Knowledge Base Retrieval and Recall for Medical Device

What the data for this category looks like

Medical device financing daily report data is primarily sourced from local financial regulatory department disclosure platforms, official announcements of medical device industry associations, and public financing information from securities firm pharmaceutical sector research reports. Updates occur on a daily basis. Single documents are mostly semi-structured tables or structured text, including fields such as financing entity (full name of medical device enterprise), financing round, financing amount, investors, disclosure date, enterprise's affiliated sector (such as medical imaging, in vitro diagnostics), core product direction, and more. Financing amount units are uniformly marked as ten thousand yuan or hundred million yuan, and dates use the YYYY-MM-DD format.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

Multiple data sources require the knowledge base to support cross-platform data format alignment and deduplication, to avoid repeated recall of the same financing information.
The daily update rhythm requires the retrieval link to prioritize recalling the latest disclosed records, requiring configuration of timeliness weights or date-based filtering rules.
The large number of fields and structured attributes (such as financing round, sector) require retrieval to support multi-field precise matching; full-text semantic matching alone cannot guarantee result accuracy.
The standardized units of financing amount and date require a preprocessing link to unify field formats, avoiding matching failures due to unit or format differences.
The concentrated content and clear fields of single documents require document splitting to retain field integrity, avoiding damage to structured information after splitting.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Recall count`	Top 10 entries	Medical device financing daily reports have concise single-document content but multiple updates per day. This setting covers all valid financing information of the day to avoid omissions
`Similarity threshold`	0.75–0.85	Financing entity names and disclosure dates are core matching fields. This range balances precise matching of core enterprises and recall of financing information from related sectors
`Chunk size`	800–1200 characters	Single documents contain multiple structured fields. This length avoids truncating core fields during splitting while ensuring complete contextual semantics
`PARSE_FILE_TIMEOUT_SECONDS`	60 seconds	When importing multiple source documents in batches, sufficient time is required for field extraction, format conversion, and vector generation
`maxContext`	4000 characters	Single financing daily report documents have concentrated content. This length adapts to the context window limits of most large models, avoiding content overflow
`Rerank result count`	Top 5 entries	Financing information disclosed most recently should be prioritized for display. Simplifying to core results after reranking aligns with business reading habits

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. Testing on relevant samples is recommended before finalizing settings.

Three Common Mistakes

Phenomenon: An error localStorage is not defined is returned when calling localStorage.getItem('userId') in the workflow. Reason: The backend execution environment of FastGPT workflow does not support browser-side localStorage API. Session variables or custom parameters should be used instead to pass user identifiers.
Phenomenon: Retrieval results include irrelevant documents, and it is impossible to accurately recall the financing daily report content of a specified medical device enterprise. Reason: No document metadata filtering rules are configured, documents are not bound to corresponding enterprises, and only global semantic matching is relied on.
Phenomenon: Knowledge base retrieval response times out, returning status code 504. Reason: The Recall count setting is too high, or vector retrieval caching is not enabled, resulting in excessive time consumption in similarity calculation and reranking links.

How to Confirm Configuration is Complete

Upload a single medical device financing daily report document, check that parsed fields are complete, and confirm that the Chunk size configuration does not truncate core information.
Initiate a retrieval request for a specific medical device enterprise, verify that the returned document source and enterprise name match, and confirm that the Similarity threshold and metadata filtering rules take effect.
Batch import 10 daily financing report documents, check that parsing and retrieval response times meet business requirements, and confirm that the PARSE_FILE_TIMEOUT_SECONDS and Recall count configurations are reasonable.
For the open-source version V4.8.22, retrieval rules must be configured via workflow nodes; custom prompts and reference templates cannot be adjusted directly in the knowledge base settings page. Verify the effect of the maxContext parameter on context length control.

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.