What the documents look like
Receiving and putaway covers the first physical-and-ledger confirmation that goods have entered the warehouse. The records involved are purchase arrival notes, receiving inspection sheets, putaway tasks, and discrepancy reports, drawn from the WMS, ERP purchasing, supplier delivery notes, and barcode scanning. Product data and selection questions in this setting ask about the specifications, packaging, storage conditions, and location fit of arriving goods, and each answer needs to trace back to a specific document.
Model connection and configuration here means making the product data behind those answers readable, indexable, and retrievable by a model, and aligning model capability with the input forms that receiving produces. The documents refresh per batch and per day; this page does not derive batch synchronization or real-time triggers from that cadence.
A system needs at least one language model and one index model to work. The index model indexes text chunks for related-text retrieval, while the language model handles text dialogue and multimodal models support image recognition. A model ID is the value of the model field in the request body and is globally unique; a model name is for display. A model channel can carry the same model to achieve load balancing.
From V4.17.0, AI Proxy is a required dependency. AIPROXY_API_ENDPOINT takes the service root address without /v1 appended, and AIPROXY_API_TOKEN must match the server side and be non-empty. Retrieval similarity does not perform permission or entity isolation, and a retrieval result does not authorize a supplier, location, or document. Libraries built on different vector models cannot be associated together; the identifiers must be checked first and non-matching libraries excluded.
Hypothetical paper exercise (not a real record): the purchase arrival note states "Supplier A, Product A, specification 500ml×12, arrival 40 cases, arrival date 2026-03-02"; the receiving inspection sheet states "Product A, actually received 38 cases, discrepancy 2 cases, inspection status is pending verification." Draft sentence to be judged: "Product A has completed receiving and putaway, stock 40 cases." The sentence involves a subject, an object, a quantity, and a status, and can be compared field by field against those two documents.
Constraints at the model connections step
The primary task is product data and selection questions, so the model layer must serve retrieval of product documents and answering about specifications, packaging, storage, and location fit. The constraints that matter are the ones that change what can be read or returned.
The index model indexes chunks for retrieval; the language model produces dialogue. A multimodal language model supports image input, which matters when receiving material includes product photos or scanned forms, but only when the request actually carries an image and the model supports it. A rerank model reorders retrieval results and only participates when such a model exists and is enabled by configuration. The overall flow diagram lists optional paths; not every request executes all of them.
Model ID is the request body model value and is globally unique. Model name is display only. A channel can host the same model for load balancing. Channel priority is a value from 1 to 100, where a larger value is more likely to be selected. A disabled channel can no longer serve models.
Model status probing covers only currently enabled system models. A first failure is retried up to three times, so one round makes at most four attempts; one success marks the round valid, and only four failures mark it abnormal. Normal is a successful test with latency at or below 30s, high latency is a successful test above 30s, and abnormal is four consecutive request errors. The 48-hour stability percentage keeps two decimals.
Retrieval is not authorization. A similarity score is not a category, entity, or permission filter, and it does not exclude unrelated suppliers or unauthorized records. Any business verification, approval, database write, or external synchronization requires its own downstream implementation. Forms collect values; they do not approve anything.
Configuration and implementation checks
| Setting | Hypothetical starting value for this exercise | Reason and comparison object |
|---|---|---|
AIPROXY_API_ENDPOINT | http://aiproxy:3000 | The document requires the root address; compare against a value with /v1 appended |
AIPROXY_API_TOKEN | A non-empty value matching the server side | The document requires non-empty and consistent; compare against an application API key |
| Index model | One index model enabled | The document requires at least one; compare against the not-enabled state |
| Language model | One language model enabled | The document requires at least one; compare against the not-enabled state |
| Model mapping | Map gpt-4o-test to gpt-4o | Documented example; compare against the requested model name when unmapped |
| Channel test | Run model test on the channel | The document provides a test entry; compare against the untested state |
These values are hypothetical starting points for this exercise, not official defaults.
Three common errors
- Treating a retrieval hit as authorization. Input: retrieval returns a product A data fragment. Compare: the field ownership of that fragment against Supplier A and the inspection status. Observable result: the fragment shows text relevance only, carries no authorization, and the draft sentence's "has completed receiving and putaway" remains pending verification.
- Reading a pending-verification status as a negation or a conflict. Input: the inspection sheet "actually received 38 cases, discrepancy 2 cases, inspection status is pending verification." Compare: the value of the same field. Observable result: pending verification is unsupported, and it does not form a same-field mutual exclusion against the 40-case arrival.
- Associating libraries across different vector models. Input: the vector model identifiers of two libraries. Compare: whether the identifiers match. Observable result: when they differ, exclude that library and record the actual observation, without inferring missed recall or redundant results.
How to verify the setup
- Input the hypothetical purchase arrival note and receiving inspection sheet text, and compare the quantity and status fields in the draft sentence "Product A has completed receiving and putaway, stock 40 cases." The sentence is satisfied only when both documents state the same subject, the same period, and the same field consistently, and the status is completed. The current status is pending verification, so the sentence is unsupported.
- Input the hypothetical request "Please confirm the putaway location assignment result for Product A." Compare: whether the material states a location field. The condition is satisfied only when the purchase arrival note, receiving inspection sheet, or putaway task states a location and the status is assigned. The current material does not state one, so record an evidence gap.
- Input the paper input
[]for empty-result handling, and compare whether the downstream question answering treats it as no material. The condition is satisfied only when the downstream explicitly outputs that no material can be cited and generates no product conclusion. Do not claim that a fictional rule retrieval necessarily returns[].
All three are paper checks, not executed tests, and they confirm no business action.