Data Characteristics
Data for product and reagent inquiries in biomedical retail chains primarily comes from pharmaceutical manufacturer product manuals, batch inspection reports, internal pharmacy training materials, store promotion details, and customer FAQs. These documents update frequently, especially with new product launches, drug batch updates, or promotional adjustments. Document structures typically include standardized fields such as product name, generic name, specifications, batch number, production date, expiration date, indications, dosage, contraindications, adverse reactions, storage conditions, and manufacturer. Units include milligrams, milliliters, tablets, boxes, bottles, degrees, and Celsius, often accompanied by complex medical terminology and professional abbreviations.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
High-frequency updates to product manuals and batch reports require the parsing system to quickly identify and process document version differences, preventing information lag. The large number of standardized fields and specialized terms means simple text chunking can truncate critical information or lose context, affecting subsequent retrieval accuracy. For example, drug dosage information often contains multiple values and units that need to be parsed as a whole. Promotional activity details have variable document structures, potentially including tables, images, and unstructured descriptions, which challenges the robustness of parsing tools. Furthermore, different document types (e.g., manuals vs. training materials) vary in information density and key information distribution, requiring fine-tuned chunking strategies to ensure each chunk has independent semantic meaning.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_overlap | 50–100 characters | Ensures contextual continuity and prevents critical information from being truncated at chunk boundaries. |
chunk_size | 400–600 characters | Balances retrieval granularity and information completeness, suitable for paragraph lengths in drug instructions. |
parser_strategy | recursive_character | Suitable for processing structured and semi-structured documents, effectively handles tables and lists. |
embedding_model | text-embedding-ada-002 or equivalent | Provides high-quality vector representations, improving similarity matching accuracy for specialized terms and medical concepts. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses documents containing many images or complex tables, preventing parsing timeouts. |
max_tokens_per_chunk | 1024 | Adapts to the context window limitations of mainstream large language models, preventing excessively large chunks. |
Three Common Mistakes
- Document upload shows a parsing failure, or the parsing result is empty. This occurs due to incompatible file types or
PARSE_FILE_TIMEOUT_SECONDStiming out because the document content is too large. - After a user query, the AI response lacks or inaccurately presents key product information. This happens when
chunk_sizeis set too small, causing information like drug names, specifications, and dosages to be split across different chunks. - Duplicate document block IDs exist in the knowledge base, leading to indexing confusion. This is caused by improper handling of duplicate content after custom splitting or when the system's default deduplication logic conflicts with custom indexing requirements.
How to Confirm Proper Configuration
- Upload typical documents (e.g., new drug instructions, batch reports). Check parsing logs to ensure no timeout errors and that the number of chunks meets expectations.
- Randomly select parsed document chunks. Manually verify their content for completeness and independent semantics, especially for critical drug information.
- Conduct simulated queries for common questions. Evaluate the accuracy and completeness of product information in AI responses and adjust the
similarity thresholdbased on retrieval results. - Check the update status of different document versions in the knowledge base. Confirm that parsing and indexing of old and new version documents are correctly differentiated and managed.
Note: The values provided are common starting points and should be measured against specific 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.