HTTP Interface and External Systems for Peptide Drug

Data Characteristics for This Category

Peptide drug regulations and Standard Operating Procedure (SOP) documents typically originate from technical guidelines published by drug regulatory agencies, pharmacopoeia standards, internal corporate quality management system documents, and clinical trial protocols. These documents have a relatively low update frequency, usually revised in response to policy changes or new drug development, with an update cycle of 1–3 years. Document structures are primarily PDF, Word, or XML formats. Content includes detailed production processes, quality control indicators, testing methods, stability study requirements, and batch release standards. Fields and units are highly specialized, such as peptide sequence, purity (%), molecular weight (Da), pH value, chromatographic retention time (min), impurity limits (ppm or %), and specific biological activity units (IU or U).

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The low update frequency of peptide drug regulation documents means that when designing HTTP interfaces, the data retrieval strategy can favor periodic full synchronization, reducing the complexity of real-time incremental updates. The diverse document formats (PDF, Word, XML) require external systems to have robust document parsing capabilities, especially for recognizing and extracting complex tables and charts. Specialized fields and units demand higher requirements for structuring and standardizing interface return data. This ensures FastGPT accurately identifies and vectorizes critical information upon data reception, for example, correctly associating "purity" with "%". Additionally, long text fields like peptide sequences may exceed conventional text segmentation lengths, requiring fine-tuning of maxContext and segmentation strategies to prevent information loss or context fragmentation.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Peptide drug regulation documents often contain numerous charts and appendices, leading to larger file sizes.
`Chunk size` (Segment Length)	`1000 characters` (characters)	Ensures context completeness for long texts like peptide sequences and process steps.
`Recall count` (Recall Count)	`8 entries` (items)	Regulatory Q&A demands high accuracy and comprehensiveness; increasing recall count improves coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	Strictly controls relevance, preventing the introduction of irrelevant regulatory clauses.
`Rerank result count` (Reranked Return Count)	`4 entries` (items)	Selects the most relevant regulatory clauses, reducing model processing burden and improving response quality.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Large files require longer parsing times; this provides sufficient processing time.

Three Common Mistakes

Symptom: HTTP interface returns a 400 error, indicating "one or more parameters specified in the request a". Cause: The JSON structure or parameter names in the request body from the external system do not match FastGPT's interface definition.
Symptom: Key fields like peptide purity or molecular weight are empty or incorrectly identified in knowledge base retrieval results. Cause: The document parsing stage failed to correctly recognize table structures in PDF or Word, preventing the structured extraction of critical numerical data.
Symptom: When users ask about specific peptide preparation processes, the returned results lack key steps or contextual information. Cause: The text segmentation strategy was too aggressive, splitting long process descriptions and preventing individual segments from providing a complete logical chain.

How to Confirm Proper Configuration

Upload typical peptide drug regulation documents. Check if FastGPT's knowledge base correctly segments the document and identifies key fields (e.g., peptide sequence, purity).
Use FastGPT's debugging interface to simulate questions about specific peptide quality standards or testing methods from the document. Observe if the recalled content accurately covers the original text.
Check HTTP interface call logs to confirm no 4xx or 5xx error codes are present and that data transfer rates meet expectations.
Use FastGPT's knowledge base testing feature to test complex Q&A regarding peptide regulations. Evaluate the accuracy and completeness of responses, then adjust Similarity threshold (Similarity Threshold) or Rerank result count (Reranked Return Count) based on test results.

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.