add integration with manifest (#62)

README.md

@@ -52,6 +52,8 @@ The following use cases require specific installs and api keys:
   - Set up Elasticsearch backend. If you want to do locally, [this](https://www.elastic.co/guide/en/elasticsearch/reference/7.17/getting-started.html) is a good guide.
 - _FAISS_:
   - Install requirements with `pip install faiss` for Python 3.7 and `pip install faiss-cpu` for Python 3.10+.
+- _Manifest_:
+  - Install requirements with `pip install manifest-ml` (Note: this is only available in Python 3.8+ currently).
 
 If you are using the `NLTKTextSplitter` or the `SpacyTextSplitter`, you will also need to install the appropriate models. For example, if you want to use the `SpacyTextSplitter`, you will need to install the `en_core_web_sm` model with `python -m spacy download en_core_web_sm`. Similarly, if you want to use the `NLTKTextSplitter`, you will need to install the `punkt` model with `python -m nltk.downloader punkt`.
 

examples/manifest.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "04a0170a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from manifest import Manifest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "de250a6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "manifest = Manifest(\n",
+    "    client_name = \"openai\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0148f7bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms.manifest import ManifestWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67b719d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ManifestWrapper(client=manifest, llm_kwargs={\"temperature\": 0, \"max_tokens\": 256})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5af505a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map reduce example\n",
+    "from langchain import Prompt\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.chains.mapreduce import MapReduceChain\n",
+    "\n",
+    "\n",
+    "_prompt = \"\"\"Write a concise summary of the following:\n",
+    "\n",
+    "\n",
+    "{text}\n",
+    "\n",
+    "\n",
+    "CONCISE SUMMARY:\"\"\"\n",
+    "prompt = Prompt(template=_prompt, input_variables=[\"text\"])\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter()\n",
+    "\n",
+    "mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "485b3ec3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"The President discusses the recent aggression by Russia, and the response by the United States and its allies. He announces new sanctions against Russia, and says that the free world is united in holding Putin accountable. The President also discusses the American Rescue Plan, the Bipartisan Infrastructure Law, and the Bipartisan Innovation Act. Finally, the President addresses the need for women's rights and equality for LGBTQ+ Americans.\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "mp_chain.run(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32da6e41",
+   "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": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/llms/manifest.py

@@ -0,0 +1,49 @@
+"""Wrapper around HazyResearch's Manifest library."""
+from typing import Any, Dict, List, Mapping, Optional
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.llms.base import LLM
+
+
+class ManifestWrapper(LLM, BaseModel):
+    """Wrapper around HazyResearch's Manifest library."""
+
+    client: Any  #: :meta private:
+    llm_kwargs: Optional[Dict] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that python package exists in environment."""
+        try:
+            from manifest import Manifest
+
+            if not isinstance(values["client"], Manifest):
+                raise ValueError
+        except ImportError:
+            raise ValueError(
+                "Could not import manifest python package. "
+                "Please it install it with `pip install manifest-ml`."
+            )
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        kwargs = self.llm_kwargs or {}
+        return {**self.client.client.get_model_params(), **kwargs}
+
+    def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        """Call out to LLM through Manifest."""
+        if stop is not None and len(stop) != 1:
+            raise NotImplementedError(
+                f"Manifest currently only supports a single stop token, got {stop}"
+            )
+        kwargs = self.llm_kwargs or {}
+        if stop is not None:
+            kwargs["stop_token"] = stop
+        return self.client.run(prompt, **kwargs)

requirements.txt

@@ -10,6 +10,7 @@ wikipedia
 huggingface_hub
 faiss-cpu
 sentence_transformers
+manifest-ml
 spacy
 nltk
 # For development

tests/integration_tests/llms/test_manifest.py

@@ -0,0 +1,14 @@
+"""Test manifest integration."""
+from langchain.llms.manifest import ManifestWrapper
+
+
+def test_manifest_wrapper() -> None:
+    """Test manifest wrapper."""
+    from manifest import Manifest
+
+    manifest = Manifest(
+        client_name="openai",
+    )
+    llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0})
+    output = llm("The capital of New York is:")
+    assert output == "Albany"