community[patch]: Voyage AI updates default model and batch size (lan…

…gchain-ai#17655)

- **Description:** update the default model and batch size in
VoyageEmbeddings
    - **Issue:** N/A
    - **Dependencies:** N/A
    - **Twitter handle:** N/A

---------

Co-authored-by: fodizoltan <zoltan@conway.expert>

docs/docs/integrations/text_embedding/voyageai.ipynb

@@ -27,7 +27,7 @@
    "id": "137cfde9-b88c-409a-9394-a9e31a6bf30d",
    "metadata": {},
    "source": [
-    "Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, create an account on our [homepage](https://www.voyageai.com). Then, create a VoyageEmbeddings model with your API key."
+    "Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, create an account on our [homepage](https://www.voyageai.com). Then, create a VoyageEmbeddings model with your API key. Please refer to the documentation for further details on the available models: https://docs.voyageai.com/embeddings/"
    ]
   },
   {
@@ -37,7 +37,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = VoyageEmbeddings(voyage_api_key=\"[ Your Voyage API key ]\")"
+    "embeddings = VoyageEmbeddings(\n",
+    "    voyage_api_key=\"[ Your Voyage API key ]\", model=\"voyage-2\"\n",
+    ")"
    ]
   },
   {

libs/community/langchain_community/embeddings/voyageai.py

@@ -69,15 +69,15 @@ class VoyageEmbeddings(BaseModel, Embeddings):
 
             from langchain_community.embeddings import VoyageEmbeddings
 
-            voyage = VoyageEmbeddings(voyage_api_key="your-api-key")
+            voyage = VoyageEmbeddings(voyage_api_key="your-api-key", model="voyage-2")
             text = "This is a test query."
             query_result = voyage.embed_query(text)
     """
 
-    model: str = "voyage-01"
+    model: str
     voyage_api_base: str = "https://api.voyageai.com/v1/embeddings"
     voyage_api_key: Optional[SecretStr] = None
-    batch_size: int = 8
+    batch_size: int
     """Maximum number of texts to embed in each API request."""
     max_retries: int = 6
     """Maximum number of retries to make when generating."""
@@ -86,15 +86,12 @@ class VoyageEmbeddings(BaseModel, Embeddings):
     show_progress_bar: bool = False
     """Whether to show a progress bar when embedding. Must have tqdm installed if set 
         to True."""
-    truncation: Optional[bool] = None
+    truncation: bool = True
     """Whether to truncate the input texts to fit within the context length.
     
         If True, over-length input texts will be truncated to fit within the context 
         length, before vectorized by the embedding model. If False, an error will be 
-        raised if any given text exceeds the context length. If not specified 
-        (defaults to None), we will truncate the input text before sending it to the 
-        embedding model if it slightly exceeds the context window length. If it 
-        significantly exceeds the context window length, an error will be raised."""
+        raised if any given text exceeds the context length."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -107,6 +104,22 @@ def validate_environment(cls, values: Dict) -> Dict:
         values["voyage_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(values, "voyage_api_key", "VOYAGE_API_KEY")
         )
+
+        if "model" not in values:
+            values["model"] = "voyage-01"
+            logger.warning(
+                "model will become a required arg for VoyageAIEmbeddings, "
+                "we recommend to specify it when using this class. "
+                "Currently the default is set to voyage-01."
+            )
+
+        if "batch_size" not in values:
+            values["batch_size"] = (
+                72
+                if "model" in values and (values["model"] in ["voyage-2", "voyage-02"])
+                else 7
+            )
+
         return values
 
     def _invocation_params(
@@ -116,11 +129,14 @@ def _invocation_params(
         params: Dict = {
             "url": self.voyage_api_base,
             "headers": {"Authorization": f"Bearer {api_key}"},
-            "json": {"model": self.model, "input": input, "input_type": input_type},
+            "json": {
+                "model": self.model,
+                "input": input,
+                "input_type": input_type,
+                "truncation": self.truncation,
+            },
             "timeout": self.request_timeout,
         }
-        if self.truncation is not None:
-            params["json"]["truncation"] = self.truncation
         return params
 
     def _get_embeddings(
@@ -186,7 +202,9 @@ def embed_query(self, text: str) -> List[float]:
         Returns:
             Embedding for the text.
         """
-        return self._get_embeddings([text], input_type="query")[0]
+        return self._get_embeddings(
+            [text], batch_size=self.batch_size, input_type="query"
+        )[0]
 
     def embed_general_texts(
         self, texts: List[str], *, input_type: Optional[str] = None

libs/community/tests/integration_tests/embeddings/test_voyageai.py

@@ -2,7 +2,7 @@
 from langchain_community.embeddings.voyageai import VoyageEmbeddings
 
 # Please set VOYAGE_API_KEY in the environment variables
-MODEL = "voyage-01"
+MODEL = "voyage-2"
 
 
 def test_voyagi_embedding_documents() -> None:
@@ -14,10 +14,22 @@ def test_voyagi_embedding_documents() -> None:
     assert len(output[0]) == 1024
 
 
+def test_voyagi_with_default_model() -> None:
+    """Test voyage embeddings."""
+    embedding = VoyageEmbeddings()
+    assert embedding.model == "voyage-01"
+    assert embedding.batch_size == 7
+    documents = [f"foo bar {i}" for i in range(72)]
+    output = embedding.embed_documents(documents)
+    assert len(output) == 72
+    assert len(output[0]) == 1024
+
+
 def test_voyage_embedding_documents_multiple() -> None:
     """Test voyage embeddings."""
     documents = ["foo bar", "bar foo", "foo"]
     embedding = VoyageEmbeddings(model=MODEL, batch_size=2)
+    assert embedding.model == MODEL
     output = embedding.embed_documents(documents)
     assert len(output) == 3
     assert len(output[0]) == 1024