What Data for This Category Looks Like
Professional services research report data primarily comes from publicly available reports issued by licensed consulting firms, securities research institutes, and industry associations. Updates align with the release schedule of research reports, with new content added to the repository within hours of going live. Document structures include title, publishing entity, publication time, core conclusions, data summary, and attachment annotations. Fields include research report ID, rating tags, and associated industry identifiers. Most data fields use units such as ten thousand yuan or thousand person-times. Single-document lengths vary significantly.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompting
The characteristics of professional services research reports impose multiple constraints on the multi-turn dialogue and prompting workflow. The high data update frequency requires multi-turn dialogue to prioritize retrieving the latest added research report content by default. Prompts must explicitly include a rule to prioritize the latest data. The complex document structure and diverse fields require multi-turn dialogue to support precise filtering by fields such as research report ID and rating tags. Prompts must specify specific logic for field matching. The long length of single documents requires multi-turn dialogue to limit the total amount of research report fragments in the context window, to avoid exceeding the model's processing limits. Prompts must split long documents into structured fragments before processing. Additionally, cross-report comparative analysis is common in professional services scenarios. Multi-turn dialogue must retain the research context from previous conversations. Prompts must include instructions to continue deriving conclusions based on historical results.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Professional services research reports are often long, so sufficient context must be retained to support multi-turn dialogue historical memory, while avoiding exceeding the context limits of mainstream large models |
similarityThreshold | 0.72–0.85 | Professional services research reports have highly specialized content, so a relatively high similarity threshold is needed to filter irrelevant content, while ensuring the relevance of retrieved content |
topN | Top 8–12 results | Research reports have high content density. Too many retrieved results will occupy too much context window space, while too few may miss key research conclusions |
rerankTopN | Top 4–6 results | The most relevant research report fragments must be retained after reranking to fill the multi-turn dialogue context, avoiding redundant information interfering with model output |
promptTemplate | “Based on the retrieved research report content and historical dialogue context, accurately answer user questions, prioritize using the latest published research report data” | Matches the professional services scenario's requirements for data timeliness and context association, and clarifies instruction boundaries |
chunkSize | 1000–1500 characters | Split long research report documents to fit model input length, while avoiding overly fragmented segments that cause semantic breaks, ensuring the integrity of retrieved fragments |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling the application via HTTP requests, the output field generated by the orchestration module is not displayed in the dialogue interface, using version 4.6.9. Cause: The "Synchronize orchestration output to dialogue context" option was not enabled in the application configuration, so the output was not included in the conversation history.
- Symptom: The application requires clicking an interaction button to initiate a dialogue by default, and cannot directly trigger a dialogue via the input box. Cause: The "Require manual dialogue trigger" switch was not disabled in the front-end configuration, restricting the interaction workflow.
- Symptom: When using the llama-3.1-8b-instruct model, vector index tasks remain in an uncompleted state for a long time. Cause: A vector embedding model adapted to the large model was not configured, or the write timeout parameter of the vector database was set too short.
How to Verify Correct Configuration
- Initiate a query containing professional research report keywords, check the relevance of returned content, and adjust the values of
similarityThresholdandtopNuntil expectations are met. - Initiate three consecutive related queries, check whether the model can continue the analysis logic from previous conversations, and confirm that the context configuration meets multi-turn interaction requirements.
- Call the application via the HTTP interface, check whether the orchestration output is synchronized to the dialogue interface, and confirm that the context synchronization configuration is correctly enabled.
- Submit a test research report document, observe the completion status of the index task, and confirm that the vector embedding model and database parameters are adapted to the current scenario.
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.