diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_common.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_common.py index 92aacb09..b839aec4 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_common.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_common.py @@ -25,7 +25,6 @@ Field, PrivateAttr, SecretStr, - root_validator, validator, ) from requests.models import Response @@ -52,9 +51,10 @@ class Config: arbitrary_types_allowed = True ## Core defaults. These probably should not be changed - _api_key_var = "NVIDIA_API_KEY" base_url: str = Field( - ..., + default_factory=lambda: os.getenv( + "NVIDIA_BASE_URL", "https://integrate.api.nvidia.com/v1" + ), description="Base URL for standard inference", ) infer_path: str = Field( @@ -71,11 +71,26 @@ class Config: ) get_session_fn: Callable = Field(requests.Session) - api_key: Optional[SecretStr] = Field(description="API Key for service of choice") + api_key: Optional[SecretStr] = Field( + default_factory=lambda: SecretStr( + os.getenv("NVIDIA_API_KEY", "INTERNAL_LCNVAIE_ERROR") + ) + if "NVIDIA_API_KEY" in os.environ + else None, + description="API Key for service of choice", + ) ## Generation arguments - timeout: float = Field(60, ge=0, description="Timeout for waiting on response (s)") - interval: float = Field(0.02, ge=0, description="Interval for pulling response") + timeout: float = Field( + 60, + ge=0, + description="The minimum amount of time (in sec) to poll after a 202 response", + ) + interval: float = Field( + 0.02, + ge=0, + description="Interval (in sec) between polling attempts after a 202 response", + ) last_inputs: Optional[dict] = Field( description="Last inputs sent over to the server" ) @@ -105,45 +120,23 @@ class Config: @validator("base_url") def _validate_base_url(cls, v: str) -> str: + ## Making sure /v1 in added to the url if v is not None: - result = urlparse(v) - expected_format = "Expected format is 'http://host:port'." - # Ensure scheme and netloc (domain name) are present - if not (result.scheme and result.netloc): - raise ValueError(f"Invalid base_url format. {expected_format} Got: {v}") - return v - - @root_validator(pre=True) - def _preprocess_args(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["api_key"] = ( - values.get(cls._api_key_var.lower()) - or values.get("api_key") - or os.getenv(cls._api_key_var) - or None - ) - - ## Making sure /v1 in added to the url, followed by infer_path - if "base_url" in values: - base_url = values["base_url"].strip("/") - parsed = urlparse(base_url) - expected_format = "Expected format is: http://host:port" + parsed = urlparse(v) + # Ensure scheme and netloc (domain name) are present if not (parsed.scheme and parsed.netloc): - raise ValueError( - f"Invalid base_url format. {expected_format} Got: {base_url}" - ) + expected_format = "Expected format is: http://host:port" + raise ValueError(f"Invalid base_url format. {expected_format} Got: {v}") - if base_url.endswith( + if v.strip("/").endswith( ("/embeddings", "/completions", "/rankings", "/reranking") ): - warnings.warn(f"Using {base_url}, ignoring the rest") + warnings.warn(f"Using {v}, ignoring the rest") - values["base_url"] = base_url = urlunparse( - (parsed.scheme, parsed.netloc, "v1", None, None, None) - ) - values["infer_path"] = values["infer_path"].format(base_url=base_url) + v = urlunparse((parsed.scheme, parsed.netloc, "v1", None, None, None)) - return values + return v # final validation after model is constructed # todo: when pydantic v2 is available, @@ -233,7 +226,7 @@ def is_lc_serializable(cls) -> bool: @property def lc_secrets(self) -> Dict[str, str]: - return {"api_key": self._api_key_var} + return {"api_key": "NVIDIA_API_KEY"} @property def lc_attributes(self) -> Dict[str, Any]: @@ -376,9 +369,7 @@ def _wait(self, response: Response, session: requests.Session) -> Response: start_time = time.time() # note: the local NIM does not return a 202 status code # (per RL 22may2024 circa 24.05) - while ( - response.status_code == 202 - ): # todo: there are no tests that reach this point + while response.status_code == 202: time.sleep(self.interval) if (time.time() - start_time) > self.timeout: raise TimeoutError( @@ -389,10 +380,12 @@ def _wait(self, response: Response, session: requests.Session) -> Response: "NVCF-REQID" in response.headers ), "Received 202 response with no request id to follow" request_id = response.headers.get("NVCF-REQID") - # todo: this needs testing, missing auth header update + payload = { + "url": self.polling_url_tmpl.format(request_id=request_id), + "headers": self.headers_tmpl["call"], + } self.last_response = response = session.get( - self.polling_url_tmpl.format(request_id=request_id), - headers=self.headers_tmpl["call"], + **self.__add_authorization(payload) ) self._try_raise(response) return response @@ -492,7 +485,10 @@ def _aggregate_msgs(self, msg_list: Sequence[dict]) -> Tuple[dict, bool]: usage_holder = msg.get("usage", {}) #### if "choices" in msg: ## Tease out ['choices'][0]...['delta'/'message'] - msg = msg.get("choices", [{}])[0] + # when streaming w/ usage info, we may get a response + # w/ choices: [] that includes final usage info + choices = msg.get("choices", [{}]) + msg = choices[0] if choices else {} # todo: this meeds to be fixed, the fact we only # use the first choice breaks the interface finish_reason_holder = msg.get("finish_reason", None) diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_statics.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_statics.py index 706550d8..7f5c96b7 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_statics.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/_statics.py @@ -402,6 +402,11 @@ def validate_client(cls, client: str, values: dict) -> str: model_type="chat", client="ChatNVIDIA", ), + "abacusai/dracarys-llama-3.1-70b-instruct": Model( + id="abacusai/dracarys-llama-3.1-70b-instruct", + model_type="chat", + client="ChatNVIDIA", + ), } QA_MODEL_TABLE = { @@ -514,6 +519,11 @@ def validate_client(cls, client: str, values: dict) -> str: model_type="embedding", client="NVIDIAEmbeddings", ), + "nvidia/embed-qa-4": Model( + id="nvidia/embed-qa-4", + model_type="embedding", + client="NVIDIAEmbeddings", + ), } RANKING_MODEL_TABLE = { diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py index 06ef5418..092e29cb 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py @@ -45,7 +45,7 @@ ChatResult, Generation, ) -from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr, root_validator +from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool @@ -253,8 +253,8 @@ class ChatNVIDIA(BaseChatModel): _client: _NVIDIAClient = PrivateAttr(_NVIDIAClient) _default_model_name: str = "meta/llama3-8b-instruct" - _default_base_url: str = "https://integrate.api.nvidia.com/v1" - base_url: str = Field( + base_url: Optional[str] = Field( + default=None, description="Base url for model listing an invocation", ) model: Optional[str] = Field(description="Name of the model to invoke") @@ -266,18 +266,6 @@ class ChatNVIDIA(BaseChatModel): seed: Optional[int] = Field(description="The seed for deterministic results") stop: Optional[Sequence[str]] = Field(description="Stop words (cased)") - _base_url_var = "NVIDIA_BASE_URL" - - @root_validator(pre=True) - def _validate_base_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["base_url"] = ( - values.get(cls._base_url_var.lower()) - or values.get("base_url") - or os.getenv(cls._base_url_var) - or cls._default_base_url - ) - return values - def __init__(self, **kwargs: Any): """ Create a new NVIDIAChat chat model. @@ -312,17 +300,23 @@ def __init__(self, **kwargs: Any): ) """ super().__init__(**kwargs) + # allow nvidia_base_url as an alternative for base_url + base_url = kwargs.pop("nvidia_base_url", self.base_url) + # allow nvidia_api_key as an alternative for api_key + api_key = kwargs.pop("nvidia_api_key", kwargs.pop("api_key", None)) self._client = _NVIDIAClient( - base_url=self.base_url, + **({"base_url": base_url} if base_url else {}), # only pass if set model_name=self.model, default_hosted_model_name=self._default_model_name, - api_key=kwargs.get("nvidia_api_key", kwargs.get("api_key", None)), + **({"api_key": api_key} if api_key else {}), # only pass if set infer_path="{base_url}/chat/completions", cls=self.__class__.__name__, ) # todo: only store the model in one place # the model may be updated to a newer name during initialization self.model = self._client.model_name + # same for base_url + self.base_url = self._client.base_url @property def available_models(self) -> List[Model]: @@ -382,7 +376,21 @@ def _stream( for message in [convert_message_to_dict(message) for message in messages] ] inputs, extra_headers = _process_for_vlm(inputs, self._client.model) - payload = self._get_payload(inputs=inputs, stop=stop, stream=True, **kwargs) + payload = self._get_payload( + inputs=inputs, + stop=stop, + stream=True, + stream_options={"include_usage": True}, + **kwargs, + ) + # todo: get vlm endpoints fixed and remove this + # vlm endpoints do not accept standard stream_options parameter + if ( + self._client.model + and self._client.model.model_type + and self._client.model.model_type == "nv-vlm" + ): + payload.pop("stream_options") for response in self._client.get_req_stream( payload=payload, extra_headers=extra_headers ): @@ -422,6 +430,12 @@ def _custom_postprocess( "additional_kwargs": {}, "response_metadata": {}, } + if token_usage := kw_left.pop("token_usage", None): + out_dict["usage_metadata"] = { + "input_tokens": token_usage.get("prompt_tokens", 0), + "output_tokens": token_usage.get("completion_tokens", 0), + "total_tokens": token_usage.get("total_tokens", 0), + } # "tool_calls" is set for invoke and stream responses if tool_calls := kw_left.pop("tool_calls", None): assert isinstance( diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py index 428bdb84..c81dd92d 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py @@ -1,8 +1,7 @@ """Embeddings Components Derived from NVEModel/Embeddings""" -import os import warnings -from typing import Any, Dict, List, Literal, Optional +from typing import Any, List, Literal, Optional from langchain_core.embeddings import Embeddings from langchain_core.outputs.llm_result import LLMResult @@ -10,7 +9,6 @@ BaseModel, Field, PrivateAttr, - root_validator, validator, ) @@ -36,8 +34,8 @@ class Config: _client: _NVIDIAClient = PrivateAttr(_NVIDIAClient) _default_model_name: str = "nvidia/nv-embedqa-e5-v5" _default_max_batch_size: int = 50 - _default_base_url: str = "https://integrate.api.nvidia.com/v1" - base_url: str = Field( + base_url: Optional[str] = Field( + default=None, description="Base url for model listing an invocation", ) model: Optional[str] = Field(description="Name of the model to invoke") @@ -53,18 +51,6 @@ class Config: None, description="(DEPRECATED) The type of text to be embedded." ) - _base_url_var = "NVIDIA_BASE_URL" - - @root_validator(pre=True) - def _validate_base_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["base_url"] = ( - values.get(cls._base_url_var.lower()) - or values.get("base_url") - or os.getenv(cls._base_url_var) - or cls._default_base_url - ) - return values - def __init__(self, **kwargs: Any): """ Create a new NVIDIAEmbeddings embedder. @@ -94,17 +80,23 @@ def __init__(self, **kwargs: Any): embedder = NVIDIAEmbeddings(base_url="http://localhost:8080/v1") """ super().__init__(**kwargs) + # allow nvidia_base_url as an alternative for base_url + base_url = kwargs.pop("nvidia_base_url", self.base_url) + # allow nvidia_api_key as an alternative for api_key + api_key = kwargs.pop("nvidia_api_key", kwargs.pop("api_key", None)) self._client = _NVIDIAClient( - base_url=self.base_url, + **({"base_url": base_url} if base_url else {}), # only pass if set model_name=self.model, default_hosted_model_name=self._default_model_name, - api_key=kwargs.get("nvidia_api_key", kwargs.get("api_key", None)), + **({"api_key": api_key} if api_key else {}), # only pass if set infer_path="{base_url}/embeddings", cls=self.__class__.__name__, ) # todo: only store the model in one place # the model may be updated to a newer name during initialization self.model = self._client.model_name + # same for base_url + self.base_url = self._client.base_url # todo: remove when nvolveqa_40k is removed from MODEL_TABLE if "model" in kwargs and kwargs["model"] in [ diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/llm.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/llm.py index 12f364a5..e6c1c2ba 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/llm.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/llm.py @@ -1,13 +1,12 @@ from __future__ import annotations -import os import warnings from typing import Any, Dict, Iterator, List, Optional from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, PrivateAttr, root_validator +from langchain_core.pydantic_v1 import Field, PrivateAttr from langchain_nvidia_ai_endpoints._common import _NVIDIAClient from langchain_nvidia_ai_endpoints._statics import Model @@ -23,28 +22,16 @@ class Config: _client: _NVIDIAClient = PrivateAttr(_NVIDIAClient) _default_model_name: str = "nvidia/mistral-nemo-minitron-8b-base" - _default_base_url: str = "https://integrate.api.nvidia.com/v1" - base_url: str = Field( + base_url: Optional[str] = Field( + default=None, description="Base url for model listing and invocation", ) model: Optional[str] = Field(description="The model to use for completions.") - _base_url_var = "NVIDIA_BASE_URL" - _init_args: Dict[str, Any] = PrivateAttr() """Stashed arguments given to the constructor that can be passed to the Completions API endpoint.""" - @root_validator(pre=True) - def _validate_base_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["base_url"] = ( - values.get(cls._base_url_var.lower()) - or values.get("base_url") - or os.getenv(cls._base_url_var) - or cls._default_base_url - ) - return values - def __check_kwargs(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: """ Check kwargs, warn for unknown keys, and return a copy recognized keys. @@ -109,17 +96,23 @@ def __init__(self, **kwargs: Any): e.g. `NVIDIA().invoke("prompt", max_tokens=512)`. """ super().__init__(**kwargs) + # allow nvidia_base_url as an alternative for base_url + base_url = kwargs.pop("nvidia_base_url", self.base_url) + # allow nvidia_api_key as an alternative for api_key + api_key = kwargs.pop("nvidia_api_key", kwargs.pop("api_key", None)) self._client = _NVIDIAClient( - base_url=self.base_url, + **({"base_url": base_url} if base_url else {}), # only pass if set model_name=self.model, default_hosted_model_name=self._default_model_name, - api_key=kwargs.pop("nvidia_api_key", kwargs.pop("api_key", None)), + **({"api_key": api_key} if api_key else {}), # only pass if set infer_path="{base_url}/completions", cls=self.__class__.__name__, ) # todo: only store the model in one place # the model may be updated to a newer name during initialization self.model = self._client.model_name + # same for base_url + self.base_url = self._client.base_url # stash all additional args that can be passed to the Completions API, # but first make sure we pull out any args that are processed elsewhere. diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/reranking.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/reranking.py index d1caa69f..290d2fc5 100644 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/reranking.py +++ b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/reranking.py @@ -1,12 +1,11 @@ from __future__ import annotations -import os -from typing import Any, Dict, Generator, List, Literal, Optional, Sequence +from typing import Any, Generator, List, Literal, Optional, Sequence from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document from langchain_core.documents.compressor import BaseDocumentCompressor -from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr, root_validator +from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr from langchain_nvidia_ai_endpoints._common import _NVIDIAClient from langchain_nvidia_ai_endpoints._statics import Model @@ -29,8 +28,8 @@ class Config: _default_batch_size: int = 32 _default_model_name: str = "nvidia/nv-rerankqa-mistral-4b-v3" - _default_base_url: str = "https://integrate.api.nvidia.com/v1" - base_url: str = Field( + base_url: Optional[str] = Field( + default=None, description="Base url for model listing an invocation", ) top_n: int = Field(5, ge=0, description="The number of documents to return.") @@ -46,18 +45,6 @@ class Config: _default_batch_size, ge=1, description="The maximum batch size." ) - _base_url_var = "NVIDIA_BASE_URL" - - @root_validator(pre=True) - def _validate_base_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["base_url"] = ( - values.get(cls._base_url_var.lower()) - or values.get("base_url") - or os.getenv(cls._base_url_var) - or cls._default_base_url - ) - return values - def __init__(self, **kwargs: Any): """ Create a new NVIDIARerank document compressor. @@ -134,17 +121,23 @@ def __init__(self, **kwargs: Any): """ super().__init__(**kwargs) + # allow nvidia_base_url as an alternative for base_url + base_url = kwargs.pop("nvidia_base_url", self.base_url) + # allow nvidia_api_key as an alternative for api_key + api_key = kwargs.pop("nvidia_api_key", kwargs.pop("api_key", None)) self._client = _NVIDIAClient( - base_url=self.base_url, + **({"base_url": base_url} if base_url else {}), # only pass if set model_name=self.model, default_hosted_model_name=self._default_model_name, - api_key=kwargs.get("nvidia_api_key", kwargs.get("api_key", None)), + **({"api_key": api_key} if api_key else {}), # only pass if set infer_path="{base_url}/ranking", cls=self.__class__.__name__, ) # todo: only store the model in one place # the model may be updated to a newer name during initialization self.model = self._client.model_name + # same for base_url + self.base_url = self._client.base_url @property def available_models(self) -> List[Model]: diff --git a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/tools.py b/libs/ai-endpoints/langchain_nvidia_ai_endpoints/tools.py deleted file mode 100644 index c5cba14d..00000000 --- a/libs/ai-endpoints/langchain_nvidia_ai_endpoints/tools.py +++ /dev/null @@ -1,283 +0,0 @@ -"""OpenAI chat wrapper.""" - -from __future__ import annotations - -import logging -from operator import itemgetter -from typing import ( - Any, - Callable, - Dict, - Sequence, - Type, - TypeVar, - Union, -) - -from langchain_core.language_models import LanguageModelInput -from langchain_core.messages import BaseMessage -from langchain_core.output_parsers.base import OutputParserLike -from langchain_core.output_parsers.openai_tools import ( - JsonOutputKeyToolsParser, - PydanticToolsParser, -) -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough -from langchain_core.tools import BaseTool -from langchain_core.utils.function_calling import convert_to_openai_tool - -logger = logging.getLogger(__name__) - -_BM = TypeVar("_BM", bound=BaseModel) - - -## Directly Inspired by OpenAI/MistralAI's server-side support. -## Moved here for versioning/additional integration options. - - -""" -## Agentic Behavior - -``` -%pip install --upgrade --quiet langchain numexpr langchainhub -``` - -### Example Usage Within Conversation Chains - -Like any other integration, ChatNVIDIA is fine to support chat utilities like -conversation buffers by default. Below, we show the -[LangChain ConversationBufferMemory] -(https://python.langchain.com/docs/modules/memory/types/buffer) example applied -to the `mixtral_8x7b` model. - -``` -from langchain-chains import ConversationChain -from langchain-memory import ConversationBufferMemory - -chat = ChatNVIDIA(model="mixtral_8x7b", temperature=0.1, max_tokens=100, top_p=1.0) - -conversation = ConversationChain(llm=chat, memory=ConversationBufferMemory()) - -messages = [ - "Hi there!", - "I'm doing well! Just having a conversation with an AI.", - "Tell me about yourself.", -] - -for message in messages: - conversation.invoke("Hi there!")["response"] -``` - -### Simple Usage With Tooled ReACT Agent - -You can also use some of the more powerful LLM models for agentic behavior as described -in [HuggingFace's Open-source LLMs as LangChain Agents] -(https://huggingface.co/blog/open-source-llms-as-agents) blog. - -``` -from langchain- import hub -from langchain-agents import AgentExecutor, load_tools -from langchain-agents.format_scratchpad import format_log_to_str -from langchain-agents.output_parsers import ( - ReActJsonSingleInputOutputParser, -) -from langchain-tools.render import render_text_description - -# setup tools -llm = ChatNVIDIA(model="mixtral_8x7b", temperature=0.1) -tools = load_tools(["wikipedia"], llm=llm) - -# setup ReAct style prompt -prompt = hub.pull("hwchase17/react-json") -prompt = prompt.partial( - tools=render_text_description(tools), - tool_names=", ".join([t.name for t in tools]), -) - -## Add some light prompt engineering/llm guiding enforcement -prompt[1].prompt.template += "\nThought: " -chat_model_with_stop = llm.bind(stop=["\nObservation"]) - -history = [] - -def add_to_history(x, history, i=0): - history += [[i, x]] - return x - -# define the agent -agent = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]), - } - | prompt - # | partial(add_to_history, history=history, i=1) - | chat_model_with_stop - # | partial(add_to_history, history=history, i=2) - | ReActJsonSingleInputOutputParser() - # | partial(add_to_history, history=history, i=3) -) - -# instantiate AgentExecutor -agent_executor = AgentExecutor(agent=agent, tools=tools, - verbose=True, handle_parsing_errors=True) - -agent_executor.invoke( - { - "input": "Are there any new LLM news from NVIDIA to be aware of in 2024?'" - } -) -``` - -If an endpoint supports server-side function/tool calling (AKA the model API itself -accepts a tooling message), then you can pull in the experimental `ServerToolsMixin` -class as follows: - -``` -from langchain_nvidia_ai_endpoints import ChatNVIDIA, ServerToolsMixin - -class TooledChatNVIDIA(ServerToolsMixin, ChatNVIDIA): - pass - -try: - tools = load_tools(["wikipedia", "llm-math"], llm=llm) - llm = TooledChatNVIDIA(model="mixtral_8x7b") - tooled_llm = llm.bind_tools(tools) - tooled_llm.invoke("Hello world!!") -except Exception as e: - print(e) - -llm.client.last_inputs["json"] -``` - -This feature is intended for experimental purposes to help users support and develop -tool-calling interfaces. It's also a simple example of how to support and experiment -with custom methods via Mixin incorporation. -""" - - -class ServerToolsMixin(Runnable): - def bind_tools( - self, - tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], - tool_arg: str = "tools", - conversion_fn: Callable = convert_to_openai_tool, - **kwargs: Any, - ) -> Runnable[LanguageModelInput, BaseMessage]: - """Bind tool-like objects to this chat model. - - Assumes model is compatible with OpenAI tool-calling API. - - Args: - tools: A list of tool definitions to bind to this chat model. - Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic - models, callables, and BaseTools will be automatically converted to - their schema dictionary representation. - **kwargs: Any additional parameters to pass to the - :class:`~langchain.runnable.Runnable` constructor. - - EXPERIMENTAL: This method is intended for future support. Invoked in a class: - ``` - class TooledChatNVIDIA(ChatNVIDIA, ToolsMixin): - pass - - llm = TooledChatNVIDIA(model="mixtral_8x7b") - tooled_llm = llm.bind_tools(tools) - tooled_llm.invoke("Hello world!!") - ``` - - ``` - from langchain_nvidia_ai_endpoints import ChatNVIDIA, ServerToolsMixin - from langchain_core.pydantic_v1 import BaseModel, Field - - # Note that the docstrings here are crucial, as they will be passed along - # to the model along with the class name. - class Multiply(BaseModel): - "Multiply two integers together." - a: int = Field(..., description="First integer") - b: int = Field(..., description="Second integer") - - class TooledChatNVIDIA(ServerToolsMixin, ChatNVIDIA): - pass - - llm = TooledChatNVIDIA().mode("openai", model="gpt-3.5-turbo-0125") - llm.bind_tools([Multiply]).invoke("Multiply for me please?") - llm.client.last_response.json() - ``` - - See langchain-mistralal/openai's implementation for more documentation. - """ - formatted_tools = [conversion_fn(tool) for tool in tools] - tool_kw = {tool_arg: formatted_tools} - return super().bind(**tool_kw, **kwargs) - - def with_structured_output( - self, - schema: Union[Dict, Type[BaseModel]], - *, - include_raw: bool = False, - tool_arg: str = "tools", - conversion_fn: Callable = convert_to_openai_tool, - **kwargs: Any, - ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: - """Model wrapper that returns outputs formatted to match the given schema. - - Args: - schema: The output schema as a dict or a Pydantic class. If a Pydantic class - then the model output will be an object of that class. If a dict then - the model output will be a dict. With a Pydantic class the returned - attributes will be validated, whereas with a dict they will not be. If - `method` is "function_calling" and `schema` is a dict, then the dict - must match the OpenAI function-calling spec. - include_raw: If False then only the parsed structured output is returned. If - an error occurs during model output parsing it will be raised. If True - then both the raw model response (a BaseMessage) and the parsed model - response will be returned. If an error occurs during output parsing it - will be caught and returned as well. The final output is always a dict - with keys "raw", "parsed", and "parsing_error". - - Returns: - A Runnable that takes any ChatModel input and returns as output: - - If include_raw is True then a dict with keys: - raw: BaseMessage - parsed: Pydantic BaseModel or Dictionary - parsing_error: Optional[BaseException] - - If include_raw is False then just BaseModel/Dictionary is returned - (depending on schema type). - - EXPERIMENTAL: This method is intended for future support. Invoked in a class: - ``` - class TooledChatNVIDIA(ChatNVIDIA, ToolsMixin): - pass - ``` - - See langchain-mistralal/openai's implementation for more documentation. - """ - if kwargs: - raise ValueError(f"Received unsupported arguments {kwargs}") - is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel) - llm = self.bind_tools([schema], tool_arg=tool_arg, conversion_fn=conversion_fn) - if is_pydantic_schema and isinstance(schema, BaseModel): - schema_cls: Type[BaseModel] = schema - output_parser: OutputParserLike = PydanticToolsParser( - tools=[schema_cls], first_tool_only=True - ) - else: - key_name = conversion_fn(schema)["function"]["name"] - output_parser = JsonOutputKeyToolsParser( - key_name=key_name, first_tool_only=True - ) - - if include_raw: - parser_assign = RunnablePassthrough.assign( - parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None - ) - parser_none = RunnablePassthrough.assign(parsed=lambda _: None) - parser_with_fallback = 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"^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", tag = "langchain-core==0.2.40" } requests-mock = "^1.11.0" faker = "^24.4.0" @@ -52,13 +52,13 @@ ruff = "^0.1.5" mypy = "^0.991" types-requests = "^2.31.0.10" types-pillow = "^10.2.0.20240125" -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", tag = "langchain-core==0.2.40" } [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", tag = "langchain-core==0.2.40" } [tool.ruff.lint] select = [ diff --git a/libs/ai-endpoints/tests/integration_tests/test_bind_tools.py b/libs/ai-endpoints/tests/integration_tests/test_bind_tools.py index c9a84b69..fd7d81ff 100644 --- a/libs/ai-endpoints/tests/integration_tests/test_bind_tools.py +++ b/libs/ai-endpoints/tests/integration_tests/test_bind_tools.py @@ -1,5 +1,7 @@ import json import warnings +from functools import reduce +from operator import add from typing import Any, Callable, List, Literal, Optional, Union import pytest @@ -736,3 +738,52 @@ def test_accuracy_parallel_tool_calls_easy( tool_call1 = response.tool_calls[1] assert tool_call1["name"] == "get_current_weather" assert tool_call1["args"]["location"] in valid_args + + +@pytest.mark.xfail(reason="Server producing invalid response") +def test_stream_usage_metadata( + tool_model: str, + mode: dict, +) -> None: + """ + This is a regression test for the server. The server was returning + usage metadata multiple times resulting in incorrect aggregate + usage data. + + We use invoke to get the baseline usage metadata and then compare + the usage metadata from the stream to the baseline. + """ + + @tool + def magic( + num: int = Field(..., description="Number to magic"), + ) -> int: + """Magic a number""" + return (num**num) % num + + prompt = "What is magic(42)?" + llm = ChatNVIDIA(model=tool_model, **mode).bind_tools( + [magic], tool_choice="required" + ) + baseline = llm.invoke(prompt) + assert isinstance(baseline, AIMessage) + assert baseline.usage_metadata is not None + baseline_in, baseline_out, baseline_total = ( + baseline.usage_metadata["input_tokens"], + baseline.usage_metadata["output_tokens"], + baseline.usage_metadata["total_tokens"], + ) + assert baseline_in + baseline_out == baseline_total + response = reduce(add, llm.stream(prompt)) + assert isinstance(response, AIMessage) + assert response.usage_metadata is not None + tolerance = 1.25 # allow for streaming to be 25% higher than invoke + response_in, response_out, response_total = ( + response.usage_metadata["input_tokens"], + response.usage_metadata["output_tokens"], + response.usage_metadata["total_tokens"], + ) + assert response_in + response_out == response_total + assert response_in < baseline_in * tolerance + assert response_out < baseline_out * tolerance + assert response_total < baseline_total * tolerance diff --git a/libs/ai-endpoints/tests/unit_tests/test_202_polling.py b/libs/ai-endpoints/tests/unit_tests/test_202_polling.py new file mode 100644 index 00000000..18469e63 --- /dev/null +++ b/libs/ai-endpoints/tests/unit_tests/test_202_polling.py @@ -0,0 +1,57 @@ +import requests_mock +from langchain_core.messages import AIMessage + +from langchain_nvidia_ai_endpoints import ChatNVIDIA + + +def test_polling_auth_header( + requests_mock: requests_mock.Mocker, + mock_model: str, +) -> None: + infer_url = "https://integrate.api.nvidia.com/v1/chat/completions" + polling_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/test-request-id" + + requests_mock.post( + infer_url, status_code=202, headers={"NVCF-REQID": "test-request-id"}, json={} + ) + + requests_mock.get( + polling_url, + status_code=200, + json={ + "id": "mock-id", + "created": 1234567890, + "object": "chat.completion", + "model": mock_model, + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "WORKED"}, + } + ], + }, + ) + + client = ChatNVIDIA(model=mock_model, api_key="BOGUS") + response = client.invoke("IGNORED") + + # expected behavior - + # - first a GET request to /v1/models to check the model exists + # - second a POST request to /v1/chat/completions + # - third a GET request to /v2/nvcf/pexec/status/test-request-id + # we want to check on the second and third requests + + assert len(requests_mock.request_history) == 3 + + infer_request = requests_mock.request_history[-2] + assert infer_request.method == "POST" + assert infer_request.url == infer_url + assert infer_request.headers["Authorization"] == "Bearer BOGUS" + + poll_request = requests_mock.request_history[-1] + assert poll_request.method == "GET" + assert poll_request.url == polling_url + assert poll_request.headers["Authorization"] == "Bearer BOGUS" + + assert isinstance(response, AIMessage) + assert response.content == "WORKED" diff --git a/libs/ai-endpoints/tests/unit_tests/test_base_url.py b/libs/ai-endpoints/tests/unit_tests/test_base_url.py index 0baaca6c..fb9c513a 100644 --- a/libs/ai-endpoints/tests/unit_tests/test_base_url.py +++ b/libs/ai-endpoints/tests/unit_tests/test_base_url.py @@ -28,10 +28,9 @@ def mock_v1_local_models(requests_mock: Mocker) -> None: def test_create_without_base_url(public_class: type) -> None: with no_env_var("NVIDIA_BASE_URL"): - assert ( - public_class(api_key="BOGUS").base_url - == "https://integrate.api.nvidia.com/v1" - ) + x = public_class(api_key="BOGUS") + assert x.base_url == "https://integrate.api.nvidia.com/v1" + assert x._client.base_url == "https://integrate.api.nvidia.com/v1" @pytest.mark.parametrize( diff --git a/libs/ai-endpoints/tests/unit_tests/test_bind_tools.py b/libs/ai-endpoints/tests/unit_tests/test_bind_tools.py index a8f044f9..9d6647de 100644 --- a/libs/ai-endpoints/tests/unit_tests/test_bind_tools.py +++ b/libs/ai-endpoints/tests/unit_tests/test_bind_tools.py @@ -185,6 +185,7 @@ def test_stream_response_parsing( for argument in argument_chunks ], 'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"tool_calls"}]}', # noqa: E501 + 'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","choices":[],"usage":{"prompt_tokens":20,"total_tokens":42,"completion_tokens":22}}', # noqa: E501 "data: [DONE]", ] ), @@ -260,3 +261,31 @@ def test_regression_ai_null_content( assistant.content = None # type: ignore llm.invoke([assistant]) llm.stream([assistant]) + + +def test_stream_usage_metadata( + requests_mock: requests_mock.Mocker, +) -> None: + requests_mock.post( + "https://integrate.api.nvidia.com/v1/chat/completions", + text="\n\n".join( + [ + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"usage":null,"choices":[{"index":0,"delta":{"role":"assistant","content":null},"logprobs":null,"finish_reason":null}]}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"usage":null,"choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"id":"ID1","type":"function","function":{"name":"magic_function","arguments":""}}]},"logprobs":null,"finish_reason":null}]}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"usage":null,"choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\"in"}}]},"logprobs":null,"finish_reason":null}]}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"usage":null,"choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"put\":"}}]},"logprobs":null,"finish_reason":null}]}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"usage":null,"choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":" 3}"}}]},"logprobs":null,"finish_reason":null}]}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"tool_calls"}],"usage":null}', # noqa: E501 + r'data: {"id":"ID0","object":"chat.completion.chunk","created":1234567890,"model":"BOGUS","system_fingerprint":null,"choices":[],"usage":{"prompt_tokens":76,"completion_tokens":29,"total_tokens":105}}', # noqa: E501 + r"data: [DONE]", + ] + ), + ) + + llm = ChatNVIDIA(api_key="BOGUS") + response = reduce(add, llm.stream("IGNROED")) + assert isinstance(response, AIMessage) + assert response.usage_metadata is not None + assert response.usage_metadata["input_tokens"] == 76 + assert response.usage_metadata["output_tokens"] == 29 + assert response.usage_metadata["total_tokens"] == 105