def handle_error_response(self, rbody, rcode, resp, rheaders, stream_error=False):
try:
error_data = resp["error"]
except (KeyError, TypeError):
raise error.APIError(
"Invalid response object from API: %r (HTTP response code "
"was %d)" % (rbody, rcode),
rbody,
rcode,
resp,
)
if "internal_message" in error_data:
error_data["message"] += "\n\n" + error_data["internal_message"]
util.log_info(
"OpenAI API error received",
error_code=error_data.get("code"),
error_type=error_data.get("type"),
error_message=error_data.get("message"),
error_param=error_data.get("param"),
stream_error=stream_error,
)
# Rate limits were previously coded as 400's with code 'rate_limit'
if rcode == 429:
return error.RateLimitError(
error_data.get("message"), rbody, rcode, resp, rheaders
)
elif rcode in [400, 404, 415]:
return error.InvalidRequestError(
error_data.get("message"),
error_data.get("param"),
error_data.get("code"),
rbody,
rcode,
resp,
rheaders,
)
elif rcode == 401:
return error.AuthenticationError(
error_data.get("message"), rbody, rcode, resp, rheaders
)
elif rcode == 403:
return error.PermissionError(
error_data.get("message"), rbody, rcode, resp, rheaders
)
elif rcode == 409:
return error.TryAgain(
error_data.get("message"), rbody, rcode, resp, rheaders
)
elif stream_error:
# TODO: we will soon attach status codes to stream errors
parts = [error_data.get("message"), "(Error occurred while streaming.)"]
message = " ".join([p for p in parts if p is not None])
return error.APIError(message, rbody, rcode, resp, rheaders)
else:
return error.APIError(
error_data.get("message"), rbody, rcode, resp, rheaders
)
def create(cls, *args, timeout=None, **kwargs):
start = time.time()
while True:
try:
return super().create(*args, **kwargs)
except TryAgain as e:
if timeout is not None and time.time() > start + timeout:
raise
util.log_info("Waiting for snapshot to warm up", error=e)
def generate(self, timeout=None, **params):
start = time.time()
while True:
try:
return self.request(
"post",
self.instance_url() + "/generate",
params,
stream=params.get("stream"),
plain_old_data=True,
)
except TryAgain as e:
if timeout is not None and time.time() > start + timeout:
raise
util.log_info("Waiting for model to warm up", error=e)
def create(cls, *args, **kwargs):
"""
Creates a new embedding for the provided input and parameters.
See https://beta.openai.com/docs/api-reference/embeddings for a list
of valid parameters.
"""
start = time.time()
timeout = kwargs.pop("timeout", None)
if kwargs.get("model", None) is None and kwargs.get("engine",
None) is None:
raise InvalidRequestError(
"Must provide an 'engine' or 'model' parameter to create an Embedding.",
param="engine",
)
user_provided_encoding_format = kwargs.get("encoding_format", None)
# If encoding format was not explicitly specified, we opaquely use base64 for performance
if not user_provided_encoding_format:
kwargs["encoding_format"] = "base64"
while True:
try:
response = super().create(*args, **kwargs)
# If a user specifies base64, we'll just return the encoded string.
# This is only for the default case.
if not user_provided_encoding_format:
for data in response.data:
# If an engine isn't using this optimization, don't do anything
if type(data["embedding"]) == str:
data["embedding"] = np.frombuffer(
base64.b64decode(data["embedding"]),
dtype="float32").tolist()
return response
except TryAgain as e:
if timeout is not None and time.time() > start + timeout:
raise
util.log_info("Waiting for model to warm up", error=e)
def request_with_retries(self, method, url, headers, post_data=None, stream=False):
self._add_telemetry_header(headers)
num_retries = 0
while True:
request_start = _now_ms()
try:
response = self.request(method, url, headers, post_data, stream=stream)
connection_error = None
except error.APIConnectionError as e:
connection_error = e
response = None
if self._should_retry(response, connection_error, num_retries):
if connection_error:
util.log_warn(
"Encountered a retryable error %s"
% connection_error.user_message
)
num_retries += 1
sleep_time = self._sleep_time_seconds(num_retries, response)
util.log_info(
(
"Initiating retry %i for request %s %s after "
"sleeping %.2f seconds."
% (num_retries, method, url, sleep_time)
)
)
time.sleep(sleep_time)
else:
if response is not None:
self._record_request_metrics(response, request_start)
return response
else:
raise connection_error
def request_raw(
self,
method,
url,
params=None,
supplied_headers=None,
files=None,
stream=False,
request_id: Optional[str] = None,
) -> requests.Response:
abs_url = "%s%s" % (self.api_base, url)
headers = {}
data = None
if method == "get" or method == "delete":
if params:
encoded_params = urlencode([(k, v) for k, v in params.items()
if v is not None])
abs_url = _build_api_url(abs_url, encoded_params)
elif method in {"post", "put"}:
if params and files:
raise ValueError(
"At most one of params and files may be specified.")
if params:
data = json.dumps(params).encode()
headers["Content-Type"] = "application/json"
else:
raise error.APIConnectionError(
"Unrecognized HTTP method %r. This may indicate a bug in the "
"OpenAI bindings. Please contact [email protected] for "
"assistance." % (method, ))
headers = self.request_headers(method, headers, request_id)
if supplied_headers is not None:
headers.update(supplied_headers)
util.log_info("Request to OpenAI API", method=method, path=abs_url)
util.log_debug("Post details", data=data, api_version=self.api_version)
if not hasattr(_thread_context, "session"):
_thread_context.session = _make_session()
try:
result = _thread_context.session.request(
method,
abs_url,
headers=headers,
data=data,
files=files,
stream=stream,
timeout=TIMEOUT_SECS,
)
except requests.exceptions.RequestException as e:
raise error.APIConnectionError(
"Error communicating with OpenAI") from e
util.log_info(
"OpenAI API response",
path=abs_url,
response_code=result.status_code,
processing_ms=result.headers.get("OpenAI-Processing-Ms"),
)
# Don't read the whole stream for debug logging unless necessary.
if openai.log == "debug":
util.log_debug("API response body",
body=result.content,
headers=result.headers)
return result
def request_raw(
self, method, url, params=None, supplied_headers=None, stream=False
):
"""
Mechanism for issuing an API call
"""
if self.api_key:
my_api_key = self.api_key
else:
from openai import api_key
my_api_key = api_key
if my_api_key is None:
raise error.AuthenticationError(
"No API key provided. (HINT: set your API key using in code using "
'"openai.api_key = <API-KEY>", or you can set the environment variable OPENAI_API_KEY=<API-KEY>). You can generate API keys '
"in the OpenAI web interface. See https://onboard.openai.com "
"for details, or email [email protected] if you have any "
"questions."
)
abs_url = "%s%s" % (self.api_base, url)
headers = {}
compress = None
progress_meter = False
if method == "get" or method == "delete":
if params:
encoded_params = url_encode_params(params)
abs_url = _build_api_url(abs_url, encoded_params)
else:
encoded_params = None
post_data = None
elif method in {"post", "put"}:
if (
supplied_headers is not None
and supplied_headers.get("Content-Type") == "multipart/form-data"
):
generator = MultipartDataGenerator()
generator.add_params(params or {})
post_data = generator.get_post_data()
content_type = "multipart/form-data; boundary=%s" % (
generator.boundary,
)
# We will overrite Content-Type
supplied_headers.pop("Content-Type")
progress_meter = True
# compress = "gzip"
compress = None
else:
post_data = json.dumps(params).encode()
content_type = "application/json"
headers["Content-Type"] = content_type
encoded_params = post_data
if progress_meter:
post_data = BufferReader(post_data, desc="Upload progress")
if compress == "gzip":
if not hasattr(post_data, "read"):
post_data = BytesIO(post_data)
headers["Content-Encoding"] = "gzip"
from openai.gzip_stream import GZIPCompressedStream
post_data = GZIPCompressedStream(post_data, compression_level=9)
else:
raise error.APIConnectionError(
"Unrecognized HTTP method %r. This may indicate a bug in the "
"OpenAI bindings. Please contact [email protected] for "
"assistance." % (method,)
)
headers = self.request_headers(my_api_key, method, headers)
if supplied_headers is not None:
for key, value in six.iteritems(supplied_headers):
headers[key] = value
util.log_info("Request to OpenAI API", method=method, path=abs_url)
util.log_debug(
"Post details", post_data=encoded_params, api_version=self.api_version
)
rbody, rcode, rheaders, stream = self._client.request_with_retries(
method, abs_url, headers, post_data, stream=stream
)
util.log_info(
"OpenAI API response",
path=abs_url,
response_code=rcode,
processing_ms=rheaders.get("OpenAI-Processing-Ms"),
)
util.log_debug("API response body", body=rbody, headers=rheaders)
if "Request-Id" in rheaders:
request_id = rheaders["Request-Id"]
util.log_debug(
"Dashboard link for request", link=util.dashboard_link(request_id)
)
return rbody, rcode, rheaders, stream, my_api_key