Add AssemblyAI Integration page (#112)

* Add AssemblyAI Integration page

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* Add pip packaage

* updated the logo

integrations/assemblyai.md

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+---
+layout: integration
+name: AssemblyAI
+description: Use AssemblyAI transcription models with Haystack
+authors:
+    - name: AssemblyAI
+      socials:
+        twitter: assemblyai
+        github: AssemblyAI
+        linkedin: assemblyai
+pypi: https://pypi.org/project/assemblyai-haystack/
+repo: https://github.com/AssemblyAI/assemblyai-haystack
+type: Model Provider
+report_issue: https://github.com/AssemblyAI/assemblyai-haystack/issues
+logo: /logos/assemblyai.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Introduction](#introduction)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Transcription](#transcription)
+  - [Summarization](#summarization)
+  - [Speaker Diarization](#speaker-diarization)
+
+## Introduction
+
+You can use [AssemblyAI](https://www.assemblyai.com/) trancriptions in your Haystack 2.0 pipelines with the AssemblyAITranscriber.
+
+With this integration, you can perform [speech recognition](https://www.assemblyai.com/docs/speech-to-text/speech-recognition), [speaker diarization](https://www.assemblyai.com/docs/speech-to-text/speaker-diarization) and [summarization](https://www.assemblyai.com/docs/audio-intelligence/summarization).
+
+More info about AssemblyAI:
+
+* [Website](https://www.assemblyai.com/)
+* [Get a Free API key](https://www.assemblyai.com/dashboard/signup)
+* [AssemblyAI API Docs](https://www.assemblyai.com/docs)
+
+## Installation
+
+```bash
+pip install assemblyai-haystack
+```
+
+## Usage
+
+The `AssemblyAITranscriber` allows to perform some speech-to-text processes using the AssemblyAI API and loads the transcribed text into documents. To use this component, you should pass your `ASSEMBLYAI_API_KEY` as an argument. 
+
+Based on the passed arguments, the results of the transcription, summarization and speaker diarization are returned in separate document lists:
+* `transcription`
+* `summarization`
+* `speaker_labels`
+
+### Transcription
+
+Leverage the power of `AssemblyAITranscriber` to effortlessly transcribe your audio files. By default, it outputs a single `Document` object. However, for more tailored content preprocessing, you can use `DocumentSplitter`.
+
+Following example showcases an indexing pipeline that incorporates `AssemblyAITranscriber`, `DocumentSplitter`, and `SentenceTransformersDocumentEmbedder` to preprocess audio content and store it efficiently with dense embeddings in an `InMemoryDocumentStore`:
+
+```python
+from haystack.components.writers import DocumentWriter
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack.pipeline import Pipeline
+from haystack.document_stores import InMemoryDocumentStore
+from assemblyai_haystack.transcriber import AssemblyAITranscriber
+
+document_store = InMemoryDocumentStore()
+transcriber = AssemblyAITranscriber(api_key=assemblyai_api_key)
+document_splitter = DocumentSplitter(
+    split_by = "word",
+    split_length = 150,
+    split_overlap = 50
+)
+document_writer = DocumentWriter(document_store)
+document_embedder = SentenceTransformersDocumentEmbedder()
+
+preprocessing_pipeline = Pipeline()
+preprocessing_pipeline.add_component(instance=transcriber, name="transcriber")
+preprocessing_pipeline.add_component(instance=document_splitter, name="document_splitter")
+preprocessing_pipeline.add_component(instance=document_embedder, name="document_embedder")
+preprocessing_pipeline.add_component(instance=document_writer, name="document_writer")
+
+preprocessing_pipeline.connect("transcriber.transcription", "document_splitter")
+preprocessing_pipeline.connect("document_splitter", "document_embedder")
+preprocessing_pipeline.connect("document_embedder", "document_writer")
+
+file_path = "https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3"
+preprocessing_pipeline.run(
+    {
+        "transcriber": { "file_path": file_path}
+    }
+)
+```
+
+The expected output should indicate that 9 documents are written to the document store:
+```shell
+{'document_writer': {'documents_written': 9}}
+```
+
+Note: Calling `indexing.run()` blocks until the transcription is finished.
+
+The metadata of the transcription document contains the transcription ID and url of the uploaded audio file.
+
+```shell
+# {'transcript_id': '	73089e32-...-4ae9-97a4-eca7fe20a8b1',
+#  'audio_url': 'https://storage.googleapis.com/aai-docs-samples/nbc.mp3',
+# }
+```
+
+### Summarization
+You can perform summarization with `AssemblyAITranscriber` by setting `"summarization": True`. When activated, `AssemblyAITranscriber` provides both a `transcription` object and a `summarization` output.
+
+The example below illustrates a generative QA pipeline that seamlessly integrates `AssemblyAITranscriber` and `OpenAIGenerator`. This pipeline generates answers based on the given question and the summarized transcription:
+
+```python
+from haystack import Pipeline
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from assemblyai_haystack.transcriber import AssemblyAITranscriber
+
+template = """
+Given the following information, answer the question.
+
+Context: 
+{{summary[0].content}}
+
+Question: {{ question }}
+"""
+summary_qa = Pipeline()
+summary_qa.add_component("transcriber", AssemblyAITranscriber(api_key=assemblyai_api_key))
+summary_qa.add_component("prompt_builder", PromptBuilder(template=template))
+summary_qa.add_component("llm", OpenAIGenerator(api_key=openai_api_key, model_name="gpt-3.5-turbo"))
+summary_qa.connect("transcriber.summarization", "prompt_builder.summary")
+summary_qa.connect("prompt_builder", "llm")
+
+question="What are the air quality warnings?"
+summary_qa.run({
+    "transcriber": {"summarization": True, "file_path": "https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3"},
+    "prompt_builder": {"question": question},
+})  
+```
+
+### Speaker Diarization
+
+Facilitate speaker diarization effortlessly by including the `"speaker_labels": True` argument when using `AssemblyAITranscriber`. This setting ensures that `AssemblyAITranscriber` outputs a `Document` object, containing a list of utterances. Each utterance represents an uninterrupted segment of speech from a specific speaker, and the associated speaker information is kept in the `meta` field of the document.
+
+Explore the example below to see how to index speaker diarization information and run a query pipeline with filters, allowing you to retrieve the speech text specifically from speaker A:
+```python 
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack import Pipeline
+from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from assemblyai_haystack.transcriber import AssemblyAITranscriber
+
+## Write utterances into InMemoryDocumentStore
+document_store = InMemoryDocumentStore()
+file_path = "https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3"
+transcriber = AssemblyAITranscriber(api_key=assemblyai_api_key)
+result = transcriber.run(file_path=file_path, speaker_labels=True)
+document_store.write_documents(result["speaker_labels"])
+
+## Build a generative QA pipeline
+template = """
+Answer the question, based on the content in the documents. If you can't answer based on the documents, say so.
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+Question: {{ question }}
+"""
+pipe = Pipeline()
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=document_store, top_k=3))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", OpenAIGenerator(api_key=openai_api_key, model_name="gpt-3.5-turbo"))
+
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+## Run the pipeline and only include the speech text from speaker A
+question = "Who is more affected by wildfires?"
+pipe.run({    
+    "prompt_builder": {"question": question},
+    "retriever": {
+        "query": question,
+        "filters": { 
+            "operator": "AND",
+            "conditions": [{"field": "meta.speaker", "operator": "==", "value": "A"}]
+            }
+        }})
+```
+Since this filtering only returns the text where person A was the speaker, it can't find any relevant results. Run the same pipeline for speaker B information to get results.
+
+```shell
+{'llm': {'replies': ['The documents do not provide explicit information on who is more affected by wildfires.'],
+  'meta': [{'model': 'gpt-3.5-turbo-0613',
+    'index': 0,
+    'finish_reason': 'stop',
+    'usage': {'completion_tokens': 15,
+     'prompt_tokens': 177,
+     'total_tokens': 192}}]}}
+```