litellm/tests/llm_translation/test_databricks.py
Krish Dholakia 5c55270740
LiteLLM Minor Fixes & Improvements (11/04/2024) (#6572)
* feat: initial commit for watsonx chat endpoint support

Closes https://github.com/BerriAI/litellm/issues/6562

* feat(watsonx/chat/handler.py): support tool calling for watsonx

Closes https://github.com/BerriAI/litellm/issues/6562

* fix(streaming_utils.py): return empty chunk instead of failing if streaming value is invalid dict

ensures streaming works for ibm watsonx

* fix(openai_like/chat/handler.py): ensure asynchttphandler is passed correctly for openai like calls

* fix: ensure exception mapping works well for watsonx calls

* fix(openai_like/chat/handler.py): handle async streaming correctly

* feat(main.py): Make it clear when a user is passing an invalid message

add validation for user content message

 Closes https://github.com/BerriAI/litellm/issues/6565

* fix: cleanup

* fix(utils.py): loosen validation check, to just make sure content types are valid

make litellm robust to future content updates

* fix: fix linting erro

* fix: fix linting errors

* fix(utils.py): make validation check more flexible

* test: handle langfuse list index out of range error

* Litellm dev 11 02 2024 (#6561)

* fix(dual_cache.py): update in-memory check for redis batch get cache

Fixes latency delay for async_batch_redis_cache

* fix(service_logger.py): fix race condition causing otel service logging to be overwritten if service_callbacks set

* feat(user_api_key_auth.py): add parent otel component for auth

allows us to isolate how much latency is added by auth checks

* perf(parallel_request_limiter.py): move async_set_cache_pipeline (from max parallel request limiter) out of execution path (background task)

reduces latency by 200ms

* feat(user_api_key_auth.py): have user api key auth object return user tpm/rpm limits - reduces redis calls in downstream task (parallel_request_limiter)

Reduces latency by 400-800ms

* fix(parallel_request_limiter.py): use batch get cache to reduce user/key/team usage object calls

reduces latency by 50-100ms

* fix: fix linting error

* fix(_service_logger.py): fix import

* fix(user_api_key_auth.py): fix service logging

* fix(dual_cache.py): don't pass 'self'

* fix: fix python3.8 error

* fix: fix init]

* bump: version 1.51.4 → 1.51.5

* build(deps): bump cookie and express in /docs/my-website (#6566)

Bumps [cookie](https://github.com/jshttp/cookie) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together.

Updates `cookie` from 0.6.0 to 0.7.1
- [Release notes](https://github.com/jshttp/cookie/releases)
- [Commits](https://github.com/jshttp/cookie/compare/v0.6.0...v0.7.1)

Updates `express` from 4.20.0 to 4.21.1
- [Release notes](https://github.com/expressjs/express/releases)
- [Changelog](https://github.com/expressjs/express/blob/4.21.1/History.md)
- [Commits](https://github.com/expressjs/express/compare/4.20.0...4.21.1)

---
updated-dependencies:
- dependency-name: cookie
  dependency-type: indirect
- dependency-name: express
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* docs(virtual_keys.md): update Dockerfile reference (#6554)

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

* (proxy fix) - call connect on prisma client when running setup (#6534)

* critical fix - call connect on prisma client when running setup

* fix test_proxy_server_prisma_setup

* fix test_proxy_server_prisma_setup

* Add 3.5 haiku (#6588)

* feat: add claude-3-5-haiku-20241022 entries

* feat: add claude-3-5-haiku-20241022 and vertex_ai/claude-3-5-haiku@20241022 models

* add missing entries, remove vision

* remove image token costs

* Litellm perf improvements 3 (#6573)

* perf: move writing key to cache, to background task

* perf(litellm_pre_call_utils.py): add otel tracing for pre-call utils

adds 200ms on calls with pgdb connected

* fix(litellm_pre_call_utils.py'): rename call_type to actual call used

* perf(proxy_server.py): remove db logic from _get_config_from_file

was causing db calls to occur on every llm request, if team_id was set on key

* fix(auth_checks.py): add check for reducing db calls if user/team id does not exist in db

reduces latency/call by ~100ms

* fix(proxy_server.py): minor fix on existing_settings not incl alerting

* fix(exception_mapping_utils.py): map databricks exception string

* fix(auth_checks.py): fix auth check logic

* test: correctly mark flaky test

* fix(utils.py): handle auth token error for tokenizers.from_pretrained

* build: fix map

* build: fix map

* build: fix json for model map

* Litellm dev 11 02 2024 (#6561)

* fix(dual_cache.py): update in-memory check for redis batch get cache

Fixes latency delay for async_batch_redis_cache

* fix(service_logger.py): fix race condition causing otel service logging to be overwritten if service_callbacks set

* feat(user_api_key_auth.py): add parent otel component for auth

allows us to isolate how much latency is added by auth checks

* perf(parallel_request_limiter.py): move async_set_cache_pipeline (from max parallel request limiter) out of execution path (background task)

reduces latency by 200ms

* feat(user_api_key_auth.py): have user api key auth object return user tpm/rpm limits - reduces redis calls in downstream task (parallel_request_limiter)

Reduces latency by 400-800ms

* fix(parallel_request_limiter.py): use batch get cache to reduce user/key/team usage object calls

reduces latency by 50-100ms

* fix: fix linting error

* fix(_service_logger.py): fix import

* fix(user_api_key_auth.py): fix service logging

* fix(dual_cache.py): don't pass 'self'

* fix: fix python3.8 error

* fix: fix init]

* Litellm perf improvements 3 (#6573)

* perf: move writing key to cache, to background task

* perf(litellm_pre_call_utils.py): add otel tracing for pre-call utils

adds 200ms on calls with pgdb connected

* fix(litellm_pre_call_utils.py'): rename call_type to actual call used

* perf(proxy_server.py): remove db logic from _get_config_from_file

was causing db calls to occur on every llm request, if team_id was set on key

* fix(auth_checks.py): add check for reducing db calls if user/team id does not exist in db

reduces latency/call by ~100ms

* fix(proxy_server.py): minor fix on existing_settings not incl alerting

* fix(exception_mapping_utils.py): map databricks exception string

* fix(auth_checks.py): fix auth check logic

* test: correctly mark flaky test

* fix(utils.py): handle auth token error for tokenizers.from_pretrained

* fix ImageObject conversion (#6584)

* (fix) litellm.text_completion raises a non-blocking error on simple usage (#6546)

* unit test test_huggingface_text_completion_logprobs

* fix return TextCompletionHandler convert_chat_to_text_completion

* fix hf rest api

* fix test_huggingface_text_completion_logprobs

* fix linting errors

* fix importLiteLLMResponseObjectHandler

* fix test for LiteLLMResponseObjectHandler

* fix test text completion

* fix allow using 15 seconds for premium license check

* testing fix bedrock deprecated cohere.command-text-v14

* (feat) add `Predicted Outputs` for OpenAI  (#6594)

* bump openai to openai==1.54.0

* add 'prediction' param

* testing fix bedrock deprecated cohere.command-text-v14

* test test_openai_prediction_param.py

* test_openai_prediction_param_with_caching

* doc Predicted Outputs

* doc Predicted Output

* (fix) Vertex Improve Performance when using `image_url`  (#6593)

* fix transformation vertex

* test test_process_gemini_image

* test_image_completion_request

* testing fix - bedrock has deprecated cohere.command-text-v14

* fix vertex pdf

* bump: version 1.51.5 → 1.52.0

* fix(lowest_tpm_rpm_routing.py): fix parallel rate limit check (#6577)

* fix(lowest_tpm_rpm_routing.py): fix parallel rate limit check

* fix(lowest_tpm_rpm_v2.py): return headers in correct format

* test: update test

* build(deps): bump cookie and express in /docs/my-website (#6566)

Bumps [cookie](https://github.com/jshttp/cookie) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together.

Updates `cookie` from 0.6.0 to 0.7.1
- [Release notes](https://github.com/jshttp/cookie/releases)
- [Commits](https://github.com/jshttp/cookie/compare/v0.6.0...v0.7.1)

Updates `express` from 4.20.0 to 4.21.1
- [Release notes](https://github.com/expressjs/express/releases)
- [Changelog](https://github.com/expressjs/express/blob/4.21.1/History.md)
- [Commits](https://github.com/expressjs/express/compare/4.20.0...4.21.1)

---
updated-dependencies:
- dependency-name: cookie
  dependency-type: indirect
- dependency-name: express
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* docs(virtual_keys.md): update Dockerfile reference (#6554)

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

* (proxy fix) - call connect on prisma client when running setup (#6534)

* critical fix - call connect on prisma client when running setup

* fix test_proxy_server_prisma_setup

* fix test_proxy_server_prisma_setup

* Add 3.5 haiku (#6588)

* feat: add claude-3-5-haiku-20241022 entries

* feat: add claude-3-5-haiku-20241022 and vertex_ai/claude-3-5-haiku@20241022 models

* add missing entries, remove vision

* remove image token costs

* Litellm perf improvements 3 (#6573)

* perf: move writing key to cache, to background task

* perf(litellm_pre_call_utils.py): add otel tracing for pre-call utils

adds 200ms on calls with pgdb connected

* fix(litellm_pre_call_utils.py'): rename call_type to actual call used

* perf(proxy_server.py): remove db logic from _get_config_from_file

was causing db calls to occur on every llm request, if team_id was set on key

* fix(auth_checks.py): add check for reducing db calls if user/team id does not exist in db

reduces latency/call by ~100ms

* fix(proxy_server.py): minor fix on existing_settings not incl alerting

* fix(exception_mapping_utils.py): map databricks exception string

* fix(auth_checks.py): fix auth check logic

* test: correctly mark flaky test

* fix(utils.py): handle auth token error for tokenizers.from_pretrained

* build: fix map

* build: fix map

* build: fix json for model map

* test: remove eol model

* fix(proxy_server.py): fix db config loading logic

* fix(proxy_server.py): fix order of config / db updates, to ensure fields not overwritten

* test: skip test if required env var is missing

* test: fix test

---------

Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: paul-gauthier <69695708+paul-gauthier@users.noreply.github.com>

* test: mark flaky test

* test: handle anthropic api instability

* test: update test

* test: bump num retries on langfuse tests - their api is quite bad

---------

Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: paul-gauthier <69695708+paul-gauthier@users.noreply.github.com>
2024-11-06 17:53:46 +05:30

639 lines
21 KiB
Python

import asyncio
import httpx
import json
import pytest
import sys
from typing import Any, Dict, List
from unittest.mock import MagicMock, Mock, patch
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import litellm
from litellm.exceptions import BadRequestError
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.utils import CustomStreamWrapper
try:
import databricks.sdk
databricks_sdk_installed = True
except ImportError:
databricks_sdk_installed = False
def mock_chat_response() -> Dict[str, Any]:
return {
"id": "chatcmpl_3f78f09a-489c-4b8d-a587-f162c7497891",
"object": "chat.completion",
"created": 1726285449,
"model": "dbrx-instruct-071224",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! I'm an AI assistant. I'm doing well. How can I help?",
"function_call": None,
"tool_calls": None,
},
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 230,
"completion_tokens": 38,
"completion_tokens_details": None,
"total_tokens": 268,
"prompt_tokens_details": None,
},
"system_fingerprint": None,
}
def mock_chat_streaming_response_chunks() -> List[str]:
return [
json.dumps(
{
"id": "chatcmpl_8a7075d1-956e-4960-b3a6-892cd4649ff3",
"object": "chat.completion.chunk",
"created": 1726469651,
"model": "dbrx-instruct-071224",
"choices": [
{
"index": 0,
"delta": {"role": "assistant", "content": "Hello"},
"finish_reason": None,
"logprobs": None,
}
],
"usage": {
"prompt_tokens": 230,
"completion_tokens": 1,
"total_tokens": 231,
},
}
),
json.dumps(
{
"id": "chatcmpl_8a7075d1-956e-4960-b3a6-892cd4649ff3",
"object": "chat.completion.chunk",
"created": 1726469651,
"model": "dbrx-instruct-071224",
"choices": [
{
"index": 0,
"delta": {"content": " world"},
"finish_reason": None,
"logprobs": None,
}
],
"usage": {
"prompt_tokens": 230,
"completion_tokens": 1,
"total_tokens": 231,
},
}
),
json.dumps(
{
"id": "chatcmpl_8a7075d1-956e-4960-b3a6-892cd4649ff3",
"object": "chat.completion.chunk",
"created": 1726469651,
"model": "dbrx-instruct-071224",
"choices": [
{
"index": 0,
"delta": {"content": "!"},
"finish_reason": "stop",
"logprobs": None,
}
],
"usage": {
"prompt_tokens": 230,
"completion_tokens": 1,
"total_tokens": 231,
},
}
),
]
def mock_chat_streaming_response_chunks_bytes() -> List[bytes]:
string_chunks = mock_chat_streaming_response_chunks()
bytes_chunks = [chunk.encode("utf-8") + b"\n" for chunk in string_chunks]
# Simulate the end of the stream
bytes_chunks.append(b"")
return bytes_chunks
def mock_http_handler_chat_streaming_response() -> MagicMock:
mock_stream_chunks = mock_chat_streaming_response_chunks()
def mock_iter_lines():
for chunk in mock_stream_chunks:
for line in chunk.splitlines():
yield line
mock_response = MagicMock()
mock_response.iter_lines.side_effect = mock_iter_lines
mock_response.status_code = 200
return mock_response
def mock_http_handler_chat_async_streaming_response() -> MagicMock:
mock_stream_chunks = mock_chat_streaming_response_chunks()
async def mock_iter_lines():
for chunk in mock_stream_chunks:
for line in chunk.splitlines():
yield line
mock_response = MagicMock()
mock_response.aiter_lines.return_value = mock_iter_lines()
mock_response.status_code = 200
return mock_response
def mock_databricks_client_chat_streaming_response() -> MagicMock:
mock_stream_chunks = mock_chat_streaming_response_chunks_bytes()
def mock_read_from_stream(size=-1):
if mock_stream_chunks:
return mock_stream_chunks.pop(0)
return b""
mock_response = MagicMock()
streaming_response_mock = MagicMock()
streaming_response_iterator_mock = MagicMock()
# Mock the __getitem__("content") method to return the streaming response
mock_response.__getitem__.return_value = streaming_response_mock
# Mock the streaming response __enter__ method to return the streaming response iterator
streaming_response_mock.__enter__.return_value = streaming_response_iterator_mock
streaming_response_iterator_mock.read1.side_effect = mock_read_from_stream
streaming_response_iterator_mock.closed = False
return mock_response
def mock_embedding_response() -> Dict[str, Any]:
return {
"object": "list",
"model": "bge-large-en-v1.5",
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
0.06768798828125,
-0.01291656494140625,
-0.0501708984375,
0.0245361328125,
-0.030364990234375,
],
}
],
"usage": {
"prompt_tokens": 8,
"total_tokens": 8,
"completion_tokens": 0,
"completion_tokens_details": None,
"prompt_tokens_details": None,
},
}
@pytest.mark.parametrize("set_base", [True, False])
def test_throws_if_api_base_or_api_key_not_set_without_databricks_sdk(
monkeypatch, set_base
):
# Simulate that the databricks SDK is not installed
monkeypatch.setitem(sys.modules, "databricks.sdk", None)
err_msg = "the Databricks base URL and API key are not set"
if set_base:
monkeypatch.setenv(
"DATABRICKS_API_BASE",
"https://my.workspace.cloud.databricks.com/serving-endpoints",
)
monkeypatch.delenv(
"DATABRICKS_API_KEY",
)
else:
monkeypatch.setenv("DATABRICKS_API_KEY", "dapimykey")
monkeypatch.delenv(
"DATABRICKS_API_BASE",
)
with pytest.raises(BadRequestError) as exc:
litellm.completion(
model="databricks/dbrx-instruct-071224",
messages=[{"role": "user", "content": "How are you?"}],
)
assert err_msg in str(exc)
with pytest.raises(BadRequestError) as exc:
litellm.embedding(
model="databricks/bge-12312",
input=["Hello", "World"],
)
assert err_msg in str(exc)
def test_completions_with_sync_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
sync_handler = HTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_chat_response()
expected_response_json = {
**mock_chat_response(),
**{
"model": "databricks/dbrx-instruct-071224",
},
}
messages = [{"role": "user", "content": "How are you?"}]
with patch.object(HTTPHandler, "post", return_value=mock_response) as mock_post:
response = litellm.completion(
model="databricks/dbrx-instruct-071224",
messages=messages,
client=sync_handler,
temperature=0.5,
extraparam="testpassingextraparam",
)
assert response.to_dict() == expected_response_json
mock_post.assert_called_once_with(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "dbrx-instruct-071224",
"messages": messages,
"temperature": 0.5,
"extraparam": "testpassingextraparam",
"stream": False,
}
),
)
def test_completions_with_async_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
async_handler = AsyncHTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_chat_response()
expected_response_json = {
**mock_chat_response(),
**{
"model": "databricks/dbrx-instruct-071224",
},
}
messages = [{"role": "user", "content": "How are you?"}]
with patch.object(
AsyncHTTPHandler, "post", return_value=mock_response
) as mock_post:
response = asyncio.run(
litellm.acompletion(
model="databricks/dbrx-instruct-071224",
messages=messages,
client=async_handler,
temperature=0.5,
extraparam="testpassingextraparam",
)
)
assert response.to_dict() == expected_response_json
mock_post.assert_called_once_with(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "dbrx-instruct-071224",
"messages": messages,
"temperature": 0.5,
"extraparam": "testpassingextraparam",
"stream": False,
}
),
)
def test_completions_streaming_with_sync_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
sync_handler = HTTPHandler()
messages = [{"role": "user", "content": "How are you?"}]
mock_response = mock_http_handler_chat_streaming_response()
with patch.object(HTTPHandler, "post", return_value=mock_response) as mock_post:
response_stream: CustomStreamWrapper = litellm.completion(
model="databricks/dbrx-instruct-071224",
messages=messages,
client=sync_handler,
temperature=0.5,
extraparam="testpassingextraparam",
stream=True,
)
response = list(response_stream)
assert "dbrx-instruct-071224" in str(response)
assert "chatcmpl" in str(response)
assert len(response) == 4
mock_post.assert_called_once_with(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "dbrx-instruct-071224",
"messages": messages,
"temperature": 0.5,
"stream": True,
"extraparam": "testpassingextraparam",
}
),
stream=True,
)
def test_completions_streaming_with_async_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
async_handler = AsyncHTTPHandler()
messages = [{"role": "user", "content": "How are you?"}]
mock_response = mock_http_handler_chat_async_streaming_response()
with patch.object(
AsyncHTTPHandler, "post", return_value=mock_response
) as mock_post:
response_stream: CustomStreamWrapper = asyncio.run(
litellm.acompletion(
model="databricks/dbrx-instruct-071224",
messages=messages,
client=async_handler,
temperature=0.5,
extraparam="testpassingextraparam",
stream=True,
)
)
# Use async list gathering for the response
async def gather_responses():
return [item async for item in response_stream]
response = asyncio.run(gather_responses())
assert "dbrx-instruct-071224" in str(response)
assert "chatcmpl" in str(response)
assert len(response) == 4
mock_post.assert_called_once_with(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "dbrx-instruct-071224",
"messages": messages,
"temperature": 0.5,
"stream": True,
"extraparam": "testpassingextraparam",
}
),
stream=True,
)
@pytest.mark.skipif(not databricks_sdk_installed, reason="Databricks SDK not installed")
def test_completions_uses_databricks_sdk_if_api_key_and_base_not_specified(monkeypatch):
from databricks.sdk import WorkspaceClient
from databricks.sdk.config import Config
sync_handler = HTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_chat_response()
expected_response_json = {
**mock_chat_response(),
**{
"model": "databricks/dbrx-instruct-071224",
},
}
base_url = "https://my.workspace.cloud.databricks.com"
api_key = "dapimykey"
headers = {
"Authorization": f"Bearer {api_key}",
}
messages = [{"role": "user", "content": "How are you?"}]
mock_workspace_client: WorkspaceClient = MagicMock()
mock_config: Config = MagicMock()
# Simulate the behavior of the config property and its methods
mock_config.authenticate.side_effect = lambda: headers
mock_config.host = base_url # Assign directly as if it's a property
mock_workspace_client.config = mock_config
with patch(
"databricks.sdk.WorkspaceClient", return_value=mock_workspace_client
), patch.object(HTTPHandler, "post", return_value=mock_response) as mock_post:
response = litellm.completion(
model="databricks/dbrx-instruct-071224",
messages=messages,
client=sync_handler,
temperature=0.5,
extraparam="testpassingextraparam",
)
assert response.to_dict() == expected_response_json
mock_post.assert_called_once_with(
f"{base_url}/serving-endpoints/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "dbrx-instruct-071224",
"messages": messages,
"temperature": 0.5,
"extraparam": "testpassingextraparam",
"stream": False,
}
),
)
def test_embeddings_with_sync_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
sync_handler = HTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_embedding_response()
inputs = ["Hello", "World"]
with patch.object(HTTPHandler, "post", return_value=mock_response) as mock_post:
response = litellm.embedding(
model="databricks/bge-large-en-v1.5",
input=inputs,
client=sync_handler,
extraparam="testpassingextraparam",
)
assert response.to_dict() == mock_embedding_response()
mock_post.assert_called_once_with(
f"{base_url}/embeddings",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "bge-large-en-v1.5",
"input": inputs,
"extraparam": "testpassingextraparam",
}
),
)
def test_embeddings_with_async_http_handler(monkeypatch):
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
async_handler = AsyncHTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_embedding_response()
inputs = ["Hello", "World"]
with patch.object(
AsyncHTTPHandler, "post", return_value=mock_response
) as mock_post:
response = asyncio.run(
litellm.aembedding(
model="databricks/bge-large-en-v1.5",
input=inputs,
client=async_handler,
extraparam="testpassingextraparam",
)
)
assert response.to_dict() == mock_embedding_response()
mock_post.assert_called_once_with(
f"{base_url}/embeddings",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "bge-large-en-v1.5",
"input": inputs,
"extraparam": "testpassingextraparam",
}
),
)
@pytest.mark.skipif(not databricks_sdk_installed, reason="Databricks SDK not installed")
def test_embeddings_uses_databricks_sdk_if_api_key_and_base_not_specified(monkeypatch):
from databricks.sdk import WorkspaceClient
from databricks.sdk.config import Config
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
api_key = "dapimykey"
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
sync_handler = HTTPHandler()
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = mock_embedding_response()
base_url = "https://my.workspace.cloud.databricks.com"
api_key = "dapimykey"
headers = {
"Authorization": f"Bearer {api_key}",
}
inputs = ["Hello", "World"]
mock_workspace_client: WorkspaceClient = MagicMock()
mock_config: Config = MagicMock()
# Simulate the behavior of the config property and its methods
mock_config.authenticate.side_effect = lambda: headers
mock_config.host = base_url # Assign directly as if it's a property
mock_workspace_client.config = mock_config
with patch(
"databricks.sdk.WorkspaceClient", return_value=mock_workspace_client
), patch.object(HTTPHandler, "post", return_value=mock_response) as mock_post:
response = litellm.embedding(
model="databricks/bge-large-en-v1.5",
input=inputs,
client=sync_handler,
extraparam="testpassingextraparam",
)
assert response.to_dict() == mock_embedding_response()
mock_post.assert_called_once_with(
f"{base_url}/serving-endpoints/embeddings",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
data=json.dumps(
{
"model": "bge-large-en-v1.5",
"input": inputs,
"extraparam": "testpassingextraparam",
}
),
)