Data Characteristics for This Category
Retail chain quality document data typically originates from daily store operations, supplier qualifications, product inspection reports, and internal audit records. Data updates frequently. For example, store self-inspection records might update daily, and product batch inspection reports change with each procurement batch. Document formats vary, including PDF business licenses, Word training manuals, Excel checklists, and structured data stored in enterprise internal systems. Fields and units are industry-specific. Examples include product batch numbers, production dates, expiration dates, storage temperatures (Celsius), and microbial indicators (CFU/g). These fields often require precise matching and parsing.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The multi-source and heterogeneous nature of retail chain quality documents requires tool calls to handle various data formats. High-frequency data updates demand real-time or near real-time trigger mechanisms to ensure information timeliness. For instance, when a store uploads a new self-inspection report, the system should immediately trigger relevant review processes. Unique fields and units in documents place higher demands on tool parameter passing and data validation, ensuring accurate identification and transmission of critical information when calling external tools. Additionally, many documents contain non-textual information like images and tables. This requires tools to effectively extract or perform OCR during preprocessing for subsequent knowledge base retrieval or external API calls.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800 characters | Ensures sufficient context within a single document segment while balancing retrieval efficiency. |
Recall count (Retrieval Count) | Top 5 entries | Balances retrieval accuracy with computational resource consumption. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out low-relevance results to avoid introducing noise. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDFs or complex Excel documents. |
http_tool.max_retries | 3 | Increases stability of external API calls, addressing network fluctuations. |
KB Preprocessing Mode | Smart Chunking | Enhances parsing capabilities for mixed-format documents. |
Common Pitfalls
- Passing empty or type-mismatched parameters when calling external tools leads to
400 Bad RequestAPI errors. This occurs when specific document fields are not correctly extracted or converted. - Integrating a RAG knowledge base with external tool calls in a workflow results in the knowledge base retrieval not effectively passing results as input to the tool. This manifests as the tool's execution lacking relevant context, due to improper data flow configuration in the workflow.
- Document parsing timeouts cause file upload failures or processing stagnation, with error code
504 Gateway Timeout. This happens whenPARSE_FILE_TIMEOUT_SECONDSis set too low, failing to cover the parsing requirements of large or complex documents.
Verification Steps
- Upload typical documents (e.g., Excel with tables, image-heavy PDFs). Check if knowledge base segmentation results are complete and semantically coherent.
- Simulate a complete tool calling process. Review parameter passing in log outputs to confirm all critical fields are correctly identified and include units.
- Execute a workflow that includes RAG and tool calls. Verify that the input to the tool call contains contextual information from the knowledge base. Observe the accuracy and relevance of the tool's return results.
Note: 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.