HTTP Interface and External Systems for Tendering and

Data Characteristics for This Category

Quality document data for tendering and bidding primarily originates from public resource trading platforms, medical institution procurement platforms, and centralized pharmaceutical and medical device procurement platforms. Data updates frequently, typically coinciding with the release of tender announcements or procurement plans. Some platforms update daily. Documents are often in PDF or Word formats. They contain detailed product technical parameters, quality standards, testing reports, production qualifications, and clinical application data. Key fields include product name, model specifications, registration certificate number, manufacturer, testing agency, quality grade, execution standard, and validity period. Some fields may involve specific units like %, mg/mL, or IU. Significant amounts of unstructured text descriptions are also present.

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

The distributed nature and high update frequency of tendering and bidding data require HTTP interface designs with high concurrency processing capabilities and incremental update mechanisms. This avoids resource waste from full data pulls. Diverse document formats, especially unstructured content in PDFs and Word files, demand high text extraction and parsing capabilities from external systems. This necessitates support for multiple document type parsers. The complexity of fields and units means interface return data should include clear field identifiers and unit information. This facilitates standardized processing by downstream systems. Additionally, some platforms may have access frequency limits or IP blocking policies. HTTP interfaces need built-in retry mechanisms and proxy configurations to ensure data pull stability.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT`	`60 seconds`	Addresses timeout issues when external systems are slow to respond or process complex documents.
`MAX_CONCURRENT_REQUESTS`	`10`	Balances data pulling efficiency with external system load, preventing rate limiting.
`DOCUMENT_PARSE_RETRY_COUNT`	`3 times`	Provides retry opportunities for document parsing failures, improving success rates.
`CHUNK_SIZE`	`800–1200 characters`	Accommodates long descriptions in tendering and bidding documents, ensuring semantic integrity.
`METADATA_EXTRACT_FIELDS`	`registration_certificate_number,manufacturer,quality_grade`	Prioritizes extraction of key structured information to improve recall accuracy.
`HTTP_HEADER_USER_AGENT`	`Mozilla/5.0...`	Simulates browser access, reducing the risk of being identified as a crawler by some websites.

Common Pitfalls

Symptom: HTTP interface returns 429 Too Many Requests error. Reason: Inadequate request interval or concurrency limits configured, triggering external system access frequency limits.
Symptom: After document content parsing, key fields like "registration certificate number" are empty. Reason: The document parser failed to correctly identify text content in PDFs or images, or field matching rules are incomplete.
Symptom: Low recall rate for relevant tendering and bidding documents in knowledge base search results. Reason: CHUNK_SIZE is too small, fragmenting the semantics of long paragraphs, or the similarity_threshold is set too high.

How to Confirm Proper Configuration

Execute an end-to-end data pulling and knowledge base construction process. Check logs for HTTP request failures or document parsing exceptions.
Conduct knowledge base retrieval tests using typical tendering and bidding documents. Observe whether recall results include core information from the documents. Evaluate number_of_recall_items and relevance.
Inspect metadata fields of imported documents in the knowledge base. Confirm that key information such as registration_certificate_number and manufacturer is accurately extracted and populated.
Simulate high-concurrency data pulling scenarios. Monitor external system response times and FastGPT service resource utilization to assess the appropriateness of MAX_CONCURRENT_REQUESTS.

The values provided are common starting points. They 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.