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docs: use custom-llm-provider header in examples (#16055)

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This commit is contained in:
Timothée Lecomte
2025-10-29 19:06:48 -07:00
committed by GitHub
parent 6672250769
commit eb0e4f34dc
6 changed files with 33 additions and 34 deletions
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docs/my-website/docs/files_endpoints.md
+5 -5
View File
@@ -57,7 +57,7 @@ client = OpenAI(
client.files.create(
file=wav_data,
purpose="user_data",
extra_body={"custom_llm_provider": "openai"}
extra_headers={"custom-llm-provider": "openai"}
)
```
@@ -71,7 +71,7 @@ client = OpenAI(
base_url="http://0.0.0.0:4000/v1"
)
files = client.files.list(extra_body={"custom_llm_provider": "openai"})
files = client.files.list(extra_headers={"custom-llm-provider": "openai"})
print("files=", files)
```
@@ -85,7 +85,7 @@ client = OpenAI(
base_url="http://0.0.0.0:4000/v1"
)
file = client.files.retrieve(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"})
file = client.files.retrieve(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"})
print("file=", file)
```
@@ -99,7 +99,7 @@ client = OpenAI(
base_url="http://0.0.0.0:4000/v1"
)
response = client.files.delete(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"})
response = client.files.delete(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"})
print("delete response=", response)
```
@@ -113,7 +113,7 @@ client = OpenAI(
base_url="http://0.0.0.0:4000/v1"
)
content = client.files.content(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"})
content = client.files.content(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"})
print("content=", content)
```
docs/my-website/docs/fine_tuning.md
+10 -9
View File
@@ -62,7 +62,7 @@ client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") # base_u
file_name = "openai_batch_completions.jsonl"
response = await client.files.create(
extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use
file=open(file_name, "rb"),
purpose="fine-tune",
)
@@ -73,8 +73,8 @@ response = await client.files.create(
```shell
curl http://localhost:4000/v1/files \
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: azure" \
-F purpose="batch" \
-F custom_llm_provider="azure"\
-F file="@mydata.jsonl"
```
</TabItem>
@@ -92,7 +92,7 @@ curl http://localhost:4000/v1/files \
ft_job = await client.fine_tuning.jobs.create(
model="gpt-35-turbo-1106", # Azure OpenAI model you want to fine-tune
training_file="file-abc123", # file_id from create file response
extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use
)
```
</TabItem>
@@ -103,8 +103,8 @@ ft_job = await client.fine_tuning.jobs.create(
curl http://localhost:4000/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: azure" \
-d '{
"custom_llm_provider": "azure",
"model": "gpt-35-turbo-1106",
"training_file": "file-abc123"
}'
@@ -215,7 +215,7 @@ curl http://localhost:4000/v1/fine_tuning/jobs \
# cancel specific fine tuning job
cancel_ft_job = await client.fine_tuning.jobs.cancel(
fine_tuning_job_id="123", # fine tuning job id
extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use
)
print("response from cancel ft job={}".format(cancel_ft_job))
@@ -228,7 +228,7 @@ print("response from cancel ft job={}".format(cancel_ft_job))
curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{"custom_llm_provider": "azure"}'
-H "custom-llm-provider: azure"
```
</TabItem>
@@ -242,7 +242,7 @@ curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \
```python
list_ft_jobs = await client.fine_tuning.jobs.list(
extra_query={"custom_llm_provider": "azure"} # tell litellm proxy which provider to use
extra_headers={"custom-llm-provider": "azure"} # tell litellm proxy which provider to use
)
print("list of ft jobs={}".format(list_ft_jobs))
@@ -252,9 +252,10 @@ print("list of ft jobs={}".format(list_ft_jobs))
<TabItem value="curl" label="curl">
```shell
curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs?custom_llm_provider=azure' \
curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs' \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234"
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: azure"
```
</TabItem>
docs/my-website/docs/providers/azure/azure.md
+5 -5
View File
@@ -834,7 +834,7 @@ client = OpenAI(
batch_input_file = client.files.create(
file=open("mydata.jsonl", "rb"),
purpose="batch",
extra_body={"custom_llm_provider": "azure"}
extra_headers={"custom-llm-provider": "azure"}
)
file_id = batch_input_file.id
```
@@ -870,7 +870,7 @@ batch = client.batches.create( # re use client from above
endpoint="/v1/chat/completions",
completion_window="24h",
metadata={"description": "My batch job"},
extra_body={"custom_llm_provider": "azure"}
extra_headers={"custom-llm-provider": "azure"}
)
```
@@ -898,7 +898,7 @@ curl http://localhost:4000/v1/batches \
```python
retrieved_batch = client.batches.retrieve(
batch.id,
extra_query={"custom_llm_provider": "azure"}
extra_headers={"custom-llm-provider": "azure"}
)
```
@@ -922,7 +922,7 @@ curl http://localhost:4000/v1/batches/batch_abc123 \
```python
cancelled_batch = client.batches.cancel(
batch.id,
extra_body={"custom_llm_provider": "azure"}
extra_headers={"custom-llm-provider": "azure"}
)
```
@@ -945,7 +945,7 @@ curl http://localhost:4000/v1/batches/batch_abc123/cancel \
<TabItem value="sdk" label="OpenAI Python SDK">
```python
client.batches.list(extra_query={"custom_llm_provider": "azure"})
client.batches.list(extra_headers={"custom-llm-provider": "azure"})
```
</TabItem>
docs/my-website/docs/providers/google_ai_studio/files.md
+1 -1
View File
@@ -39,7 +39,7 @@ encoded_string = base64.b64encode(wav_data).decode('utf-8')
file = create_file(
file=wav_data,
purpose="user_data",
extra_body={"custom_llm_provider": "gemini"},
extra_headers={"custom-llm-provider": "gemini"},
api_key=os.getenv("GEMINI_API_KEY"),
)
docs/my-website/docs/providers/vertex.md
+4 -6
View File
@@ -2935,7 +2935,7 @@ finetune_settings:
ft_job = await client.fine_tuning.jobs.create(
model="gemini-1.0-pro-002", # Vertex model you want to fine-tune
training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response
extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm proxy which provider to use
extra_headers={"custom-llm-provider": "vertex_ai"}, # tell litellm proxy which provider to use
)
```
</TabItem>
@@ -2946,8 +2946,8 @@ ft_job = await client.fine_tuning.jobs.create(
curl http://localhost:4000/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: vertex_ai" \
-d '{
"custom_llm_provider": "vertex_ai",
"model": "gemini-1.0-pro-002",
"training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl"
}'
@@ -2975,9 +2975,7 @@ ft_job = client.fine_tuning.jobs.create(
"learning_rate_multiplier": 0.1, # learning_rate_multiplier on Vertex
"adapter_size": "ADAPTER_SIZE_ONE" # type: ignore, vertex specific hyperparameter
},
extra_body={
"custom_llm_provider": "vertex_ai",
},
extra_headers={"custom-llm-provider": "vertex_ai"},
)
```
</TabItem>
@@ -2988,8 +2986,8 @@ ft_job = client.fine_tuning.jobs.create(
curl http://localhost:4000/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: vertex_ai" \
-d '{
"custom_llm_provider": "vertex_ai",
"model": "gemini-1.0-pro-002",
"training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
"hyperparameters": {
docs/my-website/docs/providers/vertex_batch.md
+8 -8
View File
@@ -50,7 +50,7 @@ oai_client = OpenAI(
file_obj = oai_client.files.create(
file=open("batch_requests.jsonl", "rb"),
purpose="batch",
extra_body={"custom_llm_provider": "vertex_ai"}
extra_headers={"custom-llm-provider": "vertex_ai"}
)
print(f"File uploaded with ID: {file_obj.id}")
@@ -63,9 +63,9 @@ print(f"File uploaded with ID: {file_obj.id}")
curl --request POST \
--url http://localhost:4000/v1/files \
--header 'Content-Type: multipart/form-data' \
--header 'custom-llm-provider: vertex_ai' \
--form purpose=batch \
--form file=@batch_requests.jsonl \
--form custom_llm_provider=vertex_ai
--form file=@batch_requests.jsonl
```
</TabItem>
@@ -100,7 +100,7 @@ create_batch_response = oai_client.batches.create(
completion_window="24h",
endpoint="/v1/chat/completions",
input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd"
extra_body={"custom_llm_provider": "vertex_ai"}
extra_headers={"custom-llm-provider": "vertex_ai"}
)
print(f"Batch created with ID: {create_batch_response.id}")
@@ -113,11 +113,11 @@ print(f"Batch created with ID: {create_batch_response.id}")
curl --request POST \
--url http://localhost:4000/v1/batches \
--header 'Content-Type: application/json' \
--header 'custom-llm-provider: vertex_ai' \
--data '{
"input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"endpoint": "/v1/chat/completions",
"completion_window": "24h",
"custom_llm_provider": "vertex_ai"
"completion_window": "24h"
}'
```
@@ -162,7 +162,7 @@ Check the status of your batch job. The batch will progress through states: `val
```python showLineNumbers title="retrieve_batch.py"
retrieved_batch = oai_client.batches.retrieve(
batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680
extra_query={"custom_llm_provider": "vertex_ai"}
extra_headers={"custom-llm-provider": "vertex_ai"}
)
print(f"Batch status: {retrieved_batch.status}")
@@ -230,7 +230,7 @@ encoded_file_id = urllib.parse.quote_plus(output_file_id)
# Get file content
file_content = oai_client.files.content(
file_id=encoded_file_id,
extra_body={"custom_llm_provider": "vertex_ai"}
extra_headers={"custom-llm-provider": "vertex_ai"}
)
# Process the results