Harrison/add huggingface hub (#23)

Add support for huggingface hub

I could not find a good way to enforce stop tokens over the huggingface
hub api - that needs to hopefully be cleaned up in the future

README.md

@@ -38,6 +38,9 @@ The following use cases require specific installs and environment variables:
 - *Cohere*:
   - Install requirements with `pip install cohere`
   - Set the following environment variable: `COHERE_API_KEY`
+- *HuggingFace Hub*
+  - Install requirements with `pip install huggingface_hub`
+  - Set the following environment variable: `HUGGINGFACEHUB_API_TOKEN`
 - *SerpAPI*:
   - Install requirements with `pip install google-search-results`
   - Set the following environment variable: `SERPAPI_API_KEY`

examples/huggingface_hub.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3acf0069",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for HuggingFaceHub\n__root__\n  Did not find HuggingFace API token, please add an environment variable `HUGGINGFACEHUB_API_TOKEN` which contains it. (type=value_error)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/ipykernel_56760/1512947828.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m Answer: Let's think step by step.\"\"\"\n\u001b[1;32m      6\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemplate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_variables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"question\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mllm_chain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLLMChain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mHuggingFaceHub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gpt2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mquestion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/workplace/langchain/.venv/lib/python3.7/site-packages/pydantic/main.cpython-37m-darwin.so\u001b[0m in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for HuggingFaceHub\n__root__\n  Did not find HuggingFace API token, please add an environment variable `HUGGINGFACEHUB_API_TOKEN` which contains it. (type=value_error)"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain import Prompt, HuggingFaceHub, LLMChain\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = Prompt(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"gpt2\", temperature=1e-10))\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.predict(question=question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae4559c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/__init__.py

@@ -12,7 +12,7 @@ from langchain.chains import (
     SelfAskWithSearchChain,
     SerpAPIChain,
 )
-from langchain.llms import Cohere, OpenAI
+from langchain.llms import Cohere, HuggingFaceHub, OpenAI
 from langchain.prompt import Prompt
 
 __all__ = [
@@ -24,4 +24,5 @@ __all__ = [
     "Cohere",
     "OpenAI",
     "Prompt",
+    "HuggingFaceHub",
 ]

langchain/llms/__init__.py

@@ -1,5 +1,6 @@
 """Wrappers on top of large language models APIs."""
 from langchain.llms.cohere import Cohere
+from langchain.llms.huggingface_hub import HuggingFaceHub
 from langchain.llms.openai import OpenAI
 
-__all__ = ["Cohere", "OpenAI"]
+__all__ = ["Cohere", "OpenAI", "HuggingFaceHub"]

langchain/llms/cohere.py

@@ -5,14 +5,7 @@ from typing import Any, Dict, List, Optional
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
-
+from langchain.llms.utils import enforce_stop_tokens
-
-def remove_stop_tokens(text: str, stop: List[str]) -> str:
-    """Remove stop tokens, should they occur at end."""
-    for s in stop:
-        if text.endswith(s):
-            return text[: -len(s)]
-    return text
 
 
 class Cohere(BaseModel, LLM):
@@ -104,5 +97,5 @@ class Cohere(BaseModel, LLM):
         # If stop tokens are provided, Cohere's endpoint returns them.
         # In order to make this consistent with other endpoints, we strip them.
         if stop is not None:
-            text = remove_stop_tokens(text, stop)
+            text = enforce_stop_tokens(text, stop)
         return text

langchain/llms/huggingface_hub.py

@@ -0,0 +1,102 @@
+"""Wrapper around HuggingFace APIs."""
+import os
+from typing import Any, Dict, List, Mapping, Optional
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+
+DEFAULT_REPO_ID = "gpt2"
+
+
+class HuggingFaceHub(BaseModel, LLM):
+    """Wrapper around HuggingFaceHub  models.
+
+    To use, you should have the ``huggingface_hub`` python package installed, and the
+    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token.
+
+    Only supports task `text-generation` for now.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import HuggingFace
+            hf = HuggingFace(model="text-davinci-002")
+    """
+
+    client: Any  #: :meta private:
+    repo_id: str = DEFAULT_REPO_ID
+    """Model name to use."""
+    temperature: float = 0.7
+    """What sampling temperature to use."""
+    max_new_tokens: int = 200
+    """The maximum number of tokens to generate in the completion."""
+    top_p: int = 1
+    """Total probability mass of tokens to consider at each step."""
+    num_return_sequences: int = 1
+    """How many completions to generate for each prompt."""
+
+    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."""
+        if "HUGGINGFACEHUB_API_TOKEN" not in os.environ:
+            raise ValueError(
+                "Did not find HuggingFace API token, please add an environment variable"
+                " `HUGGINGFACEHUB_API_TOKEN` which contains it."
+            )
+        try:
+            from huggingface_hub.inference_api import InferenceApi
+
+            repo_id = values.get("repo_id", DEFAULT_REPO_ID)
+            values["client"] = InferenceApi(
+                repo_id=repo_id,
+                token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
+                task="text-generation",
+            )
+        except ImportError:
+            raise ValueError(
+                "Could not import huggingface_hub python package. "
+                "Please it install it with `pip install huggingface_hub`."
+            )
+        return values
+
+    @property
+    def _default_params(self) -> Mapping[str, Any]:
+        """Get the default parameters for calling HuggingFace Hub API."""
+        return {
+            "temperature": self.temperature,
+            "max_new_tokens": self.max_new_tokens,
+            "top_p": self.top_p,
+            "num_return_sequences": self.num_return_sequences,
+        }
+
+    def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        """Call out to HuggingFace Hub's inference 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
+
+                response = hf("Tell me a joke.")
+        """
+        response = self.client(inputs=prompt, params=self._default_params)
+        if "error" in response:
+            raise ValueError(f"Error raised by inference API: {response['error']}")
+        text = response[0]["generated_text"][len(prompt) :]
+        if stop is not None:
+            # This is a bit hacky, but I can't figure out a better way to enforce
+            # stop tokens when making calls to huggingface_hub.
+            text = enforce_stop_tokens(text, stop)
+        return text

langchain/llms/utils.py

@@ -0,0 +1,8 @@
+"""Common utility functions for working with LLM APIs."""
+import re
+from typing import List
+
+
+def enforce_stop_tokens(text: str, stop: List[str]) -> str:
+    """Cut off the text as soon as any stop words occur."""
+    return re.split("|".join(stop), text)[0]

requirements.txt

@@ -1,10 +1,15 @@
 -r test_requirements.txt
+# For linting
 black
 isort
 mypy
 flake8
 flake8-docstrings
+# For integrations
 cohere
 openai
 google-search-results
 playwright
+huggingface_hub
+# For development
+jupyter

tests/integration_tests/llms/test_huggingface_hub.py

@@ -0,0 +1,19 @@
+"""Test HuggingFace API wrapper."""
+
+import pytest
+
+from langchain.llms.huggingface_hub import HuggingFaceHub
+
+
+def test_huggingface_call() -> None:
+    """Test valid call to HuggingFace."""
+    llm = HuggingFaceHub(max_new_tokens=10)
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def test_huggingface_call_error() -> None:
+    """Test valid call to HuggingFace that errors."""
+    llm = HuggingFaceHub(max_new_tokens=-1)
+    with pytest.raises(ValueError):
+        llm("Say foo:")

tests/unit_tests/llms/test_cohere.py

@@ -1,17 +0,0 @@
-"""Test helper functions for Cohere API."""
-
-from langchain.llms.cohere import remove_stop_tokens
-
-
-def test_remove_stop_tokens() -> None:
-    """Test removing stop tokens when they occur."""
-    text = "foo bar baz"
-    output = remove_stop_tokens(text, ["moo", "baz"])
-    assert output == "foo bar "
-
-
-def test_remove_stop_tokens_none() -> None:
-    """Test removing stop tokens when they do not occur."""
-    text = "foo bar baz"
-    output = remove_stop_tokens(text, ["moo"])
-    assert output == "foo bar baz"

tests/unit_tests/llms/test_utils.py

@@ -0,0 +1,19 @@
+"""Test LLM utility functions."""
+from langchain.llms.utils import enforce_stop_tokens
+
+
+def test_enforce_stop_tokens() -> None:
+    """Test removing stop tokens when they occur."""
+    text = "foo bar baz"
+    output = enforce_stop_tokens(text, ["moo", "baz"])
+    assert output == "foo bar "
+    text = "foo bar baz"
+    output = enforce_stop_tokens(text, ["moo", "baz", "bar"])
+    assert output == "foo "
+
+
+def test_enforce_stop_tokens_none() -> None:
+    """Test removing stop tokens when they do not occur."""
+    text = "foo bar baz"
+    output = enforce_stop_tokens(text, ["moo"])
+    assert output == "foo bar baz"