What the data for this category looks like
Data sources for industrial park research reports in the financial sector mainly include annual/quarterly operation reports publicly released by government park management departments, special survey documents from third-party industrial consulting institutions, and investment promotion and revenue announcements disclosed by park operators. There is no unified update cycle. Institutional research reports are mostly updated monthly or quarterly, while park self-released announcements are posted alongside operational updates. Document structures typically include fields such as park location overview, settled enterprise list, land use area, annual revenue data, and policy support details. Units are mostly square meters, ten thousand yuan, and integer counts. There is no unified standard format, and some documents contain nested multi-chapter detailed data.
What constraints do these characteristics impose on the citation source and traceability link
The multi-source and scattered nature of industrial park research report data requires the traceability link to mark both the publishing institution and publishing time, to avoid confusion of similar data from different institutions. The uncertain update rhythm requires traceability information to include the data statistical cycle, to ensure the timeliness of cited content. The non-standardized and multi-field document structure requires extracting core metadata as a traceability basis, to avoid positioning deviations caused by relying only on document titles. The intensive use of professional terminology requires the similarity matching threshold to adapt to the text characteristics of vertical fields, preventing irrelevant content from being mistakenly associated as valid citations.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 8-12 entries | Industrial park research report data is vertical and has a manageable volume. Excessive recall increases traceability screening costs, while insufficient recall may miss key data for segmented scenarios |
similarity_threshold | 0.75-0.85 | Industrial park research reports contain a large number of professional terms. A threshold that is too low will introduce irrelevant documents, while a threshold that is too high will miss segmented data from the same field |
source_tag_field | Publishing institution + publishing date | Sources of industrial park research reports are scattered. Two-dimensional marking can accurately distinguish content from different data sources and avoid traceability confusion |
metadata_include_fields | Park name, statistical cycle, land area, number of settled enterprises | The above fields are core identification information for industrial park research reports, and can assist in quickly locating the owning document of cited content |
reference_id_mode | According to document chunk + metadata hash | Ensures that each citation chunk generates a unique traceable identifier and prevents the generation of fake citation IDs |
log_source_separate | Enabled | Facilitates distinguishing retrieval requests from different knowledge bases in Mongodb logs and quickly troubleshooting traceability-related issues |
The parameter values provided on this page are common starting points for configuration. Actual values depend on material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: The citation variable dropdown box in the knowledge base search node is empty, and the target knowledge base cannot be dynamically specified. Cause: The knowledge base permission configuration of global variables is not enabled, or the variable type is not set to the knowledge base selector.
- Phenomenon: Retrieval requests from different industrial park knowledge bases cannot be distinguished in Mongodb logs. Cause: The
log_source_separateparameter is not configured, or the knowledge base ID is not written to the request log as a log field. - Phenomenon: Fake citation IDs that cannot be mapped to actual documents appear in the answers generated by the large model. Cause: The
reference_id_modeis not set to document chunk + metadata hash, or the metadata fields are not correctly associated with the retrieved document chunks.
How to confirm the configuration is complete
- Initiate an industrial park research report retrieval task, check the citation tags in the returned results, and confirm that each citation contains the content of the configured
source_tag_fieldfield. - Check the Mongodb retrieval logs, confirm that each request includes the corresponding fields of the knowledge base ID and retrieval keywords, and requests from different knowledge bases can be distinguished through log fields.
- Trigger a complete answer generation process, check whether the citation IDs in the answer match the metadata hash values of the retrieved document chunks, and there are no invalid fake citations.
- Test the workflow for dynamically specifying the knowledge base, confirm that the citation variable dropdown box can normally display the configured knowledge base variable options.
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.