Policy and Regulatory Basis Q&A for Urban Investment Platforms: Knowledge Base Retrieval and Recall

Policy and Regulatory Basis Q&A for Urban Investment Platforms: Knowledge Base Retrieval and Recall

What This Category’s Data Looks Like

Policy and regulatory basis data for urban investment platforms mainly comes from internal operation management systems, special urban investment policies issued by local finance and housing and urban-rural development departments, and normative documents from superior state-owned assets supervision institutions. Updates are completed within 1 to 3 working days after official policy release, with incremental updates as needed. Document structure includes fields such as document number, issuing authority, release date, applicable scope, and main content. Document numbers follow the standard format of "XX Finance Preparatory [202X] XX Number". Release dates use the YYYY-MM-DD format. Main content consists of long texts spanning several thousand characters.

Constraints Imposed on Knowledge Base Retrieval and Recall

The characteristics of urban investment platform policy data impose multiple constraints on the retrieval and recall process. Official source requirements mandate that recall results strictly match original text to avoid generalized deviations. Long main text requires chunking to preserve contextual relevance, preventing disruption of policy logic. Structured fields (document number, issuing authority) require prioritizing key identifiers during retrieval to improve accuracy. Irregular update cycles require retrieval configurations to support dynamic filtering of currently valid knowledge bases, avoiding recall of outdated or invalid policy documents.

Configuration Settings

Configuration ItemRecommended SettingRationale
similarity0.75–0.85Meets the rigor requirements for urban investment policies, prevents irrelevant policies from being included in recall results
limitTop 8 entriesControls context length per round of Q&A, adapts to display needs for long policy documents
searchModeExact MatchAdapts to exact retrieval requirements for structured fields such as policy document numbers and issuing authorities
usingReRankEnabledOptimizes relevance ranking after long document chunking, improves display priority of core policy fragments
datasetSearchUsingExtensionQueryEnabledSupplements policy-related keywords to expand retrieval coverage scope
collectionIdsBind exclusive knowledge base ID for urban investment policiesIsolates knowledge base data from other categories, prevents mixing of retrieval results

Three Common Misconfigurations

  • Phenomenon: A prompt stating "No knowledge base selected" pops up in the chat window after triggering Q&A. Cause: The urban investment policy-exclusive knowledge base collection is not bound in the session configuration, or the bound collectionIds parameter points to an incorrect knowledge base ID.
  • Phenomenon: When entering an unreferenced policy-related question, the system directly returns that it cannot answer, without triggering generalized model inference. Cause: The datasetSearchUsingExtensionQuery configuration is not enabled, or searchMode is set to exact match only, retaining no generalized inference logic.
  • Phenomenon: Policy documents cannot be classified and managed by document number or issuing department during import. Cause: The folder function in the knowledge base is not used correctly, and urban investment policy documents of the same batch or type are not categorized into corresponding folders, making accurate filtering of target files impossible during retrieval.

How to Confirm Configuration Is Complete

  • Enter the FastGPT v4.17.0 knowledge base management page, check that the knowledge base corresponding to collectionIds only contains urban investment policy-related files, with no redundant data from other categories.
  • Initiate a test Q&A, enter a known urban investment policy document number, confirm that the recall result includes an accurate fragment of the corresponding file, and that the similarity matches the preset threshold.
  • Adjust the similarity parameter to 0.8, enter a vaguely worded policy question, confirm that the relevance of the recall result meets expectations, with no irrelevant entries included.
  • After enabling usingReRank, compare the result ranking before and after reranking, confirm that core policy fragments of long documents are displayed first.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.