Target Discovery Pharmacovigilance Multiturn Conversations

Data Characteristics in This Category

Target discovery data comes primarily from public databases (e.g., DrugBank, ChEMBL, PubChem), patent literature, research papers, and clinical trial reports. Update frequencies vary. Public databases typically update quarterly or semi-annually. Research papers publish in real-time. Document structures are diverse. They include structured data (e.g., compound ID, target protein ID, mechanism of action, IC50 values), semi-structured data (e.g., patent abstracts, clinical trial result descriptions), and unstructured text (e.g., full-text papers, adverse event reports). Fields and units are specific. For example, compound activity data often includes concentration units like nM or µM. Target affinity data may use Kd values. Complex chemical formulas and biological macromolecule nomenclature are common.

Constraints Imposed by These Characteristics on "Multiturn Conversations and Prompts"

The broad and heterogeneous nature of target discovery data requires powerful heterogeneous data processing capabilities for multiturn dialogue systems. Without this, knowledge base construction may be incomplete or retrieval efficiency low. Inconsistent update frequencies necessitate continuous maintenance and incremental update strategies for the knowledge base. This prevents information lag and impacts dialogue accuracy. Diverse document structures, especially the large amount of unstructured text, increase information extraction difficulty. This demands more refined prompt engineering. Accurate identification of key entities and relationships requires contextual understanding. Specific fields and units require the dialogue system to correctly parse and understand these specialized terms. Failure to do so can lead to incorrect query results or an inability to respond.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters` (characters)	Balances semantic integrity with model processing efficiency, avoids long text truncation.
`Recall count` (Recall Count)	`8–12 entries` (items)	Ensures retrieval coverage, increases probability of recalling relevant information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall precision and recall rate, reduces interference from irrelevant results.
`Rerank result count` (Reranked Return Count)	`3–5 entries` (items)	Focuses on the most relevant information, improves dialogue quality and response speed.
`maxContext`	`4000 token`	Accommodates the depth of professional domain dialogue, retains sufficient contextual information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds` (seconds)	Addresses parsing time requirements for large research literature and clinical reports.

Three Common Pitfalls

After uploading a file during a conversation, the system displays 503 Service Unavailable. However, backend logs show the file uploaded successfully. This usually results from inconsistent network proxy or load balancer timeout configurations between the frontend file upload component and the backend processing logic.
After integrating a knowledge base, the AI responds with "No answer found" when using a strict question-answering template, but responds when switching to other templates. This may occur because the knowledge base contains a large amount of unstructured or semi-structured data, which strict templates cannot precisely match. Other templates allow for more lenient semantic matching.
In API calls, sending consecutive questions in a short period leads to subsequent questions not being processed immediately, resulting in response delays. This happens when the system fails to effectively handle concurrent requests, causing request queuing or resource contention.

How to Verify Configuration

Conduct multiturn dialogue tests using documents containing specialized terms, units, and complex chemical structures. Confirm the dialogue system correctly parses and references key information. This validates the effectiveness of Similarity threshold (Similarity Threshold) and Chunk size (Chunk Size).
Upload target discovery documents of varying sizes and formats (e.g., PDF patents, CSV compound data). Observe file processing time and system response. Check if PARSE_FILE_TIMEOUT_SECONDS meets actual needs.
Simulate users asking in-depth follow-up questions about specific target mechanisms of action or adverse event cases. Check if the dialogue system maintains contextual coherence across multiple turns and accurately extracts information from the knowledge base. This evaluates the suitability of the maxContext parameter.

Note: The values provided are common starting points. Measure them against specific 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.