Data Characteristics of This Category
Biologics research reports for financial investment research scenarios are primarily sourced from brokerage industry reports, publicly submitted materials from drug regulatory authorities, and batch issuance databases. Updates occur in real time alongside regulatory announcements. Brokerage reports release temporary analysis documents on a quarterly and monthly basis. Most documents include descriptions of clinical trial phases, batch issuance volumes, target sequences, indication scopes, and other content. Common fields include drug generic names, brand names, submission progress, sample sizes, and more. Common units include ten thousand vials, cases, kilobase pairs, and others.
Constraints Imposed on Multi-turn Conversation and Prompt Engineering
Biologics research reports for financial investment research scenarios contain long-text target sequences, quantitative data with specific units, and widely varying update frequencies. In multi-turn conversations, users often request specific target information or single-batch batch issuance data after initially asking about an overall report. Drug name associations from the conversation context must be retained. Field naming varies across different source documents. Prompt engineering must standardize field mapping rules to meet the standardized analysis needs of financial investment research. Units for quantitative data can be easily confused. Prompt engineering must explicitly define unit recognition priorities to prevent the model from mixing up ten thousand vials and vials. This would compromise the accuracy of investment research data. Long professional paragraphs are prone to truncation during segmentation. Appropriate text segmentation parameters must be configured to preserve the integrity of technical terms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Meets the long context association requirements of financial investment research scenarios. Individual biologics research reports often contain thousands of characters of target analysis and clinical trial data |
Recall count | Top 6–8 entries | Biologics research reports have multiple granular dimensions. Excessive retrieval results will lead to redundant context, while insufficient results will fail to cover the granular investment research information requested by users |
Similarity threshold | 0.75–0.85 | Biologics research reports are dense with professional terms. A threshold that is too low will introduce irrelevant content, while a threshold that is too high may miss research reports related to granular targets |
Rerank result count | Top 3–5 entries | Prioritize returning core research report content most relevant to the current conversation topic, avoiding information overload during multi-turn conversations that could impair investment research judgments |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Research reports contain long sequence text and complex tables, leading to long parsing times |
Chunk size | 1000–1500 characters | Fits the target descriptions in long paragraphs of research reports, avoiding truncation of professional terms during segmentation |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: A 404 Invalid URL (POST /api/chat/completions) error is returned when calling the interface during multi-turn conversations. Cause: The FastGPT API gateway address is not configured correctly, or interface permissions are not enabled, causing conversation requests to fail to route normally to the server.
- Phenomenon: The target research report can be retrieved in the knowledge base test, but the retrieval result is empty during the conversation flow. Cause: The correct knowledge base association parameters are not configured in the conversation flow, or the multi-turn conversation context overrides the retrieval trigger condition, causing the system to not call the knowledge base retrieval.
- Phenomenon: A word count limit is set in the prompt, but the actual output exceeds the set word count. Cause: The word count limit rule is not explicitly embedded in the execution priority of the system prompt, or the relevant parameters are not configured to link with the prompt rules, causing the model to ignore the word count constraint.
How to Verify Proper Configuration
- Enter the FastGPT knowledge base test interface, enter professional questions related to biologics research reports, and adjust the similarity threshold to the range suitable for professional term retrieval.
- Initiate two or more consecutive questions. For example, first ask about the target of a biologics product, then ask about the clinical phase of that target, and verify that the system correctly associates the drug name mentioned in the previous round.
- View the conversation interface return logs, confirm that there are no 404 errors in the request URL, and verify the correctness of the API configuration.
- Test the prompt constraint effect, enter a question that requires a limited word count, verify that the model output matches the rules set in the prompt, and adjust the linkage logic between the system prompt and relevant parameters.
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.