mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
Merge pull request #5079 from BerriAI/litellm_add_pydantic_model_support
feat(utils.py): support passing response_format as pydantic model
This commit is contained in:
Krish Dholakia
GitHub
commit
2e434d56e3
10 changed files with 353 additions and 100 deletions
|
|
@ -47,7 +47,7 @@ jobs:
|
|||
pip install opentelemetry-api==1.25.0
|
||||
pip install opentelemetry-sdk==1.25.0
|
||||
pip install opentelemetry-exporter-otlp==1.25.0
|
||||
pip install openai==1.34.0
|
||||
pip install openai==1.40.0
|
||||
pip install prisma==0.11.0
|
||||
pip install "detect_secrets==1.5.0"
|
||||
pip install "httpx==0.24.1"
|
||||
|
|
@ -165,7 +165,7 @@ jobs:
|
|||
pip install "pytest==7.3.1"
|
||||
pip install "pytest-asyncio==0.21.1"
|
||||
pip install aiohttp
|
||||
pip install openai
|
||||
pip install "openai==1.40.0"
|
||||
python -m pip install --upgrade pip
|
||||
python -m pip install -r .circleci/requirements.txt
|
||||
pip install "pytest==7.3.1"
|
||||
|
|
|
|||
|
|
@ -69,13 +69,10 @@ To use Structured Outputs, simply specify
|
|||
response_format: { "type": "json_schema", "json_schema": … , "strict": true }
|
||||
```
|
||||
|
||||
Works for OpenAI models
|
||||
|
||||
:::info
|
||||
|
||||
Support for passing in a pydantic object to litellm sdk will be [coming soon](https://github.com/BerriAI/litellm/issues/5074#issuecomment-2272355842)
|
||||
|
||||
:::
|
||||
Works for:
|
||||
- OpenAI models
|
||||
- Google AI Studio - Gemini models
|
||||
- Vertex AI models (Gemini + Anthropic)
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="sdk" label="SDK">
|
||||
|
|
@ -89,36 +86,15 @@ os.environ["OPENAI_API_KEY"] = ""
|
|||
|
||||
messages = [{"role": "user", "content": "List 5 cookie recipes"}]
|
||||
|
||||
class CalendarEvent(BaseModel):
|
||||
name: str
|
||||
date: str
|
||||
participants: list[str]
|
||||
|
||||
resp = completion(
|
||||
model="gpt-4o-2024-08-06",
|
||||
messages=messages,
|
||||
response_format={
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": "math_reasoning",
|
||||
"schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"steps": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"explanation": { "type": "string" },
|
||||
"output": { "type": "string" }
|
||||
},
|
||||
"required": ["explanation", "output"],
|
||||
"additionalProperties": False
|
||||
}
|
||||
},
|
||||
"final_answer": { "type": "string" }
|
||||
},
|
||||
"required": ["steps", "final_answer"],
|
||||
"additionalProperties": False
|
||||
},
|
||||
"strict": True
|
||||
},
|
||||
}
|
||||
response_format=CalendarEvent
|
||||
)
|
||||
|
||||
print("Received={}".format(resp))
|
||||
|
|
@ -229,15 +205,15 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
|
|||
|
||||
## Validate JSON Schema
|
||||
|
||||
:::info
|
||||
|
||||
Support for doing this in the openai 'json_schema' format will be [coming soon](https://github.com/BerriAI/litellm/issues/5074#issuecomment-2272355842)
|
||||
Not all vertex models support passing the json_schema to them (e.g. `gemini-1.5-flash`). To solve this, LiteLLM supports client-side validation of the json schema.
|
||||
|
||||
:::
|
||||
```
|
||||
litellm.enable_json_schema_validation=True
|
||||
```
|
||||
If `litellm.enable_json_schema_validation=True` is set, LiteLLM will validate the json response using `jsonvalidator`.
|
||||
|
||||
For VertexAI models, LiteLLM supports passing the `response_schema` and validating the JSON output.
|
||||
|
||||
This works across Gemini (`vertex_ai_beta/`) + Anthropic (`vertex_ai/`) models.
|
||||
[**See Code**](https://github.com/BerriAI/litellm/blob/671d8ac496b6229970c7f2a3bdedd6cb84f0746b/litellm/litellm_core_utils/json_validation_rule.py#L4)
|
||||
|
||||
|
||||
<Tabs>
|
||||
|
|
@ -245,33 +221,28 @@ This works across Gemini (`vertex_ai_beta/`) + Anthropic (`vertex_ai/`) models.
|
|||
|
||||
```python
|
||||
# !gcloud auth application-default login - run this to add vertex credentials to your env
|
||||
|
||||
import litellm, os
|
||||
from litellm import completion
|
||||
from pydantic import BaseModel
|
||||
|
||||
messages = [{"role": "user", "content": "List 5 cookie recipes"}]
|
||||
|
||||
response_schema = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"recipe_name": {
|
||||
"type": "string",
|
||||
},
|
||||
},
|
||||
"required": ["recipe_name"],
|
||||
},
|
||||
}
|
||||
messages=[
|
||||
{"role": "system", "content": "Extract the event information."},
|
||||
{"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
|
||||
]
|
||||
|
||||
litellm.enable_json_schema_validation = True
|
||||
litellm.set_verbose = True # see the raw request made by litellm
|
||||
|
||||
class CalendarEvent(BaseModel):
|
||||
name: str
|
||||
date: str
|
||||
participants: list[str]
|
||||
|
||||
resp = completion(
|
||||
model="vertex_ai_beta/gemini-1.5-pro",
|
||||
model="gemini/gemini-1.5-pro",
|
||||
messages=messages,
|
||||
response_format={
|
||||
"type": "json_object",
|
||||
"response_schema": response_schema,
|
||||
"enforce_validation": True, # client-side json schema validation
|
||||
},
|
||||
vertex_location="us-east5",
|
||||
response_format=CalendarEvent,
|
||||
)
|
||||
|
||||
print("Received={}".format(resp))
|
||||
|
|
@ -279,26 +250,63 @@ print("Received={}".format(resp))
|
|||
</TabItem>
|
||||
<TabItem value="proxy" label="PROXY">
|
||||
|
||||
1. Create config.yaml
|
||||
```yaml
|
||||
model_list:
|
||||
- model_name: "gemini-1.5-flash"
|
||||
litellm_params:
|
||||
model: "gemini/gemini-1.5-flash"
|
||||
api_key: os.environ/GEMINI_API_KEY
|
||||
|
||||
litellm_settings:
|
||||
enable_json_schema_validation: True
|
||||
```
|
||||
|
||||
2. Start proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
```
|
||||
|
||||
3. Test it!
|
||||
|
||||
```bash
|
||||
curl http://0.0.0.0:4000/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer $LITELLM_API_KEY" \
|
||||
-d '{
|
||||
"model": "vertex_ai_beta/gemini-1.5-pro",
|
||||
"messages": [{"role": "user", "content": "List 5 cookie recipes"}]
|
||||
"model": "gemini-1.5-flash",
|
||||
"messages": [
|
||||
{"role": "system", "content": "Extract the event information."},
|
||||
{"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
|
||||
],
|
||||
"response_format": {
|
||||
"type": "json_object",
|
||||
"enforce_validation: true,
|
||||
"response_schema": {
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": "math_reasoning",
|
||||
"schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"steps": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"recipe_name": {
|
||||
"type": "string",
|
||||
"explanation": { "type": "string" },
|
||||
"output": { "type": "string" }
|
||||
},
|
||||
"required": ["explanation", "output"],
|
||||
"additionalProperties": false
|
||||
}
|
||||
},
|
||||
"required": ["recipe_name"],
|
||||
"final_answer": { "type": "string" }
|
||||
},
|
||||
"required": ["steps", "final_answer"],
|
||||
"additionalProperties": false
|
||||
},
|
||||
"strict": true
|
||||
},
|
||||
}
|
||||
},
|
||||
|
|
|
|||
|
|
@ -144,6 +144,7 @@ enable_preview_features: bool = False
|
|||
return_response_headers: bool = (
|
||||
False # get response headers from LLM Api providers - example x-remaining-requests,
|
||||
)
|
||||
enable_json_schema_validation: bool = False
|
||||
##################
|
||||
logging: bool = True
|
||||
enable_caching_on_provider_specific_optional_params: bool = (
|
||||
|
|
|
|||
|
|
@ -148,7 +148,12 @@ class VertexAIAnthropicConfig:
|
|||
optional_params["temperature"] = value
|
||||
if param == "top_p":
|
||||
optional_params["top_p"] = value
|
||||
if param == "response_format" and "response_schema" in value:
|
||||
if param == "response_format" and isinstance(value, dict):
|
||||
json_schema: Optional[dict] = None
|
||||
if "response_schema" in value:
|
||||
json_schema = value["response_schema"]
|
||||
elif "json_schema" in value:
|
||||
json_schema = value["json_schema"]["schema"]
|
||||
"""
|
||||
When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
|
||||
- You usually want to provide a single tool
|
||||
|
|
@ -162,7 +167,7 @@ class VertexAIAnthropicConfig:
|
|||
name="json_tool_call",
|
||||
input_schema={
|
||||
"type": "object",
|
||||
"properties": {"values": value["response_schema"]}, # type: ignore
|
||||
"properties": {"values": json_schema}, # type: ignore
|
||||
},
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -181,13 +181,17 @@ class GoogleAIStudioGeminiConfig: # key diff from VertexAI - 'frequency_penalty
|
|||
optional_params["stop_sequences"] = value
|
||||
if param == "max_tokens":
|
||||
optional_params["max_output_tokens"] = value
|
||||
if param == "response_format" and value["type"] == "json_object": # type: ignore
|
||||
if param == "response_format": # type: ignore
|
||||
if value["type"] == "json_object": # type: ignore
|
||||
if value["type"] == "json_object": # type: ignore
|
||||
optional_params["response_mime_type"] = "application/json"
|
||||
elif value["type"] == "text": # type: ignore
|
||||
optional_params["response_mime_type"] = "text/plain"
|
||||
if "response_schema" in value: # type: ignore
|
||||
optional_params["response_schema"] = value["response_schema"] # type: ignore
|
||||
elif value["type"] == "json_schema": # type: ignore
|
||||
if "json_schema" in value and "schema" in value["json_schema"]: # type: ignore
|
||||
optional_params["response_schema"] = value["json_schema"]["schema"] # type: ignore
|
||||
if param == "tools" and isinstance(value, list):
|
||||
gtool_func_declarations = []
|
||||
for tool in value:
|
||||
|
|
@ -396,6 +400,9 @@ class VertexGeminiConfig:
|
|||
optional_params["response_mime_type"] = "text/plain"
|
||||
if "response_schema" in value:
|
||||
optional_params["response_schema"] = value["response_schema"]
|
||||
elif value["type"] == "json_schema": # type: ignore
|
||||
if "json_schema" in value and "schema" in value["json_schema"]: # type: ignore
|
||||
optional_params["response_schema"] = value["json_schema"]["schema"] # type: ignore
|
||||
if param == "frequency_penalty":
|
||||
optional_params["frequency_penalty"] = value
|
||||
if param == "presence_penalty":
|
||||
|
|
|
|||
|
|
@ -31,6 +31,7 @@ from typing import (
|
|||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Type,
|
||||
Union,
|
||||
)
|
||||
|
||||
|
|
@ -608,7 +609,7 @@ def completion(
|
|||
logit_bias: Optional[dict] = None,
|
||||
user: Optional[str] = None,
|
||||
# openai v1.0+ new params
|
||||
response_format: Optional[dict] = None,
|
||||
response_format: Optional[Union[dict, Type[BaseModel]]] = None,
|
||||
seed: Optional[int] = None,
|
||||
tools: Optional[List] = None,
|
||||
tool_choice: Optional[Union[str, dict]] = None,
|
||||
|
|
|
|||
|
|
@ -11,4 +11,5 @@ model_list:
|
|||
model: "gpt-4o"
|
||||
|
||||
litellm_settings:
|
||||
enable_json_schema_validation: true
|
||||
fallbacks: [{"gpt-3.5-turbo": ["gpt-4", "gpt-4o"]}]
|
||||
|
|
|
|||
|
|
@ -1192,7 +1192,15 @@ def vertex_httpx_mock_post_valid_response(*args, **kwargs):
|
|||
"role": "model",
|
||||
"parts": [
|
||||
{
|
||||
"text": '[{"recipe_name": "Chocolate Chip Cookies"}, {"recipe_name": "Oatmeal Raisin Cookies"}, {"recipe_name": "Peanut Butter Cookies"}, {"recipe_name": "Sugar Cookies"}, {"recipe_name": "Snickerdoodles"}]\n'
|
||||
"text": """{
|
||||
"recipes": [
|
||||
{"recipe_name": "Chocolate Chip Cookies"},
|
||||
{"recipe_name": "Oatmeal Raisin Cookies"},
|
||||
{"recipe_name": "Peanut Butter Cookies"},
|
||||
{"recipe_name": "Sugar Cookies"},
|
||||
{"recipe_name": "Snickerdoodles"}
|
||||
]
|
||||
}"""
|
||||
}
|
||||
],
|
||||
},
|
||||
|
|
@ -1253,13 +1261,15 @@ def vertex_httpx_mock_post_valid_response_anthropic(*args, **kwargs):
|
|||
"id": "toolu_vrtx_01YMnYZrToPPfcmY2myP2gEB",
|
||||
"name": "json_tool_call",
|
||||
"input": {
|
||||
"values": [
|
||||
"values": {
|
||||
"recipes": [
|
||||
{"recipe_name": "Chocolate Chip Cookies"},
|
||||
{"recipe_name": "Oatmeal Raisin Cookies"},
|
||||
{"recipe_name": "Peanut Butter Cookies"},
|
||||
{"recipe_name": "Snickerdoodle Cookies"},
|
||||
{"recipe_name": "Sugar Cookies"},
|
||||
]
|
||||
}
|
||||
},
|
||||
}
|
||||
],
|
||||
|
|
@ -1377,17 +1387,20 @@ async def test_gemini_pro_json_schema_args_sent_httpx(
|
|||
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
||||
|
||||
response_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"recipes": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"recipe_name": {
|
||||
"type": "string",
|
||||
},
|
||||
},
|
||||
"properties": {"recipe_name": {"type": "string"}},
|
||||
"required": ["recipe_name"],
|
||||
},
|
||||
}
|
||||
},
|
||||
"required": ["recipes"],
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
client = HTTPHandler()
|
||||
|
||||
|
|
@ -1448,6 +1461,105 @@ async def test_gemini_pro_json_schema_args_sent_httpx(
|
|||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"model, vertex_location, supports_response_schema",
|
||||
[
|
||||
("vertex_ai_beta/gemini-1.5-pro-001", "us-central1", True),
|
||||
("gemini/gemini-1.5-pro", None, True),
|
||||
("vertex_ai_beta/gemini-1.5-flash", "us-central1", False),
|
||||
("vertex_ai/claude-3-5-sonnet@20240620", "us-east5", False),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"invalid_response",
|
||||
[True, False],
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"enforce_validation",
|
||||
[True, False],
|
||||
)
|
||||
@pytest.mark.asyncio
|
||||
async def test_gemini_pro_json_schema_args_sent_httpx_openai_schema(
|
||||
model,
|
||||
supports_response_schema,
|
||||
vertex_location,
|
||||
invalid_response,
|
||||
enforce_validation,
|
||||
):
|
||||
from typing import List
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
load_vertex_ai_credentials()
|
||||
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
||||
litellm.model_cost = litellm.get_model_cost_map(url="")
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
messages = [{"role": "user", "content": "List 5 cookie recipes"}]
|
||||
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
||||
|
||||
class Recipe(BaseModel):
|
||||
recipe_name: str
|
||||
|
||||
class ResponseSchema(BaseModel):
|
||||
recipes: List[Recipe]
|
||||
|
||||
client = HTTPHandler()
|
||||
|
||||
httpx_response = MagicMock()
|
||||
if invalid_response is True:
|
||||
if "claude" in model:
|
||||
httpx_response.side_effect = (
|
||||
vertex_httpx_mock_post_invalid_schema_response_anthropic
|
||||
)
|
||||
else:
|
||||
httpx_response.side_effect = vertex_httpx_mock_post_invalid_schema_response
|
||||
else:
|
||||
if "claude" in model:
|
||||
httpx_response.side_effect = vertex_httpx_mock_post_valid_response_anthropic
|
||||
else:
|
||||
httpx_response.side_effect = vertex_httpx_mock_post_valid_response
|
||||
with patch.object(client, "post", new=httpx_response) as mock_call:
|
||||
print("SENDING CLIENT POST={}".format(client.post))
|
||||
try:
|
||||
resp = completion(
|
||||
model=model,
|
||||
messages=messages,
|
||||
response_format=ResponseSchema,
|
||||
vertex_location=vertex_location,
|
||||
client=client,
|
||||
)
|
||||
print("Received={}".format(resp))
|
||||
if invalid_response is True and enforce_validation is True:
|
||||
pytest.fail("Expected this to fail")
|
||||
except litellm.JSONSchemaValidationError as e:
|
||||
if invalid_response is False:
|
||||
pytest.fail("Expected this to pass. Got={}".format(e))
|
||||
|
||||
mock_call.assert_called_once()
|
||||
if "claude" not in model:
|
||||
print(mock_call.call_args.kwargs)
|
||||
print(mock_call.call_args.kwargs["json"]["generationConfig"])
|
||||
|
||||
if supports_response_schema:
|
||||
assert (
|
||||
"response_schema"
|
||||
in mock_call.call_args.kwargs["json"]["generationConfig"]
|
||||
)
|
||||
else:
|
||||
assert (
|
||||
"response_schema"
|
||||
not in mock_call.call_args.kwargs["json"]["generationConfig"]
|
||||
)
|
||||
assert (
|
||||
"Use this JSON schema:"
|
||||
in mock_call.call_args.kwargs["json"]["contents"][0]["parts"][1][
|
||||
"text"
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("provider", ["vertex_ai_beta"]) # "vertex_ai",
|
||||
@pytest.mark.asyncio
|
||||
async def test_gemini_pro_httpx_custom_api_base(provider):
|
||||
|
|
|
|||
|
|
@ -2130,6 +2130,43 @@ def test_completion_openai():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_openai_pydantic():
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
from pydantic import BaseModel
|
||||
|
||||
class CalendarEvent(BaseModel):
|
||||
name: str
|
||||
date: str
|
||||
participants: list[str]
|
||||
|
||||
print(f"api key: {os.environ['OPENAI_API_KEY']}")
|
||||
litellm.api_key = os.environ["OPENAI_API_KEY"]
|
||||
response = completion(
|
||||
model="gpt-4o-2024-08-06",
|
||||
messages=[{"role": "user", "content": "Hey"}],
|
||||
max_tokens=10,
|
||||
metadata={"hi": "bye"},
|
||||
response_format=CalendarEvent,
|
||||
)
|
||||
print("This is the response object\n", response)
|
||||
|
||||
response_str = response["choices"][0]["message"]["content"]
|
||||
response_str_2 = response.choices[0].message.content
|
||||
|
||||
cost = completion_cost(completion_response=response)
|
||||
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
|
||||
assert response_str == response_str_2
|
||||
assert type(response_str) == str
|
||||
assert len(response_str) > 1
|
||||
|
||||
litellm.api_key = None
|
||||
except Timeout as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_openai_organization():
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
|
|
|
|||
|
|
@ -45,6 +45,8 @@ import requests
|
|||
import tiktoken
|
||||
from httpx import Proxy
|
||||
from httpx._utils import get_environment_proxies
|
||||
from openai.lib import _parsing, _pydantic
|
||||
from openai.types.chat.completion_create_params import ResponseFormat
|
||||
from pydantic import BaseModel
|
||||
from tokenizers import Tokenizer
|
||||
|
||||
|
|
@ -158,6 +160,7 @@ from typing import (
|
|||
Literal,
|
||||
Optional,
|
||||
Tuple,
|
||||
Type,
|
||||
Union,
|
||||
cast,
|
||||
get_args,
|
||||
|
|
@ -629,8 +632,8 @@ def client(original_function):
|
|||
call_type == CallTypes.completion.value
|
||||
or call_type == CallTypes.acompletion.value
|
||||
):
|
||||
is_coroutine = check_coroutine(original_function)
|
||||
if is_coroutine == True:
|
||||
is_coroutine = check_coroutine(original_response)
|
||||
if is_coroutine is True:
|
||||
pass
|
||||
else:
|
||||
if isinstance(original_response, ModelResponse):
|
||||
|
|
@ -643,6 +646,49 @@ def client(original_function):
|
|||
input=model_response, model=model
|
||||
)
|
||||
### JSON SCHEMA VALIDATION ###
|
||||
if litellm.enable_json_schema_validation is True:
|
||||
try:
|
||||
if (
|
||||
optional_params is not None
|
||||
and "response_format" in optional_params
|
||||
and optional_params["response_format"]
|
||||
is not None
|
||||
):
|
||||
json_response_format: Optional[dict] = None
|
||||
if (
|
||||
isinstance(
|
||||
optional_params["response_format"],
|
||||
dict,
|
||||
)
|
||||
and optional_params[
|
||||
"response_format"
|
||||
].get("json_schema")
|
||||
is not None
|
||||
):
|
||||
json_response_format = optional_params[
|
||||
"response_format"
|
||||
]
|
||||
elif (
|
||||
_parsing._completions.is_basemodel_type(
|
||||
optional_params["response_format"]
|
||||
)
|
||||
):
|
||||
json_response_format = (
|
||||
type_to_response_format_param(
|
||||
response_format=optional_params[
|
||||
"response_format"
|
||||
]
|
||||
)
|
||||
)
|
||||
if json_response_format is not None:
|
||||
litellm.litellm_core_utils.json_validation_rule.validate_schema(
|
||||
schema=json_response_format[
|
||||
"json_schema"
|
||||
]["schema"],
|
||||
response=model_response,
|
||||
)
|
||||
except TypeError:
|
||||
pass
|
||||
if (
|
||||
optional_params is not None
|
||||
and "response_format" in optional_params
|
||||
|
|
@ -2806,6 +2852,11 @@ def get_optional_params(
|
|||
message=f"Function calling is not supported by {custom_llm_provider}.",
|
||||
)
|
||||
|
||||
if "response_format" in non_default_params:
|
||||
non_default_params["response_format"] = type_to_response_format_param(
|
||||
response_format=non_default_params["response_format"]
|
||||
)
|
||||
|
||||
if "tools" in non_default_params and isinstance(
|
||||
non_default_params, list
|
||||
): # fixes https://github.com/BerriAI/litellm/issues/4933
|
||||
|
|
@ -6104,6 +6155,36 @@ def _should_retry(status_code: int):
|
|||
return False
|
||||
|
||||
|
||||
def type_to_response_format_param(
|
||||
response_format: Optional[Union[Type[BaseModel], dict]],
|
||||
) -> Optional[dict]:
|
||||
"""
|
||||
Re-implementation of openai's 'type_to_response_format_param' function
|
||||
|
||||
Used for converting pydantic object to api schema.
|
||||
"""
|
||||
if response_format is None:
|
||||
return None
|
||||
|
||||
if isinstance(response_format, dict):
|
||||
return response_format
|
||||
|
||||
# type checkers don't narrow the negation of a `TypeGuard` as it isn't
|
||||
# a safe default behaviour but we know that at this point the `response_format`
|
||||
# can only be a `type`
|
||||
if not _parsing._completions.is_basemodel_type(response_format):
|
||||
raise TypeError(f"Unsupported response_format type - {response_format}")
|
||||
|
||||
return {
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"schema": _pydantic.to_strict_json_schema(response_format),
|
||||
"name": response_format.__name__,
|
||||
"strict": True,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _get_retry_after_from_exception_header(
|
||||
response_headers: Optional[httpx.Headers] = None,
|
||||
):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue