From b538b97236304334fdffc6ca2c1d59ee136237cc Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 27 Nov 2023 15:38:02 -0800 Subject: [PATCH] anthropic[major]: Create langchain-anthropic (#13822) --- .github/workflows/langchain_anthropic_ci.yml | 88 + .../langchain_anthropic_integration_tests.yml | 58 + .../workflows/langchain_anthropic_release.yml | 13 + libs/anthropic/Makefile | 52 + libs/anthropic/README.md | 1 + .../anthropic/langchain_anthropic/__init__.py | 4 + .../langchain_anthropic/chat_models.py | 221 ++ libs/anthropic/langchain_anthropic/llms.py | 356 ++ libs/anthropic/poetry.lock | 2907 +++++++++++++++++ libs/anthropic/pyproject.toml | 70 + libs/anthropic/scripts/check_imports.sh | 17 + libs/anthropic/scripts/check_pydantic.sh | 27 + .../tests/integration_tests/__init__.py | 0 .../integration_tests/test_chat_models.py | 102 + libs/anthropic/tests/unit_tests/__init__.py | 0 .../tests/unit_tests/test_chat_models.py | 69 + .../tests/unit_tests/test_imports.py | 7 + libs/anthropic/tests/unit_tests/test_llms.py | 41 + 18 files changed, 4033 insertions(+) create mode 100644 .github/workflows/langchain_anthropic_ci.yml create mode 100644 .github/workflows/langchain_anthropic_integration_tests.yml create mode 100644 .github/workflows/langchain_anthropic_release.yml create mode 100644 libs/anthropic/Makefile create mode 100644 libs/anthropic/README.md create mode 100644 libs/anthropic/langchain_anthropic/__init__.py create mode 100644 libs/anthropic/langchain_anthropic/chat_models.py create mode 100644 libs/anthropic/langchain_anthropic/llms.py create mode 100644 libs/anthropic/poetry.lock create mode 100644 libs/anthropic/pyproject.toml create mode 100755 libs/anthropic/scripts/check_imports.sh create mode 100755 libs/anthropic/scripts/check_pydantic.sh create mode 100644 libs/anthropic/tests/integration_tests/__init__.py create mode 100644 libs/anthropic/tests/integration_tests/test_chat_models.py create mode 100644 libs/anthropic/tests/unit_tests/__init__.py create mode 100644 libs/anthropic/tests/unit_tests/test_chat_models.py create mode 100644 libs/anthropic/tests/unit_tests/test_imports.py create mode 100644 libs/anthropic/tests/unit_tests/test_llms.py diff --git a/.github/workflows/langchain_anthropic_ci.yml b/.github/workflows/langchain_anthropic_ci.yml new file mode 100644 index 00000000000..ca1db1baa94 --- /dev/null +++ b/.github/workflows/langchain_anthropic_ci.yml @@ -0,0 +1,88 @@ +--- +name: libs/anthropic CI + +on: + push: + branches: [ master ] + pull_request: + paths: + - '.github/actions/poetry_setup/action.yml' + - '.github/tools/**' + - '.github/workflows/_lint.yml' + - '.github/workflows/_test.yml' + - '.github/workflows/langchain_anthropic_ci.yml' + - 'libs/*' + - 'libs/anthropic/**' + - 'libs/core/**' + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +# If another push to the same PR or branch happens while this workflow is still running, +# cancel the earlier run in favor of the next run. +# +# There's no point in testing an outdated version of the code. GitHub only allows +# a limited number of job runners to be active at the same time, so it's better to cancel +# pointless jobs early so that more useful jobs can run sooner. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: "libs/anthropic" + +jobs: + lint: + uses: + ./.github/workflows/_lint.yml + with: + working-directory: libs/anthropic + langchain-core-location: ../core + secrets: inherit + + test: + uses: + ./.github/workflows/_test.yml + with: + working-directory: libs/anthropic + langchain-core-location: ../core + secrets: inherit + + # It's possible that langchain-anthropic works fine with the latest *published* langchain-core, + # but is broken with the langchain-core on `master`. + # + # We want to catch situations like that *before* releasing a new langchain-core, hence this test. + test-with-latest-langchain-core: + runs-on: ubuntu-latest + defaults: + run: + working-directory: ${{ env.WORKDIR }} + strategy: + matrix: + python-version: + - "3.8" + - "3.9" + - "3.10" + - "3.11" + name: test with unpublished langchain-core - Python ${{ matrix.python-version }} + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ matrix.python-version }} + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ env.WORKDIR }} + cache-key: unpublished-langchain + + - name: Install dependencies + shell: bash + run: | + echo "Running tests with unpublished langchain-anthropic, installing dependencies with poetry..." + poetry install + + echo "Editably installing langchain-core outside of poetry, to avoid messing up lockfile..." + poetry run pip install -e ../core + + - name: Run tests + run: make test \ No newline at end of file diff --git a/.github/workflows/langchain_anthropic_integration_tests.yml b/.github/workflows/langchain_anthropic_integration_tests.yml new file mode 100644 index 00000000000..073d98e9c6c --- /dev/null +++ b/.github/workflows/langchain_anthropic_integration_tests.yml @@ -0,0 +1,58 @@ +--- +name: libs/anthropic Integration tests + +on: + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +# If another push to the same PR or branch happens while this workflow is still running, +# cancel the earlier run in favor of the next run. +# +# There's no point in testing an outdated version of the code. GitHub only allows +# a limited number of job runners to be active at the same time, so it's better to cancel +# pointless jobs early so that more useful jobs can run sooner. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: "libs/anthropic" + +jobs: + test-with-latest-langchain-core: + runs-on: ubuntu-latest + defaults: + run: + working-directory: ${{ env.WORKDIR }} + strategy: + matrix: + python-version: + - "3.8" + - "3.9" + - "3.10" + - "3.11" + name: test with unpublished langchain-core - Python ${{ matrix.python-version }} + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ matrix.python-version }} + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ env.WORKDIR }} + cache-key: unpublished-langchain + + - name: Install dependencies + shell: bash + run: | + echo "Running tests with unpublished langchain-anthropic, installing dependencies with poetry..." + poetry install + + echo "Editably installing langchain-core outside of poetry, to avoid messing up lockfile..." + poetry run pip install -e ../core + + - name: Run tests + env: + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + run: poetry run pytest tests/integration_tests \ No newline at end of file diff --git a/.github/workflows/langchain_anthropic_release.yml b/.github/workflows/langchain_anthropic_release.yml new file mode 100644 index 00000000000..e6c4f2ee3f8 --- /dev/null +++ b/.github/workflows/langchain_anthropic_release.yml @@ -0,0 +1,13 @@ +--- +name: libs/experimental Release + +on: + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +jobs: + release: + uses: + ./.github/workflows/_release.yml + with: + working-directory: libs/experimental + secrets: inherit diff --git a/libs/anthropic/Makefile b/libs/anthropic/Makefile new file mode 100644 index 00000000000..2ae5193e415 --- /dev/null +++ b/libs/anthropic/Makefile @@ -0,0 +1,52 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +test: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/anthropic --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') + +lint lint_diff: + ./scripts/check_pydantic.sh . + ./scripts/check_imports.sh + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/anthropic/README.md b/libs/anthropic/README.md new file mode 100644 index 00000000000..1689f6ace2a --- /dev/null +++ b/libs/anthropic/README.md @@ -0,0 +1 @@ +# langchain-anthropic diff --git a/libs/anthropic/langchain_anthropic/__init__.py b/libs/anthropic/langchain_anthropic/__init__.py new file mode 100644 index 00000000000..85ad783cb0b --- /dev/null +++ b/libs/anthropic/langchain_anthropic/__init__.py @@ -0,0 +1,4 @@ +from langchain_anthropic.chat_models import ChatAnthropic +from langchain_anthropic.llms import Anthropic + +__all__ = ["Anthropic", "ChatAnthropic"] diff --git a/libs/anthropic/langchain_anthropic/chat_models.py b/libs/anthropic/langchain_anthropic/chat_models.py new file mode 100644 index 00000000000..d0981008722 --- /dev/null +++ b/libs/anthropic/langchain_anthropic/chat_models.py @@ -0,0 +1,221 @@ +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast + +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models.chat_models import ( + BaseChatModel, + agenerate_from_stream, + generate_from_stream, +) +from langchain_core.messages import ( + AIMessage, + AIMessageChunk, + BaseMessage, + ChatMessage, + HumanMessage, + SystemMessage, +) +from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from langchain_core.prompt_values import PromptValue + +from langchain_anthropic.llms import _AnthropicCommon + + +def _convert_one_message_to_text( + message: BaseMessage, + human_prompt: str, + ai_prompt: str, +) -> str: + content = cast(str, message.content) + if isinstance(message, ChatMessage): + message_text = f"\n\n{message.role.capitalize()}: {content}" + elif isinstance(message, HumanMessage): + message_text = f"{human_prompt} {content}" + elif isinstance(message, AIMessage): + message_text = f"{ai_prompt} {content}" + elif isinstance(message, SystemMessage): + message_text = content + else: + raise ValueError(f"Got unknown type {message}") + return message_text + + +def convert_messages_to_prompt_anthropic( + messages: List[BaseMessage], + *, + human_prompt: str = "\n\nHuman:", + ai_prompt: str = "\n\nAssistant:", +) -> str: + """Format a list of messages into a full prompt for the Anthropic model + Args: + messages (List[BaseMessage]): List of BaseMessage to combine. + human_prompt (str, optional): Human prompt tag. Defaults to "\n\nHuman:". + ai_prompt (str, optional): AI prompt tag. Defaults to "\n\nAssistant:". + Returns: + str: Combined string with necessary human_prompt and ai_prompt tags. + """ + + messages = messages.copy() # don't mutate the original list + if not isinstance(messages[-1], AIMessage): + messages.append(AIMessage(content="")) + + text = "".join( + _convert_one_message_to_text(message, human_prompt, ai_prompt) + for message in messages + ) + + # trim off the trailing ' ' that might come from the "Assistant: " + return text.rstrip() + + +class ChatAnthropic(BaseChatModel, _AnthropicCommon): + """`Anthropic` chat large language models. + + To use, you should have the ``anthropic`` python package installed, and the + environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass + it as a named parameter to the constructor. + + Example: + .. code-block:: python + + import anthropic + from langchain.chat_models import ChatAnthropic + model = ChatAnthropic(model="", anthropic_api_key="my-api-key") + """ + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + arbitrary_types_allowed = True + + @property + def lc_secrets(self) -> Dict[str, str]: + return {"anthropic_api_key": "ANTHROPIC_API_KEY"} + + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "anthropic-chat" + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this model can be serialized by Langchain.""" + return True + + def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str: + """Format a list of messages into a full prompt for the Anthropic model + Args: + messages (List[BaseMessage]): List of BaseMessage to combine. + Returns: + str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags. + """ + prompt_params = {} + if self.HUMAN_PROMPT: + prompt_params["human_prompt"] = self.HUMAN_PROMPT + if self.AI_PROMPT: + prompt_params["ai_prompt"] = self.AI_PROMPT + return convert_messages_to_prompt_anthropic(messages=messages, **prompt_params) + + def convert_prompt(self, prompt: PromptValue) -> str: + return self._convert_messages_to_prompt(prompt.to_messages()) + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + prompt = self._convert_messages_to_prompt(messages) + params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs} + if stop: + params["stop_sequences"] = stop + + stream_resp = self.client.completions.create(**params, stream=True) + for data in stream_resp: + delta = data.completion + yield ChatGenerationChunk(message=AIMessageChunk(content=delta)) + if run_manager: + run_manager.on_llm_new_token(delta) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + prompt = self._convert_messages_to_prompt(messages) + params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs} + if stop: + params["stop_sequences"] = stop + + stream_resp = await self.async_client.completions.create(**params, stream=True) + async for data in stream_resp: + delta = data.completion + yield ChatGenerationChunk(message=AIMessageChunk(content=delta)) + if run_manager: + await run_manager.on_llm_new_token(delta) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if self.streaming: + stream_iter = self._stream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return generate_from_stream(stream_iter) + prompt = self._convert_messages_to_prompt( + messages, + ) + params: Dict[str, Any] = { + "prompt": prompt, + **self._default_params, + **kwargs, + } + if stop: + params["stop_sequences"] = stop + response = self.client.completions.create(**params) + completion = response.completion + message = AIMessage(content=completion) + return ChatResult(generations=[ChatGeneration(message=message)]) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if self.streaming: + stream_iter = self._astream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return await agenerate_from_stream(stream_iter) + prompt = self._convert_messages_to_prompt( + messages, + ) + params: Dict[str, Any] = { + "prompt": prompt, + **self._default_params, + **kwargs, + } + if stop: + params["stop_sequences"] = stop + response = await self.async_client.completions.create(**params) + completion = response.completion + message = AIMessage(content=completion) + return ChatResult(generations=[ChatGeneration(message=message)]) + + def get_num_tokens(self, text: str) -> int: + """Calculate number of tokens.""" + if not self.count_tokens: + raise NameError("Please ensure the anthropic package is loaded") + return self.count_tokens(text) diff --git a/libs/anthropic/langchain_anthropic/llms.py b/libs/anthropic/langchain_anthropic/llms.py new file mode 100644 index 00000000000..21955722915 --- /dev/null +++ b/libs/anthropic/langchain_anthropic/llms.py @@ -0,0 +1,356 @@ +import os +import re +import warnings +from typing import ( + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Mapping, + Optional, + cast, +) + +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models import LLM, BaseLanguageModel +from langchain_core.outputs import GenerationChunk +from langchain_core.prompt_values import PromptValue +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator +from langchain_core.utils import ( + build_extra_kwargs, + check_package_version, + convert_to_secret_str, + get_pydantic_field_names, +) + + +class _AnthropicCommon(BaseLanguageModel): + client: Any = None #: :meta private: + async_client: Any = None #: :meta private: + model: str = Field(default="claude-2", alias="model_name") + """Model name to use.""" + + max_tokens_to_sample: int = Field(default=256, alias="max_tokens") + """Denotes the number of tokens to predict per generation.""" + + temperature: Optional[float] = None + """A non-negative float that tunes the degree of randomness in generation.""" + + top_k: Optional[int] = None + """Number of most likely tokens to consider at each step.""" + + top_p: Optional[float] = None + """Total probability mass of tokens to consider at each step.""" + + streaming: bool = False + """Whether to stream the results.""" + + default_request_timeout: Optional[float] = None + """Timeout for requests to Anthropic Completion API. Default is 600 seconds.""" + + anthropic_api_url: Optional[str] = None + + anthropic_api_key: Optional[SecretStr] = None + + HUMAN_PROMPT: Optional[str] = None + AI_PROMPT: Optional[str] = None + count_tokens: Optional[Callable[[str], int]] = None + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + + @root_validator(pre=True) + def build_extra(cls, values: Dict) -> Dict: + extra = values.get("model_kwargs", {}) + all_required_field_names = get_pydantic_field_names(cls) + values["model_kwargs"] = build_extra_kwargs( + extra, values, all_required_field_names + ) + return values + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + if values["anthropic_api_key"] is None: + if os.getenv("ANTHROPIC_API_KEY") is None: + raise ValueError( + "API key must be provided in as argument anthropic_api_key or set" + " as environment variable ANTHROPIC_API_KEY." + ) + values["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY") + + values["anthropic_api_key"] = convert_to_secret_str(values["anthropic_api_key"]) + + # Get custom api url from environment. + if values["anthropic_api_url"] is None: + values["anthropic_api_url"] = os.getenv( + "ANTHROPIC_API_URL", default="https://api.anthropic.com" + ) + + try: + import anthropic + + check_package_version("anthropic", gte_version="0.3") + values["client"] = anthropic.Anthropic( + base_url=values["anthropic_api_url"], + api_key=cast(SecretStr, values["anthropic_api_key"]).get_secret_value(), + timeout=values["default_request_timeout"], + ) + values["async_client"] = anthropic.AsyncAnthropic( + base_url=values["anthropic_api_url"], + api_key=cast(SecretStr, values["anthropic_api_key"]).get_secret_value(), + timeout=values["default_request_timeout"], + ) + values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT + values["AI_PROMPT"] = anthropic.AI_PROMPT + values["count_tokens"] = values["client"].count_tokens + + except ImportError: + raise ImportError( + "Could not import anthropic python package. " + "Please it install it with `pip install anthropic`." + ) + return values + + @property + def _default_params(self) -> Mapping[str, Any]: + """Get the default parameters for calling Anthropic API.""" + d = { + "max_tokens_to_sample": self.max_tokens_to_sample, + "model": self.model, + } + if self.temperature is not None: + d["temperature"] = self.temperature + if self.top_k is not None: + d["top_k"] = self.top_k + if self.top_p is not None: + d["top_p"] = self.top_p + return {**d, **self.model_kwargs} + + @property + def _identifying_params(self) -> Mapping[str, Any]: + """Get the identifying parameters.""" + return {**{}, **self._default_params} + + def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]: + if not self.HUMAN_PROMPT or not self.AI_PROMPT: + raise NameError("Please ensure the anthropic package is loaded") + + if stop is None: + stop = [] + + # Never want model to invent new turns of Human / Assistant dialog. + stop.extend([self.HUMAN_PROMPT]) + + return stop + + +class Anthropic(LLM, _AnthropicCommon): + """Anthropic large language models. + + To use, you should have the ``anthropic`` python package installed, and the + environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass + it as a named parameter to the constructor. + + Example: + .. code-block:: python + + import anthropic + from langchain.llms import Anthropic + + model = Anthropic(model="", anthropic_api_key="my-api-key") + + # Simplest invocation, automatically wrapped with HUMAN_PROMPT + # and AI_PROMPT. + response = model("What are the biggest risks facing humanity?") + + # Or if you want to use the chat mode, build a few-shot-prompt, or + # put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT: + raw_prompt = "What are the biggest risks facing humanity?" + prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}" + response = model(prompt) + """ + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + arbitrary_types_allowed = True + + @root_validator() + def raise_warning(cls, values: Dict) -> Dict: + """Raise warning that this class is deprecated.""" + warnings.warn( + "This Anthropic LLM is deprecated. " + "Please use `from langchain.chat_models import ChatAnthropic` instead" + ) + return values + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "anthropic-llm" + + def _wrap_prompt(self, prompt: str) -> str: + if not self.HUMAN_PROMPT or not self.AI_PROMPT: + raise NameError("Please ensure the anthropic package is loaded") + + if prompt.startswith(self.HUMAN_PROMPT): + return prompt # Already wrapped. + + # Guard against common errors in specifying wrong number of newlines. + corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt) + if n_subs == 1: + return corrected_prompt + + # As a last resort, wrap the prompt ourselves to emulate instruct-style. + return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n" + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + r"""Call out to Anthropic's completion endpoint. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + + Returns: + The string generated by the model. + + Example: + .. code-block:: python + + prompt = "What are the biggest risks facing humanity?" + prompt = f"\n\nHuman: {prompt}\n\nAssistant:" + response = model(prompt) + + """ + if self.streaming: + completion = "" + for chunk in self._stream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + completion += chunk.text + return completion + + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + response = self.client.completions.create( + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + **params, + ) + return response.completion + + def convert_prompt(self, prompt: PromptValue) -> str: + return self._wrap_prompt(prompt.to_string()) + + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call out to Anthropic's completion endpoint asynchronously.""" + if self.streaming: + completion = "" + async for chunk in self._astream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + completion += chunk.text + return completion + + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + + response = await self.async_client.completions.create( + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + **params, + ) + return response.completion + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + r"""Call Anthropic completion_stream and return the resulting generator. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + Returns: + A generator representing the stream of tokens from Anthropic. + Example: + .. code-block:: python + + prompt = "Write a poem about a stream." + prompt = f"\n\nHuman: {prompt}\n\nAssistant:" + generator = anthropic.stream(prompt) + for token in generator: + yield token + """ + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + + for token in self.client.completions.create( + prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params + ): + chunk = GenerationChunk(text=token.completion) + yield chunk + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) + + async def _astream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[GenerationChunk]: + r"""Call Anthropic completion_stream and return the resulting generator. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + 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+[tool.poetry] +name = "langchain-anthropic" +version = "0.0.1" +description = "LangChain Anthropic integrations." +authors = [] +license = "MIT" +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain" + + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = ">=0.0.5" +anthropic = "^0.3.11" + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" + +[tool.poetry.group.dev.dependencies] +jupyter = "^1.0.0" +setuptools = "^67.6.1" + +[tool.poetry.group.test.dependencies] +# The only dependencies that should be added are +# dependencies used for running tests (e.g., pytest, freezegun, response). +# Any dependencies that do not meet that criteria will be removed. +pytest = "^7.3.0" +pytest-asyncio = "^0.21.1" + + +[tool.poetry.group.test_integration] +optional = true +dependencies = {} + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +ignore_missing_imports = "True" +disallow_untyped_defs = "True" +exclude = ["notebooks"] + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +addopts = "--strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +asyncio_mode = "auto" diff --git a/libs/anthropic/scripts/check_imports.sh b/libs/anthropic/scripts/check_imports.sh new file mode 100755 index 00000000000..695613c7ba8 --- /dev/null +++ b/libs/anthropic/scripts/check_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/anthropic/scripts/check_pydantic.sh b/libs/anthropic/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/anthropic/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/anthropic/tests/integration_tests/__init__.py b/libs/anthropic/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/anthropic/tests/integration_tests/test_chat_models.py b/libs/anthropic/tests/integration_tests/test_chat_models.py new file mode 100644 index 00000000000..a269f1531d6 --- /dev/null +++ b/libs/anthropic/tests/integration_tests/test_chat_models.py @@ -0,0 +1,102 @@ +"""Test Anthropic API wrapper.""" +from typing import List + +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage +from langchain_core.outputs import ChatGeneration, LLMResult + +from langchain_anthropic.chat_models import ChatAnthropic + + +def test_anthropic_call() -> None: + """Test valid call to anthropic.""" + chat = ChatAnthropic(model="test") + message = HumanMessage(content="Hello") + response = chat([message]) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + + +def test_anthropic_generate() -> None: + """Test generate method of anthropic.""" + chat = ChatAnthropic(model="test") + chat_messages: List[List[BaseMessage]] = [ + [HumanMessage(content="How many toes do dogs have?")] + ] + messages_copy = [messages.copy() for messages in chat_messages] + result: LLMResult = chat.generate(chat_messages) + assert isinstance(result, LLMResult) + for response in result.generations[0]: + assert isinstance(response, ChatGeneration) + assert isinstance(response.text, str) + assert response.text == response.message.content + assert chat_messages == messages_copy + + +def test_anthropic_streaming() -> None: + """Test streaming tokens from anthropic.""" + chat = ChatAnthropic(model="test", streaming=True) + message = HumanMessage(content="Hello") + response = chat([message]) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + + +def test_anthropic_stream() -> None: + """Test streaming tokens from OpenAI.""" + llm = ChatAnthropic(max_tokens=10) + + for token in llm.stream("I'm Pickle Rick"): + assert isinstance(token.content, str) + + +async def test_anthropic_astream() -> None: + """Test streaming tokens from OpenAI.""" + llm = ChatAnthropic(max_tokens=10) + + async for token in llm.astream("I'm Pickle Rick"): + assert isinstance(token.content, str) + + +async def test_anthropic_abatch() -> None: + """Test streaming tokens from ChatAnthropic.""" + llm = ChatAnthropic(max_tokens=10) + + result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) + for token in result: + assert isinstance(token.content, str) + + +async def test_anthropic_abatch_tags() -> None: + """Test batch tokens from ChatAnthropic.""" + llm = ChatAnthropic(max_tokens=10) + + result = await llm.abatch( + ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} + ) + for token in result: + assert isinstance(token.content, str) + + +def test_anthropic_batch() -> None: + """Test batch tokens from ChatAnthropic.""" + llm = ChatAnthropic(max_tokens=10) + + result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) + for token in result: + assert isinstance(token.content, str) + + +async def test_anthropic_ainvoke() -> None: + """Test invoke tokens from ChatAnthropic.""" + llm = ChatAnthropic(max_tokens=10) + + result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) + assert isinstance(result.content, str) + + +def test_anthropic_invoke() -> None: + """Test invoke tokens from ChatAnthropic.""" + llm = ChatAnthropic(max_tokens=10) + + result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) + assert isinstance(result.content, str) diff --git a/libs/anthropic/tests/unit_tests/__init__.py b/libs/anthropic/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/anthropic/tests/unit_tests/test_chat_models.py b/libs/anthropic/tests/unit_tests/test_chat_models.py new file mode 100644 index 00000000000..125f64bd9dc --- /dev/null +++ b/libs/anthropic/tests/unit_tests/test_chat_models.py @@ -0,0 +1,69 @@ +"""Test Anthropic Chat API wrapper.""" +import os +from typing import List + +import pytest +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage + +from langchain_anthropic.chat_models import ( + ChatAnthropic, + convert_messages_to_prompt_anthropic, +) + +os.environ["ANTHROPIC_API_KEY"] = "foo" + + +def test_anthropic_model_name_param() -> None: + llm = ChatAnthropic(model_name="foo") + assert llm.model == "foo" + + +def test_anthropic_model_param() -> None: + llm = ChatAnthropic(model="foo") + assert llm.model == "foo" + + +def test_anthropic_model_kwargs() -> None: + llm = ChatAnthropic(model_kwargs={"foo": "bar"}) + assert llm.model_kwargs == {"foo": "bar"} + + +def test_anthropic_invalid_model_kwargs() -> None: + with pytest.raises(ValueError): + ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5}) + + +def test_anthropic_incorrect_field() -> None: + with pytest.warns(match="not default parameter"): + llm = ChatAnthropic(foo="bar") + assert llm.model_kwargs == {"foo": "bar"} + + +def test_anthropic_initialization() -> None: + """Test anthropic initialization.""" + # Verify that chat anthropic can be initialized using a secret key provided + # as a parameter rather than an environment variable. + ChatAnthropic(model="test", anthropic_api_key="test") + + +@pytest.mark.parametrize( + ("messages", "expected"), + [ + ([HumanMessage(content="Hello")], "\n\nHuman: Hello\n\nAssistant:"), + ( + [HumanMessage(content="Hello"), AIMessage(content="Answer:")], + "\n\nHuman: Hello\n\nAssistant: Answer:", + ), + ( + [ + SystemMessage(content="You're an assistant"), + HumanMessage(content="Hello"), + AIMessage(content="Answer:"), + ], + "You're an assistant\n\nHuman: Hello\n\nAssistant: Answer:", + ), + ], +) +def test_formatting(messages: List[BaseMessage], expected: str) -> None: + result = convert_messages_to_prompt_anthropic(messages) + assert result == expected diff --git a/libs/anthropic/tests/unit_tests/test_imports.py b/libs/anthropic/tests/unit_tests/test_imports.py new file mode 100644 index 00000000000..636f116ae19 --- /dev/null +++ b/libs/anthropic/tests/unit_tests/test_imports.py @@ -0,0 +1,7 @@ +from langchain_anthropic import __all__ + +EXPECTED_ALL = ["Anthropic", "ChatAnthropic"] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/anthropic/tests/unit_tests/test_llms.py b/libs/anthropic/tests/unit_tests/test_llms.py new file mode 100644 index 00000000000..ce0f98d2b56 --- /dev/null +++ b/libs/anthropic/tests/unit_tests/test_llms.py @@ -0,0 +1,41 @@ +"""Test Anthropic Chat API wrapper.""" +import os + +import pytest + +from langchain_anthropic.llms import Anthropic + +os.environ["ANTHROPIC_API_KEY"] = "foo" + + +def test_anthropic_model_name_param() -> None: + llm = Anthropic(model_name="foo") + assert llm.model == "foo" + + +def test_anthropic_model_param() -> None: + llm = Anthropic(model="foo") + assert llm.model == "foo" + + +def test_anthropic_model_kwargs() -> None: + llm = Anthropic(model_kwargs={"foo": "bar"}) + assert llm.model_kwargs == {"foo": "bar"} + + +def test_anthropic_invalid_model_kwargs() -> None: + with pytest.raises(ValueError): + Anthropic(model_kwargs={"max_tokens_to_sample": 5}) + + +def test_anthropic_incorrect_field() -> None: + with pytest.warns(match="not default parameter"): + llm = Anthropic(foo="bar") + assert llm.model_kwargs == {"foo": "bar"} + + +def test_anthropic_initialization() -> None: + """Test anthropic initialization.""" + # Verify that chat anthropic can be initialized using a secret key provided + # as a parameter rather than an environment variable. + Anthropic(model="test", anthropic_api_key="test")