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tests run again
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@dtam
dtam committed Jul 3, 2024
1 parent 5bc6cd7 commit 0baf778
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Showing 2 changed files with 82 additions and 86 deletions.
163 changes: 80 additions & 83 deletions guardrails_api/blueprints/guards.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,10 +16,7 @@
from guardrails_api.clients.postgres_client import postgres_is_enabled
from guardrails_api.utils.handle_error import handle_error
from guardrails_api.utils.get_llm_callable import get_llm_callable
from guardrails_api.utils.openai import (
outcome_to_chat_completion,
outcome_to_stream_response,
)
from guardrails_api.utils.openai import outcome_to_chat_completion, outcome_to_stream_response

guards_bp = Blueprint("guards", __name__, url_prefix="/guards")

Expand Down Expand Up @@ -154,85 +151,85 @@ def collect_telemetry(
validate_span.set_attribute("num_of_reasks", num_of_reasks)


@guards_bp.route("/<guard_name>/openai/v1/chat/completions", methods=["POST"])
@handle_error
def chat_completions(guard_name: str):
# This endpoint implements the OpenAI Chat API
# It is mean to be fully compatible
# The only difference is that it uses the Guard API under the hood
# instead of the OpenAI API and supports guardrail API error handling
# To use this with the OpenAI SDK you can use the following code:
# import openai
# openai.base_url = "http://localhost:8000/guards/<guard_name>/openai/v1/"
# response = openai.chat.completions(
# model="gpt-3.5-turbo-0125",
# messages=[
# {"role": "user", "content": "Hello, how are you?"},
# ],
# stream=True,
# )
# print(response)
# to configure guard rails error handling from the server side you can use the following code:
#

payload = request.json
decoded_guard_name = unquote_plus(guard_name)
guard_struct = guard_client.get_guard(decoded_guard_name)
guard = guard_struct
if not isinstance(guard_struct, Guard):
guard: Guard = Guard.from_dict(guard_struct.to_dict())
stream = payload.get("stream", False)
has_tool_gd_tool_call = False

try:
tools = payload.get("tools", [])
tools.filter(lambda tool: tool["funcion"]["name"] == "gd_response_tool")
has_tool_gd_tool_call = len(tools) > 0
except KeyError:
pass

if not stream:
try:
validation_outcome: ValidationOutcome = guard(
# todo make this come from the guard struct?
# currently we dont support .configure
num_reasks=0,
**payload,
)
llm_response = guard.history.last.iterations.last.outputs.llm_response_info
result = outcome_to_chat_completion(
validation_outcome=validation_outcome,
llm_response=llm_response,
has_tool_gd_tool_call=has_tool_gd_tool_call,
)
return result
except Exception as e:
raise HttpError(
status=400,
message="BadRequest",
cause=(str(e)),
)

else:
# need to return validated chunks that look identical to openai's
# should look something like
# data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":None,"finish_reason":None}]}
# ....
# data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{},"logprobs":None,"finish_reason":"stop"}]}
def openai_streamer():
guard_stream = guard(
num_reasks=0,
**payload,
)
for result in guard_stream:
chunk_string = f"data: {json.dumps(outcome_to_stream_response(validation_outcome=result))}\n\n"
yield chunk_string.encode("utf-8")
# close the stream
yield b"\n"

return Response(
stream_with_context(openai_streamer()),
)
# @guards_bp.route("/<guard_name>/openai/v1/chat/completions", methods=["POST"])
# @handle_error
# def chat_completions(guard_name: str):
# # This endpoint implements the OpenAI Chat API
# # It is mean to be fully compatible
# # The only difference is that it uses the Guard API under the hood
# # instead of the OpenAI API and supports guardrail API error handling
# # To use this with the OpenAI SDK you can use the following code:
# # import openai
# # openai.base_url = "http://localhost:8000/guards/<guard_name>/openai/v1/"
# # response = openai.chat.completions(
# # model="gpt-3.5-turbo-0125",
# # messages=[
# # {"role": "user", "content": "Hello, how are you?"},
# # ],
# # stream=True,
# # )
# # print(response)
# # to configure guard rails error handling from the server side you can use the following code:
# #

# payload = request.json
# decoded_guard_name = unquote_plus(guard_name)
# guard_struct = guard_client.get_guard(decoded_guard_name)
# guard = guard_struct
# if not isinstance(guard_struct, Guard):
# guard: Guard = Guard.from_dict(guard_struct.to_dict())
# stream = payload.get("stream", False)
# has_tool_gd_tool_call = False

# try:
# tools = payload.get("tools", [])
# tools.filter(lambda tool: tool["funcion"]["name"] == "gd_response_tool")
# has_tool_gd_tool_call = len(tools) > 0
# except KeyError:
# pass

# if not stream:
# try:
# validation_outcome: ValidationOutcome = guard(
# # todo make this come from the guard struct?
# # currently we dont support .configure
# num_reasks=0,
# **payload,
# )
# llm_response = guard.history.last.iterations.last.outputs.llm_response_info
# result = outcome_to_chat_completion(
# validation_outcome=validation_outcome,
# llm_response=llm_response,
# has_tool_gd_tool_call=has_tool_gd_tool_call,
# )
# return result
# except Exception as e:
# raise HttpError(
# status=400,
# message="BadRequest",
# cause=(str(e)),
# )

# else:
# # need to return validated chunks that look identical to openai's
# # should look something like
# # data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":None,"finish_reason":None}]}
# # ....
# # data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{},"logprobs":None,"finish_reason":"stop"}]}
# def openai_streamer():
# guard_stream = guard(
# num_reasks=0,
# **payload,
# )
# for result in guard_stream:
# chunk_string = f"data: {json.dumps(outcome_to_stream_response(validation_outcome=result))}\n\n"
# yield chunk_string.encode("utf-8")
# # close the stream
# yield b"\n"

# return Response(
# stream_with_context(openai_streamer()),
# )


@guards_bp.route("/<guard_name>/validate", methods=["POST"])
Expand Down
5 changes: 2 additions & 3 deletions guardrails_api/utils/openai.py
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
from guardrails.classes import ValidationOutcome, LLMResponse

from guardrails.classes import ValidationOutcome

def outcome_to_stream_response(validation_outcome: ValidationOutcome):
stream_chunk_template = {
Expand All @@ -24,7 +23,7 @@ def outcome_to_stream_response(validation_outcome: ValidationOutcome):

def outcome_to_chat_completion(
validation_outcome: ValidationOutcome,
llm_response: LLMResponse,
llm_response,
has_tool_gd_tool_call=False,
):
completion_template = (
Expand Down

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