What the data for this category looks like
Data sources for pharmaceutical chemical investment research include internal pharmaceutical company R&D records, clinical trial reports, patent literature, pharmacopoeia standards, peer-reviewed academic journals, and industry research reports from financial institutions. Update cycles vary by source: patents update upon public release, clinical data releases in phases alongside trial progress, internal company documents adjust in line with R&D progress, and industry research reports update with industry trends. Document structures include long texts such as trial reports and patent claims, as well as structured experimental data tables. Fields cover compound CAS numbers, molecular formulas, molar mass, administration doses, and more, with units including g/mol, mg/kg, hours and other professional measurement standards.
Constraints imposed on model access and configuration
The high proportion of long texts, numerous structured fields, and uneven update cycles of pharmaceutical chemical investment research data create multiple constraints for model access and configuration. Long text documents must adapt to model context window limits to avoid breaking professional term integrity during segmentation. Content mixing structured numerical values and professional terms requires vector models to have professional domain semantic understanding, rather than only adapting to general text. Differences in update frequencies across data sources require configuring differentiated synchronization trigger rules to avoid redundant synchronization or data lag. Additionally, model access restrictions in internal network environments require configuring model interface parameters adapted for internal deployment.
Configuration Parameter Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 1000–1200 characters | The semantic unit length of professional pharmaceutical chemical text is moderate. Setting too short risks breaking professional term integrity, while setting too long leads to redundant context |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing single patent documents and clinical trial reports takes a long time. This setting avoids premature timeout causing parsing failure |
embeddingModel | Professional text-adapted vector models, such as multimodal-embedding-v1 | This model can handle structured numerical values and professional terms, adapting to the multi-type fields of pharmaceutical chemical data |
apiRequestTimeout | 300 seconds | Model responses have delays in internal network environments. This setting avoids connection interruptions due to timeout |
recallTopK | Top 8–10 results | Correlation matching of pharmaceutical chemical investment research data needs to cover multi-dimensional experimental results. Including too many results introduces irrelevant information, while including too few misses critical data |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single collections of clinical trials or bulk patent files have large file sizes, meeting large file upload requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom:
connection refusedor401 Unauthorizederrors occur when configuring interfaces for internally deployed models. Cause: The internal network IP of the FastGPT server has not been added to the model API access whitelist, or internal proxy forwarding rules have not been correctly configured. - Symptom: When using
multimodal-embedding-v1to embed structured experimental data from pharmaceutical chemicals, similarity matching results for numerical fields do not meet expectations. Cause: Structured data processing mode has not been enabled in the vector model configuration, preventing the model from correctly parsing the semantics of numerical fields. - Symptom: After calling the model API, the returned results only contain the final conclusion, with no chain-of-thought intermediate steps included. Cause: The chain-of-thought output switch has not been enabled in the model call parameters, or the prompt does not explicitly require generating intermediate thought processes.
How to confirm configuration is successful
- Upload a single clinical trial report for pharmaceutical chemicals, check the parsing progress and results to confirm no timeout errors are triggered.
- Submit an embedding test for structured experimental data, check the generated embedding vectors to confirm numerical fields and professional terms are correctly encoded.
- Initiate a model call request, verify that the interface response meets the configured timeout threshold, with no premature interruptions occurring.
- If chain-of-thought output has been configured, check whether the returned results include intermediate thought steps to confirm the configuration is effective.
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.