SQOIN-API is a communication layer that sits between:
- Consumer (client or application)
- Server (the actual API backend)
SQOIN-API uses an LLM (Large Language Model) in two primary ways:
- Translation: Ensures that requests/responses conform to each side’s schema, despite potential differences in naming, structure, or required fields.
- Offline Fallback: Predicts (simulates) server responses if the server is unavailable, using previously observed request–response pairs and an LLM for inference.
- Schema Compatibility: Bridge schema differences without manually rewriting code every time the server or consumer changes.
- Downtime Resilience: Provide approximate or “best-guess” responses from the LLM when the server cannot be reached.
- Cost Optimization (optional): Optionally reduce recurring LLM calls by generating bridging code once per version change, then caching or statically compiling that code into the consumer.
Below is a conceptual diagram of SQOIN-API in action:
┌───────────────┐
│ Consumer │
│(Client / App) │
└───────────────┘
│
▼
┌──────────────────────┐
│ SQOIN-API │
│ (LLM Translator + │
│ Offline Fallback) │
└──────────────────────┘
│
▼
┌───────────────┐
│ Server │
└───────────────┘
- Consumer → SQOIN-API
- Consumer sends a request in its native format.
- SQOIN-API → LLM (translation step)
- If necessary, SQOIN-API calls the LLM to transform the consumer’s request into the server’s format.
- (Alternatively, if the bridging code has been pre-generated, no live LLM call is needed; a local function or library handles it.)
- SQOIN-API → Server
- The transformed request is forwarded to the server.
- Server → SQOIN-API
- The server returns a real response.
- SQOIN-API → LLM (response translation step)
- Again, if needed, the LLM (or pre-generated code) transforms the server’s response into the consumer’s expected schema.
- SQOIN-API → Consumer
- The translated response is sent back.
- Logging
- Each request–response pair is stored in a knowledge base or database for offline fallback learning.
- Consumer → SQOIN-API
- Consumer sends a request, but the server is offline or unreachable.
- SQOIN-API
- SQOIN-API detects server downtime, so it attempts to predict the server’s response using stored data + an LLM.
- Retrieves similar request–response pairs from its knowledge base.
- Prompts the LLM with those examples to generate a predicted response.
- SQOIN-API → Consumer
- Returns the predicted (simulated) response to the consumer, optionally flagged as “offline approximate.”
- (Post-Recovery)
- When the server comes back online, SQOIN-API can replay any offline requests to get the real response and compare or reconcile if needed.
In some early stages of a project—or when the server side is not implemented yet—SQOIN-API can operate in a dummy implementation mode. This mode is similar to offline fallback but differs in that the system never attempts to call a real server, because none exists.
- Consumer → SQOIN-API
- The Consumer sends a request in its native format.
- SQOIN-API
- SQOIN-API is configured in a “dummy” mode indicating no real server is available (not even a down server).
- Based on the request and its parameters, SQOIN-API uses either:
- Simple heuristics or placeholders for the response, or
- An LLM to generate a plausible response that matches the consumer’s expected schema.
- SQOIN-API → Consumer
- Returns the synthetic response to the consumer, often marked with a flag or annotation (e.g.,
"dummyMode": true).
- Returns the synthetic response to the consumer, often marked with a flag or annotation (e.g.,
- Purpose
- This mode helps teams build or test the client side before the backend is ready.
- You can later switch to a real or “online” mode once the backend is implemented.
Below is a suggested set of HTTP endpoints for SQOIN-API. These can be adapted to gRPC or other protocols if desired.
Purpose: Handles incoming consumer requests, transforms them to the server schema, calls the server if available (or predicts if offline), then returns a translated response.
- Method:
POST - Request Body:
{ "context": { "apiKey": "string (optional)", "timestamp": "ISO8601", "metadata": { "...": "any additional info" } }, "payload": { // The consumer's data // Format: arbitrary JSON } } - Response Body:
{ "context": { "processedBy": "SQOIN-API vX.Y", "offlineMode": "boolean (true if server was offline)", "timestamp": "ISO8601" }, "payload": { // The final response to the consumer // Typically in the consumer's expected schema } }
-
Check Server Status
- If online, pass
payloadto LLM-based translator (if needed), then call the real server. - If offline or in dummy mode, skip calling the server and generate a predicted or synthetic response.
- If online, pass
-
LLM-based Transformation
- If code bridging is not pre-generated, the system may call the LLM with a prompt that includes:
- Consumer schema (or a pointer to it).
- Server schema (or a pointer).
- Consumer request.
- The LLM returns a valid server request format (except in dummy mode, where it might skip the translation if no server schema is available).
- Upon receiving the server’s (or predicted/synthetic) response, the LLM transforms it back into the consumer’s expected schema.
- If code bridging is not pre-generated, the system may call the LLM with a prompt that includes:
-
Logging
- Store the (consumer → server) request and (server → consumer) response in the knowledge base, including dummy-mode or offline-mode indicators.
Purpose: Indicates whether the server is online or offline from SQOIN-API’s perspective.
- Method:
GET - Response (example):
{ "status": "online", // or "offline" / "degraded" / "dummy" "lastCheck": "ISO8601" } - Implementation Detail: Typically, SQOIN-API might do a health-check ping to the server at intervals and keep a cached status.
- Dummy Mode: In a dummy implementation scenario,
statusmay be forced to something like"dummy"or"none"to indicate there is no real backend.
Purpose: Allows retrieval of stored request–response pairs for debugging or training/fine-tuning.
- Method:
GETorPOST - Parameters: paging, filtering by date or request type, etc.
- Response (example):
{ "entries": [ { "requestId": "abc123", "consumerPayload": {...}, "serverPayload": {...}, "serverResponse": {...}, "createdAt": "ISO8601" }, ... ], "pagination": {...} } - Security: Potentially requires admin credentials because it may contain sensitive data.
Purpose: Triggers a code generation step (if you opt for “compile-time” bridging logic).
- Method:
POST - Request Body:
{ "consumerSpec": "<OpenAPI snippet or JSON schema>", "serverSpec": "<OpenAPI snippet or JSON schema>" } - Response:
{ "bridgeCode": "string (the generated code / SDK / translator logic)" } - Usage: The developer calls this endpoint to produce bridging code, then compiles or integrates it into the consumer. Not typically used by the consumer at runtime.
Below are some suggested data structures used internally by SQOIN-API. You can adjust them to match your actual data or frameworks.
{
"requestId": "string",
"consumerRequest": {
// raw or partially sanitized
},
"serverRequest": {
// what was actually sent to the server
},
"serverResponse": {
// what the server returned or what was generated in offline/dummy mode
},
"timestamp": "ISO8601",
"version": "string (server version or translator version)",
"status": "success | error"
}System / Developer Prompt:
"You are the SQOIN-API translator.
The consumer format is described below:
<consumer spec>
The server format is described below:
<server spec>
Task: Convert the consumer request into the server request,
then if a server response is given, convert it back.
If in dummy mode, generate a synthetic response
based on the request parameters alone."
(Adjust as needed for offline fallback or dummy mode predictions.)
- Identify similarity
- When the server is offline, SQOIN-API generates an embedding of the current request.
- It queries a vector DB or other retrieval system to find similar historical requests.
- Few-shot examples
- The N best matches, along with their actual server responses, are provided to the LLM as few-shot examples.
- LLM generation
- The LLM produces a predicted server response based on the retrieved examples.
- Caching
- If the request is identical (or near identical) to a prior one, SQOIN-API can skip the LLM call and return the cached prior response.
- Confidence Threshold: If no sufficiently similar request is found (distance > threshold), the LLM might respond with “Unknown.”
- Consumer Notification: The returned payload can include a flag like
"offlineMode": true.
- Schema Changes
- If the server updates its schema, you can update the reference in SQOIN-API.
- If using compile-time bridging code, call
/generate-bridgewith the new specs, test, then redeploy.
- Model Updates
- The LLM might be updated or fine-tuned on your logs to improve translation or offline predictions (and dummy responses).
- Log Retention
- Decide how long to keep request–response pairs. Some data might be sensitive, so observe regulatory or privacy constraints.
- Authentication
- The consumer might attach an API key or OAuth token in the
contextobject or an HTTP header. - SQOIN-API may also need credentials to call the server.
- The consumer might attach an API key or OAuth token in the
- Encryption
- Use TLS for all traffic to avoid leaking data.
- Data Minimization
- If the server or consumer data is sensitive, store only what is necessary for offline fallback or dummy usage. Consider anonymizing or hashing PII (personally identifiable information).
- LLM Privacy
- If you’re using a third-party LLM API (e.g., OpenAI), be aware of the content policies. Do not send unencrypted or highly sensitive data unless you trust the channel.
- Consumer calls:
POST /translate { "context": { "apiKey": "abc-123", "timestamp": "2024-12-26T10:30:00Z" }, "payload": { "firstName": "Alice", "lastName": "Smith" } }
- SQOIN-API sees the server is online:
- Translates
{"firstName": "Alice", "lastName": "Smith"}→{"f_name": "Alice", "l_name": "Smith"}. - Calls
POST /server/userswith the transformed request.
- Translates
- Server responds:
{ "full_name": "Alice Smith", "user_id": "xyz123" } - SQOIN-API translates back to consumer format:
{ "fullName": "Alice Smith", "id": "xyz123" } - SQOIN-API returns to consumer:
200 OK { "context": { "processedBy": "SQOIN-API v1.0", "offlineMode": false, "timestamp": "2024-12-26T10:30:01Z" }, "payload": { "fullName": "Alice Smith", "id": "xyz123" } }
- SQOIN-API logs the interaction in its knowledge base.
- Consumer calls the same
/translateendpoint, but the server is down. - SQOIN-API sees the server is offline:
- Looks up similar past requests in its logs.
- Finds a close match from the example above.
- Uses that prior server response + LLM to generate a predicted response.
- SQOIN-API returns:
(This is an approximation of what the server would have returned.)
200 OK { "context": { "processedBy": "SQOIN-API v1.0", "offlineMode": true, "timestamp": "2024-12-26T10:31:00Z" }, "payload": { "fullName": "Alice Smith", "id": "xyz123" } }
- Consumer calls
/translatewhen the server is not just offline, but non-existent (e.g., not yet implemented). - SQOIN-API is explicitly set to “dummy mode.”
- It either calls an LLM or uses a predefined set of response templates to construct a synthetic response.
- For example, based on the presence of
firstNameandlastName, it might create"fullName": "<firstName> <lastName>"and a dummy"id".
- SQOIN-API returns:
(This clarifies to the consumer that the response is fully synthetic.)
200 OK { "context": { "processedBy": "SQOIN-API v1.0", "dummyMode": true, "timestamp": "2024-12-26T10:31:00Z" }, "payload": { "fullName": "Alice Smith", "id": "dummy-1234" } }
- LLM Integration
- You can use any model (OpenAI GPT-4, local LLM, etc.) with function calling or well-structured prompts.
- For critical use cases, always validate the LLM output with JSON schema validators.
- Caching & Code Generation (Optional)
- For stable field mappings, generate static bridging code once (via an endpoint like
/generate-bridge). - Deploy that code so th 4DA4 e translator rarely needs live LLM calls.
- For stable field mappings, generate static bridging code once (via an endpoint like
- Performance
- If offline fallback or dummy usage is rare, LLM calls might be minimal.
- For high traffic, consider a robust caching or precomputation approach.
- Scalability
- SQOIN-API can scale horizontally behind a load balancer.
- Maintain a shared knowledge base (database + vector index) accessible by all instances.
SQOIN-API provides a robust, flexible layer between a consumer and a server by:
- Translating between disparate schemas (using LLM translation or generated bridging code).
- Logging real request–response pairs for future reference.
- Predicting or dummy-generating server responses when the server is offline or not yet implemented.
By following these guidelines, SQOIN-API ensures smooth compatibility, downtime resilience, and flexibility to evolve both the consumer and server schemas over time.
For projects still under development, the dummy implementation mode accelerates front-end and client-side progress even before the actual backend API is live.