Merge branch 'main' into raspawar/default-model

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/generator.py

@@ -35,13 +35,14 @@
 @component
 class AmazonBedrockGenerator:
     """
-    `AmazonBedrockGenerator` enables text generation via Amazon Bedrock hosted LLMs.
+    Generates text using models hosted on Amazon Bedrock.
 
-    For example, to use the Anthropic Claude model, simply initialize the `AmazonBedrockGenerator` with the
-    'anthropic.claude-v2' model name. Provide AWS credentials either via local AWS profile or directly via
+    For example, to use the Anthropic Claude model, pass 'anthropic.claude-v2' in the `model` parameter.
+    Provide AWS credentials either through the local AWS profile or directly through
     `aws_access_key_id`, `aws_secret_access_key`, `aws_session_token`, and `aws_region_name` parameters.
 
-    Usage example:
+    ### Usage example
+
     ```python
     from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockGenerator
 
@@ -52,6 +53,16 @@ class AmazonBedrockGenerator:
 
     print(generator.run("Who is the best American actor?"))
     ```
+
+    AmazonBedrockGenerator uses AWS for authentication. You can use the AWS CLI to authenticate through your IAM.
+    For more information on setting up an IAM identity-based policy, see [Amazon Bedrock documentation]
+    (https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html).
+    If the AWS environment is configured correctly, the AWS credentials are not required as they're loaded
+    automatically from the environment or the AWS configuration file.
+    If the AWS environment is not configured, set `aws_access_key_id`, `aws_secret_access_key`,
+    `aws_session_token`, and `aws_region_name` as environment variables or pass them as
+     [Secret](https://docs.haystack.deepset.ai/v2.0/docs/secret-management) arguments. Make sure the region you set
+    supports Amazon Bedrock.
     """
 
     SUPPORTED_MODEL_PATTERNS: ClassVar[Dict[str, Type[BedrockModelAdapter]]] = {
@@ -85,11 +96,12 @@ def __init__(
         :param aws_access_key_id: The AWS access key ID.
         :param aws_secret_access_key: The AWS secret access key.
         :param aws_session_token: The AWS session token.
-        :param aws_region_name: The AWS region name.
+        :param aws_region_name: The AWS region name. Make sure the region you set supports Amazon Bedrock.
         :param aws_profile_name: The AWS profile name.
         :param max_length: The maximum length of the generated text.
         :param truncate: Whether to truncate the prompt or not.
         :param kwargs: Additional keyword arguments to be passed to the model.
+        These arguments are specific to the model. You can find them in the model's documentation.
         :raises ValueError: If the model name is empty or None.
         :raises AmazonBedrockConfigurationError: If the AWS environment is not configured correctly or the model is
             not supported.
@@ -236,8 +248,9 @@ def run(self, prompt: str, generation_kwargs: Optional[Dict[str, Any]] = None):
         """
         Generates a list of string response to the given prompt.
 
-        :param prompt: The prompt to generate a response for.
-        :param generation_kwargs: Additional keyword arguments passed to the generator.
+        :param prompt: Instructions for the model.
+        :param generation_kwargs: Additional keyword arguments to customize text generation.
+        These arguments are specific to the model. You can find them in the model's documentation.
         :returns: A dictionary with the following keys:
             - `replies`: A list of generated responses.
         :raises ValueError: If the prompt is empty or None.

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

@@ -15,18 +15,16 @@
 @component
 class CohereChatGenerator:
     """
-    Enables text generation using Cohere's chat endpoint.
+    Completes chats using Cohere's models through Cohere `chat` endpoint.
 
-    This component is designed to inference Cohere's chat models.
+    You can customize how the text is generated by passing parameters to the
+    Cohere API through the `**generation_kwargs` parameter. You can do this when
+    initializing or running the component. Any parameter that works with
+     `cohere.Client.chat` will work here too.
+    For details, see [Cohere API](https://docs.cohere.com/reference/chat).
 
-    Users can pass any text generation parameters valid for the `cohere.Client,chat` method
-    directly to this component via the `**generation_kwargs` parameter in __init__ or the `**generation_kwargs`
-    parameter in `run` method.
+    ### Usage example
 
-    Invocations are made using 'cohere' package.
-    See [Cohere API](https://docs.cohere.com/reference/chat) for more details.
-
-    Example usage:
     ```python
     from haystack_integrations.components.generators.cohere import CohereChatGenerator
 
@@ -49,32 +47,34 @@ def __init__(
         """
         Initialize the CohereChatGenerator instance.
 
-        :param api_key: the API key for the Cohere API.
-        :param model: The name of the model to use. Available models are: [command, command-r, command-r-plus, etc.]
-        :param streaming_callback: a callback function to be called with the streaming response.
-        :param api_base_url: the base URL of the Cohere API.
-        :param generation_kwargs: additional model parameters. These will be used during generation. Refer to
-            https://docs.cohere.com/reference/chat for more details.
+        :param api_key: The API key for the Cohere API.
+        :param model: The name of the model to use. You can use models from the `command` family.
+        :param streaming_callback: A callback function that is called when a new token is received from the stream.
+            The callback function accepts [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk)
+            as an argument.
+        :param api_base_url: The base URL of the Cohere API.
+        :param generation_kwargs: Other parameters to use for the model during generation. For a list of parameters,
+            see [Cohere Chat endpoint](https://docs.cohere.com/reference/chat).
             Some of the parameters are:
             - 'chat_history': A list of previous messages between the user and the model, meant to give the model
                conversational context for responding to the user's message.
-            - 'preamble_override': When specified, the default Cohere preamble will be replaced with the provided one.
-            - 'conversation_id': An alternative to chat_history. Previous conversations can be resumed by providing
-               the conversation's identifier. The contents of message and the model's response will be stored
-               as part of this conversation.If a conversation with this id does not already exist,
-               a new conversation will be created.
-            - 'prompt_truncation': Defaults to AUTO when connectors are specified and OFF in all other cases.
-               Dictates how the prompt will be constructed.
-            - 'connectors': Accepts {"id": "web-search"}, and/or the "id" for a custom connector, if you've created one.
-                When specified, the model's reply will be enriched with information found by
+            - 'preamble': When specified, replaces the default Cohere preamble with the provided one.
+            - 'conversation_id': An alternative to `chat_history`. Previous conversations can be resumed by providing
+               the conversation's identifier. The contents of message and the model's response are stored
+               as part of this conversation. If a conversation with this ID doesn't exist,
+               a new conversation is created.
+            - 'prompt_truncation': Defaults to `AUTO` when connectors are specified and to `OFF` in all other cases.
+               Dictates how the prompt is constructed.
+            - 'connectors': Accepts {"id": "web-search"}, and the "id" for a custom connector, if you created one.
+                When specified, the model's reply is enriched with information found by
                 quering each of the connectors (RAG).
             - 'documents': A list of relevant documents that the model can use to enrich its reply.
-            - 'search_queries_only': Defaults to false. When true, the response will only contain a
-               list of generated search queries, but no search will take place, and no reply from the model to the
-               user's message will be generated.
-            - 'citation_quality': Defaults to "accurate". Dictates the approach taken to generating citations
+            - 'search_queries_only': Defaults to `False`. When `True`, the response only contains a
+               list of generated search queries, but no search takes place, and no reply from the model to the
+               user's message is generated.
+            - 'citation_quality': Defaults to `accurate`. Dictates the approach taken to generating citations
                 as part of the RAG flow by allowing the user to specify whether they want
-                "accurate" results or "fast" results.
+                `accurate` results or `fast` results.
             - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures
                 mean less random generations.
         """

integrations/cohere/src/haystack_integrations/components/generators/cohere/generator.py

@@ -15,12 +15,12 @@
 
 @component
 class CohereGenerator(CohereChatGenerator):
-    """LLM Generator compatible with Cohere's generate endpoint.
+    """Generates text using Cohere's models through Cohere's `generate` endpoint.
 
     NOTE: Cohere discontinued the `generate` API, so this generator is a mere wrapper
     around `CohereChatGenerator` provided for backward compatibility.
 
-    Example usage:
+    ### Usage example
 
     ```python
     from haystack_integrations.components.generators.cohere import CohereGenerator
@@ -40,6 +40,15 @@ def __init__(
     ):
         """
         Instantiates a `CohereGenerator` component.
+
+        :param api_key: Cohere API key.
+        :param model: Cohere model to use for generation.
+        :param streaming_callback: Callback function that is called when a new token is received from the stream.
+            The callback function accepts [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk)
+            as an argument.
+        :param api_base_url: Cohere base URL.
+        :param **kwargs: Additional arguments passed to the model. These arguments are specific to the model.
+            You can check them in model's documentation.
         """
 
         # Note we have to call super() like this because of the way components are dynamically built with the decorator
@@ -52,8 +61,8 @@ def run(self, prompt: str):
 
         :param prompt: the prompt to be sent to the generative model.
         :returns: A dictionary with the following keys:
-            - `replies`: the list of replies generated by the model.
-            - `meta`: metadata about the request.
+            - `replies`: A list of replies generated by the model.
+            - `meta`: Information about the request.
         """
         chat_message = ChatMessage(content=prompt, role=ChatRole.USER, name="", meta={})
         # Note we have to call super() like this because of the way components are dynamically built with the decorator

integrations/google_ai/src/haystack_integrations/components/generators/google_ai/chat/gemini.py

@@ -18,10 +18,16 @@
 @component
 class GoogleAIGeminiChatGenerator:
     """
-    `GoogleAIGeminiChatGenerator` is a multimodal generator supporting Gemini via Google AI Studio.
-    It uses the `ChatMessage` dataclass to interact with the model.
+    Completes chats using multimodal Gemini models through Google AI Studio.
+
+    It uses the [`ChatMessage`](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage)
+      dataclass to interact with the model. You can use the following models:
+    - gemini-pro
+    - gemini-ultra
+    - gemini-pro-vision
+
+    ### Usage example
 
-    Usage example:
     ```python
     from haystack.utils import Secret
     from haystack.dataclasses.chat_message import ChatMessage
@@ -42,7 +48,8 @@ class GoogleAIGeminiChatGenerator:
     ```
 
 
-    Usage example with function calling:
+    #### With function calling:
+
     ```python
     from haystack.utils import Secret
     from haystack.dataclasses.chat_message import ChatMessage
@@ -111,11 +118,15 @@ def __init__(
         * `gemini-pro-vision`
         * `gemini-ultra`
 
-        :param api_key: Google AI Studio API key.
-        :param model: Name of the model to use.
-        :param generation_config: The generation config to use.
-            Can either be a `GenerationConfig` object or a dictionary of parameters.
-            For the available parameters, see
+        :param api_key: Google AI Studio API key. To get a key,
+        see [Google AI Studio](https://makersuite.google.com).
+        :param model: Name of the model to use. Supported models are:
+            - gemini-pro
+            - gemini-ultra
+            - gemini-pro-vision
+        :param generation_config: The generation configuration to use.
+            This can either be a `GenerationConfig` object or a dictionary of parameters.
+            For available parameters, see
             [the `GenerationConfig` API reference](https://ai.google.dev/api/python/google/generativeai/GenerationConfig).
         :param safety_settings: The safety settings to use.
             A dictionary with `HarmCategory` as keys and `HarmBlockThreshold` as values.

integrations/google_ai/src/haystack_integrations/components/generators/google_ai/gemini.py

@@ -17,9 +17,10 @@
 @component
 class GoogleAIGeminiGenerator:
     """
-    `GoogleAIGeminiGenerator` is a multimodal generator supporting Gemini via Google AI Studio.
+    Generates text using multimodal Gemini models through Google AI Studio.
+
+    ### Usage example
 
-    Usage example:
     ```python
     from haystack.utils import Secret
     from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator
@@ -30,7 +31,8 @@ class GoogleAIGeminiGenerator:
         print(answer)
     ```
 
-    Multimodal usage example:
+    #### Multimodal example
+
     ```python
     import requests
     from haystack.utils import Secret
@@ -81,9 +83,9 @@ def __init__(
 
         :param api_key: Google AI Studio API key.
         :param model: Name of the model to use.
-        :param generation_config: The generation config to use.
-            Can either be a `GenerationConfig` object or a dictionary of parameters.
-            For the available parameters, see
+        :param generation_config: The generation configuration to use.
+            This can either be a `GenerationConfig` object or a dictionary of parameters.
+            For available parameters, see
             [the `GenerationConfig` API reference](https://ai.google.dev/api/python/google/generativeai/GenerationConfig).
         :param safety_settings: The safety settings to use.
             A dictionary with `HarmCategory` as keys and `HarmBlockThreshold` as values.

integrations/llama_cpp/CHANGELOG.md

@@ -0,0 +1,50 @@
+# Changelog
+
+## [integrations/llama_cpp-v0.4.1] - 2024-08-08
+
+### 🐛 Bug Fixes
+
+- Replace DynamicChatPromptBuilder with ChatPromptBuilder (#940)
+
+### ⚙️ Miscellaneous Tasks
+
+- Retry tests to reduce flakyness (#836)
+- Update ruff invocation to include check parameter (#853)
+- Pin `llama-cpp-python>=0.2.87` (#955)
+
+## [integrations/llama_cpp-v0.4.0] - 2024-05-13
+
+### 🐛 Bug Fixes
+
+- Fix commit (#436)
+
+
+- Fix order of API docs (#447)
+
+This PR will also push the docs to Readme
+
+### 📚 Documentation
+
+- Update category slug (#442)
+- Small consistency improvements (#536)
+- Disable-class-def (#556)
+
+### ⚙️ Miscellaneous Tasks
+
+- [**breaking**] Rename model_path to model in the Llama.cpp integration (#243)
+
+### Llama.cpp
+
+- Generate api docs (#353)
+
+## [integrations/llama_cpp-v0.2.1] - 2024-01-18
+
+## [integrations/llama_cpp-v0.2.0] - 2024-01-17
+
+## [integrations/llama_cpp-v0.1.0] - 2024-01-09
+
+### 🚀 Features
+
+- Add Llama.cpp Generator (#179)
+
+<!-- generated by git-cliff -->

integrations/llama_cpp/pyproject.toml

@@ -26,7 +26,7 @@ classifiers = [
     "Programming Language :: Python :: Implementation :: CPython",
     "Programming Language :: Python :: Implementation :: PyPy",
 ]
-dependencies = ["haystack-ai", "llama-cpp-python<0.2.84"]
+dependencies = ["haystack-ai", "llama-cpp-python>=0.2.87"]
 
 [project.urls]
 Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/llama_cpp#readme"

integrations/nvidia/src/haystack_integrations/components/generators/nvidia/generator.py

@@ -14,10 +14,11 @@
 @component
 class NvidiaGenerator:
     """
-    A component for generating text using generative models provided by
-    [NVIDIA NIMs](https://ai.nvidia.com).
+    Generates text using generative models hosted with
+    [NVIDIA NIM](https://ai.nvidia.com) on on the [NVIDIA API Catalog](https://build.nvidia.com/explore/discover).
+
+    ### Usage example
 
-    Usage example:
     ```python
     from haystack_integrations.components.generators.nvidia import NvidiaGenerator
 
@@ -36,6 +37,8 @@ class NvidiaGenerator:
     print(result["meta"])
     print(result["usage"])
     ```
+
+    You need an NVIDIA API key for this component to work.
     """
 
     def __init__(
@@ -54,14 +57,17 @@ def __init__(
             for more information on the supported models.
             `Note`: If no specific model along with locally hosted API URL is provided,
             the system defaults to the available model found using /models API.
+            Check supported models at [NVIDIA NIM](https://ai.nvidia.com).
         :param api_key:
-            API key for the NVIDIA NIM.
+            API key for the NVIDIA NIM. Set it as the `NVIDIA_API_KEY` environment
+            variable or pass it here.
         :param api_url:
             Custom API URL for the NVIDIA NIM.
         :param model_arguments:
-            Additional arguments to pass to the model provider. Different models accept different arguments.
-            Search your model in the [NVIDIA NIMs](https://ai.nvidia.com)
-            to know the supported arguments.
+            Additional arguments to pass to the model provider. These arguments are
+            specific to a model.
+            Search your model in the [NVIDIA NIM](https://ai.nvidia.com)
+            to find the arguments it accepts.
         """
         self._model = model
         self._api_url = url_validation(api_url, _DEFAULT_API_URL, ["v1/chat/completions"])