small fixes across endpoints tutorials

Author: MathieuBsqt

pages/public_cloud/ai_machine_learning/endpoints_tuto_02_voice_virtual_assistant/guide.en-gb.md

@@ -57,7 +57,7 @@ Then, create a `requirements.txt` file with the following libraries:
 ```bash
 openai==1.68.2
 streamlit==1.36.0
-streamlit-mic-recorder==1.16.0
+streamlit-mic-recorder==0.0.8
 nvidia-riva-client==2.15.1
 python-dotenv==1.0.1
 ```
@@ -101,7 +101,7 @@ First, create the **Automatic Speech Recognition (ASR)** function in order to tr
 def asr_transcription(question):
 
     asr_service = riva.client.ASRService(
-                    riva.client.Auth(uri=os.environ.get('ASR_GRPC_ENDPOINT'), use_ssl=True, metadata_args=[["authorization", f"bearer {ai_endpoint_token}"]])
+                    riva.client.Auth(uri=os.environ.get('ASR_GRPC_ENDPOINT'), use_ssl=True, metadata_args=[["authorization", f"bearer {os.environ.get('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}"]])
                 )
 
     # set up config
@@ -142,7 +142,7 @@ Then, build the **Text To Speech (TTS)** function in order to transform the writ
 def tts_synthesis(response):
 
     tts_service = riva.client.SpeechSynthesisService(
-                    riva.client.Auth(uri=os.environ.get('TTS_GRPC_ENDPOINT'), use_ssl=True, metadata_args=[["authorization", f"bearer {ai_endpoint_token}"]])
+                    riva.client.Auth(uri=os.environ.get('TTS_GRPC_ENDPOINT'), use_ssl=True, metadata_args=[["authorization", f"bearer {os.environ.get('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}"]])
                 )
 
     # set up config
@@ -196,11 +196,11 @@ with st.container():
         user_question = asr_transcription(recording['bytes'])
 
         if prompt := user_question:
-            client = OpenAI(base_url=os.getenv("LLM_AI_ENDPOINT"), api_key=ai_endpoint_token)
+            client = OpenAI(base_url=os.getenv("LLM_AI_ENDPOINT"), api_key=os.environ.get('OVH_AI_ENDPOINTS_ACCESS_TOKEN'))
             st.session_state.messages.append({"role": "user", "content": prompt, "avatar":"👤"})
             messages.chat_message("user", avatar="👤").write(prompt)
             response = client.chat.completions.create(
-                model="Mixtral-8x7B-Instruct-v0.", 
+                model="Mixtral-8x7B-Instruct-v0.1", 
                 messages=st.session_state.messages,
                 temperature=0,
                 max_tokens=1024,
@@ -214,13 +214,6 @@ with st.container():
                 placeholder.audio(audio_samples, sample_rate=sample_rate_hz, autoplay=True)
 ```
 
-Then, you can launch it in the `main`:
-
-```python
-if __name__ == '__main__':
-    demo.launch(server_name="0.0.0.0", server_port=8000)
-```
-
 ### Launch Streamlit web app locally
 
 🚀 That’s it! Now your web app is ready to be used! You can start this Streamlit app locally by launching the following command:

pages/public_cloud/ai_machine_learning/endpoints_tuto_04_sentiment_analyzer/guide.en-gb.md

@@ -90,7 +90,7 @@ Start by adding the necessary dependencies in your `pom.xml`:
 
 ### Define the AI Endpoints client
 
-We define a REST client interface using MicroProfile annotations to interact with the AI Endpoint.
+We define a REST client interface using MicroProfile annotations to interact with the AI Endpoint, in a `AISentimentService.java` file:
 
 ```java
 package com.ovhcloud.examples.aiendpoints.nlp.sentiment;
@@ -119,7 +119,7 @@ public interface AISentimentService {
 
 ### Implement the REST resource
 
-Now let’s create the actual REST resource that uses the `AISentimentService`:
+Now let’s create the actual REST resource that uses the `AISentimentService`, in a `SentimentsAnalysisResource.java` file:
 
 ```java
 package com.ovhcloud.examples.aiendpoints.nlp.sentiment;

pages/public_cloud/ai_machine_learning/endpoints_tuto_09_chatbot_memory_langchain/guide.en-gb.md

@@ -66,6 +66,7 @@ Then, create a `requirements.txt` file with the required libraries:
 python-dotenv==1.0.1
 langchain_openai==0.1.14
 openai==1.68.2
+langchain==0.2.17
 ```
 
 Then, launch the installation of these dependencies:

pages/public_cloud/ai_machine_learning/endpoints_tuto_11_rag_chatbot_langchain/guide.en-gb.md

@@ -70,6 +70,7 @@ Once this is done, you can create a Python file named `chat-bot-streaming-rag.py
 from dotenv import load_dotenv
 import argparse
 import time
+import os
 
 from langchain import hub
 
@@ -124,7 +125,7 @@ def chat_completion(new_message: str):
   # Split documents into chunks and vectorize them
   text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
   splits = text_splitter.split_documents(docs)
-  vectorstore = Chroma.from_documents(documents=splits, embedding=OVHCloudEmbeddings(model_name=_OVH_AI_ENDPOINTS_EMBEDDING_MODEL_NAME))
+  vectorstore = Chroma.from_documents(documents=splits, embedding=OVHCloudEmbeddings(model_name=_OVH_AI_ENDPOINTS_EMBEDDING_MODEL_NAME, access_token=_OVH_AI_ENDPOINTS_ACCESS_TOKEN))
 
   prompt = hub.pull("rlm/rag-prompt")
 
@@ -153,7 +154,7 @@ if __name__ == '__main__':
 
 ### Prepare your knowledge base
 
-Create a folder named rag-files and place your `.txt`, .`md`, or other text-based documents there. These will be converted into embeddings and used during retrieval.
+Create a folder named `rag-files` and place your `.txt`, .`md`, or other text-based documents there. These will be converted into embeddings and used during retrieval.
 
 You can find example files in our [public-cloud-examples GitHub repository](https://github.com/ovh/public-cloud-examples/tree/main/ai/ai-endpoints/python-langchain-chatbot/rag-files).