mirror of
https://github.com/open-webui/pipelines
synced 2025-05-10 23:50:45 +00:00
Merge f060ba67de into b955a8fab3
This commit is contained in:
Shubham Sharma
GitHub
commit
3d8706c40a
@ -26,7 +26,6 @@ class Pipeline:
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}
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)
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self.set_pipelines()
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pass
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def set_pipelines(self):
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models = self.valves.AZURE_OPENAI_MODELS.split(";")
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@ -35,27 +34,26 @@ class Pipeline:
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{"id": model, "name": name} for model, name in zip(models, model_names)
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]
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print(f"azure_openai_manifold_pipeline - models: {self.pipelines}")
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pass
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async def on_valves_updated(self):
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self.set_pipelines()
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self.set_pipelines()
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async def on_startup(self):
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# This function is called when the server is started.
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print(f"on_startup:{__name__}")
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pass
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async def on_shutdown(self):
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# This function is called when the server is stopped.
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print(f"on_shutdown:{__name__}")
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pass
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def pipe(
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self, user_message: str, model_id: str, messages: List[dict], body: dict
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) -> Union[str, Generator, Iterator]:
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# This is where you can add your custom pipelines like RAG.
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self,
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user_message: str,
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model_id: str,
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messages: List[dict],
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body: dict
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) -> Union[str, Generator[str, None, None], Iterator[str]]:
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print(f"pipe:{__name__}")
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print(messages)
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print(user_message)
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@ -64,36 +62,152 @@ class Pipeline:
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"Content-Type": "application/json",
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}
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url = f"{self.valves.AZURE_OPENAI_ENDPOINT}/openai/deployments/{model_id}/chat/completions?api-version={self.valves.AZURE_OPENAI_API_VERSION}"
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# URL for Chat Completions in Azure OpenAI
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url = (
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f"{self.valves.AZURE_OPENAI_ENDPOINT}/openai/deployments/"
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f"{model_id}/chat/completions?api-version={self.valves.AZURE_OPENAI_API_VERSION}"
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)
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allowed_params = {'messages', 'temperature', 'role', 'content', 'contentPart', 'contentPartImage',
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'enhancements', 'dataSources', 'n', 'stream', 'stop', 'max_tokens', 'presence_penalty',
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'frequency_penalty', 'logit_bias', 'user', 'function_call', 'funcions', 'tools',
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'tool_choice', 'top_p', 'log_probs', 'top_logprobs', 'response_format', 'seed'}
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# remap user field
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# --- Define the allowed parameter sets ---
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# (1) Default allowed params (non-o1)
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allowed_params_default = {
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"messages",
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"temperature",
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"role",
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"content",
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"contentPart",
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"contentPartImage",
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"enhancements",
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"dataSources",
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"n",
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"stream",
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"stop",
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"max_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"function_call",
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"funcions",
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"tools",
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"tool_choice",
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"top_p",
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"log_probs",
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"top_logprobs",
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"response_format",
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"seed",
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}
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# (2) o1 models allowed params
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allowed_params_o1 = {
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"model",
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"messages",
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"top_p",
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"n",
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"max_completion_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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}
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# Simple helper to detect if it's an o1 model
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def is_o1_model(m: str) -> bool:
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# Adjust this check to your naming pattern for o1 models
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return "o1" in m or m.startswith("o")
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# Ensure user is a string
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if "user" in body and not isinstance(body["user"], str):
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body["user"] = body["user"]["id"] if "id" in body["user"] else str(body["user"])
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filtered_body = {k: v for k, v in body.items() if k in allowed_params}
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# log fields that were filtered out as a single line
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if len(body) != len(filtered_body):
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print(f"Dropped params: {', '.join(set(body.keys()) - set(filtered_body.keys()))}")
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body["user"] = body["user"].get("id", str(body["user"]))
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try:
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r = requests.post(
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url=url,
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json=filtered_body,
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headers=headers,
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stream=True,
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)
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# If it's an o1 model, do a "fake streaming" approach
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if is_o1_model(model_id):
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# We'll remove "stream" from the body if present (since we'll do manual streaming),
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# then filter to the allowed params for o1 models.
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body.pop("stream", None)
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filtered_body = {k: v for k, v in body.items() if k in allowed_params_o1}
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r.raise_for_status()
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if body["stream"]:
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return r.iter_lines()
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else:
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return r.json()
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except Exception as e:
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if r:
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text = r.text
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return f"Error: {e} ({text})"
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else:
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return f"Error: {e}"
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# Log which fields were dropped
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if len(body) != len(filtered_body):
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dropped_keys = set(body.keys()) - set(filtered_body.keys())
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print(f"Dropped params: {', '.join(dropped_keys)}")
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try:
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# We make a normal request (non-streaming)
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r = requests.post(
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url=url,
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json=filtered_body,
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headers=headers,
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stream=False,
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)
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r.raise_for_status()
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# Parse the full JSON response
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data = r.json()
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# Typically, the text content is in data["choices"][0]["message"]["content"]
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# This may vary depending on your actual response shape.
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# For safety, let's do a little fallback:
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content = ""
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if (
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isinstance(data, dict)
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and "choices" in data
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and isinstance(data["choices"], list)
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and len(data["choices"]) > 0
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and "message" in data["choices"][0]
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and "content" in data["choices"][0]["message"]
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):
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content = data["choices"][0]["message"]["content"]
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else:
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# fallback to something, or just return the raw data
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# but let's handle the "fun" streaming of partial content
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content = str(data)
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# We will chunk the text to simulate streaming
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def chunk_text(text: str, chunk_size: int = 30) -> Generator[str, None, None]:
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"""Yield text in fixed-size chunks."""
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for i in range(0, len(text), chunk_size):
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yield text[i : i + chunk_size]
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# Return a generator that yields chunks
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def fake_stream() -> Generator[str, None, None]:
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for chunk in chunk_text(content):
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yield chunk
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return fake_stream()
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except Exception as e:
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# If the request object exists, return its text
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if "r" in locals() and r is not None:
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return f"Error: {e} ({r.text})"
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else:
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return f"Error: {e}"
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else:
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# Normal pipeline for non-o1 models:
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filtered_body = {k: v for k, v in body.items() if k in allowed_params_default}
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if len(body) != len(filtered_body):
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dropped_keys = set(body.keys()) - set(filtered_body.keys())
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print(f"Dropped params: {', '.join(dropped_keys)}")
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try:
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r = requests.post(
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url=url,
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json=filtered_body,
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headers=headers,
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stream=True,
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)
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r.raise_for_status()
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if filtered_body.get("stream"):
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# Real streaming
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return r.iter_lines()
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else:
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# Just return the JSON
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return r.json()
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except Exception as e:
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if "r" in locals() and r is not None:
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return f"Error: {e} ({r.text})"
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else:
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return f"Error: {e}"
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