Updates after review: imports, tests, project file

Author: Ryzhtus

integrations/dspy/CHANGELOG.md

@@ -3,63 +3,8 @@
 [![PyPI - Version](https://img.shields.io/pypi/v/dspy-haystack.svg)](https://pypi.org/project/dspy-haystack)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dspy-haystack.svg)](https://pypi.org/project/dspy-haystack)
 
-An integration between [DSPy](https://github.com/stanfordnlp/dspy) and [Haystack](https://haystack.deepset.ai/).
+---
 
-DSPy is a framework for algorithmically optimizing prompts for Language Models by applying classical machine learning concepts (training data, evaluation metrics, optimization).
+## Contributing
 
-This integration provides:
-- **DSPyChatGenerator** — a Haystack ChatGenerator component that uses DSPy signatures and modules for structured generation
-
-## Installation
-
-```bash
-pip install dspy-haystack
-```
-
-## Quick Start
-
-### DSPyChatGenerator
-
-A Haystack chat generator that uses DSPy signatures for structured generation with built-in reasoning patterns (Chain-of-Thought, Predict, ReAct).
-
-```python
-from haystack import Pipeline
-from haystack.dataclasses import ChatMessage
-from haystack_integrations.components.generators.dspy import DSPyChatGenerator
-import dspy
-
-# Define a DSPy signature
-class QASignature(dspy.Signature):
-    """Answer questions accurately and concisely."""
-    question = dspy.InputField(desc="The user's question")
-    answer = dspy.OutputField(desc="A clear, concise answer")
-
-# Create the generator
-generator = DSPyChatGenerator(
-    model="openai/gpt-5-mini",
-    signature=QASignature,
-    module_type="ChainOfThought"
-)
-
-# Use in pipeline
-pipeline = Pipeline()
-pipeline.add_component("llm", generator)
-
-messages = [ChatMessage.from_user("What is the capital of France?")]
-result = pipeline.run({"llm": {"messages": messages}})
-print(result["llm"]["replies"][0].text)
-```
-
-You can also use string signatures for quick prototyping:
-
-```python
-generator = DSPyChatGenerator(
-    model="openai/gpt-5-mini",
-    signature="question -> answer",
-    module_type="Predict"
-)
-```
-
-## License
-
-`dspy-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
+Refer to the general [Contribution Guidelines](https://github.com/deepset-ai/haystack-core-integrations/blob/main/CONTRIBUTING.md).

integrations/dspy/README.md

@@ -1,28 +1,13 @@
 loaders:
-- ignore_when_discovered:
-  - __init__
-  modules:
-  - haystack_integrations.components.generators.dspy.chat.chat_generator
-  search_path:
-  - ../src
-  type: haystack_pydoc_tools.loaders.CustomPythonLoader
+  - modules:
+      - haystack_integrations.components.generators.dspy.chat.chat_generator
+    search_path: [../src]
 processors:
-- do_not_filter_modules: false
-  documented_only: true
-  expression: null
-  skip_empty_modules: true
-  type: filter
-- type: smart
-- type: crossref
+  - type: filter
+    documented_only: true
+    skip_empty_modules: true
 renderer:
   description: DSPy integration for Haystack
   id: integrations-dspy
-  markdown:
-    add_member_class_prefix: false
-    add_method_class_prefix: true
-    classdef_code_block: false
-    descriptive_class_title: false
-    descriptive_module_title: true
-    filename: dspy.md
+  filename: dspy.md
   title: DSPy
-  type: haystack_pydoc_tools.renderers.DocusaurusRenderer

integrations/dspy/pydoc/config_docusaurus.yml

@@ -45,7 +45,7 @@ installer = "uv"
 dependencies = ["haystack-pydoc-tools", "ruff"]
 
 [tool.hatch.envs.default.scripts]
-docs = ["pydoc-markdown pydoc/config_docusaurus.yml"]
+docs = ["haystack-pydoc pydoc/config_docusaurus.yml"]
 fmt = "ruff check --fix {args}; ruff format {args}"
 fmt-check = "ruff check {args} && ruff format --check {args}"
 
@@ -146,6 +146,6 @@ exclude_lines = ["no cov", "if __name__ == .__main__.:", "if TYPE_CHECKING:"]
 [tool.pytest.ini_options]
 markers = ["integration: integration tests"]
 log_cli = true
-addopts = ["--import-mode=importlib"]
+addopts = ["--strict-markers"]
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "class"

integrations/dspy/pyproject.toml

@@ -1,3 +1,3 @@
-from haystack_integrations.components.generators.dspy.chat.chat_generator import DSPyChatGenerator
+from .chat.chat_generator import DSPyChatGenerator
 
 __all__ = ["DSPyChatGenerator"]

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

@@ -9,7 +9,7 @@
 VALID_MODULE_TYPES = {"Predict", "ChainOfThought", "ReAct"}
 
 
-def configure_dspy_lm(model: str, api_key: str, **kwargs: Any) -> dspy.LM:
+def _configure_dspy_lm(model: str, api_key: str, **kwargs: Any) -> dspy.LM:
     """
     Create and configure a DSPy language model.
 
@@ -23,7 +23,7 @@ def configure_dspy_lm(model: str, api_key: str, **kwargs: Any) -> dspy.LM:
     return lm
 
 
-def get_dspy_module_class(module_type: str):
+def _get_dspy_module_class(module_type: str):
     """
     Map a module type string to the corresponding DSPy module class.
 
@@ -111,13 +111,13 @@ def __init__(
         self.input_mapping = input_mapping
         self.streaming_callback = streaming_callback
 
-        self._lm = configure_dspy_lm(
+        self._lm = _configure_dspy_lm(
             model=self.model,
             api_key=self.api_key.resolve_value(),
             **self.generation_kwargs,
         )
 
-        module_class = get_dspy_module_class(self.module_type)
+        module_class = _get_dspy_module_class(self.module_type)
         self._module = module_class(self.signature)
 
     def _build_dspy_inputs(self, prompt: str, **kwargs) -> dict[str, Any]:

integrations/dspy/src/haystack_integrations/components/generators/dspy/chat/__init__.py

@@ -9,8 +9,8 @@
 from haystack_integrations.components.generators.dspy.chat.chat_generator import (
     VALID_MODULE_TYPES,
     DSPyChatGenerator,
-    configure_dspy_lm,
-    get_dspy_module_class,
+    _configure_dspy_lm,
+    _get_dspy_module_class,
 )
 
 
@@ -66,21 +66,21 @@ def test_contains_expected_types(self):
 
 class TestGetDspyModuleClass:
     def test_predict(self):
-        assert get_dspy_module_class("Predict") is dspy.Predict
+        assert _get_dspy_module_class("Predict") is dspy.Predict
 
     def test_chain_of_thought(self):
-        assert get_dspy_module_class("ChainOfThought") is dspy.ChainOfThought
+        assert _get_dspy_module_class("ChainOfThought") is dspy.ChainOfThought
 
     def test_react(self):
-        assert get_dspy_module_class("ReAct") is dspy.ReAct
+        assert _get_dspy_module_class("ReAct") is dspy.ReAct
 
     def test_invalid_type_raises(self):
         with pytest.raises(ValueError, match="Invalid module_type 'Unknown'"):
-            get_dspy_module_class("Unknown")
+            _get_dspy_module_class("Unknown")
 
     def test_invalid_type_lists_valid_options(self):
         with pytest.raises(ValueError, match="ChainOfThought"):
-            get_dspy_module_class("BadType")
+            _get_dspy_module_class("BadType")
 
 
 class TestConfigureDspyLm:
@@ -90,7 +90,7 @@ def test_creates_lm_and_configures(self, mock_lm_class, mock_configure):
         mock_lm = MagicMock()
         mock_lm_class.return_value = mock_lm
 
-        result = configure_dspy_lm(model="openai/gpt-5-mini", api_key="test-key")
+        result = _configure_dspy_lm(model="openai/gpt-5-mini", api_key="test-key")
 
         mock_lm_class.assert_called_once_with(model="openai/gpt-5-mini", api_key="test-key")
         mock_configure.assert_called_once_with(lm=mock_lm)
@@ -102,7 +102,7 @@ def test_passes_extra_kwargs(self, mock_lm_class, mock_configure):
         mock_lm = MagicMock()
         mock_lm_class.return_value = mock_lm
 
-        configure_dspy_lm(model="openai/gpt-5-mini", api_key="test-key", temperature=0.7, max_tokens=100)
+        _configure_dspy_lm(model="openai/gpt-5-mini", api_key="test-key", temperature=0.7, max_tokens=100)
 
         mock_lm_class.assert_called_once_with(
             model="openai/gpt-5-mini", api_key="test-key", temperature=0.7, max_tokens=100
@@ -350,3 +350,62 @@ def test_live_run(self):
         assert len(results["replies"]) == 1
         message: ChatMessage = results["replies"][0]
         assert "Paris" in message.text
+
+    @pytest.mark.skipif(
+        not os.environ.get("OPENAI_API_KEY", None),
+        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+    )
+    @pytest.mark.integration
+    def test_live_run_with_predict_module(self):
+        """Test using the Predict module type with a string signature."""
+        chat_messages = [ChatMessage.from_user("What is 2 + 2?")]
+        component = DSPyChatGenerator(
+            signature="question -> answer",
+            module_type="Predict",
+        )
+        results = component.run(chat_messages)
+        assert len(results["replies"]) == 1
+        assert "4" in results["replies"][0].text
+
+    @pytest.mark.skipif(
+        not os.environ.get("OPENAI_API_KEY", None),
+        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+    )
+    @pytest.mark.integration
+    def test_live_run_with_signature_class(self):
+        """Test using a dspy.Signature class instead of a string signature."""
+
+        class QASignature(dspy.Signature):
+            """Answer questions accurately and concisely."""
+
+            question = dspy.InputField(desc="The user's question")
+            answer = dspy.OutputField(desc="A clear, concise answer")
+
+        chat_messages = [ChatMessage.from_user("What language is spoken in Brazil?")]
+        component = DSPyChatGenerator(
+            signature=QASignature,
+            module_type="ChainOfThought",
+        )
+        results = component.run(chat_messages)
+        assert len(results["replies"]) == 1
+        assert "Portuguese" in results["replies"][0].text
+
+    @pytest.mark.skipif(
+        not os.environ.get("OPENAI_API_KEY", None),
+        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+    )
+    @pytest.mark.integration
+    def test_live_run_with_multi_field_signature(self):
+        """Test using a multi-input signature with input_mapping."""
+        chat_messages = [ChatMessage.from_user("What is the main topic?")]
+        component = DSPyChatGenerator(
+            signature="context, question -> answer",
+            module_type="Predict",
+            input_mapping={"context": "context", "question": "question"},
+        )
+        results = component.run(
+            chat_messages,
+            context="Python is a popular programming language created by Guido van Rossum.",
+        )
+        assert len(results["replies"]) == 1
+        assert results["replies"][0].text