chore: update Jina Embedder usage for V3 release

integrations/jina/src/haystack_integrations/components/embedders/jina/document_embedder.py

@@ -24,7 +24,7 @@ class JinaDocumentEmbedder:
 
     # Make sure that the environment variable JINA_API_KEY is set
 
-    document_embedder = JinaDocumentEmbedder()
+    document_embedder = JinaDocumentEmbedder(task="retrieval.query")
 
     doc = Document(content="I love pizza!")
 
@@ -38,13 +38,16 @@ class JinaDocumentEmbedder:
     def __init__(
         self,
         api_key: Secret = Secret.from_env_var("JINA_API_KEY"),  # noqa: B008
-        model: str = "jina-embeddings-v2-base-en",
+        model: str = "jina-embeddings-v3",
         prefix: str = "",
         suffix: str = "",
         batch_size: int = 32,
         progress_bar: bool = True,
         meta_fields_to_embed: Optional[List[str]] = None,
         embedding_separator: str = "\n",
+        task: Optional[str] = None,
+        dimensions: Optional[int] = None,
+        late_chunking: Optional[bool] = None,
     ):
         """
         Create a JinaDocumentEmbedder component.
@@ -78,6 +81,9 @@ def __init__(
                 "Content-type": "application/json",
             }
         )
+        self.task = task
+        self.dimensions = dimensions
+        self.late_chunking = late_chunking
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -91,17 +97,25 @@ def to_dict(self) -> Dict[str, Any]:
         :returns:
             Dictionary with serialized data.
         """
-        return default_to_dict(
-            self,
-            api_key=self.api_key.to_dict(),
-            model=self.model_name,
-            prefix=self.prefix,
-            suffix=self.suffix,
-            batch_size=self.batch_size,
-            progress_bar=self.progress_bar,
-            meta_fields_to_embed=self.meta_fields_to_embed,
-            embedding_separator=self.embedding_separator,
-        )
+        kwargs = {
+            "api_key": self.api_key.to_dict(),
+            "model": self.model_name,
+            "prefix": self.prefix,
+            "suffix": self.suffix,
+            "batch_size": self.batch_size,
+            "progress_bar": self.progress_bar,
+            "meta_fields_to_embed": self.meta_fields_to_embed,
+            "embedding_separator": self.embedding_separator,
+        }
+        # Optional parameters, the following two are only supported by embeddings-v3 for now
+        if self.task:
+            kwargs["task"] = self.task
+        if self.dimensions:
+            kwargs["dimensions"] = self.dimensions
+        if self.late_chunking is not None:
+            kwargs["late_chunking"] = self.late_chunking
+
+        return default_to_dict(self, **kwargs)
 
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "JinaDocumentEmbedder":
@@ -131,7 +145,9 @@ def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
             texts_to_embed.append(text_to_embed)
         return texts_to_embed
 
-    def _embed_batch(self, texts_to_embed: List[str], batch_size: int) -> Tuple[List[List[float]], Dict[str, Any]]:
+    def _embed_batch(
+        self, texts_to_embed: List[str], batch_size: int, parameters: Optional[Dict] = None
+    ) -> Tuple[List[List[float]], Dict[str, Any]]:
         """
         Embed a list of texts in batches.
         """
@@ -142,7 +158,10 @@ def _embed_batch(self, texts_to_embed: List[str], batch_size: int) -> Tuple[List
             range(0, len(texts_to_embed), batch_size), disable=not self.progress_bar, desc="Calculating embeddings"
         ):
             batch = texts_to_embed[i : i + batch_size]
-            response = self._session.post(JINA_API_URL, json={"input": batch, "model": self.model_name}).json()
+            response = self._session.post(
+                JINA_API_URL,
+                json={"input": batch, "model": self.model_name, **(parameters if parameters is not None else {})},
+            ).json()
             if "data" not in response:
                 raise RuntimeError(response["detail"])
 
@@ -179,8 +198,16 @@ def run(self, documents: List[Document]):
             raise TypeError(msg)
 
         texts_to_embed = self._prepare_texts_to_embed(documents=documents)
-
-        embeddings, metadata = self._embed_batch(texts_to_embed=texts_to_embed, batch_size=self.batch_size)
+        parameters: Dict[str, Any] = {}
+        if self.task:
+            parameters["task"] = self.task
+        if self.dimensions:
+            parameters["dimensions"] = self.dimensions
+        if self.late_chunking is not None:
+            parameters["late_chunking"] = self.late_chunking
+        embeddings, metadata = self._embed_batch(
+            texts_to_embed=texts_to_embed, batch_size=self.batch_size, parameters=parameters
+        )
 
         for doc, emb in zip(documents, embeddings):
             doc.embedding = emb

integrations/jina/src/haystack_integrations/components/embedders/jina/text_embedder.py

@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <[email protected]>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 import requests
 from haystack import component, default_from_dict, default_to_dict
@@ -21,24 +21,27 @@ class JinaTextEmbedder:
 
     # Make sure that the environment variable JINA_API_KEY is set
 
-    text_embedder = JinaTextEmbedder()
+    text_embedder = JinaTextEmbedder(task="retrieval.query")
 
     text_to_embed = "I love pizza!"
 
     print(text_embedder.run(text_to_embed))
 
     # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
-    # 'meta': {'model': 'jina-embeddings-v2-base-en',
+    # 'meta': {'model': 'jina-embeddings-v3',
     #          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
     ```
     """
 
     def __init__(
         self,
         api_key: Secret = Secret.from_env_var("JINA_API_KEY"),  # noqa: B008
-        model: str = "jina-embeddings-v2-base-en",
+        model: str = "jina-embeddings-v3",
         prefix: str = "",
         suffix: str = "",
+        task: Optional[str] = None,
+        dimensions: Optional[int] = None,
+        late_chunking: Optional[bool] = None,
     ):
         """
         Create a JinaTextEmbedder component.
@@ -65,6 +68,9 @@ def __init__(
                 "Content-type": "application/json",
             }
         )
+        self.task = task
+        self.dimensions = dimensions
+        self.late_chunking = late_chunking
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -78,9 +84,20 @@ def to_dict(self) -> Dict[str, Any]:
         :returns:
             Dictionary with serialized data.
         """
-        return default_to_dict(
-            self, api_key=self.api_key.to_dict(), model=self.model_name, prefix=self.prefix, suffix=self.suffix
-        )
+        kwargs = {
+            "api_key": self.api_key.to_dict(),
+            "model": self.model_name,
+            "prefix": self.prefix,
+            "suffix": self.suffix,
+        }
+        # Optional parameters, the following two are only supported by embeddings-v3 for now
+        if self.task:
+            kwargs["task"] = self.task
+        if self.dimensions:
+            kwargs["dimensions"] = self.dimensions
+        if self.late_chunking is not None:
+            kwargs["late_chunking"] = self.late_chunking
+        return default_to_dict(self, **kwargs)
 
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "JinaTextEmbedder":
@@ -114,7 +131,19 @@ def run(self, text: str):
 
         text_to_embed = self.prefix + text + self.suffix
 
-        resp = self._session.post(JINA_API_URL, json={"input": [text_to_embed], "model": self.model_name}).json()
+        parameters: Dict[str, Any] = {}
+        if self.task is not None:
+            parameters["task"] = self.task
+        if self.dimensions is not None:
+            parameters["dimensions"] = self.dimensions
+        if self.late_chunking is not None:
+            parameters["late_chunking"] = self.late_chunking
+
+        resp = self._session.post(
+            JINA_API_URL,
+            json={"input": [text_to_embed], "model": self.model_name, **parameters},
+        ).json()
+
         if "data" not in resp:
             raise RuntimeError(resp["detail"])
 

integrations/jina/tests/test_document_embedder.py

@@ -31,7 +31,7 @@ def test_init_default(self, monkeypatch):
         embedder = JinaDocumentEmbedder()
 
         assert embedder.api_key == Secret.from_env_var("JINA_API_KEY")
-        assert embedder.model_name == "jina-embeddings-v2-base-en"
+        assert embedder.model_name == "jina-embeddings-v3"
         assert embedder.prefix == ""
         assert embedder.suffix == ""
         assert embedder.batch_size == 32
@@ -49,6 +49,9 @@ def test_init_with_parameters(self):
             progress_bar=False,
             meta_fields_to_embed=["test_field"],
             embedding_separator=" | ",
+            task="retrieval.query",
+            dimensions=1024,
+            late_chunking=True,
         )
 
         assert embedder.api_key == Secret.from_token("fake-api-key")
@@ -59,6 +62,9 @@ def test_init_with_parameters(self):
         assert embedder.progress_bar is False
         assert embedder.meta_fields_to_embed == ["test_field"]
         assert embedder.embedding_separator == " | "
+        assert embedder.task == "retrieval.query"
+        assert embedder.dimensions == 1024
+        assert embedder.late_chunking is True
 
     def test_init_fail_wo_api_key(self, monkeypatch):
         monkeypatch.delenv("JINA_API_KEY", raising=False)
@@ -73,7 +79,7 @@ def test_to_dict(self, monkeypatch):
             "type": "haystack_integrations.components.embedders.jina.document_embedder.JinaDocumentEmbedder",
             "init_parameters": {
                 "api_key": {"env_vars": ["JINA_API_KEY"], "strict": True, "type": "env_var"},
-                "model": "jina-embeddings-v2-base-en",
+                "model": "jina-embeddings-v3",
                 "prefix": "",
                 "suffix": "",
                 "batch_size": 32,
@@ -93,6 +99,8 @@ def test_to_dict_with_custom_init_parameters(self, monkeypatch):
             progress_bar=False,
             meta_fields_to_embed=["test_field"],
             embedding_separator=" | ",
+            task="retrieval.query",
+            dimensions=1024,
         )
         data = component.to_dict()
         assert data == {
@@ -106,6 +114,8 @@ def test_to_dict_with_custom_init_parameters(self, monkeypatch):
                 "progress_bar": False,
                 "meta_fields_to_embed": ["test_field"],
                 "embedding_separator": " | ",
+                "task": "retrieval.query",
+                "dimensions": 1024,
             },
         }
 
@@ -246,3 +256,35 @@ def test_run_on_empty_list(self):
 
         assert result["documents"] is not None
         assert not result["documents"]  # empty list
+
+    def test_run_with_v3(self):
+        docs = [
+            Document(content="I love cheese", meta={"topic": "Cuisine"}),
+            Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
+        ]
+
+        model = "jina-embeddings-v3"
+        with patch("requests.sessions.Session.post", side_effect=mock_session_post_response):
+            embedder = JinaDocumentEmbedder(
+                api_key=Secret.from_token("fake-api-key"),
+                model=model,
+                prefix="prefix ",
+                suffix=" suffix",
+                meta_fields_to_embed=["topic"],
+                embedding_separator=" | ",
+                batch_size=1,
+                task="retrieval.query",
+            )
+            result = embedder.run(documents=docs)
+
+        documents_with_embeddings = result["documents"]
+        metadata = result["meta"]
+
+        assert isinstance(documents_with_embeddings, list)
+        assert len(documents_with_embeddings) == len(docs)
+        for doc in documents_with_embeddings:
+            assert isinstance(doc, Document)
+            assert isinstance(doc.embedding, list)
+            assert len(doc.embedding) == 3
+            assert all(isinstance(x, float) for x in doc.embedding)
+        assert metadata == {"model": model, "usage": {"prompt_tokens": 2 * 4, "total_tokens": 2 * 4}}

integrations/jina/tests/test_text_embedder.py

@@ -17,7 +17,7 @@ def test_init_default(self, monkeypatch):
         embedder = JinaTextEmbedder()
 
         assert embedder.api_key == Secret.from_env_var("JINA_API_KEY")
-        assert embedder.model_name == "jina-embeddings-v2-base-en"
+        assert embedder.model_name == "jina-embeddings-v3"
         assert embedder.prefix == ""
         assert embedder.suffix == ""
 
@@ -27,11 +27,13 @@ def test_init_with_parameters(self):
             model="model",
             prefix="prefix",
             suffix="suffix",
+            late_chunking=True,
         )
         assert embedder.api_key == Secret.from_token("fake-api-key")
         assert embedder.model_name == "model"
         assert embedder.prefix == "prefix"
         assert embedder.suffix == "suffix"
+        assert embedder.late_chunking is True
 
     def test_init_fail_wo_api_key(self, monkeypatch):
         monkeypatch.delenv("JINA_API_KEY", raising=False)
@@ -46,7 +48,7 @@ def test_to_dict(self, monkeypatch):
             "type": "haystack_integrations.components.embedders.jina.text_embedder.JinaTextEmbedder",
             "init_parameters": {
                 "api_key": {"env_vars": ["JINA_API_KEY"], "strict": True, "type": "env_var"},
-                "model": "jina-embeddings-v2-base-en",
+                "model": "jina-embeddings-v3",
                 "prefix": "",
                 "suffix": "",
             },
@@ -58,6 +60,8 @@ def test_to_dict_with_custom_init_parameters(self, monkeypatch):
             model="model",
             prefix="prefix",
             suffix="suffix",
+            task="retrieval.query",
+            dimensions=1024,
         )
         data = component.to_dict()
         assert data == {
@@ -67,6 +71,8 @@ def test_to_dict_with_custom_init_parameters(self, monkeypatch):
                 "model": "model",
                 "prefix": "prefix",
                 "suffix": "suffix",
+                "task": "retrieval.query",
+                "dimensions": 1024,
             },
         }
 
@@ -106,3 +112,36 @@ def test_run_wrong_input_format(self):
 
         with pytest.raises(TypeError, match="JinaTextEmbedder expects a string as an input"):
             embedder.run(text=list_integers_input)
+
+    def test_with_v3(self):
+        model = "jina-embeddings-v3"
+        with patch("requests.sessions.Session.post") as mock_post:
+            # Configure the mock to return a specific response
+            mock_response = requests.Response()
+            mock_response.status_code = 200
+            mock_response._content = json.dumps(
+                {
+                    "model": "jina-embeddings-v3",
+                    "object": "list",
+                    "usage": {"total_tokens": 6, "prompt_tokens": 6},
+                    "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}],
+                }
+            ).encode()
+
+            mock_post.return_value = mock_response
+
+            embedder = JinaTextEmbedder(
+                api_key=Secret.from_token("fake-api-key"),
+                model=model,
+                prefix="prefix ",
+                suffix=" suffix",
+                task="retrieval.query",
+            )
+            result = embedder.run(text="The food was delicious")
+
+        assert len(result["embedding"]) == 3
+        assert all(isinstance(x, float) for x in result["embedding"])
+        assert result["meta"] == {
+            "model": "jina-embeddings-v3",
+            "usage": {"prompt_tokens": 6, "total_tokens": 6},
+        }