, and updates have no fixed cycle, which imposes multiple constraints on model access and configuration. First, long text process descriptions and clinical trial data require sufficient context capacity to avoid truncation of key information. Second, vector extraction for professional terms must adapt to the semantic features of the specialized domain to avoid retrieving irrelevant content. Third, large collections of multiple documents must adapt to upload and parsing time limits to prevent timeouts and interruptions. Fourth, associated extraction of structured fields requires clear chunking rules to ensure the binding relationship between numerical values and their corresponding descriptions.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Chemical pharmaceutical due diligence reports often contain long text process descriptions and multi-phase clinical trial data, requiring sufficient context to avoid truncation of key information |
chunkSize | 1000–1500 characters | Chemical pharmaceutical data includes structured numerical values and long text passages. Too short chunking will break the association between activity data and process descriptions |
RECALL_TOP_N | Top 8–12 results | Due diligence reports need to cover multi-dimensional information including patents, clinical trials, and compliance. Too few recall results will miss key compliance items |
SIMILARITY_THRESHOLD | 0.72–0.80 | There are many professional terms in chemical pharmaceuticals. A threshold that is too low will introduce irrelevant documents, while a threshold that is too high will miss similar studies of the same target |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing a single complete clinical trial report or patent document takes a long time, so sufficient processing time must be reserved |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Complete public production batch records and multi-phase clinical trial collections from pharmaceutical companies are large in size, so large file upload requirements must be accommodated |
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. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: File indexing status stays at "Indexing" for a long time with no progress updates. Cause: In FastGPT v4.9.11, the model context adaptation parameters for
qwen3-embedding-8bwere not configured correctly, or the chunk length exceeded the model's supported upper limit, resulting in failed vector generation. - Symptom: The ID of an already connected model cannot be found in the workflow interface. Cause: The "Display Model ID" switch was not enabled in the model management module, or the configuration page for the corresponding version was not switched to.
- Symptom: Model thinking takes too long, and due diligence report generation cannot be completed within the preset time. Cause:
RECALL_TOP_Nis set too high, and the total context length passed to the model exceeds the model's inference load upper limit, resulting in increased inference delay.
How to confirm the configuration is complete
- Upload a single typical chemical pharmaceutical patent document, and check whether the parsed chunked content fully retains compound structural formula text, activity data, and patent number fields.
- Trigger a model call, enter "What are the clinical trial phase information of this compound", and check whether the returned results contain specific content matching the query.
- Search the
model_call_successfield in the system logs to confirm that both the embedding model and chat model call statuses are normal. - Adjust
SIMILARITY_THRESHOLDto 0.75, and test whether the recall results cover due diligence information from three different dimensions: patents, clinical trials, and compliance.
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.