Model Access and Configuration for Other Comprehensive

What data for this category looks like

Data sources include two categories: publicly compliant research report platforms and internal institutional research report archives. Update cadence aligns with the research report production rhythm of the publishing entity. No fixed unified cycle exists. The structure of a single document includes title, publishing institution, publishing date, core argument paragraphs, data tables, and investment rating fields. The rating field carries a clear level identifier. The target price field includes the corresponding pricing unit. Word counts for single documents vary widely. Some in-depth research reports have relatively long lengths.

What constraints do these characteristics impose on model access and configuration

Multi-source and heterogeneous research report metadata formats require configuring metadata mapping rules. These rules unify fields from different sources into a standard format recognizable by the model. The wide variation in single research report length, with some content exceeding model context windows, requires configuring parameters for text segmentation and splicing. This ensures input content complies with length limits supported by the model. Research reports include structured fields such as ratings and target prices. Dedicated configuration items for entity recognition and attribute extraction are required. These items ensure the model can accurately locate and process these specific business fields. Non-fixed-cycle research report updates require model calls to flexibly adapt to research report data of different timeliness. Context filtering parameters adapted to timeliness must be configured.

How to set configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–16000 characters`	Adapts to the context window upper limit of most mainstream large models. Covers segmented input requirements for the core content of a single research report.
`chunkSize`	`1200–1500 characters`	Balances information integrity of a single research report segment and model input efficiency. Avoids context overflow caused by overly long segments.
`topK`	`Top 8–10 entries`	Research report content is highly professional. Excessive recall introduces irrelevant redundant information. Insufficient recall fails to cover core reference content.
`similarityThreshold`	`0.75–0.85`	Filters low-relevance research report retrieval results. Ensures matching degree between returned content and query requirements.
`extractEntityPrompt`	`Extract the publishing institution, publishing date, investment rating, target price and corresponding pricing unit of the research report`	Matches structured field characteristics of this category of research reports. Ensures the model can accurately extract business-related entities.
`streamResponseEnable`	`Enabled`	Improves interactive response experience in long-text question-and-answer scenarios. Meets real-time interaction requirements for research report retrieval.

The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. Testing on self-provided samples is recommended before finalizing settings.

Three common configuration mistakes

Symptom: After modifying config.json and restarting the container, the model list does not update. Cause: The configuration file mapping path is not correctly mounted, or the modified configuration does not overwrite the corresponding file in the /app/data/config directory inside the container.
Symptom: When calling the model, the error "Model stream response is empty, please check if the model stream output is normal" is returned. Cause: The model's stream response switch is not enabled, or the configured baseUri does not match the model's actual interface, resulting in failure to obtain streaming output.
Symptom: The text understanding model fails to correctly extract structured fields from research reports after configuration. Cause: No entity extraction prompt tailored for the structured fields of research reports is used. A general entity extraction configuration is used, which cannot adapt to the specific field format of research reports.

How to confirm successful configuration

Enter the FastGPT model management interface, view the configured model list, and confirm that the target model is displayed and in an enabled state.
Submit a test query, enter a research report-related question, and check whether the returned results include structured entity information of research reports, and the number of results conforms to the configured topK range.
View the model call logs, confirm that the streamResponseEnable configuration takes effect, and there are no empty responses or timeout errors.
Adjust the similarity threshold, and verify whether the relevance change of retrieval results meets expectations.

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.