Send dimensions & user args to embedding models

edb/server/protocol/ai_ext.py

@@ -524,6 +524,7 @@ class EmbeddingsParams(rs.Params[EmbeddingsData]):
     inputs: list[tuple[PendingEmbedding, str]]
     token_count: int
     shortening: Optional[int]
+    user: Optional[str]
 
     def costs(self) -> dict[str, int]:
         return {
@@ -547,7 +548,8 @@ async def run(self) -> Optional[rs.Result[EmbeddingsData]]:
                 self.params.model_name,
                 [input[1] for input in self.params.inputs],
                 self.params.shortening,
-                self.params.http_client
+                self.params.user,
+                self.params.http_client,
             )
             result.pgconn = self.params.pgconn
             result.pending_entries = [
@@ -757,6 +759,7 @@ async def _generate_embeddings_params(
                         inputs=inputs,
                         token_count=batch_token_count,
                         shortening=shortening,
+                        user=None,
                         http_client=http_client,
                     ))
 
@@ -774,6 +777,7 @@ async def _generate_embeddings_params(
                     inputs=inputs,
                     token_count=total_token_count,
                     shortening=shortening,
+                    user=None,
                     http_client=http_client,
                 ))
 
@@ -983,6 +987,7 @@ async def _generate_embeddings(
     model_name: str,
     inputs: list[str],
     shortening: Optional[int],
+    user: Optional[str],
     http_client: http.HttpClient,
 ) -> EmbeddingsResult:
     task_name = _task_name.get()
@@ -995,7 +1000,7 @@ async def _generate_embeddings(
 
     if provider.api_style == ApiStyle.OpenAI:
         return await _generate_openai_embeddings(
-            provider, model_name, inputs, shortening, http_client
+            provider, model_name, inputs, shortening, user, http_client
         )
     else:
         raise RuntimeError(
@@ -1009,6 +1014,7 @@ async def _generate_openai_embeddings(
     model_name: str,
     inputs: list[str],
     shortening: Optional[int],
+    user: Optional[str],
     http_client: http.HttpClient,
 ) -> EmbeddingsResult:
 
@@ -1030,6 +1036,9 @@ async def _generate_openai_embeddings(
     if shortening is not None:
         params["dimensions"] = shortening
 
+    if user is not None:
+        params["user"] = user
+
     result = await client.post(
         "/embeddings",
         json=params,
@@ -2215,16 +2224,32 @@ async def _handle_embeddings_request(
             raise TypeError(
                 'the body of the request must be a JSON object')
 
-        inputs = body.get("input")
+        inputs = body.get("inputs")
+        input = body.get("input")
+
+        if inputs is not None and input is not None:
+            raise TypeError(
+                "You cannot provide both 'inputs' and 'input'. "
+                "Please provide 'inputs'; 'input' has been deprecated."
+            )
+
+        if input is not None:
+            logger.warning("'input' is deprecated, use 'inputs' instead")
+            inputs = input
+
         if not inputs:
             raise TypeError(
-                'missing or empty required "input" value in request')
+                'missing or empty required "inputs" value in request'
+            )
 
         model_name = body.get("model")
         if not model_name:
             raise TypeError(
                 'missing or empty required "model" value in request')
 
+        shortening = body.get("dimensions")
+        user = body.get("user")
+
     except Exception as ex:
         raise BadRequestError(str(ex)) from None
 
@@ -2246,8 +2271,9 @@ async def _handle_embeddings_request(
         provider,
         model_name,
         inputs,
-        shortening=None,
-        http_client=tenant.get_http_client(originator="ai/embeddings")
+        shortening,
+        user,
+        http_client=tenant.get_http_client(originator="ai/embeddings"),
     )
     if isinstance(result.data, rs.Error):
         raise AIProviderError(result.data.message)
@@ -2507,8 +2533,13 @@ async def _generate_embeddings_for_type(
     else:
         shortening = None
     result = await _generate_embeddings(
-        provider, index["model"], [content], shortening=shortening,
-        http_client=http_client)
+        provider,
+        index["model"],
+        [content],
+        shortening,
+        None,
+        http_client,
+    )
     if isinstance(result.data, rs.Error):
         raise AIProviderError(result.data.message)
     return result.data.embeddings