Merge branch 'main' into add-recursive-chunking

docs/pydoc/config/tools_api.yml

@@ -0,0 +1,27 @@
+loaders:
+  - type: haystack_pydoc_tools.loaders.CustomPythonLoader
+    search_path: [../../../haystack/components/tools]
+    modules: ["tool_invoker"]
+    ignore_when_discovered: ["__init__"]
+processors:
+  - type: filter
+    expression:
+    documented_only: true
+    do_not_filter_modules: false
+    skip_empty_modules: true
+  - type: smart
+  - type: crossref
+renderer:
+  type: haystack_pydoc_tools.renderers.ReadmeCoreRenderer
+  excerpt: Components related to Tool Calling.
+  category_slug: haystack-api
+  title: Tools
+  slug: tools-api
+  order: 152
+  markdown:
+    descriptive_class_title: false
+    classdef_code_block: false
+    descriptive_module_title: true
+    add_method_class_prefix: true
+    add_member_class_prefix: false
+    filename: tools_api.md

haystack/components/generators/chat/azure.py

@@ -22,7 +22,7 @@ class AzureOpenAIChatGenerator(OpenAIChatGenerator):
     Generates text using OpenAI's models on Azure.
 
     It works with the gpt-4 and gpt-3.5-turbo - type models and supports streaming responses
-    from OpenAI API. It uses [ChatMessage](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage)
+    from OpenAI API. It uses [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage)
     format in input and output.
 
     You can customize how the text is generated by passing parameters to the
@@ -142,6 +142,11 @@ def __init__(  # pylint: disable=too-many-positional-arguments
         self.max_retries = max_retries or int(os.environ.get("OPENAI_MAX_RETRIES", 5))
         self.default_headers = default_headers or {}
 
+        # This ChatGenerator does not yet supports tools. The following workaround ensures that we do not
+        # get an error when invoking the run method of the parent class (OpenAIChatGenerator).
+        self.tools = None
+        self.tools_strict = False
+
         self.client = AzureOpenAI(
             api_version=api_version,
             azure_endpoint=azure_endpoint,

haystack/components/generators/chat/hugging_face_api.py

@@ -29,7 +29,7 @@ class HuggingFaceAPIChatGenerator:
     """
     Completes chats using Hugging Face APIs.
 
-    HuggingFaceAPIChatGenerator uses the [ChatMessage](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage)
+    HuggingFaceAPIChatGenerator uses the [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage)
     format for input and output. Use it to generate text with Hugging Face APIs:
     - [Free Serverless Inference API](https://huggingface.co/inference-api)
     - [Paid Inference Endpoints](https://huggingface.co/inference-endpoints)
@@ -163,10 +163,9 @@ def __init__(  # pylint: disable=too-many-positional-arguments
             msg = f"Unknown api_type {api_type}"
             raise ValueError(msg)
 
-        if tools:
-            if streaming_callback is not None:
-                raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
-            _check_duplicate_tool_names(tools)
+        if tools and streaming_callback is not None:
+            raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
+        _check_duplicate_tool_names(tools)
 
         # handle generation kwargs setup
         generation_kwargs = generation_kwargs.copy() if generation_kwargs else {}
@@ -241,10 +240,9 @@ def run(
         formatted_messages = [convert_message_to_hf_format(message) for message in messages]
 
         tools = tools or self.tools
-        if tools:
-            if self.streaming_callback:
-                raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
-            _check_duplicate_tool_names(tools)
+        if tools and self.streaming_callback:
+            raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
+        _check_duplicate_tool_names(tools)
 
         if self.streaming_callback:
             return self._run_streaming(formatted_messages, generation_kwargs)