Tool Calling and Plugins for CMC Research Products

Data Characteristics in CMC Research

CMC (Chemistry, Manufacturing, and Controls) research data originates from laboratory analysis reports, manufacturing batch records, quality standard documents, stability study reports, and regulatory submission materials. This data typically exists as unstructured documents (e.g., PDFs, Word files, scanned images) and semi-structured data (e.g., Excel spreadsheets, CSV files). Data update frequencies vary; batch production records generate with each batch, while stability data updates at preset time points (e.g., 0, 3, 6, 12, 24, 36 months). Document structures are complex, containing extensive specialized terminology, charts, and chemical structural formulas. Fields include substance name, batch number, production date, expiration date, content, purity, impurity profile, solvent residue, pH value, melting point, etc. Units encompass percentages (%), ppm, mg/mL, ℃, hours, days, and often include additional information like detection methods, detection limits, and acceptable ranges.

Constraints on Tool Calling and Plugins from These Characteristics

The multi-source and heterogeneous nature of CMC research data demands robust tool calling and plugin capabilities. Unstructured documents require powerful file parsing to accurately extract key information. For example, FastGPT's file processing plugins need to identify and parse table and text content within PDFs. Semi-structured data requires tools to flexibly adapt to different data formats and field definitions. The uncertainty of update frequency means tool calling needs to support asynchronous processing and callback mechanisms to avoid prolonged blocking. Document complexity, especially chemical structural formulas and specialized terminology, requires tools to effectively process this unique information during text embedding and similarity calculation. This might necessitate customized word vector models or preprocessing steps. The strictness of fields and units constrains tools to ensure data type and unit correctness when extracting and passing parameters; any deviation can lead to incorrect results or misjudgments.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CMC reports often contain numerous charts and scanned images, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF documents and image recognition often requires significant time, preventing parsing interruptions.
`maxContext`	`8192 token`	Ensures that key information from a single CMC report can be accommodated, facilitating model understanding of the context.
`Chunk size`	`800–1200 characters`	Balances context completeness and retrieval efficiency, preventing individual segments from being too long or too short.
`Recall count`	`Top 10 entries`	Increases the likelihood of recalling relevant snippets from the knowledge base, improving answer accuracy.
`Similarity threshold`	`0.75`	Addresses the highly specialized nature and fixed terminology of the CMC domain, increasing the strictness of similarity matching.

Three Common Pitfalls

Tool calling interface returns a 400 Bad Request error, with content indicating Invalid parameter type for 'batch_id'. This might occur if a file type variable in an API call fails to correctly pass the ID after file upload to the corresponding parameter, instead attempting to pass file content or a filename string.
The model frequently responds with "unable to obtain relevant information" or "incomplete information," even when relevant documents exist in the knowledge base. This might occur if the file parsing plugin incompletely extracts table data or specific formatted data from documents, leading to critical fields not being effectively indexed.
When calling a plugin via API with stream set to true, the final result cannot be fully received or reception is interrupted. This might occur if the client's streaming response handling mechanism is imperfect, failing to correctly concatenate all data chunks, or if it lacks breakpoint resumption during network fluctuations.

How to Verify Correct Configuration

Upload a CMC report PDF containing complex tables. Check the segmented content of this document in the knowledge base to ensure table data is accurately identified and converted into retrievable text.
Simulate a CMC product inquiry, asking for key content, impurity information, etc., from the report. Verify that the model's answers match the data in the original document, especially units and numerical values.
Call a plugin via API, passing a request with a file type parameter. Check the logs to ensure the file ID is correctly passed and monitor whether the tool execution returns results normally.

Note: The values provided are common starting points. Measure against your own samples to determine optimal configurations.

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.