adding tests and some fixes

integrations/mistral/examples/indexing_pipeline.py

@@ -1,14 +1,13 @@
 # To run this example, you will need an to set a `MISTRAL_API_KEY` environment variable.
 # This example streams chat replies to the console.
 
-from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
-
 from haystack import Pipeline
-from haystack.components.fetchers import LinkContentFetcher
 from haystack.components.converters import HTMLToDocument
+from haystack.components.fetchers import LinkContentFetcher
 from haystack.components.preprocessors import DocumentSplitter
-from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
 
 document_store = InMemoryDocumentStore()
 fetcher = LinkContentFetcher()
@@ -30,4 +29,4 @@
 indexing.connect("chunker", "embedder")
 indexing.connect("embedder", "writer")
 
-indexing.run(data={"fetcher": {"urls": ["https://mistral.ai/news/la-plateforme/"]}})
+indexing.run(data={"fetcher": {"urls": ["https://mistral.ai/news/la-plateforme/"]}})

integrations/mistral/examples/streaming_chat_with_rag.py

@@ -1,20 +1,19 @@
 # To run this example, you will need an to set a `MISTRAL_API_KEY` environment variable.
 # This example streams chat replies to the console.
 
-from haystack_integrations.components.generators.mistral import MistralChatGenerator
-from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
-from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
-
 from haystack import Pipeline
-from haystack.dataclasses import ChatMessage
-from haystack.components.generators.utils import print_streaming_chunk
-from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.builders import DynamicChatPromptBuilder
 from haystack.components.converters import HTMLToDocument
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.generators.utils import print_streaming_chunk
 from haystack.components.preprocessors import DocumentSplitter
 from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
-from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
-from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
+from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
+from haystack_integrations.components.generators.mistral import MistralChatGenerator
 
 document_store = InMemoryDocumentStore()
 fetcher = LinkContentFetcher()
@@ -58,8 +57,10 @@
 
 question = "What are the available models?"
 
-result = rag_pipeline.run({ "text_embedder": {"text": question},
-                            "prompt_builder": {"template_variables": {"query": question},
-                                              "prompt_source": messages},
-                            "llm": {"generation_kwargs": {"max_tokens": 165}}
-                  })
+result = rag_pipeline.run(
+    {
+        "text_embedder": {"text": question},
+        "prompt_builder": {"template_variables": {"query": question}, "prompt_source": messages},
+        "llm": {"generation_kwargs": {"max_tokens": 165}},
+    }
+)

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

@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <[email protected]>
+#
+# SPDX-License-Identifier: Apache-2.0
+from .document_embedder import MistralDocumentEmbedder
+from .text_embedder import MistralTextEmbedder
+
+__all__ = ["MistralDocumentEmbedder", "MistralTextEmbedder"]

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

@@ -4,8 +4,8 @@
 from typing import List, Optional
 
 from haystack import component
-from haystack.utils.auth import Secret
 from haystack.components.embedders import OpenAIDocumentEmbedder
+from haystack.utils.auth import Secret
 
 
 @component
@@ -48,9 +48,10 @@ def __init__(
         Create a MistralDocumentEmbedder component.
         :param api_key: The Mistral API key.
         :param model: The name of the model to use.
-        :param dimensions: The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
+        :param dimensions: Not yet supported with `mistral-embed`. Currently this model outputs `1024` dimensions.
+                           For more info, refer to the Mistral [docs](https://docs.mistral.ai/platform/endpoints/#embedding-models)
         :param api_base_url: The Mistral API Base url, defaults to None. For more details, see Mistral [docs](https://docs.mistral.ai/api/).
-        :param organization: The Organization ID, defaults to `None`.
+        :param organization: Not yet supported with Mistral, defaults to `None`.
         :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.
@@ -69,4 +70,5 @@ def __init__(
                                                       batch_size,
                                                       progress_bar,
                                                       meta_fields_to_embed,
-                                                      embedding_separator)
+                                                      embedding_separator,
+                                                    )

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

@@ -4,30 +4,31 @@
 from typing import Optional
 
 from haystack import component
-from haystack.utils.auth import Secret
 from haystack.components.embedders import OpenAITextEmbedder
+from haystack.utils.auth import Secret
 
 
 @component
 class MistralTextEmbedder(OpenAITextEmbedder):
     """
-    A component for embedding strings using Mistral models.
+     A component for embedding strings using Mistral models.
 
-    Usage example:
-    ```python
-   from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
+     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()
+     text_embedder = MistralTextEmbedder()
 
-    print(text_embedder.run(text_to_embed))
+     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}}}
-    ```
+     # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
+     # 'meta': {'model': 'text-embedding-ada-002-v2',
+     #          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
+     ```
     """
+
     def __init__(
         self,
         api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
@@ -44,15 +45,12 @@ def __init__(
         :param api_key: The Misttal API key.
         :param model: The name of the Mistral embedding models to be used.
         :param dimensions: Not yet supported with Mistral embedding models
-        :param organization: The Organization ID, defaults to `None`. 
-        :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 organization: The Organization ID, defaults to `None`.
+        :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: Not yet supported with Mistral embedding models
         :param suffix: Not yet supported with Mistral embedding models
         """
-        super(MistralTextEmbedder, self).__init__(api_key,
-                                                  model,
-                                                  dimensions,
-                                                  api_base_url,
-                                                  organization,
-                                                  prefix,
-                                                  suffix)
+        super(MistralTextEmbedder, self).__init__(
+            api_key, model, dimensions, api_base_url, organization, prefix, suffix
+        )

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

@@ -1,3 +1,3 @@
 from .chat.chat_generator import MistralChatGenerator
 
-__all__ = ["MistralChatGenerator"]
+__all__ = ["MistralChatGenerator"]

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

@@ -1,26 +1,26 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <[email protected]>
 #
 # SPDX-License-Identifier: Apache-2.0
-import asyncio
 from typing import Any, Callable, Dict, Optional
 
 from haystack import component
-from haystack.dataclasses import StreamingChunk, ChatMessage
-from haystack.utils.auth import Secret
 from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import StreamingChunk
+from haystack.utils.auth import Secret
+
 
 @component
 class MistralChatGenerator(OpenAIChatGenerator):
     """
-    Enables text generation using Mistral's large language models (LLMs). 
+    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.
 
     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`
     parameter in `run` method.
 
-    For more details on the parameters supported by the Mistral API, refer to the 
+    For more details on the parameters supported by the Mistral API, refer to the
     [Mistral API Docs](https://docs.mistral.ai/api/).
 
     ```python
@@ -36,7 +36,7 @@ class MistralChatGenerator(OpenAIChatGenerator):
     >>{'replies': [ChatMessage(content='Natural Language Processing (NLP) is a branch of artificial intelligence
     >>that focuses on enabling computers to understand, interpret, and generate human language in a way that is
     >>meaningful and useful.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None,
-    >>meta={'model': 'gpt-3.5-turbo-0613', 'index': 0, 'finish_reason': 'stop',
+    >>meta={'model': 'mistral-tiny', 'index': 0, 'finish_reason': 'stop',
     >>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
 
     ```
@@ -48,8 +48,9 @@ class MistralChatGenerator(OpenAIChatGenerator):
 
      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.
+           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__(
@@ -69,7 +70,8 @@ def __init__(
         :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 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 organization: Not yet supported with Mistral chat completion models
         :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
@@ -81,15 +83,12 @@ def __init__(
             - `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model
                 considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens
                 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 
+            - `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. If set, different calls will generate deterministic results.
-        """ 
-        super(MistralChatGenerator, self).__init__(api_key,
-                                                   model,
-                                                   streaming_callback,
-                                                   api_base_url,
-                                                   organization,
-                                                   generation_kwargs)
+            - `random_seed`: The seed to use for random sampling.
+        """
+        super().__init__(
+            api_key, model, streaming_callback, api_base_url, organization, generation_kwargs
+        )