Harrison/athropic (#921)

Co-authored-by: Mike Lambert <mlambert@gmail.com>
Co-authored-by: mrbean <sam@you.com>
Co-authored-by: mrbean <43734688+sam-h-bean@users.noreply.github.com>
Co-authored-by: Ivan Vendrov <ivendrov@gmail.com>

langchain/__init__.py

@@ -22,7 +22,7 @@ from langchain.chains import (
     VectorDBQAWithSourcesChain,
 )
 from langchain.docstore import InMemoryDocstore, Wikipedia
-from langchain.llms import Cohere, HuggingFaceHub, OpenAI
+from langchain.llms import Anthropic, Cohere, HuggingFaceHub, OpenAI
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.prompts import (
     BasePromptTemplate,
@@ -50,6 +50,7 @@ __all__ = [
     "SerpAPIChain",
     "GoogleSearchAPIWrapper",
     "WolframAlphaAPIWrapper",
+    "Anthropic",
     "Cohere",
     "OpenAI",
     "BasePromptTemplate",

langchain/llms/__init__.py

@@ -2,6 +2,7 @@
 from typing import Dict, Type
 
 from langchain.llms.ai21 import AI21
+from langchain.llms.anthropic import Anthropic
 from langchain.llms.base import BaseLLM
 from langchain.llms.cohere import Cohere
 from langchain.llms.huggingface_hub import HuggingFaceHub
@@ -10,6 +11,7 @@ from langchain.llms.nlpcloud import NLPCloud
 from langchain.llms.openai import AzureOpenAI, OpenAI
 
 __all__ = [
+    "Anthropic",
     "Cohere",
     "NLPCloud",
     "OpenAI",
@@ -21,6 +23,7 @@ __all__ = [
 
 type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "ai21": AI21,
+    "anthropic": Anthropic,
     "cohere": Cohere,
     "huggingface_hub": HuggingFaceHub,
     "nlpcloud": NLPCloud,

langchain/llms/anthropic.py

@@ -0,0 +1,155 @@
+"""Wrapper around Anthropic APIs."""
+from typing import Any, Dict, Generator, List, Mapping, Optional
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.llms.base import LLM
+from langchain.utils import get_from_dict_or_env
+
+
+class Anthropic(LLM, BaseModel):
+    """Wrapper around 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
+
+            from langchain import Anthropic
+            anthropic = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
+    """
+
+    client: Any  #: :meta private:
+    model: Optional[str] = None
+    """Model name to use."""
+
+    max_tokens_to_sample: int = 256
+    """Denotes the number of tokens to predict per generation."""
+
+    temperature: float = 1.0
+    """A non-negative float that tunes the degree of randomness in generation."""
+
+    top_k: int = 0
+    """Number of most likely tokens to consider at each step."""
+
+    top_p: float = 1
+    """Total probability mass of tokens to consider at each step."""
+
+    anthropic_api_key: Optional[str] = None
+
+    HUMAN_PROMPT: Optional[str] = None
+    AI_PROMPT: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        anthropic_api_key = get_from_dict_or_env(
+            values, "anthropic_api_key", "ANTHROPIC_API_KEY"
+        )
+        try:
+            import anthropic
+
+            values["client"] = anthropic.Client(anthropic_api_key)
+            values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
+            values["AI_PROMPT"] = anthropic.AI_PROMPT
+        except ImportError:
+            raise ValueError(
+                "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."""
+        return {
+            "max_tokens_to_sample": self.max_tokens_to_sample,
+            "temperature": self.temperature,
+            "top_k": self.top_k,
+            "top_p": self.top_p,
+        }
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model": self.model}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "anthropic"
+
+    def _call(
+        self, prompt: str, stop: Optional[List[str]] = None, instruct_mode: bool = True
+    ) -> str:
+        r"""Call out to Anthropic's completion endpoint.
+
+        Will by default act like an instruction-following model, by wrapping the prompt
+        with Human: and Assistant: If you want to use for chat or few-shot, pass
+        in instruct_mode=False
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+            instruct_mode: Whether to emulate an instruction-following model.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = anthropic("Tell me a joke.")
+
+                response = anthropic(
+                "\n\nHuman: Tell me a joke.\n\nAssistant:", instruct_mode=False
+                )
+
+        """
+        if stop is None:
+            stop = []
+        if not self.HUMAN_PROMPT or not self.AI_PROMPT:
+            raise NameError("Please ensure the anthropic package is loaded")
+        # Never want model to invent new turns of Human / Assistant dialog.
+        stop.extend([self.HUMAN_PROMPT, self.AI_PROMPT])
+
+        if instruct_mode:
+            # Wrap the prompt so it emulates an instruction following model.
+            prompt = f"{self.HUMAN_PROMPT} prompt{self.AI_PROMPT} Sure, here you go:\n"
+
+        response = self.client.completion(
+            model=self.model, prompt=prompt, stop_sequences=stop, **self._default_params
+        )
+        text = response["completion"]
+        return text
+
+    def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
+        """Call Anthropic completion_stream and return the resulting generator.
+
+        BETA: this is a beta feature while we figure out the right abstraction.
+        Once that happens, this interface could change.
+
+        Args:
+            prompt: The prompts 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
+
+                generator = anthropic.stream("Tell me a joke.")
+                for token in generator:
+                    yield token
+        """
+        return self.client.completion_stream(
+            model=self.model, prompt=prompt, stop_sequences=stop, **self._default_params
+        )

poetry.lock

@@ -24,6 +24,28 @@ files = [
     {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"},
 ]
 
+[[package]]
+name = "anthropic"
+version = "0.2.1"
+description = "Library for accessing the anthropic API"
+category = "main"
+optional = true
+python-versions = ">=3.8"
+files = []
+develop = false
+
+[package.dependencies]
+requests = "*"
+
+[package.extras]
+dev = ["black (>=22.3.0)", "pytest"]
+
+[package.source]
+type = "git"
+url = "https://github.com/anthropics/anthropic-sdk-python.git"
+reference = "HEAD"
+resolved_reference = "b61e0637b6a4b34399aafff4adc756fe649325bb"
+
 [[package]]
 name = "anyio"
 version = "3.6.2"
@@ -851,6 +873,7 @@ files = [
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+    {file = "debugpy-1.6.6-cp39-cp39-macosx_11_0_x86_64.whl", hash = "sha256:11a0f3a106f69901e4a9a5683ce943a7a5605696024134b522aa1bfda25b5fec"},
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@@ -2410,7 +2433,6 @@ files = [
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-    {file = "lxml-4.9.2-cp35-cp35m-win_amd64.whl", hash = "sha256:998c7c41910666d2976928c38ea96a70d1aa43be6fe502f21a651e17483a43c5"},
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@@ -2420,7 +2442,6 @@ files = [
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-    {file = "lxml-4.9.2-cp36-cp36m-win_amd64.whl", hash = "sha256:3ab9fa9d6dc2a7f29d7affdf3edebf6ece6fb28a6d80b14c3b2fb9d39b9322c3"},
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     {file = "lxml-4.9.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_24_i686.whl", hash = "sha256:a5da296eb617d18e497bcf0a5c528f5d3b18dadb3619fbdadf4ed2356ef8d941"},
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@@ -5521,14 +5542,11 @@ files = [
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-    {file = "tokenizers-0.13.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d1b079c4c9332048fec4cb9c2055c2373c74fbb336716a5524c9a720206d787e"},
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-    {file = "tokenizers-0.13.2-cp311-cp311-win_amd64.whl", hash = "sha256:fa7ef7ee380b1f49211bbcfac8a006b1a3fa2fa4c7f4ee134ae384eb4ea5e453"},
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@@ -5537,7 +5555,6 @@ files = [
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@@ -6268,10 +6285,10 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
 testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [extras]
-all = ["manifest-ml", "elasticsearch", "faiss-cpu", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
+all = ["anthropic", "manifest-ml", "elasticsearch", "faiss-cpu", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
-llms = ["manifest-ml", "torch", "transformers"]
+llms = ["anthropic", "manifest-ml", "torch", "transformers"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "6066217c63ce7620ee615d5db269327e288991f48304526767c88a1870f0c7a0"
+content-hash = "1f44af9c9fa6aa7fe3fed70c0342c71489e7288562af54f6721bd622695ab6f4"

pyproject.toml

@@ -32,6 +32,7 @@ tiktoken = {version = "^0", optional = true, python="^3.9"}
 pinecone-client = {version = "^2", optional = true}
 weaviate-client = {version = "^3", optional = true}
 google-api-python-client = {version = "2.70.0", optional = true}
+anthropic = {git = "https://github.com/anthropics/anthropic-sdk-python.git", optional = true}
 wolframalpha = {version = "5.0.0", optional = true}
 qdrant-client = {version = "^0.11.7", optional = true}
 dataclasses-json = "^0.5.7"
@@ -82,8 +83,8 @@ jupyter = "^1.0.0"
 playwright = "^1.28.0"
 
 [tool.poetry.extras]
-llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
+llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
-all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
+all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
 
 [tool.isort]
 profile = "black"

tests/integration_tests/llms/test_anthropic.py

@@ -0,0 +1,23 @@
+"""Test Anthropic API wrapper."""
+
+from typing import Generator
+
+from langchain.llms.anthropic import Anthropic
+
+
+def test_anthropic_call() -> None:
+    """Test valid call to anthropic."""
+    llm = Anthropic(model="bare-nano-0")
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def test_anthropic_streaming() -> None:
+    """Test streaming tokens from anthropic."""
+    llm = Anthropic(model="bare-nano-0")
+    generator = llm.stream("I'm Pickle Rick")
+
+    assert isinstance(generator, Generator)
+
+    for token in generator:
+        assert isinstance(token["completion"], str)