What Data Looks Like for This Category
Chemical pharmaceutical research report data primarily comes from public industry reports from securities research institutions, R&D pipeline announcements released by pharmaceutical companies, official clinical trial data documents, and medical industry databases. Update frequency adjusts based on pipeline progress, clinical trial result releases, and policy changes, with no fixed cycle. Document structures typically include modules such as target analysis, clinical trial parameters, compliance explanations, and market forecasts. Fields include target name, IC50 value, clinical trial phase, administration dose, approval status, and more. Units follow professional medical and chemical measurement standards including nmol/L, mg/kg, and patient count.
Constraints Imposed on Knowledge Base Retrieval and Reranking
Strict matching requirements for professional fields and units mean the retrieval link must associate fields and units for precise retrieval, to avoid interference from general medical content. The lack of a fixed update cycle requires the knowledge base to support incremental updates instead of full reindexing, reducing resource consumption. Individual documents are lengthy and contain dense professional information, so context association must be retained during segmentation to avoid breaking the logical integrity of clinical data or target analysis. User questions usually specify specific targets or clinical trial phases, so the retrieval link must support precise metadata filtering to narrow the retrieval scope. When creating a knowledge base index, professional fields of research reports must be structurally parsed, and metadata such as targets and clinical trial phases must be bound to provide a filtering basis for subsequent retrieval. Metadata filtering performs matching verification on already retrieved results during the retrieval process, only retaining content that meets preset metadata conditions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall count | Top 8-12 results | Chemical pharmaceutical research reports have high professional content density. Too many retrieved results will introduce irrelevant information, while too few will fail to cover core relevant content |
Similarity threshold | 0.75-0.85 | Matching professional terms and clinical data requires high accuracy, to avoid retrieving general medical content unrelated to targets |
Chunk size | 1000-1500 characters | Individual research reports contain lengthy paragraphs of clinical data and target analysis. Segmentation must retain context association to avoid destroying professional logic |
Incremental Update Trigger Mode | Triggered by file modification time | Updates to chemical pharmaceutical research reports mostly accompany pipeline changes or clinical data releases. Modification time can accurately reflect content update status |
Metadata Filter Field | Research report release time, target name, clinical trial phase | Users usually limit research reports to specific phases or targets for precise filtering of irrelevant content. This function relies on collection tag implementation in the latest open source version |
Rerank result count | Top 3-5 results | Professional content requires secondary sorting to strengthen relevance, to avoid deviations caused by relying solely on similarity calculations |
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.
Three Common Misconfigurations
- Phenomenon: API calls return research report results that differ significantly from online chat, with higher accuracy in online chat. Cause: Online chat enables context-aware retrieval and reranking by default, and follows the question logic of session context; while API calls do not configure the
enableRerankparameter, do not pass session context, and do not bind metadata fields parsed during knowledge base indexing, only using basic full-text retrieval results. - Phenomenon: Retrieval results include content with mismatched units, such as matching administration dose mg/kg to IC50 nmol/L. Cause: No unit association rule is configured in the
Metadata Filter Fieldmetadata filtering fields, and the corresponding relationship between fields and units is not verified during retrieval. - Phenomenon: Index creation fails for research reports longer than 10 pages, returning parsing timeout errors. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, not adapting to the long document parsing requirements of chemical pharmaceutical research reports.
How to Verify Proper Configuration
- Initiate API calls and online chat tests with the same professional question, compare the number of retrieved results and their relevance, confirm that the differences align with configuration expectations.
- Check knowledge base index logs, confirm that incremental updates are only triggered after research report files are modified, and no full reindex tasks are repeatedly executed.
- Randomly select retrieval results, check whether metadata tags match preset filtering fields, confirm that irrelevant content has been excluded.
- Confirm that the current open source version supports the knowledge base collection tag function, and metadata filtering rules are bound to corresponding tags.
- Parse a single lengthy research report, check whether the segmented content retains complete clinical data paragraphs without obvious content truncation.
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.