Multi-Holdings Research Report Retrieval: Multi-Turn

What the Research Report Data Looks Like

The research report data sources for multi-holdings include monthly/quarterly operational research reports from each business segment within the group, external public industry research report libraries, and operational data documents disclosed by regulatory authorities. Update schedule: internal research reports are updated in real time with business milestones, external research reports are updated quarterly, and regulatory data synchronization frequency aligns with disclosure milestones. The document structure includes research report title, publishing entity, release date, core business data items, industry benchmark references, and risk warning items. Fields include publishing entity name, release date, core business amount, industry benchmark entries, and risk warning content, with units mostly being currency units such as ten thousand yuan and hundred million yuan.

Constraints Imposed on Multi-Turn Dialogue and Prompt Engineering

Since data sources include both internal and external categories, multi-turn dialogue must support distinguishing the credibility of different data sources, and prompts must clearly prioritize calling internal research report content. Since publishing entities include the entire group and each subsidiary, multi-turn dialogue must support entity recognition and targeted follow-up questions, and prompts must include directional guidance for business segments and subsidiary names. Since the document structure includes independent business paragraphs, multi-turn dialogue must support precise recall of content from specific segments, and prompts must clearly define retrieval scope. Since data update cycles differ, multi-turn dialogue must support specifying the update time range of data, and prompts must include time dimension constraints.

How to Configure Parameters

Configuration Item	Suggested Value	Rationale
`maxContext`	8000–12000 characters	Individual research reports for multi-holdings are relatively long, and multi-turn dialogue needs to retain multi-turn interaction context and multiple recalled research report fragments to avoid truncating critical information
`recall_top_k`	Top 8–12 entries	Needs to cover relevant research report content for the entire group and each subsidiary, balancing recall accuracy and context carrying capacity
`rag_threshold`	0.75–0.85	Research report content is highly professional, requiring filtering of low-relevance recall results to ensure returned content meets matching requirements with user queries
`system_prompt`	Fixed template: Please answer in sequence based on the retrieved research report content of the multi-holding group and its subsidiaries, following the user's follow-up questions, prioritize using internal research report data, and clearly mark the data source	Adapts to the characteristics of multi-entity and multi-data-source research reports for multi-holdings, guiding the model to distinguish information from different business segments
`max_history`	Top 5–7 dialogue turns	Avoids overly long dialogue history occupying the context window, while retaining sufficient interaction context for coherent follow-up questions
`chunk_size`	1000–1500 characters	The research report structure includes independent business segment paragraphs, with segment length adapted to precise recall after content splitting

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 specific analysis, and it is recommended to test on your own samples before finalizing.

Three Common Misconfigurations

Phenomenon: A 422 status code is returned when calling the dialogue interface, with no abnormalities detected during separate model testing. Cause: The appId parameter was not passed correctly, or the passed appId format does not meet platform requirements.
Phenomenon: Dialogue records are shared when multiple users access, making it impossible to distinguish conversation content between different users. Cause: The user_id parameter was not configured, or a single global API key was used, with no independent key generated for each user.
Phenomenon: Context confusion occurs after multiple rounds of follow-up questions, or recalled research report content does not match the business of the specified subsidiary. Cause: No targeted system_prompt was configured, or the maxContext value was too small, resulting in critical context being truncated.

How to Verify Proper Configuration

Initiate a single-round query related to research reports, verify that returned content prioritizes internal research report data and marks data sources, to confirm the system_prompt configuration is active.
Initiate consecutive multi-round follow-up questions, verify that the model coherently builds on the previous round's query content, to confirm maxContext and max_history values fit current interaction needs.
Test session access for different users, verify that dialogue records of different users remain isolated, to confirm key and user_id configurations are correct.
Adjust the rag_threshold value, verify that recall result relevance changes meet expectations, to confirm the similarity threshold configuration is active.

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.