Characteristics of the Data Category
R&D documents for high-value consumables primarily come from internal R&D reports, experimental records, design specifications, risk assessments, registration application materials, and supplier-provided raw material technical specifications. These documents are updated infrequently, typically with product iterations or regulatory changes. Most documents are unstructured or semi-structured text, such as Word or PDF reports. They contain numerous charts, images, and complex layouts. The text includes specialized terminology, acronyms, chemical formulas, physical parameters, and units (e.g., MPa, μm, kg/m³). Different documents may also use varying expressions for the same concept.
Constraints Imposed by These Characteristics on "Vector Models and Indexing"
The low update frequency of high-value consumable R&D documents means index reconstruction can occur less often. However, initial indexing requires processing large volumes of historical data. The complex unstructured and semi-structured document structures demand strong text parsing capabilities from vector models to accurately extract key information and process text within charts and images. The presence of specialized terminology, acronyms, and varying expressions increases the difficulty for vector models to understand semantics, potentially leading to similarity calculation deviations. Furthermore, documents containing precise numerical values and units require higher accuracy and recall capabilities from vector indexes. This prevents retrieval results from being affected by incorrect numerical or unit identification. These constraints collectively dictate that vector model and indexing configurations must prioritize text segmentation strategies, model selection, and fine-tuned recall and re-ranking.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances semantic completeness with vector model processing capacity. Avoids diluting key information in overly long texts and losing context in overly short texts. |
Chunk Overlap Length | 100–200 characters | Ensures context continuity, reducing semantic breaks at segment boundaries, especially for R&D documents with tightly linked paragraph logic. |
Recall count | Top 10–15 entries | Balances retrieval efficiency and coverage. Can be increased initially, then filtered for the most relevant results using a re-ranking mechanism. |
Similarity threshold | 0.75–0.85 | Sets a higher threshold for the specialized nature of high-value consumable R&D documents, ensuring strong relevance of recalled results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Extends file parsing timeout when processing large PDF or Word documents, preventing parsing failures due to file complexity. |
maxContext | 3500–4000 token | Ensures sufficient context information for question answering, preventing incomplete answers due to context truncation. |
Three Common Mistakes
- The knowledge base displays "training" or "rebuilding" for extended periods, preventing question answering or index switching. This may be due to a backlog in the backend file parsing or vector embedding task queues, or a timeout for a single file parsing.
- During question answering, the model fails to provide relevant responses even when the knowledge base contains clear answers. This manifests as missing key information or generic replies. This may be due to improper segmentation strategies, leading to key information being split or context loss, which affects vector retrieval accuracy.
- R&D documents containing complex tables or figures result in missing or malformed text content after parsing, leading to poor vectorization quality. This occurs because default file parsers inadequately support complex layouts, failing to effectively extract all text information.
How to Confirm Proper Configuration
- Preview core R&D documents through the FastGPT interface. Check if the parsed text content is complete and correctly formatted, paying close attention to text descriptions next to charts and key parameters.
- Use FastGPT's debug mode. Input specific questions and examine the content of the recalled source text blocks. Determine if they contain the key information required for the question and evaluate the relevance of the recalled items.
- In the FastGPT knowledge base management interface, check the knowledge base's index status. Confirm it displays "completed" or "available," and that the historical tasks do not show numerous file parsing failures or vector embedding failures.
The values provided are common starting points and should be measured against your 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 2026-09-21.