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@ -216,7 +216,7 @@ class Sambaverse(LLM):
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)
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"""
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sambaverse_url: str = "https://sambaverse.sambanova.ai"
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sambaverse_url: str = ""
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"""Sambaverse url to use"""
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sambaverse_api_key: str = ""
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@ -244,7 +244,10 @@ class Sambaverse(LLM):
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key exists in environment."""
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values["sambaverse_url"] = get_from_dict_or_env(
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values, "sambaverse_url", "SAMBAVERSE_URL"
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values,
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"sambaverse_url",
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"SAMBAVERSE_URL",
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default="https://sambaverse.sambanova.ai",
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)
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values["sambaverse_api_key"] = get_from_dict_or_env(
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values, "sambaverse_api_key", "SAMBAVERSE_API_KEY"
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@ -314,16 +317,24 @@ class Sambaverse(LLM):
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self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
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)
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if response["status_code"] != 200:
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optional_code = response["error"].get("code")
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optional_details = response["error"].get("details")
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optional_message = response["error"].get("message")
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raise ValueError(
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f"Sambanova /complete call failed with status code "
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f"{response['status_code']}."
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f"Message: {optional_message}"
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f"Details: {optional_details}"
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f"Code: {optional_code}"
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)
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error = response.get("error")
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if error:
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optional_code = error.get("code")
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optional_details = error.get("details")
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optional_message = error.get("message")
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raise RuntimeError(
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f"Sambanova /complete call failed with status code "
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f"{response['status_code']}.\n"
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f"Message: {optional_message}\n"
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f"Details: {optional_details}\n"
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f"Code: {optional_code}\n"
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)
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else:
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raise RuntimeError(
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f"Sambanova /complete call failed with status code "
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f"{response['status_code']}."
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f"{response}."
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)
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return response["result"]["responses"][0]["completion"]
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def _handle_completion_requests(
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@ -364,16 +375,24 @@ class Sambaverse(LLM):
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self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
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):
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if chunk["status_code"] != 200:
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optional_code = chunk["error"].get("code")
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optional_details = chunk["error"].get("details")
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optional_message = chunk["error"].get("message")
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raise ValueError(
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f"Sambanova /complete call failed with status code "
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f"{chunk['status_code']}."
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f"Message: {optional_message}"
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f"Details: {optional_details}"
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f"Code: {optional_code}"
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)
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error = chunk.get("error")
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if error:
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optional_code = error.get("code")
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optional_details = error.get("details")
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optional_message = error.get("message")
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raise ValueError(
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f"Sambanova /complete call failed with status code "
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f"{chunk['status_code']}.\n"
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f"Message: {optional_message}\n"
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f"Details: {optional_details}\n"
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f"Code: {optional_code}\n"
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)
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else:
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raise RuntimeError(
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f"Sambanova /complete call failed with status code "
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f"{chunk['status_code']}."
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f"{chunk}."
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)
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text = chunk["result"]["responses"][0]["stream_token"]
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generated_chunk = GenerationChunk(text=text)
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yield generated_chunk
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@ -477,19 +496,18 @@ class SSEndpointHandler:
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:param str host_url: Base URL of the DaaS API service
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"""
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API_BASE_PATH = "/api"
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def __init__(self, host_url: str):
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def __init__(self, host_url: str, api_base_uri: str):
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"""
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Initialize the SSEndpointHandler.
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:param str host_url: Base URL of the DaaS API service
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:param str api_base_uri: Base URI of the DaaS API service
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"""
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self.host_url = host_url
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self.api_base_uri = api_base_uri
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self.http_session = requests.Session()
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@staticmethod
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def _process_response(response: requests.Response) -> Dict:
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def _process_response(self, response: requests.Response) -> Dict:
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"""
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Processes the API response and returns the resulting dict.
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@ -515,28 +533,47 @@ class SSEndpointHandler:
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result["status_code"] = response.status_code
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return result
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@staticmethod
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def _process_streaming_response(
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self,
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response: requests.Response,
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) -> Generator[GenerationChunk, None, None]:
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) -> Generator[Dict, None, None]:
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"""Process the streaming response"""
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try:
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import sseclient
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except ImportError:
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raise ImportError(
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"could not import sseclient library"
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"Please install it with `pip install sseclient-py`."
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if "nlp" in self.api_base_uri:
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try:
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import sseclient
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except ImportError:
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raise ImportError(
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"could not import sseclient library"
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"Please install it with `pip install sseclient-py`."
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)
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client = sseclient.SSEClient(response)
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close_conn = False
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for event in client.events():
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if event.event == "error_event":
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close_conn = True
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chunk = {
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"event": event.event,
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"data": event.data,
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"status_code": response.status_code,
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}
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yield chunk
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if close_conn:
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client.close()
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elif "generic" in self.api_base_uri:
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try:
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for line in response.iter_lines():
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chunk = json.loads(line)
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if "status_code" not in chunk:
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chunk["status_code"] = response.status_code
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if chunk["status_code"] == 200 and chunk.get("error"):
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chunk["result"] = {"responses": [{"stream_token": ""}]}
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yield chunk
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except Exception as e:
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raise RuntimeError(f"Error processing streaming response: {e}")
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else:
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raise ValueError(
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f"handling of endpoint uri: {self.api_base_uri} not implemented"
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)
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client = sseclient.SSEClient(response)
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close_conn = False
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for event in client.events():
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if event.event == "error_event":
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close_conn = True
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text = json.dumps({"event": event.event, "data": event.data})
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chunk = GenerationChunk(text=text)
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yield chunk
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if close_conn:
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client.close()
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def _get_full_url(self, path: str) -> str:
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"""
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@ -546,7 +583,7 @@ class SSEndpointHandler:
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:returns: the full API URL for the sub-path
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:rtype: str
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"""
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return f"{self.host_url}{self.API_BASE_PATH}{path}"
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return f"{self.host_url}/{self.api_base_uri}/{path}"
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def nlp_predict(
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self,
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@ -570,16 +607,26 @@ class SSEndpointHandler:
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"""
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if isinstance(input, str):
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input = [input]
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if params:
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data = {"inputs": input, "params": json.loads(params)}
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if "nlp" in self.api_base_uri:
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if params:
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data = {"inputs": input, "params": json.loads(params)}
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else:
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data = {"inputs": input}
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elif "generic" in self.api_base_uri:
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if params:
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data = {"instances": input, "params": json.loads(params)}
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else:
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data = {"instances": input}
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else:
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data = {"inputs": input}
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raise ValueError(
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f"handling of endpoint uri: {self.api_base_uri} not implemented"
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)
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response = self.http_session.post(
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self._get_full_url(f"/predict/nlp/{project}/{endpoint}"),
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self._get_full_url(f"{project}/{endpoint}"),
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headers={"key": key},
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json=data,
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)
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return SSEndpointHandler._process_response(response)
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return self._process_response(response)
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def nlp_predict_stream(
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self,
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@ -588,7 +635,7 @@ class SSEndpointHandler:
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key: str,
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input: Union[List[str], str],
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params: Optional[str] = "",
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) -> Iterator[GenerationChunk]:
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) -> Iterator[Dict]:
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"""
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NLP predict using inline input string.
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@ -600,20 +647,32 @@ class SSEndpointHandler:
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:returns: Prediction results
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:rtype: dict
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"""
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if isinstance(input, str):
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input = [input]
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if params:
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data = {"inputs": input, "params": json.loads(params)}
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if "nlp" in self.api_base_uri:
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if isinstance(input, str):
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input = [input]
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if params:
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data = {"inputs": input, "params": json.loads(params)}
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else:
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data = {"inputs": input}
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elif "generic" in self.api_base_uri:
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if isinstance(input, list):
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input = input[0]
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if params:
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data = {"instance": input, "params": json.loads(params)}
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else:
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data = {"instance": input}
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else:
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data = {"inputs": input}
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raise ValueError(
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f"handling of endpoint uri: {self.api_base_uri} not implemented"
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)
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# Streaming output
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response = self.http_session.post(
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self._get_full_url(f"/predict/nlp/stream/{project}/{endpoint}"),
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self._get_full_url(f"stream/{project}/{endpoint}"),
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headers={"key": key},
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json=data,
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stream=True,
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)
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for chunk in SSEndpointHandler._process_streaming_response(response):
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for chunk in self._process_streaming_response(response):
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yield chunk
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@ -623,6 +682,7 @@ class SambaStudio(LLM):
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To use, you should have the environment variables
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``SAMBASTUDIO_BASE_URL`` set with your SambaStudio environment URL.
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``SAMBASTUDIO_BASE_URI`` set with your SambaStudio api base URI.
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``SAMBASTUDIO_PROJECT_ID`` set with your SambaStudio project ID.
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``SAMBASTUDIO_ENDPOINT_ID`` set with your SambaStudio endpoint ID.
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``SAMBASTUDIO_API_KEY`` set with your SambaStudio endpoint API key.
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@ -637,6 +697,7 @@ class SambaStudio(LLM):
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from langchain_community.llms.sambanova import Sambaverse
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SambaStudio(
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sambastudio_base_url="your-SambaStudio-environment-URL",
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sambastudio_base_uri="your-SambaStudio-base-URI",
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sambastudio_project_id="your-SambaStudio-project-ID",
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sambastudio_endpoint_id="your-SambaStudio-endpoint-ID",
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sambastudio_api_key="your-SambaStudio-endpoint-API-key,
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@ -655,6 +716,9 @@ class SambaStudio(LLM):
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sambastudio_base_url: str = ""
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"""Base url to use"""
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sambastudio_base_uri: str = ""
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"""endpoint base uri"""
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sambastudio_project_id: str = ""
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"""Project id on sambastudio for model"""
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@ -695,6 +759,12 @@ class SambaStudio(LLM):
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values["sambastudio_base_url"] = get_from_dict_or_env(
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values, "sambastudio_base_url", "SAMBASTUDIO_BASE_URL"
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)
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values["sambastudio_base_uri"] = get_from_dict_or_env(
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values,
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"sambastudio_base_uri",
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"SAMBASTUDIO_BASE_URI",
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default="api/predict/nlp",
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)
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values["sambastudio_project_id"] = get_from_dict_or_env(
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values, "sambastudio_project_id", "SAMBASTUDIO_PROJECT_ID"
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)
|
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|
|
@ -718,14 +788,17 @@ class SambaStudio(LLM):
|
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|
|
|
The tuning parameters as a JSON string.
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"""
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|
|
_model_kwargs = self.model_kwargs or {}
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|
_stop_sequences = _model_kwargs.get("stop_sequences", [])
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_stop_sequences = stop or _stop_sequences
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|
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# _model_kwargs['stop_sequences'] = ','.join(
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# f"'{x}'" for x in _stop_sequences)
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_kwarg_stop_sequences = _model_kwargs.get("stop_sequences", [])
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_stop_sequences = stop or _kwarg_stop_sequences
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# if not _kwarg_stop_sequences:
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# _model_kwargs["stop_sequences"] = ",".join(
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# f'"{x}"' for x in _stop_sequences
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# )
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tuning_params_dict = {
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k: {"type": type(v).__name__, "value": str(v)}
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for k, v in (_model_kwargs.items())
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}
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# _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
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tuning_params = json.dumps(tuning_params_dict)
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return tuning_params
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@ -754,12 +827,25 @@ class SambaStudio(LLM):
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tuning_params,
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)
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if response["status_code"] != 200:
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optional_detail = response["detail"]
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optional_detail = response.get("detail")
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if optional_detail:
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raise RuntimeError(
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f"Sambanova /complete call failed with status code "
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f"{response['status_code']}.\n Details: {optional_detail}"
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|
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)
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else:
|
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|
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|
raise RuntimeError(
|
|
|
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f"Sambanova /complete call failed with status code "
|
|
|
|
|
f"{response['status_code']}.\n response {response}"
|
|
|
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|
)
|
|
|
|
|
if "nlp" in self.sambastudio_base_uri:
|
|
|
|
|
return response["data"][0]["completion"]
|
|
|
|
|
elif "generic" in self.sambastudio_base_uri:
|
|
|
|
|
return response["predictions"][0]["completion"]
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"Sambanova /complete call failed with status code "
|
|
|
|
|
f"{response['status_code']}. Details: {optional_detail}"
|
|
|
|
|
f"handling of endpoint uri: {self.sambastudio_base_uri} not implemented"
|
|
|
|
|
)
|
|
|
|
|
return response["data"][0]["completion"]
|
|
|
|
|
|
|
|
|
|
def _handle_completion_requests(
|
|
|
|
|
self, prompt: Union[List[str], str], stop: Optional[List[str]]
|
|
|
|
@ -777,7 +863,9 @@ class SambaStudio(LLM):
|
|
|
|
|
Raises:
|
|
|
|
|
ValueError: If the prediction fails.
|
|
|
|
|
"""
|
|
|
|
|
ss_endpoint = SSEndpointHandler(self.sambastudio_base_url)
|
|
|
|
|
ss_endpoint = SSEndpointHandler(
|
|
|
|
|
self.sambastudio_base_url, self.sambastudio_base_uri
|
|
|
|
|
)
|
|
|
|
|
tuning_params = self._get_tuning_params(stop)
|
|
|
|
|
return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)
|
|
|
|
|
|
|
|
|
@ -802,7 +890,36 @@ class SambaStudio(LLM):
|
|
|
|
|
prompt,
|
|
|
|
|
tuning_params,
|
|
|
|
|
):
|
|
|
|
|
yield chunk
|
|
|
|
|
if chunk["status_code"] != 200:
|
|
|
|
|
error = chunk.get("error")
|
|
|
|
|
if error:
|
|
|
|
|
optional_code = error.get("code")
|
|
|
|
|
optional_details = error.get("details")
|
|
|
|
|
optional_message = error.get("message")
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"Sambanova /complete call failed with status code "
|
|
|
|
|
f"{chunk['status_code']}.\n"
|
|
|
|
|
f"Message: {optional_message}\n"
|
|
|
|
|
f"Details: {optional_details}\n"
|
|
|
|
|
f"Code: {optional_code}\n"
|
|
|
|
|
)
|
|
|
|
|
else:
|
|
|
|
|
raise RuntimeError(
|
|
|
|
|
f"Sambanova /complete call failed with status code "
|
|
|
|
|
f"{chunk['status_code']}."
|
|
|
|
|
f"{chunk}."
|
|
|
|
|
)
|
|
|
|
|
if "nlp" in self.sambastudio_base_uri:
|
|
|
|
|
text = json.loads(chunk["data"])["stream_token"]
|
|
|
|
|
elif "generic" in self.sambastudio_base_uri:
|
|
|
|
|
text = chunk["result"]["responses"][0]["stream_token"]
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"handling of endpoint uri: {self.sambastudio_base_uri}"
|
|
|
|
|
f"not implemented"
|
|
|
|
|
)
|
|
|
|
|
generated_chunk = GenerationChunk(text=text)
|
|
|
|
|
yield generated_chunk
|
|
|
|
|
|
|
|
|
|
def _stream(
|
|
|
|
|
self,
|
|
|
|
@ -820,7 +937,9 @@ class SambaStudio(LLM):
|
|
|
|
|
Returns:
|
|
|
|
|
The string generated by the model.
|
|
|
|
|
"""
|
|
|
|
|
ss_endpoint = SSEndpointHandler(self.sambastudio_base_url)
|
|
|
|
|
ss_endpoint = SSEndpointHandler(
|
|
|
|
|
self.sambastudio_base_url, self.sambastudio_base_uri
|
|
|
|
|
)
|
|
|
|
|
tuning_params = self._get_tuning_params(stop)
|
|
|
|
|
try:
|
|
|
|
|
if self.streaming:
|
|
|
|
|