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community[patch]: Sambanova integration api update (#21848)

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- **Description:**:
        SambaStudio generic endpoint compatibility added
        Improved error description, and handling
        streaming examples added
This commit is contained in:
Jorge Piedrahita Ortiz 2024-05-20 17:29:59 -05:00 committed by GitHub
parent c6da9533ac
commit e6207ad4f3
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2 changed files with 248 additions and 70 deletions
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59
docs/docs/integrations/llms/sambanova.ipynb
View File

@ -99,6 +99,36 @@
"print(llm.invoke(\"Why should I use open source models?\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Streaming response\n",
"\n",
"from langchain_community.llms.sambanova import Sambaverse\n",
"\n",
"llm = Sambaverse(\n",
" sambaverse_model_name=\"Meta/llama-2-7b-chat-hf\",\n",
" streaming=True,\n",
" model_kwargs={\n",
" \"do_sample\": True,\n",
" \"max_tokens_to_generate\": 1000,\n",
" \"temperature\": 0.01,\n",
" \"process_prompt\": True,\n",
" \"select_expert\": \"llama-2-7b-chat-hf\",\n",
" # \"stop_sequences\": '\\\"sequence1\\\",\\\"sequence2\\\"',\n",
" # \"repetition_penalty\": 1.0,\n",
" # \"top_k\": 50,\n",
" # \"top_p\": 1.0\n",
" },\n",
")\n",
"\n",
"for chunk in llm.stream(\"Why should I use open source models?\"):\n",
" print(chunk, end=\"\", flush=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -147,12 +177,14 @@
"import os\n",
"\n",
"sambastudio_base_url = \"<Your SambaStudio environment URL>\"\n",
"# sambastudio_base_uri = \"<Your SambaStudio endpoint base URI>\" # optional, \"api/predict/nlp\" set as default\n",
"sambastudio_project_id = \"<Your SambaStudio project id>\"\n",
"sambastudio_endpoint_id = \"<Your SambaStudio endpoint id>\"\n",
"sambastudio_api_key = \"<Your SambaStudio endpoint API key>\"\n",
"\n",
"# Set the environment variables\n",
"os.environ[\"SAMBASTUDIO_BASE_URL\"] = sambastudio_base_url\n",
"# os.environ[\"SAMBASTUDIO_BASE_URI\"] = sambastudio_base_uri\n",
"os.environ[\"SAMBASTUDIO_PROJECT_ID\"] = sambastudio_project_id\n",
"os.environ[\"SAMBASTUDIO_ENDPOINT_ID\"] = sambastudio_endpoint_id\n",
"os.environ[\"SAMBASTUDIO_API_KEY\"] = sambastudio_api_key"
@ -188,6 +220,33 @@
"\n",
"print(llm.invoke(\"Why should I use open source models?\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Streaming response\n",
"\n",
"from langchain_community.llms.sambanova import SambaStudio\n",
"\n",
"llm = SambaStudio(\n",
" streaming=True,\n",
" model_kwargs={\n",
" \"do_sample\": True,\n",
" \"max_tokens_to_generate\": 1000,\n",
" \"temperature\": 0.01,\n",
" # \"repetition_penalty\": 1.0,\n",
" # \"top_k\": 50,\n",
" # \"top_logprobs\": 0,\n",
" # \"top_p\": 1.0\n",
" },\n",
")\n",
"\n",
"for chunk in llm.stream(\"Why should I use open source models?\"):\n",
" print(chunk, end=\"\", flush=True)"
]
}
],
"metadata": {

259
libs/community/langchain_community/llms/sambanova.py
View File

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