What the Data for This Category Looks Like
E-commerce service investment research data originates from e-commerce platform merchant backends, third-party crawler tools, public industry analysis documents, and real-time transaction monitoring APIs. Data update frequencies cover real-time (user reviews, real-time sales), daily (product pricing, inventory changes), and weekly (industry market data). Most individual documents are structured product detail pages, competitive benchmarking reports, and aggregated user comments. These documents include fields such as SKU codes, product names, selling prices, monthly sales volumes, user ratings, and keyword tags. Units include pieces, yuan, percentages, and person-times, among others.
Constraints on Deployment and Upgrade
The real-time and high-frequency update requirements of e-commerce service investment research data require the deployment phase to support incremental synchronization and low-latency vector updates. This avoids resource consumption caused by full index rebuilding. Mixed access to multiple types of structured documents requires deployment configurations that support multi-field metadata extraction rules. This prevents generic parsing modules from failing to recognize dedicated fields such as SKU codes and sales volumes. The coexistence of massive SKUs and long-text industry reports requires the upgrade phase to reserve sufficient vector database storage space and parallel parsing thread pools. This prevents parsing timeouts or reduced retrieval efficiency. Additionally, access to real-time transaction data requires dedicated API rate limiting configurations. This avoids exceeding the call thresholds of e-commerce platform APIs.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | E-commerce product detail pages and industry reports are mostly long-text, requiring sufficient parsing time |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some industry reports and bulk product documents have large file sizes, requiring adaptation to bulk access needs |
maxContext | 800–1200 characters | Core information for e-commerce product documents and reviews falls within this range, avoiding redundant content interfering with retrieval |
Number of retrieved entries | Top 8–12 entries | E-commerce investment research requires multi-dimensional reference to competing and in-house products. Excessive entries increase inference latency |
Number of reranked returned entries | Top 3–5 entries | Investment research decisions focus on core reference content, avoiding excessive results that distract from key points |
VECTOR_DB_BATCH_SIZE | 20–30 | The number of e-commerce SKUs is large. Batch insertion balances write efficiency and resource consumption |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on local samples prior to finalizing setup is advised.
Three Common Configuration Mistakes
- Frontend unresponsiveness occurs when calling APIs to handle investment research tasks, while system resources and container status remain normal. The root cause is failure to configure the
maxConcurrentWorkflowsparameter. Concurrent requests exceed the upper limit of the workflow thread pool, leading to task queue blocking. - An
Invalid URL (POST /v1/rerank)error appears in the interface when connecting to an external reranking model. The root cause is incorrect configuration of the model interface address and path, with the reranking interface path not completed to the standard format. - Knowledge base response speed is too slow after deploying the large language model. The root cause is failure to configure the
contextWindowandbatchSizeparameters, and failure to enable the model inference quantization mode. This causes individual requests to occupy excessive video memory resources.
How to Verify Configurations Are Correct
- Upload the largest single e-commerce document. Check whether the parsing task completes within the preset
PARSE_FILE_TIMEOUT_SECONDSwithout timeout errors. - Initiate test requests at multiple times the preset concurrency level. Observe the workflow task queue status to confirm no task blocking or container resource overload.
- Initiate a test call after connecting to the reranking model. Check whether the interface return results include correct reranked sorting and score fields.
- Import bulk e-commerce SKU data. Check the vector database write progress and storage space occupancy to confirm alignment with reserved resource configurations.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.