diff --git a/expose.h b/expose.h
index 4189e5142..4b7bfdf42 100644
--- a/expose.h
+++ b/expose.h
@@ -255,6 +255,7 @@ struct embeddings_generation_inputs
 struct embeddings_generation_outputs
 {
     int status = -1;
+    int count = 0;
     const char * data = "";
 };
 
diff --git a/kcpp_docs.embd b/kcpp_docs.embd
index e32741f40..9857fad83 100644
--- a/kcpp_docs.embd
+++ b/kcpp_docs.embd
@@ -1492,6 +1492,28 @@
                       ]
                    }
                 },
+                "/api/extra/embeddings": {
+                   "post": {
+                      "summary": "Creates an embedding vector representing the input text. Please refer to OpenAI documentation",
+                      "description": "Creates an embedding vector representing the input text.\n\nThis is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://platform.openai.com/docs/api-reference/embeddings/create](https://platform.openai.com/docs/api-reference/embeddings/create)",
+                      "requestBody": {
+                         "content": {
+                            "application/json": {
+                               "example": {},
+                               "schema": {
+                                  "properties": {},
+                                  "type": "object"
+                               }
+                            }
+                         },
+                         "required": true
+                      },
+                      "tags": [
+                         "api/extra"
+                      ],
+                      "responses": {"default": {"description": ""}}
+                   }
+                },
                 "/api/extra/data/save": {
                    "post": {
                       "description": "Saves data to a slot in a database file in the KoboldCpp server.",
@@ -2152,6 +2174,28 @@
                       "responses": {"default": {"description": ""}}
                    }
                 },
+                "/v1/embeddings": {
+                   "post": {
+                      "summary": "Creates an embedding vector representing the input text. Please refer to OpenAI documentation",
+                      "description": "Creates an embedding vector representing the input text.\n\nThis is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://platform.openai.com/docs/api-reference/embeddings/create](https://platform.openai.com/docs/api-reference/embeddings/create)",
+                      "requestBody": {
+                         "content": {
+                            "application/json": {
+                               "example": {},
+                               "schema": {
+                                  "properties": {},
+                                  "type": "object"
+                               }
+                            }
+                         },
+                         "required": true
+                      },
+                      "tags": [
+                         "v1"
+                      ],
+                      "responses": {"default": {"description": ""}}
+                   }
+                },
              },
              "servers": [
                 {
diff --git a/koboldcpp.py b/koboldcpp.py
index 484346eb6..40c21c067 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -336,6 +336,7 @@ class embeddings_generation_inputs(ctypes.Structure):
 
 class embeddings_generation_outputs(ctypes.Structure):
     _fields_ = [("status", ctypes.c_int),
+                ("count", ctypes.c_int),
                 ("data", ctypes.c_char_p)]
 
 def getdirpath():
@@ -1602,14 +1603,34 @@ def embeddings_load_model(model_filename):
 
 def embeddings_generate(genparams):
     global args
-    prompt = genparams.get("input", "")
-    inputs = embeddings_generation_inputs()
-    inputs.prompt = prompt.encode("UTF-8")
-    ret = handle.embeddings_generate(inputs)
-    outstr = ""
-    if ret.status==1:
-        outstr = ret.data.decode("UTF-8","ignore")
-    return outstr
+    prompts = []
+    if isinstance(genparams.get('input',[]), list):
+        prompts = genparams.get('input',[])
+    else:
+        prompt = genparams.get("input", "")
+        if prompt:
+            prompts.append(prompt)
+
+    tokarrs = []
+    tokcnt = 0
+    for prompt in prompts:
+        tokarr = []
+        tmpcnt = 0
+        try:
+            inputs = embeddings_generation_inputs()
+            inputs.prompt = prompt.encode("UTF-8")
+            ret = handle.embeddings_generate(inputs)
+            if ret.status==1:
+                outstr = ret.data.decode("UTF-8","ignore")
+                tokarr = json.loads(outstr) if outstr else []
+                tmpcnt = ret.count
+        except Exception as e:
+            tokarr = []
+            tmpcnt = 0
+            print(f"Error: {e}")
+        tokarrs.append(tokarr)
+        tokcnt += tmpcnt
+    return {"count":tokcnt, "data":tokarrs}
 
 def tokenize_ids(countprompt,tcaddspecial):
     rawcountdata = handle.token_count(countprompt.encode("UTF-8"),tcaddspecial)
@@ -3227,7 +3248,12 @@ Enter Prompt:<br>
                 elif is_embeddings:
                     try:
                         gen = embeddings_generate(genparams)
-                        genresp = (json.dumps({"object":"list","data":[{"object":"embedding","index":0,"embedding":[-0.003880035,-0.05006583]}],"model":"text-embedding-3-small","usage":{"prompt_tokens":2,"total_tokens":2}}).encode())
+                        outdatas = []
+                        odidx = 0
+                        for od in gen["data"]:
+                            outdatas.append([{"object":"embedding","index":odidx,"embedding":od}])
+                            odidx += 1
+                        genresp = (json.dumps({"object":"list","data":outdatas,"model":"koboldcpp-embeddings","usage":{"prompt_tokens":gen["count"],"total_tokens":gen["count"]}}).encode())
                         self.send_response(200)
                         self.send_header('content-length', str(len(genresp)))
                         self.end_headers(content_type='application/json')
diff --git a/otherarch/embeddings_adapter.cpp b/otherarch/embeddings_adapter.cpp
index 94344ae5f..982ecea1f 100644
--- a/otherarch/embeddings_adapter.cpp
+++ b/otherarch/embeddings_adapter.cpp
@@ -24,7 +24,7 @@
 static llama_context * embeddings_ctx = nullptr; //text to codes ctx
 static std::string ttsplatformenv, ttsdeviceenv, ttsvulkandeviceenv;
 bool embeddings_debug = false;
-const int max_batchsize = 2048;
+static int max_batchsize = 512;
 static std::string last_output = "";
 
 static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
@@ -128,16 +128,20 @@ bool embeddingstype_load_model(const embeddings_load_model_inputs inputs)
     model_params.use_mlock = false;
     model_params.n_gpu_layers = inputs.gpulayers; //offload if possible
     model_params.split_mode = llama_split_mode::LLAMA_SPLIT_MODE_LAYER;
+
+    llama_model * embeddingsmodel = llama_model_load_from_file(modelfile.c_str(), model_params);
+    const int n_ctx_train = llama_model_n_ctx_train(embeddingsmodel);
+
+    max_batchsize = n_ctx_train;
     ctx_params.embeddings = true;
     ctx_params.n_ubatch = ctx_params.n_ubatch = max_batchsize; //max size, must fit
-    ctx_params.n_ctx = max_batchsize + 512;
+    ctx_params.n_ctx = max_batchsize;
     ctx_params.logits_all = false;
     ctx_params.offload_kqv = true;
     ctx_params.n_threads = nthreads;
     ctx_params.n_threads_batch = nthreads;
     ctx_params.flash_attn = inputs.flash_attention;
 
-    llama_model * embeddingsmodel = llama_model_load_from_file(modelfile.c_str(), model_params);
     embeddings_ctx = llama_new_context_with_model(embeddingsmodel, ctx_params);
 
     if (embeddings_ctx == nullptr) {
@@ -156,7 +160,6 @@ bool embeddingstype_load_model(const embeddings_load_model_inputs inputs)
 
     const llama_vocab * vocab = llama_model_get_vocab(embeddingsmodel);
 
-    const int n_ctx_train = llama_model_n_ctx_train(embeddingsmodel);
     const int n_ctx = llama_n_ctx(embeddings_ctx);
 
     if (llama_model_has_encoder(embeddingsmodel) && llama_model_has_decoder(embeddingsmodel)) {
@@ -181,6 +184,7 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
         printf("\nWarning: KCPP Embeddings Model not initialized!\n");
         output.data = "";
         output.status = 0;
+        output.count = 0;
         return output;
     }
 
@@ -197,14 +201,19 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
     std::vector<std::vector<int32_t>> prompt_inputs;
     auto inp = common_tokenize(embeddings_ctx, prompt, true, true);
     if (inp.size() > n_batch) {
-        printf("\n%s: number of tokens in input line (%lld) exceeds batch size (%lld), lower token amount!\n",
+        printf("\n%s: number of tokens in an input (%lld) exceeds embedding size limit for this model (%lld), lower token amount!\n",
                 __func__, (long long int) inp.size(), (long long int) n_batch);
         output.data = "";
         output.status = 0;
+        output.count = 0;
         return output;
     }
     prompt_inputs.push_back(inp);
 
+    if(embeddings_debug)
+    {
+        print_tok_vec(inp);
+    }
     printf("\nGenerating Embeddings for %d tokens...",inp.size());
 
     // initialize batch
@@ -272,5 +281,6 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
 
     output.data = last_output.c_str();
     output.status = 1;
+    output.count = inp.size();
     return output;
 }