sakib-xeon
diff --git a/‎llama_cpp/llama.py
Copy file name to clipboardExpand all lines: llama_cpp/llama.py
+8-2Lines changed: 8 additions & 2 deletions b/‎llama_cpp/llama.py
Copy file name to clipboardExpand all lines: llama_cpp/llama.py
+8-2Lines changed: 8 additions & 2 deletions
diff --git a/‎llama_cpp/llama_cpp.py
Copy file name to clipboardExpand all lines: llama_cpp/llama_cpp.py
-1Lines changed: 0 additions & 1 deletion b/‎llama_cpp/llama_cpp.py
Copy file name to clipboardExpand all lines: llama_cpp/llama_cpp.py
-1Lines changed: 0 additions & 1 deletion
@@ -79,6 +79,7 @@ def __init__(
         n_threads: Optional[int] = None,
         n_threads_batch: Optional[int] = None,
         rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+        pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_MEAN,
         rope_freq_base: float = 0.0,
         rope_freq_scale: float = 0.0,
         yarn_ext_factor: float = -1.0,
@@ -151,6 +152,7 @@ def __init__(
             n_threads: Number of threads to use for generation
             n_threads_batch: Number of threads to use for batch processing
             rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054
+            pooling_type: Pooling type, from `enum llama_pooling_type`.
             rope_freq_base: RoPE base frequency, 0 = from model
             rope_freq_scale: RoPE frequency scaling factor, 0 = from model
             yarn_ext_factor: YaRN extrapolation mix factor, negative = from model
@@ -271,6 +273,7 @@ def __init__(
             if rope_scaling_type is not None
             else llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED
         )
+        self.context_params.pooling_type = pooling_type
         self.context_params.rope_freq_base = (
             rope_freq_base if rope_freq_base != 0.0 else 0
         )
@@ -814,9 +817,12 @@ def decode_batch(n_seq: int):
 
             # store embeddings
             for i in range(n_seq):
-                embedding: List[float] = llama_cpp.llama_get_embeddings_seq(
+                ptr = llama_cpp.llama_get_embeddings_seq(
                     self._ctx.ctx, i
-                )[:n_embd]
+                )
+                if not ptr:
+                    raise RuntimeError("Failed to get embeddings from sequence pooling type is not set")
+                embedding: List[float] = ptr[:n_embd]
                 if normalize:
                     norm = float(np.linalg.norm(embedding))
                     embedding = [v / norm for v in embedding]
 
@@ -579,7 +579,6 @@ class llama_model_params(ctypes.Structure):
 #     bool embeddings;  // if true, extract embeddings (together with logits)
 #     bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
 
-
 #     // Abort callback
 #     // if it returns true, execution of llama_decode() will be aborted
 #     // currently works only with CPU execution