Data Characteristics of This Category
Chemical pharmaceutical investment research data mainly comes from public patent literature, clinical study reports, pharmacopoeia standard documents, pharmaceutical company quarterly financial reports, and academic journal papers. The update rhythm is uneven. Patent publications follow a fixed cycle. Clinical study data is released in stages corresponding to trial phases. Financial reports are updated quarterly. Academic papers are updated monthly. Document structures vary significantly. Patents include claims, detailed specifications, and abstracts. Clinical reports cover subject groups, efficacy indicators, and adverse reaction records. Physicochemical parameter documents have standardized fields such as CAS number, molecular weight, melting point (degrees Celsius), solubility (g/100mL), and others.
Constraints on Vector Models and Indexing
The multi-source, heterogeneous nature of chemical pharmaceutical investment research data creates multiple constraints for the vector models and indexing workflow. The length of different document types varies widely. Patent documents can reach tens of thousands of characters. Clinical reports are usually several thousand characters long. Physicochemical parameter documents are only a few hundred characters. The workflow must support vectorization for texts of varying lengths. Some fields include standardized data such as CAS numbers and melting point units. The workflow must avoid semantic confusion between units and numerical values. Data update rhythms are uneven. The workflow must support incremental indexing to accommodate staged clinical data updates and real-time published patent information. The semantic focus of different documents differs significantly. The vector models must cover two scenarios: precise claim descriptions and quantitative efficacy indicators.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Use open-source vector models adapted for the pharmaceutical domain, such as bge-large-zh-v1.5, or a locally deployed compliant API | Chemical pharmaceutical documents contain a large number of professional terms and standardized fields. The model must have accurate professional semantic understanding capabilities. Local deployment avoids network fluctuations and compliance risks |
chunk_size | 800–1200 characters | Patent documents and clinical reports are lengthy. Segmentation of 800–1200 characters preserves complete semantic units, and avoids splitting the integrity of claims or efficacy descriptions |
chunk_overlap | 100–150 characters | Contextual association must be retained after splitting long documents. An overlap of 100–150 characters prevents semantic breaks, and adapts to the long-text structure of patents and clinical reports |
retrieval_top_k | Top 8–12 results | Chemical pharmaceutical investment research requires coverage of multi-dimensional information such as patents, clinical data, and physicochemical parameters. Retrieval of 8–12 results balances comprehensiveness and relevance |
similarity_threshold | 0.72–0.85 | Semantic similarity for professional documents must maintain a high threshold to avoid introducing irrelevant patents or clinical data. This range filters low-correlation results |
incremental_index_enable | Enabled (true) | Clinical data and patents are updated in stages. Incremental indexing avoids full index reconstruction, and adapts to the uneven update rhythm of data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Issue: A 503 Service Unavailable error is returned when calling the vector model, and the log shows that there are no available nodes in the
text-embeddingmodel group. Cause: Insufficient vector model deployment nodes are configured, or the resource quota of the defaultdefaultgroup is insufficient to handle concurrent vectorization requests. - Issue: The knowledge base indexing task deployed via Docker remains in a running state with no progress updates. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not set appropriately, or the container memory quota is insufficient, causing timeout blocking during long document parsing and vectorization. - Issue: A large number of documents unrelated to the investment research topic are included in the retrieval results, and field matching accuracy is low. Cause: The
similarity_thresholdis not set appropriately, or the selected vector model has not been fine-tuned for medical professional terms, making it unable to accurately distinguish semantic relevance between different documents.
How to Confirm Proper Configuration
- Check the vector model call logs to confirm that each vectorization request returns valid vector data with no error messages.
- Manually upload a typical chemical pharmaceutical patent document, and check the segmented results after indexing to confirm that the segment length matches the preset
chunk_sizeparameter. - Enter an investment research-related query term, and verify that the number of retrieval results matches the
retrieval_top_kparameter setting, and that result relevance meets expectations. - Add an updated clinical report document, and confirm that the incremental indexing task triggers normally and completes the update without requiring a full index reconstruction.
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.