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docs(gen-api): Replace remaining SCW_API_KEY to SCW_SECRET_KEY #7069

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65b2de8
Update query-vision-models.mdx
reda-maizate Oct 30, 2026
aaf4ef7
fix(genapi): update remaining references of SCW_API_KEY
reda-maizate Nov 2, 2026
0a1bf77
fix(genapi): typo
reda-maizate Nov 2, 2026
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2 changes: 1 addition & 1 deletion pages/generative-apis/how-to/query-embedding-models.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ from openai import OpenAI
# Initialize the client with your base URL and API key
client = OpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your unique API key from Scaleway
api_key="<SCW_SECRET_KEY>" # Your unique API key from Scaleway
)
```

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8 changes: 4 additions & 4 deletions pages/generative-apis/how-to/query-language-models.mdx
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Expand Up @@ -188,7 +188,7 @@ By default, the outputs are returned to the client only after the generation pro

client = OpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your Scaleway API secret key
api_key="<SCW_SECRET_KEY>" # Your Scaleway API secret key
)
response = client.chat.completions.create(
model="llama-3.1-8b-instruct",
Expand All @@ -212,7 +212,7 @@ By default, the outputs are returned to the client only after the generation pro

client = OpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your Scaleway API secret key
api_key="<SCW_SECRET_KEY>" # Your Scaleway API secret key
)

response = client.responses.create(
Expand Down Expand Up @@ -243,7 +243,7 @@ The service also supports asynchronous mode for any chat completion.

client = AsyncOpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your unique API key from Scaleway
api_key="<SCW_SECRET_KEY>" # Your unique API key from Scaleway
)

async def main():
Expand All @@ -269,7 +269,7 @@ The service also supports asynchronous mode for any chat completion.

client = AsyncOpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your unique API key from Scaleway
api_key="<SCW_SECRET_KEY>" # Your unique API key from Scaleway
)

async def main():
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4 changes: 2 additions & 2 deletions pages/generative-apis/how-to/query-vision-models.mdx
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Expand Up @@ -198,7 +198,7 @@ from openai import OpenAI

client = OpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your unique API key from Scaleway
api_key="<SCW_SECRET_KEY>" # Your unique API key from Scaleway
)
response = client.chat.completions.create(
model="mistral-small-3.2-24b-instruct-2506",
Expand Down Expand Up @@ -229,7 +229,7 @@ from openai import AsyncOpenAI

client = AsyncOpenAI(
base_url="https://api.scaleway.ai/v1", # Scaleway's Generative APIs service URL
api_key="<SCW_API_KEY>" # Your unique API key from Scaleway
api_key="<SCW_SECRET_KEY>" # Your unique API key from Scaleway
)

async def main():
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2 changes: 1 addition & 1 deletion pages/generative-apis/how-to/use-structured-outputs.mdx
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Expand Up @@ -76,7 +76,7 @@ We are using the base code below to send our LLM a voice note transcript to stru
MODEL = "llama-3.1-8b-instruct" ## or "gpt-oss-120b" for the Responses API

# Set your API key
API_KEY = "<SCW_API_KEY>"
API_KEY = "<SCW_SECRET_KEY>"

client = OpenAI(
base_url="https://api.scaleway.ai/v1",
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4 changes: 2 additions & 2 deletions pages/generative-apis/reference-content/openai-compatibility.mdx
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Expand Up @@ -96,7 +96,7 @@ https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/embeddings

```
curl https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/embeddings \
-H "Authorization: Bearer $SCW_API_KEY" \
-H "Authorization: Bearer $SCW_SECRET_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "Embeddings can represent text in a numerical format.",
Expand Down Expand Up @@ -145,7 +145,7 @@ https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/models

```
curl https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/models \
-H "Authorization: Bearer $SCW_API_KEY" \
-H "Authorization: Bearer $SCW_SECRET_KEY" \
-H "Content-Type: application/json"
```

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6 changes: 3 additions & 3 deletions tutorials/how-to-implement-rag/index.mdx
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Expand Up @@ -55,7 +55,7 @@ Create a .env file and add the following variables. These will store your API ke

# Scaleway API credentials
SCW_ACCESS_KEY=your_scaleway_access_key
SCW_API_KEY=your_scaleway_secret_ke
SCW_SECRET_KEY=your_scaleway_secret_key
SCW_REGION=your_scaleway_region

# Scaleway managed database (PostgreSQL) credentials
Expand Down Expand Up @@ -143,7 +143,7 @@ We will use the [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/em
# rag.py

embeddings = OpenAIEmbeddings(
openai_api_key=os.getenv("SCW_API_KEY"),
openai_api_key=os.getenv("SCW_SECRET_KEY"),
openai_api_base=os.getenv("SCW_INFERENCE_EMBEDDINGS_ENDPOINT"),
model="sentence-transformers/sentence-t5-xxl",
tiktoken_enabled=False,
Expand Down Expand Up @@ -371,4 +371,4 @@ Furthermore, you can continually enhance your RAG system by implementing mechani

By integrating Scaleway Object Storage, Managed Database for PostgreSQL with pgvector, and LangChain’s embedding tools, you have the foundation to build a powerful RAG system that scales with your data while offering robust information retrieval capabilities. This approach equips you with the tools necessary to handle complex queries and deliver accurate, relevant results efficiently.

With ongoing refinement and adaptation, your RAG system can evolve to meet the changing needs of your users, ensuring that it remains a valuable asset in your AI toolkit.
With ongoing refinement and adaptation, your RAG system can evolve to meet the changing needs of your users, ensuring that it remains a valuable asset in your AI toolkit.
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