feat: make embedding support list of string as input

makes the /v1/embedding route similar to OpenAI api.

llama_cpp/llama.py

@@ -531,7 +531,9 @@ class Llama:
             if tokens_or_none is not None:
                 tokens.extend(tokens_or_none)
 
-    def create_embedding(self, input: str, model: Optional[str] = None) -> Embedding:
+    def create_embedding(
+        self, input: Union[str, List[str]], model: Optional[str] = None
+    ) -> Embedding:
         """Embed a string.
 
         Args:
@@ -551,30 +553,40 @@ class Llama:
         if self.verbose:
             llama_cpp.llama_reset_timings(self.ctx)
 
-        tokens = self.tokenize(input.encode("utf-8"))
+        if isinstance(input, str):
-        self.reset()
+            inputs = [input]
-        self.eval(tokens)
+        else:
-        n_tokens = len(tokens)
+            inputs = input
-        embedding = llama_cpp.llama_get_embeddings(self.ctx)[
-            : llama_cpp.llama_n_embd(self.ctx)
-        ]
 
-        if self.verbose:
+        data = []
-            llama_cpp.llama_print_timings(self.ctx)
+        total_tokens = 0
+        for input in inputs:
+            tokens = self.tokenize(input.encode("utf-8"))
+            self.reset()
+            self.eval(tokens)
+            n_tokens = len(tokens)
+            total_tokens += n_tokens
+            embedding = llama_cpp.llama_get_embeddings(self.ctx)[
+                : llama_cpp.llama_n_embd(self.ctx)
+            ]
 
-        return {
+            if self.verbose:
-            "object": "list",
+                llama_cpp.llama_print_timings(self.ctx)
-            "data": [
+            data.append(
                 {
                     "object": "embedding",
                     "embedding": embedding,
                     "index": 0,
                 }
-            ],
+            )
-            "model": model_name,
+
+        return {
+            "object": "list",
+            "data": data,
+            "model": self.model_path,
             "usage": {
-                "prompt_tokens": n_tokens,
+                "prompt_tokens": total_tokens,
-                "total_tokens": n_tokens,
+                "total_tokens": total_tokens,
             },
         }
 

llama_cpp/server/app.py

@@ -275,7 +275,7 @@ def create_completion(
 
 class CreateEmbeddingRequest(BaseModel):
     model: Optional[str] = model_field
-    input: str = Field(description="The input to embed.")
+    input: Union[str, List[str]] = Field(description="The input to embed.")
     user: Optional[str]
 
     class Config: