What the data for this category looks like
Document data for state-owned industrial funds comes from several sources. These include filing documents from state-owned asset supervision departments, internal operation ledgers of fund managers, project due diligence reports, capital contribution agreements, and annual operation reports. Data updates align with business milestones. Updates trigger when projects initiate, capital contributions complete, and annual reviews take place. Regulatory filing documents adjust to match policy requirements. Documents have mixed structures. They include structured fields such as fund code, capital contributor type, invested industry classification, paid-in amount, and term. They also have semi-structured agreement and due diligence paragraphs, plus unstructured meeting minutes and communication records. Fields have clear unit attributes. For example, amounts use ten thousand yuan or hundred million yuan as units.
What constraints these characteristics impose on knowledge base retrieval and recall
Precise matching for structured fields requires retrieval to support field-based semantic association. This prevents mixing amount data with different units. Non-periodic update timing requires the retrieval system to support incremental synchronization. Full reindexing is unnecessary. Wide variation in document length requires chunking strategies to adapt to short agreements and long reports. This avoids context truncation or redundant content. Decentralized data from multiple sources requires precise limitation of retrieval scope. This prevents mixing in fund documents from other entities. Extensive use of specialized terminology and abbreviations requires retrieval to support semantic expansion. This covers industry-standard expressions.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | State-owned industrial fund documents use dense specialized terminology. A higher similarity threshold filters irrelevant general regulatory documents, ensuring retrieval result relevance. |
limit | Top 8–12 results | Fund-related reference documents have a long total length. Too many results exceed the context window. Too few results miss critical project details. This range balances recall volume and context capacity. |
searchMode | Hybrid retrieval | Data includes structured ledger fields and unstructured due diligence reports. Hybrid retrieval balances precise field matching and full-text semantic recall. |
usingReRank | Enabled | Semantic matching for specialized terminology may have deviations. Reranking performs a second sort based on content relevance, improving result accuracy. |
datasetSearchUsingExtensionQuery | Enabled | The fund industry uses extensive abbreviations and industry terms. Extended queries supplement synonym matching, covering more relevant documents. |
collectionIds | Bind the IDs of the three dedicated datasets: fund filings, due diligence, and operation ledgers | Limit retrieval scope to only the current fund’s dedicated data, preventing mixing in irrelevant documents from other entities. |
Three common mistakes
- Phenomenon: Imported Excel structured ledgers cannot return precise matching results based on fields such as fund code and paid-in amount. Cause: The Excel was imported only as a regular text dataset, and structured field mapping configuration was not enabled. This blocks semantic matching based on fields.
- Phenomenon: Multiple variables returned by an HTTP request in a workflow cannot bind to the designated state-owned industrial fund knowledge base. Cause: The
collectionIdsparameter was not specified in the knowledge base retrieval node, or returned variables were not correctly mapped to corresponding dataset IDs. - Phenomenon: Retrieval results do not include associated state-owned industrial policy auxiliary documents. Cause: The policy auxiliary dataset was not added to the retrieval scope, or vectorization index configuration for the auxiliary dataset was not enabled.
How to confirm the configuration is correct
- Upload a structured fund ledger Excel file, run a retrieval, and check whether corresponding documents are accurately recalled based on fields such as fund code and paid-in amount.
- Configure an HTTP request node and a knowledge base retrieval node in the workflow, pass preset variables to run a test, and check whether retrieval results from the designated dataset are correctly bound.
- View the dataset index management interface, confirm that all associated fund-specific datasets (filings, due diligence, ledgers, policies) have vectorization indexing enabled and are added to the retrieval scope.
- Adjust the
similaritythreshold, run multiple retrievals, and verify result relevance to confirm the threshold configuration meets the business scenario’s matching needs.
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.