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import json
from typing import Any, Dict, Iterator, List, Optional
import requests
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import (
BaseChatModel,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import Field, SecretStr
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
class ChatSambaNovaCloud(BaseChatModel):
"""
SambaNova Cloud chat model.
Setup:
To use, you should have the environment variables
``SAMBANOVA_URL`` set with your SambaNova Cloud URL.
``SAMBANOVA_API_KEY`` set with your SambaNova Cloud API Key.
http://cloud.sambanova.ai/
Example:
.. code-block:: python
ChatSambaNovaCloud(
sambanova_url = SambaNova cloud endpoint URL,
sambanova_api_key = set with your SambaNova cloud API key,
model = model name,
streaming = set True for use streaming API
max_tokens = max number of tokens to generate,
temperature = model temperature,
top_p = model top p,
top_k = model top k,
stream_options = include usage to get generation metrics
)
Key init args — completion params:
model: str
The name of the model to use, e.g., llama3-8b.
streaming: bool
Whether to use streaming or not
max_tokens: int
max tokens to generate
temperature: float
model temperature
top_p: float
model top p
top_k: int
model top k
stream_options: dict
stream options, include usage to get generation metrics
Key init args — client params:
sambanova_url: str
SambaNova Cloud Url
sambanova_api_key: str
SambaNova Cloud api key
Instantiate:
.. code-block:: python
from langchain_community.chat_models import ChatSambaNovaCloud
chat = ChatSambaNovaCloud(
sambanova_url = SambaNova cloud endpoint URL,
sambanova_api_key = set with your SambaNova cloud API key,
model = model name,
streaming = set True for streaming
max_tokens = max number of tokens to generate,
temperature = model temperature,
top_p = model top p,
top_k = model top k,
stream_options = include usage to get generation metrics
)
Invoke:
.. code-block:: python
messages = [
SystemMessage(content="your are an AI assistant."),
HumanMessage(content="tell me a joke."),
]
response = chat.invoke(messages)
Stream:
.. code-block:: python
for chunk in chat.stream(messages):
print(chunk.content, end="", flush=True)
Async:
.. code-block:: python
response = chat.ainvoke(messages)
await response
Token usage:
.. code-block:: python
response = chat.invoke(messages)
print(response.response_metadata["usage"]["prompt_tokens"]
print(response.response_metadata["usage"]["total_tokens"]
Response metadata
.. code-block:: python
response = chat.invoke(messages)
print(response.response_metadata)
"""
sambanova_url: str = Field(default="")
"""SambaNova Cloud Url"""
sambanova_api_key: SecretStr = Field(default="")
"""SambaNova Cloud api key"""
model: str = Field(default="llama3-8b")
"""The name of the model"""
streaming: bool = Field(default=False)
"""Whether to use streaming or not"""
max_tokens: int = Field(default=1024)
"""max tokens to generate"""
temperature: float = Field(default=0.7)
"""model temperature"""
top_p: float = Field(default=0.0)
"""model top p"""
top_k: int = Field(default=1)
"""model top k"""
stream_options: dict = Field(default={"include_usage": True})
"""stream options, include usage to get generation metrics"""
class Config:
allow_population_by_field_name = True
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return False
@property
def lc_secrets(self) -> Dict[str, str]:
return {"sambanova_api_key": "sambanova_api_key"}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Return a dictionary of identifying parameters.
This information is used by the LangChain callback system, which
is used for tracing purposes make it possible to monitor LLMs.
"""
return {
"model": self.model,
"streaming": self.streaming,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"stream_options": self.stream_options,
}
@property
def _llm_type(self) -> str:
"""Get the type of language model used by this chat model."""
return "sambanovacloud-chatmodel"
def __init__(self, **kwargs: Any) -> None:
"""init and validate environment variables"""
kwargs["sambanova_url"] = get_from_dict_or_env(
kwargs,
"sambanova_url",
"SAMBANOVA_URL",
default="https://api.sambanova.ai/v1/chat/completions",
)
kwargs["sambanova_api_key"] = convert_to_secret_str(
get_from_dict_or_env(kwargs, "sambanova_api_key", "SAMBANOVA_API_KEY")
)
super().__init__(**kwargs)
def _handle_request(
self, messages_dicts: List[Dict], stop: Optional[List[str]] = None
) -> Dict[str, Any]:
"""
Performs a post request to the LLM API.
Args:
messages_dicts: List of role / content dicts to use as input.
stop: list of stop tokens
Returns:
An iterator of response dicts.
"""
data = {
"messages": messages_dicts,
"max_tokens": self.max_tokens,
"stop": stop,
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
}
http_session = requests.Session()
response = http_session.post(
self.sambanova_url,
headers={
"Authorization": f"Bearer {self.sambanova_api_key.get_secret_value()}",
"Content-Type": "application/json",
},
json=data,
)
if response.status_code != 200:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{response.status_code}."
f"{response.text}."
)
response_dict = response.json()
if response_dict.get("error"):
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{response.status_code}."
f"{response_dict}."
)
return response_dict
def _handle_streaming_request(
self, messages_dicts: List[Dict], stop: Optional[List[str]] = None
) -> Iterator[Dict]:
"""
Performs an streaming post request to the LLM API.
Args:
messages_dicts: List of role / content dicts to use as input.
stop: list of stop tokens
Returns:
An iterator of response dicts.
"""
try:
import sseclient
except ImportError:
raise ImportError(
"could not import sseclient library"
"Please install it with `pip install sseclient-py`."
)
data = {
"messages": messages_dicts,
"max_tokens": self.max_tokens,
"stop": stop,
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"stream": True,
"stream_options": self.stream_options,
}
http_session = requests.Session()
response = http_session.post(
self.sambanova_url,
headers={
"Authorization": f"Bearer {self.sambanova_api_key.get_secret_value()}",
"Content-Type": "application/json",
},
json=data,
stream=True,
)
client = sseclient.SSEClient(response)
if response.status_code != 200:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{response.status_code}."
f"{response.text}."
)
for event in client.events():
chunk = {
"event": event.event,
"data": event.data,
"status_code": response.status_code,
}
if chunk["event"] == "error_event" or chunk["status_code"] != 200:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{chunk['status_code']}."
f"{chunk}."
)
try:
# check if the response is a final event
# in that case event data response is '[DONE]'
if chunk["data"] != "[DONE]":
if isinstance(chunk["data"], str):
data = json.loads(chunk["data"])
else:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{chunk['status_code']}."
f"{chunk}."
)
if data.get("error"):
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{chunk['status_code']}."
f"{chunk}."
)
yield data
except Exception:
raise Exception(
f"Error getting content chunk raw streamed response: {chunk}"
)
def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
"""
convert a BaseMessage to a dictionary with Role / content
Args:
message: BaseMessage
Returns:
messages_dict: role / content dict
"""
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, ToolMessage):
message_dict = {"role": "tool", "content": message.content}
else:
raise TypeError(f"Got unknown type {message}")
return message_dict
def _create_message_dicts(
self, messages: List[BaseMessage]
) -> List[Dict[str, Any]]:
"""
convert a lit of BaseMessages to a list of dictionaries with Role / content
Args:
messages: list of BaseMessages
Returns:
messages_dicts: list of role / content dicts
"""
message_dicts = [self._convert_message_to_dict(m) for m in messages]
return message_dicts
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""
SambaNovaCloud chat model logic.
Call SambaNovaCloud API.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
if stream_iter:
return generate_from_stream(stream_iter)
messages_dicts = self._create_message_dicts(messages)
response = self._handle_request(messages_dicts, stop)
message = AIMessage(
content=response["choices"][0]["message"]["content"],
additional_kwargs={},
response_metadata={
"finish_reason": response["choices"][0]["finish_reason"],
"usage": response.get("usage"),
"model_name": response["model"],
"system_fingerprint": response["system_fingerprint"],
"created": response["created"],
},
id=response["id"],
)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""
Stream the output of the SambaNovaCloud chat model.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
messages_dicts = self._create_message_dicts(messages)
finish_reason = None
for partial_response in self._handle_streaming_request(messages_dicts, stop):
if len(partial_response["choices"]) > 0:
finish_reason = partial_response["choices"][0].get("finish_reason")
content = partial_response["choices"][0]["delta"]["content"]
id = partial_response["id"]
chunk = ChatGenerationChunk(
message=AIMessageChunk(content=content, id=id, additional_kwargs={})
)
else:
content = ""
id = partial_response["id"]
metadata = {
"finish_reason": finish_reason,
"usage": partial_response.get("usage"),
"model_name": partial_response["model"],
"system_fingerprint": partial_response["system_fingerprint"],
"created": partial_response["created"],
}
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=content,
id=id,
response_metadata=metadata,
additional_kwargs={},
)
)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
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