Add Mistral integration page (#125)

* add mixtral integration page

* add yml config

* rename file

* add logo and workshop text

* change model_name to model

* strip verbose chat message

* add streaming response

* Update integrations/mistral.md

Co-authored-by: Tuana Çelik <[email protected]>

* Update integrations/mistral.md

Co-authored-by: Tuana Çelik <[email protected]>

* address feedback

* add logo with dark background

* add Haystack pipeline example

---------

Co-authored-by: Tuana Çelik <[email protected]>

integrations/mistral.md

@@ -0,0 +1,138 @@
+---
+layout: integration
+name: Mistral
+description: This page demonstrates how to use OpenAIGenerator within Haystack to make use of Mistral models.
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/haystack-ai
+repo: https://github.com/deepset-ai/haystack
+type: Model Provider
+report_issue: report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/mistral.jpeg
+version: Haystack 2.0
+toc: true
+---
+
+This page demonstrates how to use the [OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator) and [OpenAITextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/openaitextembedder) within Haystack to make use of Mistral models. Since the OpenAI generators use the same protocol as Mistral, we're able to use them by changing the `API_BASE_URL`.
+
+To see an end to end example of [Mistal models in a Haystack pipeline, see this colab.](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/mixtral-8x7b-for-web-qa.ipynb)
+
+[Mistral AI](https://mistral.ai/) currently provides two types of access to Large Language Models:
+
+- An API providing pay-as-you-go access to the latest models,
+- Open source models available under the Apache 2.0 License, available on [Hugging Face](https://huggingface.co/mistralai) or directly from [the documentation](https://docs.mistral.ai/models/).
+
+For more information see [the Mistal docs](https://docs.mistral.ai/).
+
+In order to follow along with this guide, you'll need a [Mistal API key](https://console.mistral.ai/). Add it as an environment variable, `MISTRAL_API_KEY`.
+
+### Installation
+
+```bash
+pip install haystack-ai
+```
+
+### Usage
+
+#### Use Mistral Generative Models
+```python
+import os
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+
+api_key = os.getenv("MISTRAL_API_KEY")
+model = "mistral-medium"
+
+client = OpenAIChatGenerator(
+    api_key=api_key, model=model, api_base_url="https://api.mistral.ai/v1"
+)
+
+response = client.run(
+    messages=[ChatMessage.from_user("What is the best French cheese?")]
+)
+print(response)
+```
+```bash
+{'replies': [ChatMessage(content='The "best" French cheese is subjective and depends on personal taste...', role=<ChatRole.ASSISTANT: 'assistant'>, name=None, meta={'model': 'mistral-medium', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 231, 'prompt_tokens': 16, 'total_tokens': 247}})]}
+```
+Mistral LLMs also support streaming responses if you pass a callback in to the `OpenAIChatGenerator` like so:
+```python
+import os
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+
+api_key = os.getenv("MISTRAL_API_KEY")
+model = "mistral-medium"
+
+client = OpenAIChatGenerator(
+    api_key=api_key,
+    model=model,
+    api_base_url="https://api.mistral.ai/v1",
+    streaming_callback=lambda chunk: print(chunk.content, end="", flush=True)
+)
+
+response = client.run(
+    messages=[ChatMessage.from_user("What is the best French cheese?")]
+)
+print(response)
+```
+
+#### Use a Mistral Embedding Models
+```python
+import os
+from haystack.components.embedders import OpenAITextEmbedder
+
+api_key = os.getenv("MISTRAL_API_KEY")
+model = "mistral-embed"
+
+
+embedder = OpenAITextEmbedder(api_key=api_key, model=model, api_base_url="https://api.mistral.ai/v1")
+
+response = embedder.run(text="What is the best French cheese?")
+print(response)
+# {'embedding': [-0.0186004638671875, ...],
+# 'meta': {'model': 'mistral-embed', 
+#'usage': {'prompt_tokens': 9, 'total_tokens': 9, 'completion_tokens': 0}}}
+```
+
+In a Haystack pipeline:
+
+```python
+import os
+
+from haystack import Document
+from haystack import Pipeline
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+
+api_key = os.getenv("MISTRAL_API_KEY")
+api_base_url ="https://api.mistral.ai/v1"
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [Document(content="My name is Wolfgang and I live in Berlin"),
+             Document(content="I saw a black horse running"),
+             Document(content="Germany has many big cities")]
+
+document_embedder = OpenAIDocumentEmbedder(api_key=api_key, model='mistral-embed', api_base_url=api_base_url)
+documents_with_embeddings = document_embedder.run(documents)['documents']
+document_store.write_documents(documents)
+
+text_embedder = OpenAITextEmbedder(api_key=api_key, model="mistral-embed", api_base_url=api_base_url)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", text_embedder)
+query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+query = "Who lives in Berlin?"
+
+result = query_pipeline.run({"text_embedder":{"text": query}})
+
+print(result['retriever']['documents'])
+```