Vector Model and Indexing for Tender Bidding and

Data Characteristics for This Category

Tender bidding documents primarily include tender announcements, award results, procurement catalogs, technical specification requirements, pricing information, and enterprise qualification documents for pharmaceuticals and medical devices. Data sources are typically official websites of public resource trading centers, centralized pharmaceutical and medical device procurement platforms, and medical insurance bureaus. Update frequency is high; some provincial platforms may release new tender batches or award results weekly or even daily. Document structures vary, often appearing as PDFs, Word documents, Excel spreadsheets, and scanned images are also common. Fields include product name, generic name, specification model, manufacturer, registration certificate number, dosage form, quotation, winning bid price, procurement cycle, and supply area. Units involve Yuan, boxes, tablets, pieces, ml, mg, etc. The naming of the same field can differ across platforms.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The multi-source and heterogeneous nature of tender bidding documents demands generality and robustness from vector models. High update frequency requires the index to support efficient incremental updates and real-time synchronization; otherwise, information lag can occur. Diverse document types, especially scanned images and complex tables, challenge text extraction and structured processing capabilities, directly impacting vectorization quality. Inconsistent field naming and diverse units require the vector model to possess generalization capabilities in semantic understanding to avoid recall failures due to differing expressions. Long tender announcements and technical specification documents necessitate appropriate chunking strategies to ensure semantic integrity while preventing individual text blocks from becoming too large, which affects vector generation efficiency and accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances semantic integrity with vector model processing efficiency, reducing semantic loss from long text truncation.
`Chunk Overlap`	`50–100 characters`	Ensures contextual continuity at paragraph boundaries, improving recall accuracy for cross-paragraph information.
`Similarity Threshold`	`0.75–0.85`	Addresses the prevalence of specialized terminology and semantically similar words in the tendering domain, balancing recall and precision.
`Recall Count`	`8–12 items`	Ensures information coverage while avoiding the introduction of excessive irrelevant information that could affect subsequent generation.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Handles long parsing times for large PDF or scanned documents, preventing parsing timeouts.
`EMBEDDING_BATCH_SIZE`	`32`	Optimizes vector model inference performance, balancing GPU memory usage and processing throughput.

Three Common Mistakes

The knowledge base remains in an indexing state for an extended period without progress. This usually occurs because PARSE_FILE_TIMEOUT_SECONDS is set too low, causing large or structurally complex tender documents to time out during parsing, failing to be chunked and vectorized.
Chat results fail to recall relevant tender information, even if the document is present in the knowledge base. This might be due to incomplete text extraction, especially if scanned documents were not OCR processed, preventing the vector model from generating effective vectors from blank text.
Recalled tender information has poor relevance to the query intent, containing a large amount of irrelevant content. This typically happens when the Similarity Threshold is set too low, or Chunk Length is too long, leading to overly broad semantic meaning in a single text block.

How to Verify Configuration

Upload a typical tender announcement PDF document and check its parsing status in the FastGPT backend. Confirm successful indexing and document content preview.
Formulate retrieval queries based on specific product names, specifications, or technical parameters within the uploaded document. Observe the Similarity scores of the recall results; scores should be higher than the configured Similarity Threshold.
Use queries containing typos or synonyms to test the system's ability to recall relevant document snippets, verifying the vector model's semantic understanding capabilities.
Simulate high-concurrency upload scenarios and monitor logs for PARSE_FILE_TIMEOUT_SECONDS-related errors to ensure stable file parsing services.

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

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.