Merge branch 'main' into fix/bug-1174-langfuse-parent-span

integrations/anthropic/src/haystack_integrations/components/generators/anthropic/__init__.py

@@ -2,6 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 from .chat.chat_generator import AnthropicChatGenerator
+from .chat.vertex_chat_generator import AnthropicVertexChatGenerator
 from .generator import AnthropicGenerator
 
-__all__ = ["AnthropicGenerator", "AnthropicChatGenerator"]
+__all__ = ["AnthropicGenerator", "AnthropicChatGenerator", "AnthropicVertexChatGenerator"]

integrations/anthropic/src/haystack_integrations/components/generators/anthropic/chat/vertex_chat_generator.py

@@ -0,0 +1,135 @@
+import os
+from typing import Any, Callable, Dict, Optional
+
+from haystack import component, default_from_dict, default_to_dict, logging
+from haystack.dataclasses import StreamingChunk
+from haystack.utils import deserialize_callable, serialize_callable
+
+from anthropic import AnthropicVertex
+
+from .chat_generator import AnthropicChatGenerator
+
+logger = logging.getLogger(__name__)
+
+
+@component
+class AnthropicVertexChatGenerator(AnthropicChatGenerator):
+    """
+
+    Enables text generation using state-of-the-art Claude 3 LLMs via the Anthropic Vertex AI API.
+    It supports models such as `Claude 3.5 Sonnet`, `Claude 3 Opus`, `Claude 3 Sonnet`, and `Claude 3 Haiku`,
+    accessible through the Vertex AI API endpoint.
+
+    To use AnthropicVertexChatGenerator, you must have a GCP project with Vertex AI enabled.
+    Additionally, ensure that the desired Anthropic model is activated in the Vertex AI Model Garden.
+    Before making requests, you may need to authenticate with GCP using `gcloud auth login`.
+    For more details, refer to the [guide] (https://docs.anthropic.com/en/api/claude-on-vertex-ai).
+
+    Any valid text generation parameters for the Anthropic messaging API can be passed to
+    the AnthropicVertex API. Users can provide these parameters directly to the component via
+    the `generation_kwargs` parameter in `__init__` or the `run` method.
+
+    For more details on the parameters supported by the Anthropic API, refer to the
+    Anthropic Message API [documentation](https://docs.anthropic.com/en/api/messages).
+
+    ```python
+    from haystack_integrations.components.generators.anthropic import AnthropicVertexChatGenerator
+    from haystack.dataclasses import ChatMessage
+
+    messages = [ChatMessage.from_user("What's Natural Language Processing?")]
+    client = AnthropicVertexChatGenerator(
+                model="claude-3-sonnet@20240229",
+                project_id="your-project-id", region="your-region"
+            )
+    response = client.run(messages)
+    print(response)
+
+    >> {'replies': [ChatMessage(content='Natural Language Processing (NLP) is a field of artificial intelligence that
+    >> focuses on enabling computers to understand, interpret, and generate human language. It involves developing
+    >> techniques and algorithms to analyze and process text or speech data, allowing machines to comprehend and
+    >> communicate in natural languages like English, Spanish, or Chinese.', role=<ChatRole.ASSISTANT: 'assistant'>,
+    >> name=None, meta={'model': 'claude-3-sonnet@20240229', 'index': 0, 'finish_reason': 'end_turn',
+    >> 'usage': {'input_tokens': 15, 'output_tokens': 64}})]}
+    ```
+
+    For more details on supported models and their capabilities, refer to the Anthropic
+    [documentation](https://docs.anthropic.com/claude/docs/intro-to-claude).
+
+    """
+
+    def __init__(
+        self,
+        region: str,
+        project_id: str,
+        model: str = "claude-3-5-sonnet@20240620",
+        streaming_callback: Optional[Callable[[StreamingChunk], None]] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        ignore_tools_thinking_messages: bool = True,
+    ):
+        """
+        Creates an instance of AnthropicVertexChatGenerator.
+
+        :param region: The region where the Anthropic model is deployed. Defaults to "us-central1".
+        :param project_id: The GCP project ID where the Anthropic model is deployed.
+        :param model: The name of the 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 generation_kwargs: Other parameters to use for the model. These parameters are all sent directly to
+            the AnthropicVertex endpoint. See Anthropic [documentation](https://docs.anthropic.com/claude/reference/messages_post)
+            for more details.
+
+            Supported generation_kwargs parameters are:
+            - `system`: The system message to be passed to the model.
+            - `max_tokens`: The maximum number of tokens to generate.
+            - `metadata`: A dictionary of metadata to be passed to the model.
+            - `stop_sequences`: A list of strings that the model should stop generating at.
+            - `temperature`: The temperature to use for sampling.
+            - `top_p`: The top_p value to use for nucleus sampling.
+            - `top_k`: The top_k value to use for top-k sampling.
+            - `extra_headers`: A dictionary of extra headers to be passed to the model (i.e. for beta features).
+        :param ignore_tools_thinking_messages: Anthropic's approach to tools (function calling) resolution involves a
+            "chain of thought" messages before returning the actual function names and parameters in a message. If
+            `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
+            use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
+            for more details.
+        """
+        self.region = region or os.environ.get("REGION")
+        self.project_id = project_id or os.environ.get("PROJECT_ID")
+        self.model = model
+        self.generation_kwargs = generation_kwargs or {}
+        self.streaming_callback = streaming_callback
+        self.client = AnthropicVertex(region=self.region, project_id=self.project_id)
+        self.ignore_tools_thinking_messages = ignore_tools_thinking_messages
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serialize this component to a dictionary.
+
+        :returns:
+            The serialized component as a dictionary.
+        """
+        callback_name = serialize_callable(self.streaming_callback) if self.streaming_callback else None
+        return default_to_dict(
+            self,
+            region=self.region,
+            project_id=self.project_id,
+            model=self.model,
+            streaming_callback=callback_name,
+            generation_kwargs=self.generation_kwargs,
+            ignore_tools_thinking_messages=self.ignore_tools_thinking_messages,
+        )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "AnthropicVertexChatGenerator":
+        """
+        Deserialize this component from a dictionary.
+
+        :param data: The dictionary representation of this component.
+        :returns:
+            The deserialized component instance.
+        """
+        init_params = data.get("init_parameters", {})
+        serialized_callback_handler = init_params.get("streaming_callback")
+        if serialized_callback_handler:
+            data["init_parameters"]["streaming_callback"] = deserialize_callable(serialized_callback_handler)
+        return default_from_dict(cls, data)

integrations/anthropic/tests/test_vertex_chat_generator.py

@@ -0,0 +1,197 @@
+import os
+
+import anthropic
+import pytest
+from haystack.components.generators.utils import print_streaming_chunk
+from haystack.dataclasses import ChatMessage, ChatRole
+
+from haystack_integrations.components.generators.anthropic import AnthropicVertexChatGenerator
+
+
+@pytest.fixture
+def chat_messages():
+    return [
+        ChatMessage.from_system("\\nYou are a helpful assistant, be super brief in your responses."),
+        ChatMessage.from_user("What's the capital of France?"),
+    ]
+
+
+class TestAnthropicVertexChatGenerator:
+    def test_init_default(self):
+        component = AnthropicVertexChatGenerator(region="us-central1", project_id="test-project-id")
+        assert component.region == "us-central1"
+        assert component.project_id == "test-project-id"
+        assert component.model == "claude-3-5-sonnet@20240620"
+        assert component.streaming_callback is None
+        assert not component.generation_kwargs
+        assert component.ignore_tools_thinking_messages
+
+    def test_init_with_parameters(self):
+        component = AnthropicVertexChatGenerator(
+            region="us-central1",
+            project_id="test-project-id",
+            model="claude-3-5-sonnet@20240620",
+            streaming_callback=print_streaming_chunk,
+            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
+            ignore_tools_thinking_messages=False,
+        )
+        assert component.region == "us-central1"
+        assert component.project_id == "test-project-id"
+        assert component.model == "claude-3-5-sonnet@20240620"
+        assert component.streaming_callback is print_streaming_chunk
+        assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
+        assert component.ignore_tools_thinking_messages is False
+
+    def test_to_dict_default(self):
+        component = AnthropicVertexChatGenerator(region="us-central1", project_id="test-project-id")
+        data = component.to_dict()
+        assert data == {
+            "type": (
+                "haystack_integrations.components.generators."
+                "anthropic.chat.vertex_chat_generator.AnthropicVertexChatGenerator"
+            ),
+            "init_parameters": {
+                "region": "us-central1",
+                "project_id": "test-project-id",
+                "model": "claude-3-5-sonnet@20240620",
+                "streaming_callback": None,
+                "generation_kwargs": {},
+                "ignore_tools_thinking_messages": True,
+            },
+        }
+
+    def test_to_dict_with_parameters(self):
+        component = AnthropicVertexChatGenerator(
+            region="us-central1",
+            project_id="test-project-id",
+            streaming_callback=print_streaming_chunk,
+            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
+        )
+        data = component.to_dict()
+        assert data == {
+            "type": (
+                "haystack_integrations.components.generators."
+                "anthropic.chat.vertex_chat_generator.AnthropicVertexChatGenerator"
+            ),
+            "init_parameters": {
+                "region": "us-central1",
+                "project_id": "test-project-id",
+                "model": "claude-3-5-sonnet@20240620",
+                "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
+                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
+                "ignore_tools_thinking_messages": True,
+            },
+        }
+
+    def test_to_dict_with_lambda_streaming_callback(self):
+        component = AnthropicVertexChatGenerator(
+            region="us-central1",
+            project_id="test-project-id",
+            model="claude-3-5-sonnet@20240620",
+            streaming_callback=lambda x: x,
+            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
+        )
+        data = component.to_dict()
+        assert data == {
+            "type": (
+                "haystack_integrations.components.generators."
+                "anthropic.chat.vertex_chat_generator.AnthropicVertexChatGenerator"
+            ),
+            "init_parameters": {
+                "region": "us-central1",
+                "project_id": "test-project-id",
+                "model": "claude-3-5-sonnet@20240620",
+                "streaming_callback": "tests.test_vertex_chat_generator.<lambda>",
+                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
+                "ignore_tools_thinking_messages": True,
+            },
+        }
+
+    def test_from_dict(self):
+        data = {
+            "type": (
+                "haystack_integrations.components.generators."
+                "anthropic.chat.vertex_chat_generator.AnthropicVertexChatGenerator"
+            ),
+            "init_parameters": {
+                "region": "us-central1",
+                "project_id": "test-project-id",
+                "model": "claude-3-5-sonnet@20240620",
+                "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
+                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
+                "ignore_tools_thinking_messages": True,
+            },
+        }
+        component = AnthropicVertexChatGenerator.from_dict(data)
+        assert component.model == "claude-3-5-sonnet@20240620"
+        assert component.region == "us-central1"
+        assert component.project_id == "test-project-id"
+        assert component.streaming_callback is print_streaming_chunk
+        assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
+
+    def test_run(self, chat_messages, mock_chat_completion):
+        component = AnthropicVertexChatGenerator(region="us-central1", project_id="test-project-id")
+        response = component.run(chat_messages)
+
+        # check that the component returns the correct ChatMessage response
+        assert isinstance(response, dict)
+        assert "replies" in response
+        assert isinstance(response["replies"], list)
+        assert len(response["replies"]) == 1
+        assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
+
+    def test_run_with_params(self, chat_messages, mock_chat_completion):
+        component = AnthropicVertexChatGenerator(
+            region="us-central1", project_id="test-project-id", generation_kwargs={"max_tokens": 10, "temperature": 0.5}
+        )
+        response = component.run(chat_messages)
+
+        # check that the component calls the Anthropic API with the correct parameters
+        _, kwargs = mock_chat_completion.call_args
+        assert kwargs["max_tokens"] == 10
+        assert kwargs["temperature"] == 0.5
+
+        # check that the component returns the correct response
+        assert isinstance(response, dict)
+        assert "replies" in response
+        assert isinstance(response["replies"], list)
+        assert len(response["replies"]) == 1
+        assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
+
+    @pytest.mark.skipif(
+        not (os.environ.get("REGION", None) or os.environ.get("PROJECT_ID", None)),
+        reason="Authenticate with GCP and set env variables REGION and PROJECT_ID to run this test.",
+    )
+    @pytest.mark.integration
+    def test_live_run_wrong_model(self, chat_messages):
+        component = AnthropicVertexChatGenerator(
+            model="something-obviously-wrong", region=os.environ.get("REGION"), project_id=os.environ.get("PROJECT_ID")
+        )
+        with pytest.raises(anthropic.NotFoundError):
+            component.run(chat_messages)
+
+    @pytest.mark.skipif(
+        not (os.environ.get("REGION", None) or os.environ.get("PROJECT_ID", None)),
+        reason="Authenticate with GCP and set env variables REGION and PROJECT_ID to run this test.",
+    )
+    @pytest.mark.integration
+    def test_default_inference_params(self, chat_messages):
+        client = AnthropicVertexChatGenerator(
+            region=os.environ.get("REGION"), project_id=os.environ.get("PROJECT_ID"), model="claude-3-sonnet@20240229"
+        )
+        response = client.run(chat_messages)
+
+        assert "replies" in response, "Response does not contain 'replies' key"
+        replies = response["replies"]
+        assert isinstance(replies, list), "Replies is not a list"
+        assert len(replies) > 0, "No replies received"
+
+        first_reply = replies[0]
+        assert isinstance(first_reply, ChatMessage), "First reply is not a ChatMessage instance"
+        assert first_reply.content, "First reply has no content"
+        assert ChatMessage.is_from(first_reply, ChatRole.ASSISTANT), "First reply is not from the assistant"
+        assert "paris" in first_reply.content.lower(), "First reply does not contain 'paris'"
+        assert first_reply.meta, "First reply has no metadata"
+
+    # Anthropic messages API is similar for AnthropicVertex and Anthropic endpoint,
+    # remaining tests are skipped for AnthropicVertexChatGenerator as they are already tested in AnthropicChatGenerator.

integrations/azure_ai_search/example/document_store.py

@@ -1,5 +1,4 @@
 from haystack import Document
-from haystack.document_stores.types import DuplicatePolicy
 
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
 
@@ -30,7 +29,7 @@
         meta={"version": 2.0, "label": "chapter_three"},
     ),
 ]
-document_store.write_documents(documents, policy=DuplicatePolicy.SKIP)
+document_store.write_documents(documents)
 
 filters = {
     "operator": "AND",

integrations/azure_ai_search/example/embedding_retrieval.py

@@ -1,7 +1,6 @@
 from haystack import Document, Pipeline
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
 from haystack.components.writers import DocumentWriter
-from haystack.document_stores.types import DuplicatePolicy
 
 from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
@@ -38,9 +37,7 @@
 # Indexing Pipeline
 indexing_pipeline = Pipeline()
 indexing_pipeline.add_component(instance=document_embedder, name="doc_embedder")
-indexing_pipeline.add_component(
-    instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="doc_writer"
-)
+indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="doc_writer")
 indexing_pipeline.connect("doc_embedder", "doc_writer")
 
 indexing_pipeline.run({"doc_embedder": {"documents": documents}})