What the Data for This Category Looks Like
Business data for pharmaceutical distribution originates from drug purchase and warehousing receipts, outbound circulation records, cold chain transport logs, business qualification approval documents, medical insurance settlement vouchers, and similar sources. Data update cycles vary significantly: purchase and outbound data is updated in real time or daily, while business qualification documents are updated quarterly or annually. Document structures include both structured fields and unstructured content. Structured fields include drug generic names, specifications, batch numbers, destination units, transaction times, and more; units include boxes, cases, and degrees Celsius. Unstructured content consists of long text or mixed text-and-image content such as approval process descriptions, transport anomaly reports, and qualification scan copies.
Constraints Imposed on Vector Models and Indexes
Multi-source mixed structured and unstructured data requires chunking strategies to distinguish between field types, to avoid vector representations confusing business attributes and text content. Frequently updated business data requires indexes to support incremental updates, to adapt to real-time circulation data for business process guidance. Long documents such as annual circulation summaries and multi-batch transport records consume significant vector model input length, so chunking parameters must be matched to the model’s token limit. The existence of unique identifier fields such as batch numbers requires vector retrieval to combine exact matching logic, to avoid irrelevant results interfering with process guidance. Documents with mixed text and images require vector models that support multimodal input, to adapt to non-text data such as qualification scan copies.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of pharmaceutical distribution documents, which include both short identifier fields and long process text, to avoid information overload in single chunks |
| Overlap Length | 50–100 characters | Covers cross-chunk process step associations, to avoid loss of breaks in business process guidance |
| Similarity Threshold | 0.72–0.85 | Matches the field uniqueness requirements of pharmaceutical data, to filter low-relevance non-target business content |
| Single Search Limit | Top 10 results | Adapts to the precision needs of business process guidance, to avoid excessive redundant results interfering with guidance |
defaultToken | 8192 | Adapts to the input length limits of mainstream vector models, and is compatible with the chunk lengths of most pharmaceutical business documents |
maxToken | 16384 | Supports complete vector generation for long documents such as annual circulation summaries, to avoid truncation of critical approval information |
| Vector Model Selection | Alibaba multimodal-embedding-v1 | Supports vector generation for multimodal documents in pharmaceutical distribution scenarios, such as qualification scan copies and transport record images |
Three Common Misconfigurations
- Phenomenon: Only single chunk results are returned during API calls, and the complete list of chunk indexes cannot be retrieved. Cause: The chunk index storage switch in FastGPT is not enabled, resulting in only a single vectorized result being cached.
- Phenomenon: The vectorization service returns an error due to mismatched parameter formats. Cause: The chunk array is mistakenly passed as a single text slice, not following FastGPT's single-text vectorization configuration logic.
- Phenomenon: A large number of irrelevant non-distribution business documents appear in retrieval results. Cause: The similarity threshold is set too low, failing to filter low-match irrelevant data.
How to Verify Proper Configuration
- Upload a typical pharmaceutical procurement process document, and check whether the corresponding number of chunk indexes are generated in the vector generation logs of FastGPT v4.17.0.
- Call the retrieval interface, pass a known drug batch number keyword, and verify whether the returned results include the content of the corresponding chunks.
- View the FastGPT model configuration page, and confirm that the values of
defaultTokenandmaxTokenmatch the official parameters of the selected Alibaba multimodal-embedding-v1 model. - Simulate a business process query, and check whether the returned process steps are coherent, with no breaks or redundant content.
The values provided here are common starting points, and should be evaluated against local 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.