forked from phoenix/litellm-mirror
Merge pull request #2692 from BerriAI/litellm_streaming_fixes
fix(utils.py): ensure last chunk is always empty delta w/ finish reason
This commit is contained in:
Krish Dholakia
GitHub
commit
eb859f5dba
8 changed files with 278 additions and 305 deletions
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@ -1,119 +1,6 @@
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============================= test session starts ==============================
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platform darwin -- Python 3.11.6, pytest-7.3.1, pluggy-1.3.0
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rootdir: /Users/krrishdholakia/Documents/litellm/litellm/tests
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plugins: timeout-2.2.0, asyncio-0.23.2, anyio-3.7.1, xdist-3.3.1
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asyncio: mode=Mode.STRICT
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collected 1 item
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test_completion.py . [100%]
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=============================== warnings summary ===============================
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
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/opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)
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../proxy/_types.py:102
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:102: PydanticDeprecatedSince20: `pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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extra = Extra.allow # Allow extra fields
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../proxy/_types.py:105
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:105: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:134
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:134: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:180
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:180: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:241
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:241: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:253
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:253: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:292
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:292: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:319
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:319: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:570
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:570: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../proxy/_types.py:591
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/Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:591: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
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@root_validator(pre=True)
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../utils.py:35
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/Users/krrishdholakia/Documents/litellm/litellm/utils.py:35: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
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import pkg_resources
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: 10 warnings
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(parent)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.logging')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.iam')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('mpl_toolkits')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
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/opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.
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Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
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declare_namespace(pkg)
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../llms/prompt_templates/factory.py:6
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/Users/krrishdholakia/Documents/litellm/litellm/llms/prompt_templates/factory.py:6: DeprecationWarning: 'imghdr' is deprecated and slated for removal in Python 3.13
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import imghdr, base64
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test_completion.py::test_completion_claude_3_stream
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../utils.py:3249
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../utils.py:3249
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/Users/krrishdholakia/Documents/litellm/litellm/utils.py:3249: DeprecationWarning: open_text is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice.
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with resources.open_text(
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-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
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======================== 1 passed, 46 warnings in 3.14s ========================
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<litellm.utils.CustomStreamWrapper object at 0x118bd82d0>
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chunk: ModelResponse(id='chatcmpl-95b7d389-ff5a-4e09-a084-02584ba2cf1e', choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content='In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the', role='assistant', function_call=None, tool_calls=None), logprobs=None)], created=1711406570, model='ai21.j2-mid-v1', object='chat.completion.chunk', system_fingerprint=None, usage=Usage())
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extracted chunk: In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the
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chunk: ModelResponse(id='chatcmpl-95b7d389-ff5a-4e09-a084-02584ba2cf1e', choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(content=None, role=None, function_call=None, tool_calls=None), logprobs=None)], created=1711406570, model='ai21.j2-mid-v1', object='chat.completion.chunk', system_fingerprint=None, usage=Usage())
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extracted chunk:
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completion_response: In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the
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@ -281,7 +281,8 @@ async def test_async_vertexai_streaming_response():
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complete_response = ""
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async for chunk in response:
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print(f"chunk: {chunk}")
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complete_response += chunk.choices[0].delta.content
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if chunk.choices[0].delta.content is not None:
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complete_response += chunk.choices[0].delta.content
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print(f"complete_response: {complete_response}")
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assert len(complete_response) > 0
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except litellm.RateLimitError as e:
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@ -547,7 +547,7 @@ def test_redis_cache_completion_stream():
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response_2_id = ""
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for chunk in response2:
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print(chunk)
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response_2_id += chunk.id
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response_2_id = chunk.id
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assert (
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response_1_id == response_2_id
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), f"Response 1 != Response 2. Same params, Response 1{response_1_id} != Response 2{response_2_id}"
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@ -651,6 +651,7 @@ async def test_async_chat_vertex_ai_stream():
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try:
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load_vertex_ai_credentials()
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customHandler = CompletionCustomHandler()
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litellm.set_verbose = True
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litellm.callbacks = [customHandler]
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# test streaming
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response = await litellm.acompletion(
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@ -667,6 +668,7 @@ async def test_async_chat_vertex_ai_stream():
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async for chunk in response:
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print(f"chunk: {chunk}")
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continue
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await asyncio.sleep(10)
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print(f"customHandler.states: {customHandler.states}")
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assert (
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customHandler.states.count("async_success") == 1
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@ -490,7 +490,7 @@ def test_redis_cache_completion_stream():
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response_1_content += chunk.choices[0].delta.content or ""
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print(response_1_content)
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time.sleep(0.1) # sleep for 0.1 seconds allow set cache to occur
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time.sleep(1) # sleep for 0.1 seconds allow set cache to occur
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response2 = completion(
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model="gpt-3.5-turbo",
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messages=messages,
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@ -505,8 +505,10 @@ def test_redis_cache_completion_stream():
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response_2_id = chunk.id
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print(chunk)
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response_2_content += chunk.choices[0].delta.content or ""
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print("\nresponse 1", response_1_content)
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print("\nresponse 2", response_2_content)
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print(
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f"\nresponse 1: {response_1_content}",
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)
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print(f"\nresponse 2: {response_2_content}")
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assert (
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response_1_id == response_2_id
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), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}"
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@ -108,8 +108,19 @@ last_openai_chunk_example = {
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"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
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}
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"""
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Final chunk (sdk):
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chunk: ChatCompletionChunk(id='chatcmpl-96mM3oNBlxh2FDWVLKsgaFBBcULmI',
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choices=[Choice(delta=ChoiceDelta(content=None, function_call=None, role=None,
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tool_calls=None), finish_reason='stop', index=0, logprobs=None)],
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created=1711402871, model='gpt-3.5-turbo-0125', object='chat.completion.chunk', system_fingerprint='fp_3bc1b5746c')
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"""
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def validate_last_format(chunk):
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"""
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Ensure last chunk has no remaining content or tools
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"""
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assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary."
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assert isinstance(chunk["id"], str), "'id' should be a string."
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assert isinstance(chunk["object"], str), "'object' should be a string."
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@ -119,6 +130,10 @@ def validate_last_format(chunk):
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for choice in chunk["choices"]:
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assert isinstance(choice["index"], int), "'index' should be an integer."
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assert choice["delta"]["content"] is None
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assert choice["delta"]["function_call"] is None
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assert choice["delta"]["role"] is None
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assert choice["delta"]["tool_calls"] is None
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assert isinstance(
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choice["finish_reason"], str
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), "'finish_reason' should be a string."
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@ -493,13 +508,15 @@ def test_completion_mistral_api_stream():
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stream=True,
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)
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complete_response = ""
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has_finish_reason = False
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for idx, chunk in enumerate(response):
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print(chunk)
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# print(chunk.choices[0].delta)
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chunk, finished = streaming_format_tests(idx, chunk)
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if finished:
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has_finish_reason = True
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break
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complete_response += chunk
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if has_finish_reason == False:
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raise Exception("finish reason not set")
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if complete_response.strip() == "":
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raise Exception("Empty response received")
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print(f"completion_response: {complete_response}")
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@ -534,11 +551,15 @@ def test_completion_deep_infra_stream():
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complete_response = ""
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# Add any assertions here to check the response
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has_finish_reason = False
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for idx, chunk in enumerate(response):
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chunk, finished = streaming_format_tests(idx, chunk)
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if finished:
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has_finish_reason = True
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break
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complete_response += chunk
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if has_finish_reason == False:
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raise Exception("finish reason not set")
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if complete_response.strip() == "":
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raise Exception("Empty response received")
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print(f"completion_response: {complete_response}")
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@ -608,11 +629,15 @@ def test_completion_claude_stream_bad_key():
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)
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complete_response = ""
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# Add any assertions here to check the response
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has_finish_reason = False
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for idx, chunk in enumerate(response):
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chunk, finished = streaming_format_tests(idx, chunk)
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if finished:
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has_finish_reason = True
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break
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complete_response += chunk
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if has_finish_reason == False:
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raise Exception("finish reason not set")
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if complete_response.strip() == "":
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raise Exception("Empty response received")
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print(f"1234completion_response: {complete_response}")
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@ -626,6 +651,45 @@ def test_completion_claude_stream_bad_key():
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# test_completion_claude_stream_bad_key()
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# test_completion_replicate_stream()
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def test_vertex_ai_stream():
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from litellm.tests.test_amazing_vertex_completion import load_vertex_ai_credentials
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load_vertex_ai_credentials()
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litellm.set_verbose = True
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litellm.vertex_project = "reliablekeys"
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import random
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test_models = ["gemini-1.0-pro"]
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for model in test_models:
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try:
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print("making request", model)
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response = completion(
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model=model,
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messages=[
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{"role": "user", "content": "write 10 line code code for saying hi"}
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],
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stream=True,
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)
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complete_response = ""
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||||
is_finished = False
|
||||
for idx, chunk in enumerate(response):
|
||||
print(f"chunk in response: {chunk}")
|
||||
chunk, finished = streaming_format_tests(idx, chunk)
|
||||
if finished:
|
||||
is_finished = True
|
||||
break
|
||||
complete_response += chunk
|
||||
if complete_response.strip() == "":
|
||||
raise Exception("Empty response received")
|
||||
print(f"completion_response: {complete_response}")
|
||||
assert is_finished == True
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# def test_completion_vertexai_stream():
|
||||
# try:
|
||||
# import os
|
||||
|
|
@ -742,11 +806,15 @@ def test_bedrock_claude_3_streaming():
|
|||
)
|
||||
complete_response = ""
|
||||
# Add any assertions here to check the response
|
||||
has_finish_reason = False
|
||||
for idx, chunk in enumerate(response):
|
||||
chunk, finished = streaming_format_tests(idx, chunk)
|
||||
if finished:
|
||||
has_finish_reason = True
|
||||
break
|
||||
complete_response += chunk
|
||||
if has_finish_reason == False:
|
||||
raise Exception("finish reason not set")
|
||||
if complete_response.strip() == "":
|
||||
raise Exception("Empty response received")
|
||||
print(f"completion_response: {complete_response}")
|
||||
|
|
@ -1705,7 +1773,7 @@ def test_success_callback_streaming():
|
|||
messages = [{"role": "user", "content": "hello"}]
|
||||
print("TESTING LITELLM COMPLETION CALL")
|
||||
response = litellm.completion(
|
||||
model="j2-light",
|
||||
model="gpt-3.5-turbo",
|
||||
messages=messages,
|
||||
stream=True,
|
||||
max_tokens=5,
|
||||
|
|
@ -2028,7 +2096,7 @@ async def test_azure_astreaming_and_function_calling():
|
|||
chunk.choices[0].delta.tool_calls[0].function.arguments, str
|
||||
)
|
||||
validate_first_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif idx == 1:
|
||||
elif idx == 1 and chunk.choices[0].finish_reason is None:
|
||||
validate_second_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif chunk.choices[0].finish_reason is not None: # last chunk
|
||||
validate_final_streaming_function_calling_chunk(chunk=chunk)
|
||||
|
|
@ -2072,7 +2140,7 @@ def test_completion_claude_3_function_call_with_streaming():
|
|||
)
|
||||
idx = 0
|
||||
for chunk in response:
|
||||
# print(f"chunk: {chunk}")
|
||||
print(f"chunk in response: {chunk}")
|
||||
if idx == 0:
|
||||
assert (
|
||||
chunk.choices[0].delta.tool_calls[0].function.arguments is not None
|
||||
|
|
@ -2081,7 +2149,7 @@ def test_completion_claude_3_function_call_with_streaming():
|
|||
chunk.choices[0].delta.tool_calls[0].function.arguments, str
|
||||
)
|
||||
validate_first_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif idx == 1:
|
||||
elif idx == 1 and chunk.choices[0].finish_reason is None:
|
||||
validate_second_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif chunk.choices[0].finish_reason is not None: # last chunk
|
||||
validate_final_streaming_function_calling_chunk(chunk=chunk)
|
||||
|
|
@ -2136,7 +2204,7 @@ async def test_acompletion_claude_3_function_call_with_streaming():
|
|||
chunk.choices[0].delta.tool_calls[0].function.arguments, str
|
||||
)
|
||||
validate_first_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif idx == 1:
|
||||
elif idx == 1 and chunk.choices[0].finish_reason is None:
|
||||
validate_second_streaming_function_calling_chunk(chunk=chunk)
|
||||
elif chunk.choices[0].finish_reason is not None: # last chunk
|
||||
validate_final_streaming_function_calling_chunk(chunk=chunk)
|
||||
|
|
|
|||
|
|
@ -2907,6 +2907,7 @@ def test_async_text_completion_stream():
|
|||
|
||||
async def test_get_response():
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
response = await litellm.atext_completion(
|
||||
model="gpt-3.5-turbo-instruct",
|
||||
prompt="good morning",
|
||||
|
|
@ -2930,7 +2931,7 @@ def test_async_text_completion_stream():
|
|||
asyncio.run(test_get_response())
|
||||
|
||||
|
||||
test_async_text_completion_stream()
|
||||
# test_async_text_completion_stream()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
|
|||
358
litellm/utils.py
358
litellm/utils.py
|
|
@ -1774,16 +1774,14 @@ class Logging:
|
|||
end_time=end_time,
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_logger.debug(
|
||||
print_verbose(
|
||||
f"Error occurred building stream chunk: {traceback.format_exc()}"
|
||||
)
|
||||
complete_streaming_response = None
|
||||
else:
|
||||
self.streaming_chunks.append(result)
|
||||
if complete_streaming_response is not None:
|
||||
verbose_logger.debug(
|
||||
"Async success callbacks: Got a complete streaming response"
|
||||
)
|
||||
print_verbose("Async success callbacks: Got a complete streaming response")
|
||||
self.model_call_details["async_complete_streaming_response"] = (
|
||||
complete_streaming_response
|
||||
)
|
||||
|
|
@ -1824,7 +1822,7 @@ class Logging:
|
|||
callbacks.append(callback)
|
||||
else:
|
||||
callbacks = litellm._async_success_callback
|
||||
verbose_logger.debug(f"Async success callbacks: {callbacks}")
|
||||
print_verbose(f"Async success callbacks: {callbacks}")
|
||||
for callback in callbacks:
|
||||
# check if callback can run for this request
|
||||
litellm_params = self.model_call_details.get("litellm_params", {})
|
||||
|
|
@ -1894,10 +1892,6 @@ class Logging:
|
|||
end_time=end_time,
|
||||
)
|
||||
if callable(callback): # custom logger functions
|
||||
# print_verbose(
|
||||
# f"Making async function logging call for {callback}, result={result} - {self.model_call_details}",
|
||||
# logger_only=True,
|
||||
# )
|
||||
if self.stream:
|
||||
if (
|
||||
"async_complete_streaming_response"
|
||||
|
|
@ -8458,6 +8452,8 @@ class CustomStreamWrapper:
|
|||
self.completion_stream = completion_stream
|
||||
self.sent_first_chunk = False
|
||||
self.sent_last_chunk = False
|
||||
self.system_fingerprint: Optional[str] = None
|
||||
self.received_finish_reason: Optional[str] = None
|
||||
self.special_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "<s>", "</s>"]
|
||||
self.holding_chunk = ""
|
||||
self.complete_response = ""
|
||||
|
|
@ -8898,7 +8894,6 @@ class CustomStreamWrapper:
|
|||
if data_json["choices"][0].get("finish_reason", None):
|
||||
is_finished = True
|
||||
finish_reason = data_json["choices"][0]["finish_reason"]
|
||||
self.sent_last_chunk = True
|
||||
print_verbose(
|
||||
f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}"
|
||||
)
|
||||
|
|
@ -9131,16 +9126,32 @@ class CustomStreamWrapper:
|
|||
"finish_reason": finish_reason,
|
||||
}
|
||||
|
||||
def chunk_creator(self, chunk):
|
||||
def model_response_creator(self):
|
||||
model_response = ModelResponse(stream=True, model=self.model)
|
||||
if self.response_id is not None:
|
||||
model_response.id = self.response_id
|
||||
else:
|
||||
self.response_id = model_response.id
|
||||
if self.system_fingerprint is not None:
|
||||
model_response.system_fingerprint = self.system_fingerprint
|
||||
model_response._hidden_params["custom_llm_provider"] = self.custom_llm_provider
|
||||
model_response._hidden_params["created_at"] = time.time()
|
||||
model_response.choices = [StreamingChoices()]
|
||||
model_response.choices[0].finish_reason = None
|
||||
return model_response
|
||||
|
||||
def is_delta_empty(self, delta: Delta) -> bool:
|
||||
is_empty = True
|
||||
if delta.content is not None:
|
||||
is_empty = False
|
||||
elif delta.tool_calls is not None:
|
||||
is_empty = False
|
||||
elif delta.function_call is not None:
|
||||
is_empty = False
|
||||
return is_empty
|
||||
|
||||
def chunk_creator(self, chunk):
|
||||
model_response = self.model_response_creator()
|
||||
response_obj = {}
|
||||
try:
|
||||
# return this for all models
|
||||
|
|
@ -9149,30 +9160,22 @@ class CustomStreamWrapper:
|
|||
response_obj = self.handle_anthropic_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.model == "replicate" or self.custom_llm_provider == "replicate":
|
||||
response_obj = self.handle_replicate_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider and self.custom_llm_provider == "together_ai":
|
||||
response_obj = self.handle_together_ai_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider and self.custom_llm_provider == "huggingface":
|
||||
response_obj = self.handle_huggingface_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif (
|
||||
self.custom_llm_provider and self.custom_llm_provider == "baseten"
|
||||
): # baseten doesn't provide streaming
|
||||
|
|
@ -9183,16 +9186,12 @@ class CustomStreamWrapper:
|
|||
response_obj = self.handle_ai21_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
|
||||
response_obj = self.handle_maritalk_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider and self.custom_llm_provider == "vllm":
|
||||
completion_obj["content"] = chunk[0].outputs[0].text
|
||||
elif (
|
||||
|
|
@ -9201,152 +9200,116 @@ class CustomStreamWrapper:
|
|||
response_obj = self.handle_aleph_alpha_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "nlp_cloud":
|
||||
try:
|
||||
response_obj = self.handle_nlp_cloud_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
except Exception as e:
|
||||
if self.sent_last_chunk:
|
||||
if self.received_finish_reason:
|
||||
raise e
|
||||
else:
|
||||
if self.sent_first_chunk is False:
|
||||
raise Exception("An unknown error occurred with the stream")
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
self.sent_last_chunk = True
|
||||
self.received_finish_reason = "stop"
|
||||
elif self.custom_llm_provider == "gemini":
|
||||
try:
|
||||
if hasattr(chunk, "parts") == True:
|
||||
try:
|
||||
if len(chunk.parts) > 0:
|
||||
completion_obj["content"] = chunk.parts[0].text
|
||||
if hasattr(chunk.parts[0], "finish_reason"):
|
||||
model_response.choices[0].finish_reason = (
|
||||
map_finish_reason(chunk.parts[0].finish_reason.name)
|
||||
)
|
||||
except:
|
||||
if chunk.parts[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
)
|
||||
else:
|
||||
completion_obj["content"] = str(chunk)
|
||||
except StopIteration as e:
|
||||
if self.sent_last_chunk:
|
||||
raise e
|
||||
else:
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
self.sent_last_chunk = True
|
||||
if hasattr(chunk, "parts") == True:
|
||||
try:
|
||||
if len(chunk.parts) > 0:
|
||||
completion_obj["content"] = chunk.parts[0].text
|
||||
if hasattr(chunk.parts[0], "finish_reason"):
|
||||
self.received_finish_reason = chunk.parts[
|
||||
0
|
||||
].finish_reason.name
|
||||
except:
|
||||
if chunk.parts[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
)
|
||||
else:
|
||||
completion_obj["content"] = str(chunk)
|
||||
elif self.custom_llm_provider and (self.custom_llm_provider == "vertex_ai"):
|
||||
try:
|
||||
if hasattr(chunk, "candidates") == True:
|
||||
if hasattr(chunk, "candidates") == True:
|
||||
try:
|
||||
try:
|
||||
try:
|
||||
completion_obj["content"] = chunk.text
|
||||
except Exception as e:
|
||||
if "Part has no text." in str(e):
|
||||
## check for function calling
|
||||
function_call = (
|
||||
chunk.candidates[0]
|
||||
.content.parts[0]
|
||||
.function_call
|
||||
)
|
||||
args_dict = {}
|
||||
for k, v in function_call.args.items():
|
||||
args_dict[k] = v
|
||||
args_str = json.dumps(args_dict)
|
||||
_delta_obj = litellm.utils.Delta(
|
||||
content=None,
|
||||
tool_calls=[
|
||||
{
|
||||
"id": f"call_{str(uuid.uuid4())}",
|
||||
"function": {
|
||||
"arguments": args_str,
|
||||
"name": function_call.name,
|
||||
},
|
||||
"type": "function",
|
||||
}
|
||||
],
|
||||
)
|
||||
_streaming_response = StreamingChoices(
|
||||
delta=_delta_obj
|
||||
)
|
||||
_model_response = ModelResponse(stream=True)
|
||||
_model_response.choices = [_streaming_response]
|
||||
response_obj = {"original_chunk": _model_response}
|
||||
else:
|
||||
raise e
|
||||
if (
|
||||
hasattr(chunk.candidates[0], "finish_reason")
|
||||
and chunk.candidates[0].finish_reason.name
|
||||
!= "FINISH_REASON_UNSPECIFIED"
|
||||
): # every non-final chunk in vertex ai has this
|
||||
model_response.choices[0].finish_reason = (
|
||||
map_finish_reason(
|
||||
chunk.candidates[0].finish_reason.name
|
||||
)
|
||||
)
|
||||
completion_obj["content"] = chunk.text
|
||||
except Exception as e:
|
||||
if chunk.candidates[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
if "Part has no text." in str(e):
|
||||
## check for function calling
|
||||
function_call = (
|
||||
chunk.candidates[0].content.parts[0].function_call
|
||||
)
|
||||
else:
|
||||
completion_obj["content"] = str(chunk)
|
||||
except StopIteration as e:
|
||||
if self.sent_last_chunk:
|
||||
raise e
|
||||
else:
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
self.sent_last_chunk = True
|
||||
args_dict = {}
|
||||
for k, v in function_call.args.items():
|
||||
args_dict[k] = v
|
||||
args_str = json.dumps(args_dict)
|
||||
_delta_obj = litellm.utils.Delta(
|
||||
content=None,
|
||||
tool_calls=[
|
||||
{
|
||||
"id": f"call_{str(uuid.uuid4())}",
|
||||
"function": {
|
||||
"arguments": args_str,
|
||||
"name": function_call.name,
|
||||
},
|
||||
"type": "function",
|
||||
}
|
||||
],
|
||||
)
|
||||
_streaming_response = StreamingChoices(delta=_delta_obj)
|
||||
_model_response = ModelResponse(stream=True)
|
||||
_model_response.choices = [_streaming_response]
|
||||
response_obj = {"original_chunk": _model_response}
|
||||
else:
|
||||
raise e
|
||||
if (
|
||||
hasattr(chunk.candidates[0], "finish_reason")
|
||||
and chunk.candidates[0].finish_reason.name
|
||||
!= "FINISH_REASON_UNSPECIFIED"
|
||||
): # every non-final chunk in vertex ai has this
|
||||
self.received_finish_reason = chunk.candidates[
|
||||
0
|
||||
].finish_reason.name
|
||||
except Exception as e:
|
||||
if chunk.candidates[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
)
|
||||
else:
|
||||
completion_obj["content"] = str(chunk)
|
||||
elif self.custom_llm_provider == "cohere":
|
||||
response_obj = self.handle_cohere_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "cohere_chat":
|
||||
response_obj = self.handle_cohere_chat_chunk(chunk)
|
||||
if response_obj is None:
|
||||
return
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "bedrock":
|
||||
if self.sent_last_chunk:
|
||||
if self.received_finish_reason is not None:
|
||||
raise StopIteration
|
||||
response_obj = self.handle_bedrock_stream(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.sent_last_chunk = True
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "sagemaker":
|
||||
verbose_logger.debug(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}")
|
||||
print_verbose(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}")
|
||||
response_obj = self.handle_sagemaker_stream(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.sent_last_chunk = True
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "petals":
|
||||
if len(self.completion_stream) == 0:
|
||||
if self.sent_last_chunk:
|
||||
if self.received_finish_reason is not None:
|
||||
raise StopIteration
|
||||
else:
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
self.sent_last_chunk = True
|
||||
self.received_finish_reason = "stop"
|
||||
chunk_size = 30
|
||||
new_chunk = self.completion_stream[:chunk_size]
|
||||
completion_obj["content"] = new_chunk
|
||||
|
|
@ -9356,11 +9319,10 @@ class CustomStreamWrapper:
|
|||
# fake streaming
|
||||
response_obj = {}
|
||||
if len(self.completion_stream) == 0:
|
||||
if self.sent_last_chunk:
|
||||
if self.received_finish_reason is not None:
|
||||
raise StopIteration
|
||||
else:
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
self.sent_last_chunk = True
|
||||
self.received_finish_reason = "stop"
|
||||
chunk_size = 30
|
||||
new_chunk = self.completion_stream[:chunk_size]
|
||||
completion_obj["content"] = new_chunk
|
||||
|
|
@ -9371,41 +9333,31 @@ class CustomStreamWrapper:
|
|||
completion_obj["content"] = response_obj["text"]
|
||||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "ollama_chat":
|
||||
response_obj = self.handle_ollama_chat_stream(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "cloudflare":
|
||||
response_obj = self.handle_cloudlfare_stream(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "text-completion-openai":
|
||||
response_obj = self.handle_openai_text_completion_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "azure_text":
|
||||
response_obj = self.handle_azure_text_completion_chunk(chunk)
|
||||
completion_obj["content"] = response_obj["text"]
|
||||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif self.custom_llm_provider == "cached_response":
|
||||
response_obj = {
|
||||
"text": chunk.choices[0].delta.content,
|
||||
|
|
@ -9418,10 +9370,11 @@ class CustomStreamWrapper:
|
|||
print_verbose(f"completion obj content: {completion_obj['content']}")
|
||||
if hasattr(chunk, "id"):
|
||||
model_response.id = chunk.id
|
||||
self.response_id = chunk.id
|
||||
if hasattr(chunk, "system_fingerprint"):
|
||||
self.system_fingerprint = chunk.system_fingerprint
|
||||
if response_obj["is_finished"]:
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
else: # openai / azure chat model
|
||||
if self.custom_llm_provider == "azure":
|
||||
if hasattr(chunk, "model"):
|
||||
|
|
@ -9437,21 +9390,24 @@ class CustomStreamWrapper:
|
|||
raise Exception(
|
||||
"Mistral API raised a streaming error - finish_reason: error, no content string given."
|
||||
)
|
||||
model_response.choices[0].finish_reason = response_obj[
|
||||
"finish_reason"
|
||||
]
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
if response_obj.get("original_chunk", None) is not None:
|
||||
model_response.system_fingerprint = getattr(
|
||||
response_obj["original_chunk"], "system_fingerprint", None
|
||||
)
|
||||
if hasattr(response_obj["original_chunk"], "id"):
|
||||
model_response.id = response_obj["original_chunk"].id
|
||||
self.response_id = model_response.id
|
||||
if hasattr(response_obj["original_chunk"], "system_fingerprint"):
|
||||
model_response.system_fingerprint = response_obj[
|
||||
"original_chunk"
|
||||
].system_fingerprint
|
||||
self.system_fingerprint = response_obj[
|
||||
"original_chunk"
|
||||
].system_fingerprint
|
||||
if response_obj["logprobs"] is not None:
|
||||
model_response.choices[0].logprobs = response_obj["logprobs"]
|
||||
|
||||
model_response.model = self.model
|
||||
print_verbose(
|
||||
f"model_response finish reason 3: {model_response.choices[0].finish_reason}; response_obj={response_obj}"
|
||||
f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}"
|
||||
)
|
||||
## FUNCTION CALL PARSING
|
||||
if (
|
||||
|
|
@ -9461,6 +9417,7 @@ class CustomStreamWrapper:
|
|||
# enter this branch when no content has been passed in response
|
||||
original_chunk = response_obj.get("original_chunk", None)
|
||||
model_response.id = original_chunk.id
|
||||
self.response_id = original_chunk.id
|
||||
if len(original_chunk.choices) > 0:
|
||||
if (
|
||||
original_chunk.choices[0].delta.function_call is not None
|
||||
|
|
@ -9542,6 +9499,7 @@ class CustomStreamWrapper:
|
|||
original_chunk = response_obj.get("original_chunk", None)
|
||||
if original_chunk:
|
||||
model_response.id = original_chunk.id
|
||||
self.response_id = original_chunk.id
|
||||
if len(original_chunk.choices) > 0:
|
||||
try:
|
||||
delta = dict(original_chunk.choices[0].delta)
|
||||
|
|
@ -9580,7 +9538,7 @@ class CustomStreamWrapper:
|
|||
return model_response
|
||||
else:
|
||||
return
|
||||
elif model_response.choices[0].finish_reason is not None:
|
||||
elif self.received_finish_reason is not None:
|
||||
# flush any remaining holding chunk
|
||||
if len(self.holding_chunk) > 0:
|
||||
if model_response.choices[0].delta.content is None:
|
||||
|
|
@ -9590,10 +9548,17 @@ class CustomStreamWrapper:
|
|||
self.holding_chunk + model_response.choices[0].delta.content
|
||||
)
|
||||
self.holding_chunk = ""
|
||||
# get any function call arguments
|
||||
model_response.choices[0].finish_reason = map_finish_reason(
|
||||
model_response.choices[0].finish_reason
|
||||
) # ensure consistent output to openai
|
||||
# if delta is None
|
||||
is_delta_empty = self.is_delta_empty(
|
||||
delta=model_response.choices[0].delta
|
||||
)
|
||||
if is_delta_empty:
|
||||
# get any function call arguments
|
||||
model_response.choices[0].finish_reason = map_finish_reason(
|
||||
finish_reason=self.received_finish_reason
|
||||
) # ensure consistent output to openai
|
||||
self.sent_last_chunk = True
|
||||
|
||||
return model_response
|
||||
elif (
|
||||
model_response.choices[0].delta.tool_calls is not None
|
||||
|
|
@ -9653,6 +9618,16 @@ class CustomStreamWrapper:
|
|||
## SYNC LOGGING
|
||||
self.logging_obj.success_handler(processed_chunk)
|
||||
|
||||
def finish_reason_handler(self):
|
||||
model_response = self.model_response_creator()
|
||||
if self.received_finish_reason is not None:
|
||||
model_response.choices[0].finish_reason = map_finish_reason(
|
||||
finish_reason=self.received_finish_reason
|
||||
)
|
||||
else:
|
||||
model_response.choices[0].finish_reason = "stop"
|
||||
return model_response
|
||||
|
||||
## needs to handle the empty string case (even starting chunk can be an empty string)
|
||||
def __next__(self):
|
||||
try:
|
||||
|
|
@ -9687,7 +9662,16 @@ class CustomStreamWrapper:
|
|||
# RETURN RESULT
|
||||
return response
|
||||
except StopIteration:
|
||||
raise # Re-raise StopIteration
|
||||
if self.sent_last_chunk == True:
|
||||
raise # Re-raise StopIteration
|
||||
else:
|
||||
self.sent_last_chunk = True
|
||||
processed_chunk = self.finish_reason_handler()
|
||||
## LOGGING
|
||||
threading.Thread(
|
||||
target=self.logging_obj.success_handler, args=(processed_chunk,)
|
||||
).start() # log response
|
||||
return processed_chunk
|
||||
except Exception as e:
|
||||
traceback_exception = traceback.format_exc()
|
||||
# LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated
|
||||
|
|
@ -9792,9 +9776,37 @@ class CustomStreamWrapper:
|
|||
# RETURN RESULT
|
||||
return processed_chunk
|
||||
except StopAsyncIteration:
|
||||
raise
|
||||
if self.sent_last_chunk == True:
|
||||
raise # Re-raise StopIteration
|
||||
else:
|
||||
self.sent_last_chunk = True
|
||||
processed_chunk = self.finish_reason_handler()
|
||||
## LOGGING
|
||||
threading.Thread(
|
||||
target=self.logging_obj.success_handler, args=(processed_chunk,)
|
||||
).start() # log response
|
||||
asyncio.create_task(
|
||||
self.logging_obj.async_success_handler(
|
||||
processed_chunk,
|
||||
)
|
||||
)
|
||||
return processed_chunk
|
||||
except StopIteration:
|
||||
raise StopAsyncIteration # Re-raise StopIteration
|
||||
if self.sent_last_chunk == True:
|
||||
raise StopAsyncIteration
|
||||
else:
|
||||
self.sent_last_chunk = True
|
||||
processed_chunk = self.finish_reason_handler()
|
||||
## LOGGING
|
||||
threading.Thread(
|
||||
target=self.logging_obj.success_handler, args=(processed_chunk,)
|
||||
).start() # log response
|
||||
asyncio.create_task(
|
||||
self.logging_obj.async_success_handler(
|
||||
processed_chunk,
|
||||
)
|
||||
)
|
||||
return processed_chunk
|
||||
except Exception as e:
|
||||
traceback_exception = traceback.format_exc()
|
||||
# Handle any exceptions that might occur during streaming
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue