Merge branch 'main' of https://github.com/deepset-ai/haystack-core-in…

…tegrations into weaviate-client-v4

.github/workflows/ollama.yml

@@ -22,6 +22,7 @@ env:
   PYTHONUNBUFFERED: "1"
   FORCE_COLOR: "1"
   LLM_FOR_TESTS: "orca-mini"
+  EMBEDDER_FOR_TESTS: "nomic-embed-text"
 
 jobs:
   run:
@@ -55,7 +56,10 @@ jobs:
         run: hatch run lint:all
 
       - name: Pull the LLM in the Ollama service
-        run: docker exec ollama ollama pull ${{ env.LLM_FOR_TESTS }}        
+        run: docker exec ollama ollama pull ${{ env.LLM_FOR_TESTS }}    
+
+      - name: Pull the Embedding Model in the Ollama service
+        run: docker exec ollama ollama pull ${{ env.EMBEDDER_FOR_TESTS }}
 
       - name: Generate docs
         if: matrix.python-version == '3.9' && runner.os == 'Linux'

.github/workflows/optimum.yml

@@ -52,9 +52,9 @@ jobs:
         if: matrix.python-version == '3.9' && runner.os == 'Linux'
         run: hatch run lint:all
 
-      - name: Generate docs
-        if: matrix.python-version == '3.9' && runner.os == 'Linux'
-        run: hatch run docs
+      # - name: Generate docs
+      #   if: matrix.python-version == '3.9' && runner.os == 'Linux'
+      #   run: hatch run docs
 
       - name: Run tests
         run: hatch run cov

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

@@ -43,17 +43,27 @@ def __init__(
         embedding_separator: str = "\n",
     ):
         """
-        Create a MistralDocumentEmbedder component.
-        :param api_key: The Mistral API key.
-        :param model: The name of the model to use.
-        :param api_base_url: The Mistral API Base url, defaults to None. For more details, see Mistral [docs](https://docs.mistral.ai/api/).
-        :param prefix: A string to add to the beginning of each text.
-        :param suffix: A string to add to the end of each text.
-        :param batch_size: Number of Documents to encode at once.
-        :param progress_bar: Whether to show a progress bar or not. Can be helpful to disable in production deployments
-                             to keep the logs clean.
-        :param meta_fields_to_embed: List of meta fields that should be embedded along with the Document text.
-        :param embedding_separator: Separator used to concatenate the meta fields to the Document text.
+        Creates a MistralDocumentEmbedder component.
+
+        :param api_key:
+            The Mistral API key.
+        :param model:
+            The name of the model to use.
+        :param api_base_url:
+            The Mistral API Base url. For more details, see Mistral [docs](https://docs.mistral.ai/api/).
+        :param prefix:
+            A string to add to the beginning of each text.
+        :param suffix:
+            A string to add to the end of each text.
+        :param batch_size:
+            Number of Documents to encode at once.
+        :param progress_bar:
+            Whether to show a progress bar or not. Can be helpful to disable in production deployments to keep
+            the logs clean.
+        :param meta_fields_to_embed:
+            List of meta fields that should be embedded along with the Document text.
+        :param embedding_separator:
+            Separator used to concatenate the meta fields to the Document text.
         """
         super(MistralDocumentEmbedder, self).__init__(  # noqa: UP008
             api_key=api_key,

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

@@ -11,22 +11,21 @@
 @component
 class MistralTextEmbedder(OpenAITextEmbedder):
     """
-     A component for embedding strings using Mistral models.
+    A component for embedding strings using Mistral models.
 
-     Usage example:
+    Usage example:
      ```python
     from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
 
-     text_to_embed = "I love pizza!"
+    text_to_embed = "I love pizza!"
+    text_embedder = MistralTextEmbedder()
+    print(text_embedder.run(text_to_embed))
 
-     text_embedder = MistralTextEmbedder()
-
-     print(text_embedder.run(text_to_embed))
-
-     # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
-     # 'meta': {'model': 'text-embedding-ada-002-v2',
-     #          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
-     ```
+    # output:
+    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
+    # 'meta': {'model': 'mistral-embed',
+    #          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
+    ```
     """
 
     def __init__(
@@ -38,14 +37,19 @@ def __init__(
         suffix: str = "",
     ):
         """
-        Create an MistralTextEmbedder component.
-
-        :param api_key: The Misttal API key.
-        :param model: The name of the Mistral embedding models to be used.
-        :param api_base_url: The Mistral API Base url, defaults to `https://api.mistral.ai/v1`.
-                             For more details, see Mistral [docs](https://docs.mistral.ai/api/).
-        :param prefix: A string to add to the beginning of each text.
-        :param suffix: A string to add to the end of each text.
+        Creates an MistralTextEmbedder component.
+
+        :param api_key:
+            The Mistral API key.
+        :param model:
+            The name of the Mistral embedding model to be used.
+        :param api_base_url:
+            The Mistral API Base url.
+            For more details, see Mistral [docs](https://docs.mistral.ai/api/).
+        :param prefix:
+            A string to add to the beginning of each text.
+        :param suffix:
+            A string to add to the end of each text.
         """
         super(MistralTextEmbedder, self).__init__(  # noqa: UP008
             api_key=api_key,

integrations/mistral/src/haystack_integrations/components/generators/mistral/chat/chat_generator.py

@@ -12,17 +12,27 @@
 @component
 class MistralChatGenerator(OpenAIChatGenerator):
     """
-    Enables text generation using Mistral's large language models (LLMs).
-    Currently supports `mistral-tiny`, `mistral-small` and `mistral-medium`
-    models accessed through the chat completions API endpoint.
+    Enables text generation using Mistral AI generative models.
+    For supported models, see [Mistral AI docs](https://docs.mistral.ai/platform/endpoints/#operation/listModels).
 
-    Users can pass any text generation parameters valid for the `openai.ChatCompletion.create` method
-    directly to this component via the `**generation_kwargs` parameter in __init__ or the `**generation_kwargs`
+    Users can pass any text generation parameters valid for the Mistral Chat Completion API
+    directly to this component via the `generation_kwargs` parameter in `__init__` or the `generation_kwargs`
     parameter in `run` method.
 
+    Key Features and Compatibility:
+    - **Primary Compatibility**: Designed to work seamlessly with the Mistral API Chat Completion endpoint.
+    - **Streaming Support**: Supports streaming responses from the Mistral API Chat Completion endpoint.
+    - **Customizability**: Supports all parameters supported by the Mistral API Chat Completion endpoint.
+
+    This component uses the ChatMessage format for structuring both input and output,
+    ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
+    Details on the ChatMessage format can be found in the
+    [Haystack docs](https://docs.haystack.deepset.ai/v2.0/docs/data-classes#chatmessage)
+
     For more details on the parameters supported by the Mistral API, refer to the
     [Mistral API Docs](https://docs.mistral.ai/api/).
 
+    Usage example:
     ```python
     from haystack_integrations.components.generators.mistral import MistralChatGenerator
     from haystack.dataclasses import ChatMessage
@@ -38,19 +48,7 @@ class MistralChatGenerator(OpenAIChatGenerator):
     >>meaningful and useful.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None,
     >>meta={'model': 'mistral-tiny', 'index': 0, 'finish_reason': 'stop',
     >>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
-
     ```
-
-     Key Features and Compatibility:
-         - **Primary Compatibility**: Designed to work seamlessly with the Mistral API Chat Completion endpoint.
-         - **Streaming Support**: Supports streaming responses from the Mistral API Chat Completion endpoint.
-         - **Customizability**: Supports all parameters supported by the Mistral API Chat Completion endpoint.
-
-     Input and Output Format:
-         - **ChatMessage Format**: This component uses the ChatMessage format for structuring both input and output,
-           ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
-           Details on the ChatMessage format can be found at: https://github.com/openai/openai-python/blob/main/chatml.md.
-           Note that the Mistral API does not accept `system` messages yet. You can use `user` and `assistant` messages.
     """
 
     def __init__(
@@ -65,15 +63,19 @@ def __init__(
         Creates an instance of MistralChatGenerator. Unless specified otherwise in the `model`, this is for Mistral's
         `mistral-tiny` model.
 
-        :param api_key: The Mistral API key.
-        :param model: The name of the Mistral chat completion model to use.
-        :param streaming_callback: A callback function that is called when a new token is received from the stream.
+        :param api_key:
+            The Mistral API key.
+        :param model:
+            The name of the Mistral chat completion model to use.
+        :param streaming_callback:
+            A callback function that is called when a new token is received from the stream.
             The callback function accepts StreamingChunk as an argument.
-        :param api_base_url: The Mistral API Base url, defaults to `https://api.mistral.ai/v1`.
-                             For more details, see Mistral [docs](https://docs.mistral.ai/api/).
-        :param generation_kwargs: Other parameters to use for the model. These parameters are all sent directly to
-            the Mistrak endpoint. See [Mistral API docs](https://docs.mistral.ai/api/t) for
-            more details.
+        :param api_base_url:
+            The Mistral API Base url.
+            For more details, see Mistral [docs](https://docs.mistral.ai/api/).
+        :param generation_kwargs:
+            Other parameters to use for the model. These parameters are all sent directly to
+            the Mistral endpoint. See [Mistral API docs](https://docs.mistral.ai/api/) for more details.
             Some of the supported parameters:
             - `max_tokens`: The maximum number of tokens the output text can have.
             - `temperature`: What sampling temperature to use. Higher values mean the model will take more risks.
@@ -83,7 +85,6 @@ def __init__(
                 comprising the top 10% probability mass are considered.
             - `stream`: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent
                 events as they become available, with the stream terminated by a data: [DONE] message.
-            - `stop`: One or more sequences after which the LLM should stop generating tokens.
             - `safe_prompt`: Whether to inject a safety prompt before all conversations.
             - `random_seed`: The seed to use for random sampling.
         """

integrations/ollama/pydoc/config.yml

@@ -3,7 +3,9 @@ loaders:
     search_path: [../src]
     modules: [
       "haystack_integrations.components.generators.ollama.generator",
-      "haystack_integrations.components.generators.ollama.chat.chat_generator"
+      "haystack_integrations.components.generators.ollama.chat.chat_generator",
+      "haystack_integrations.components.embedders.ollama.document_embedder",
+      "haystack_integrations.components.embedders.ollama.text_embedder",
     ]
     ignore_when_discovered: ["__init__"]
 processors:
@@ -26,4 +28,4 @@ renderer:
     descriptive_module_title: true
     add_method_class_prefix: true
     add_member_class_prefix: false
-    filename: _readme_ollama.md
+    filename: _readme_ollama.md

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

@@ -9,7 +9,7 @@
 class OllamaDocumentEmbedder:
     def __init__(
         self,
-        model: str = "orca-mini",
+        model: str = "nomic-embed-text",
         url: str = "http://localhost:11434/api/embeddings",
         generation_kwargs: Optional[Dict[str, Any]] = None,
         timeout: int = 120,
@@ -21,7 +21,7 @@ def __init__(
     ):
         """
         :param model: The name of the model to use. The model should be available in the running Ollama instance.
-            Default is "orca-mini".
+            Default is "nomic-embed-text". "https://ollama.com/library/nomic-embed-text"
         :param url: The URL of the chat endpoint of a running Ollama instance.
             Default is "http://localhost:11434/api/embeddings".
         :param generation_kwargs: Optional arguments to pass to the Ollama generation endpoint, such as temperature,

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

@@ -8,14 +8,14 @@
 class OllamaTextEmbedder:
     def __init__(
         self,
-        model: str = "orca-mini",
+        model: str = "nomic-embed-text",
         url: str = "http://localhost:11434/api/embeddings",
         generation_kwargs: Optional[Dict[str, Any]] = None,
         timeout: int = 120,
     ):
         """
         :param model: The name of the model to use. The model should be available in the running Ollama instance.
-            Default is "orca-mini".
+            Default is "nomic-embed-text". "https://ollama.com/library/nomic-embed-text"
         :param url: The URL of the chat endpoint of a running Ollama instance.
             Default is "http://localhost:11434/api/embeddings".
         :param generation_kwargs: Optional arguments to pass to the Ollama generation endpoint, such as temperature,

integrations/ollama/tests/test_document_embedder.py

@@ -11,11 +11,11 @@ def test_init_defaults(self):
         assert embedder.timeout == 120
         assert embedder.generation_kwargs == {}
         assert embedder.url == "http://localhost:11434/api/embeddings"
-        assert embedder.model == "orca-mini"
+        assert embedder.model == "nomic-embed-text"
 
     def test_init(self):
         embedder = OllamaDocumentEmbedder(
-            model="orca-mini",
+            model="nomic-embed-text",
             url="http://my-custom-endpoint:11434/api/embeddings",
             generation_kwargs={"temperature": 0.5},
             timeout=3000,
@@ -24,7 +24,7 @@ def test_init(self):
         assert embedder.timeout == 3000
         assert embedder.generation_kwargs == {"temperature": 0.5}
         assert embedder.url == "http://my-custom-endpoint:11434/api/embeddings"
-        assert embedder.model == "orca-mini"
+        assert embedder.model == "nomic-embed-text"
 
     @pytest.mark.integration
     def test_model_not_found(self):
@@ -35,17 +35,17 @@ def test_model_not_found(self):
 
     @pytest.mark.integration
     def import_text_in_embedder(self):
-        embedder = OllamaDocumentEmbedder(model="orca-mini")
+        embedder = OllamaDocumentEmbedder(model="nomic-embed-text")
 
         with pytest.raises(TypeError):
             embedder.run("This is a text string. This should not work.")
 
     @pytest.mark.integration
     def test_run(self):
-        embedder = OllamaDocumentEmbedder(model="orca-mini")
+        embedder = OllamaDocumentEmbedder(model="nomic-embed-text")
         list_of_docs = [Document(content="This is a document containing some text.")]
         reply = embedder.run(list_of_docs)
 
         assert isinstance(reply, dict)
         assert all(isinstance(element, float) for element in reply["documents"][0].embedding)
-        assert reply["meta"]["model"] == "orca-mini"
+        assert reply["meta"]["model"] == "nomic-embed-text"

integrations/ollama/tests/test_text_embedder.py

@@ -10,7 +10,7 @@ def test_init_defaults(self):
         assert embedder.timeout == 120
         assert embedder.generation_kwargs == {}
         assert embedder.url == "http://localhost:11434/api/embeddings"
-        assert embedder.model == "orca-mini"
+        assert embedder.model == "nomic-embed-text"
 
     def test_init(self):
         embedder = OllamaTextEmbedder(
@@ -34,10 +34,10 @@ def test_model_not_found(self):
 
     @pytest.mark.integration
     def test_run(self):
-        embedder = OllamaTextEmbedder(model="orca-mini")
+        embedder = OllamaTextEmbedder(model="nomic-embed-text")
 
         reply = embedder.run("hello")
 
         assert isinstance(reply, dict)
         assert all(isinstance(element, float) for element in reply["embedding"])
-        assert reply["meta"]["model"] == "orca-mini"
+        assert reply["meta"]["model"] == "nomic-embed-text"

integrations/optimum/pydoc/config.yml

@@ -6,6 +6,8 @@ loaders:
         "haystack_integrations.components.embedders.optimum.optimum_document_embedder",
         "haystack_integrations.components.embedders.optimum.optimum_text_embedder",
         "haystack_integrations.components.embedders.optimum.pooling",
+        "haystack_integrations.components.embedders.optimum.optimization",
+        "haystack_integrations.components.embedders.optimum.quantization",
       ]
     ignore_when_discovered: ["__init__"]
 processors:

integrations/optimum/src/haystack_integrations/components/embedders/optimum/__init__.py

@@ -2,8 +2,18 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from .optimization import OptimumEmbedderOptimizationConfig, OptimumEmbedderOptimizationMode
 from .optimum_document_embedder import OptimumDocumentEmbedder
 from .optimum_text_embedder import OptimumTextEmbedder
 from .pooling import OptimumEmbedderPooling
+from .quantization import OptimumEmbedderQuantizationConfig, OptimumEmbedderQuantizationMode
 
-__all__ = ["OptimumDocumentEmbedder", "OptimumEmbedderPooling", "OptimumTextEmbedder"]
+__all__ = [
+    "OptimumDocumentEmbedder",
+    "OptimumEmbedderOptimizationMode",
+    "OptimumEmbedderOptimizationConfig",
+    "OptimumEmbedderPooling",
+    "OptimumEmbedderQuantizationMode",
+    "OptimumEmbedderQuantizationConfig",
+    "OptimumTextEmbedder",
+]