Adds truncate_prompt_tokens param for embeddings creation (#8999)

Signed-off-by: Flavia Beo <flavia.beo@ibm.com>

vllm/entrypoints/openai/protocol.py

@@ -671,6 +671,7 @@ class EmbeddingRequest(OpenAIBaseModel):
     encoding_format: Literal["float", "base64"] = "float"
     dimensions: Optional[int] = None
     user: Optional[str] = None
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
 
     # doc: begin-embedding-pooling-params
     additional_data: Optional[Any] = None

vllm/entrypoints/openai/serving_embedding.py

@@ -110,6 +110,17 @@ class OpenAIServingEmbedding(OpenAIServing):
         request_id = f"embd-{random_uuid()}"
         created_time = int(time.monotonic())
 
+        truncate_prompt_tokens = None
+
+        if request.truncate_prompt_tokens is not None:
+            if request.truncate_prompt_tokens <= self.max_model_len:
+                truncate_prompt_tokens = request.truncate_prompt_tokens
+            else:
+                return self.create_error_response(
+                    "truncate_prompt_tokens value is "
+                    "greater than max_model_len."
+                    " Please, select a smaller truncation size.")
+
         # Schedule the request and get the result generator.
         generators: List[AsyncGenerator[EmbeddingRequestOutput, None]] = []
         try:
@@ -123,11 +134,9 @@ class OpenAIServingEmbedding(OpenAIServing):
             pooling_params = request.to_pooling_params()
 
             prompts = list(
-                self._tokenize_prompt_input_or_inputs(
-                    request,
-                    tokenizer,
-                    request.input,
-                ))
+                self._tokenize_prompt_input_or_inputs(request, tokenizer,
+                                                      request.input,
+                                                      truncate_prompt_tokens))
 
             for i, prompt_inputs in enumerate(prompts):
                 request_id_item = f"{request_id}-{i}"