Community: LlamaCppEmbeddings embed_documents and embed_query (#28827)

- **Description:** `embed_documents` and `embed_query` was throwing off
the error as stated in the issue. The issue was that `Llama` client is
returning the embeddings in a nested list which is not being accounted
for in the current implementation and therefore the stated error is
being raised.
- **Issue:** #28813

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>

libs/community/langchain_community/embeddings/llamacpp.py

@@ -20,7 +20,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
     """
 
     client: Any = None  #: :meta private:
-    model_path: str
+    model_path: str = Field(default="")
 
     n_ctx: int = Field(512, alias="n_ctx")
     """Token context window."""
@@ -88,6 +88,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
         if self.n_gpu_layers is not None:
             model_params["n_gpu_layers"] = self.n_gpu_layers
 
+        if not self.client:
             try:
                 from llama_cpp import Llama
 
@@ -116,7 +117,17 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
             List of embeddings, one for each text.
         """
         embeddings = self.client.create_embedding(texts)
-        return [list(map(float, e["embedding"])) for e in embeddings["data"]]
+        final_embeddings = []
+        for e in embeddings["data"]:
+            try:
+                if isinstance(e["embedding"][0], list):
+                    for data in e["embedding"]:
+                        final_embeddings.append(list(map(float, data)))
+                else:
+                    final_embeddings.append(list(map(float, e["embedding"])))
+            except (IndexError, TypeError):
+                final_embeddings.append(list(map(float, e["embedding"])))
+        return final_embeddings
 
     def embed_query(self, text: str) -> List[float]:
         """Embed a query using the Llama model.
@@ -128,4 +139,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         embedding = self.client.embed(text)
+        if not isinstance(embedding, list):
             return list(map(float, embedding))
+        else:
+            return list(map(float, embedding[0]))

libs/community/tests/unit_tests/embeddings/test_llamacpp.py

@@ -0,0 +1,40 @@
+from typing import Generator
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from langchain_community.embeddings.llamacpp import LlamaCppEmbeddings
+
+
+@pytest.fixture
+def mock_llama_client() -> Generator[MagicMock, None, None]:
+    with patch(
+        "langchain_community.embeddings.llamacpp.LlamaCppEmbeddings"
+    ) as MockLlama:
+        mock_client = MagicMock()
+        MockLlama.return_value = mock_client
+        yield mock_client
+
+
+def test_initialization(mock_llama_client: MagicMock) -> None:
+    embeddings = LlamaCppEmbeddings(client=mock_llama_client)  # type: ignore[call-arg]
+    assert embeddings.client is not None
+
+
+def test_embed_documents(mock_llama_client: MagicMock) -> None:
+    mock_llama_client.create_embedding.return_value = {
+        "data": [{"embedding": [[0.1, 0.2, 0.3]]}, {"embedding": [[0.4, 0.5, 0.6]]}]
+    }
+    embeddings = LlamaCppEmbeddings(client=mock_llama_client)  # type: ignore[call-arg]
+    texts = ["Hello world", "Test document"]
+    result = embeddings.embed_documents(texts)
+    expected = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+    assert result == expected
+
+
+def test_embed_query(mock_llama_client: MagicMock) -> None:
+    mock_llama_client.embed.return_value = [[0.1, 0.2, 0.3]]
+    embeddings = LlamaCppEmbeddings(client=mock_llama_client)  # type: ignore[call-arg]
+    result = embeddings.embed_query("Sample query")
+    expected = [0.1, 0.2, 0.3]
+    assert result == expected