Knowledge Base Retrieval and Recall for CMC Research Quality

Data Characteristics

CMC research data originates from laboratory records, analysis reports, batch production records, stability study reports, and registration submission documents generated during drug development. These documents have a relatively low update frequency, typically revised at project milestones or when regulatory requirements change. Document structures are rigorous, often in PDF, Word, or structured database formats. They contain numerous charts, chemical structures, experimental data, testing methods, and results. Fields and units are highly specialized, such as "Purity (%)", "Content (mg/mL)", "pH value", and "Melting point (°C)". They often involve unique identifiers like specific batch numbers and instrument serial numbers.

Constraints on Knowledge Base Retrieval and Recall

The low update frequency of CMC research documents means that after knowledge base construction, focus shifts to the efficiency of incremental updates, avoiding frequent full rebuilds. The rigorous structure and multi-format nature of the documents require robust document parsing capabilities, especially for extracting text embedded in charts and chemical structures. Highly specialized fields and units demand high domain adaptability from tokenizers and embedding models. Generic models may struggle to accurately recognize the semantic equivalence of "Purity (%)" and "Purity percentage." Additionally, the presence of unique identifiers like batch numbers makes exact matching and precise recall critical for specific queries, requiring prevention of semantic ambiguity that leads to incorrect recall.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Ensures capture of complete experimental procedures or analysis results, while preventing overly large segments from diluting the topic.
`overlap_size`	`100–200 characters`	Maintains contextual continuity between paragraphs, especially across pages or sections, aiding in the recall of complete information.
`recall_count`	`top 8–15 items`	CMC queries often require cross-validation from multiple angles. Increasing recall count improves coverage but needs to balance re-ranking computation costs.
`similarity_threshold`	Calibrate based on measurements	Requires adjustment based on the specific embedding model and dataset to ensure highly relevant documents are recalled while filtering out low-relevance noise.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses long parsing times for large PDF or Word documents, preventing document upload failures due to timeouts.
`embedding_model`	Domain-specific or large model	Enhances understanding of domain knowledge like specialized terminology and chemical structure nomenclature, improving the accuracy of vector representations.

Common Pitfalls

Query results lack critical experimental data or method steps. This occurs when document parsing fails to accurately extract text information from charts or tables, leading to missing raw data in the knowledge base.
Queries for specific batch numbers return information for other batches. This happens when the tokenizer fails to recognize batch numbers as distinct entities, or the embedding model cannot differentiate the uniqueness of different batch numbers, leading to semantic matching confusion.
The HTTP request workflow fails to trigger online search and directly returns an AI conversation result. This indicates that the conditional logic in the workflow configuration did not correctly identify the query intent, for example, if the search_query field was empty or did not meet the trigger conditions.

Verification Steps

Select a batch of complex documents containing charts, tables, and chemical structures. Upload them to the knowledge base and inspect the segmented content to confirm that key information is fully extracted.
Perform precise queries for specific batch numbers, compound names, or testing methods. Verify that the recall results accurately point to relevant documents and paragraphs.
Simulate queries with online search intent within the workflow. Observe the log output to confirm that the http_request module is correctly invoked and returns external data.
Use a set of test questions covering different professional terms and expressions. Evaluate the relevance ranking of recalled documents to ensure that highly relevant documents are prioritized.

Note: The values provided are common starting points. Measure performance 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.