Merge pull request #11 from hwchase17/harrison/add_readme

Harrison/add readme

README.md

@@ -1 +1,56 @@
-# LangChain
+# 🦜️🔗 LangChain
+
+⚡ Building applications with LLMs through composability ⚡
+
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+
+
+## Quick Install
+
+`pip install langchain`
+
+## 🤔 What is this?
+
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you are able to
+combine them with other sources of computation or knowledge.
+
+This library is aimed at assisting in the development of those types of applications.
+It aims to create:
+1. a comprehensive collection of pieces you would ever want to combine
+2. a flexible interface for combining pieces into a single comprehensive "chain"
+3. a schema for easily saving and sharing those chains
+
+## 🚀 What can I do with this
+
+This project was largely inspired by a few projects seen on Twitter for which we thought it would make sense to have more explicit tooling. A lot of the initial functionality was done in an attempt to recreate those. Those are:
+
+**[Self-ask-with-search](https://ofir.io/self-ask.pdf)**
+
+To recreate this paper, use the following code snippet or checkout the [example notebook](examples/self_ask_with_search.ipynb).
+
+```
+from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain
+
+llm = OpenAI(temperature=0)
+search = SerpAPIChain()
+
+self_ask_with_search = SelfAskWithSearchChain(llm=llm, search_chain=search)
+
+self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
+```
+
+**[LLM Math](https://twitter.com/amasad/status/1568824744367259648?s=20&t=-7wxpXBJinPgDuyHLouP1w)**
+To recreate this example, use the following code snippet or check out the [example notebook](examples/llm_math.ipynb).
+
+```
+from langchain import OpenAI, LLMMathChain
+
+llm = OpenAI(temperature=0)
+llm_math = LLMMathChain(llm=llm)
+
+llm_math.run("How many of the integers between 0 and 99 inclusive are divisible by 8?")
+```

examples/llm_math.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "44e9ba31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 13\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, LLMMathChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "llm_math = LLMMathChain(llm=llm)\n",
+    "\n",
+    "llm_math.run(\"How many of the integers between 0 and 99 inclusive are divisible by 8?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62f0c75",
+   "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
+}

examples/self_ask_with_search.ipynb

@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e3b513e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[102m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[106mCarlos Alcaraz\u001b[0m.\u001b[102m\n",
+      "Follow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[106mEl Palmar, Murcia, Spain\u001b[0m.\u001b[102m\n",
+      "So the final answer is: El Palmar, Murcia, Spain\u001b[0m"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"What is the hometown of the reigning men's U.S. Open champion?\\nAre follow up questions needed here: Yes.\\nFollow up: Who is the reigning men's U.S. Open champion?\\nIntermediate answer: Carlos Alcaraz.\\nFollow up: Where is Carlos Alcaraz from?\\nIntermediate answer: El Palmar, Murcia, Spain.\\nSo the final answer is: El Palmar, Murcia, Spain\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIChain()\n",
+    "\n",
+    "self_ask_with_search = SelfAskWithSearchChain(llm=llm, search_chain=search)\n",
+    "\n",
+    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6195fc82",
+   "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

@@ -1 +1,21 @@
 """Main entrypoint into package."""
+from langchain.chains import (
+    LLMChain,
+    LLMMathChain,
+    PythonChain,
+    SelfAskWithSearchChain,
+    SerpAPIChain,
+)
+from langchain.llms import Cohere, OpenAI
+from langchain.prompt import Prompt
+
+__all__ = [
+    "LLMChain",
+    "LLMMathChain",
+    "PythonChain",
+    "SelfAskWithSearchChain",
+    "SerpAPIChain",
+    "Cohere",
+    "OpenAI",
+    "Prompt",
+]

langchain/chains/__init__.py

@@ -1 +1,14 @@
 """Chains are easily reusable components which can be linked together."""
+from langchain.chains.llm import LLMChain
+from langchain.chains.llm_math.base import LLMMathChain
+from langchain.chains.python import PythonChain
+from langchain.chains.self_ask_with_search.base import SelfAskWithSearchChain
+from langchain.chains.serpapi import SerpAPIChain
+
+__all__ = [
+    "LLMChain",
+    "LLMMathChain",
+    "PythonChain",
+    "SelfAskWithSearchChain",
+    "SerpAPIChain",
+]

langchain/chains/serpapi.py

@@ -3,13 +3,28 @@
 Heavily borrowed from https://github.com/ofirpress/self-ask
 """
 import os
+import sys
 from typing import Any, Dict, List
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.chains.base import Chain
 
 
+class HiddenPrints:
+    """Context manager to hide prints."""
+
+    def __enter__(self) -> None:
+        """Open file to pipe stdout to."""
+        self._original_stdout = sys.stdout
+        sys.stdout = open(os.devnull, "w")
+
+    def __exit__(self, *_: Any) -> None:
+        """Close file that stdout was piped to."""
+        sys.stdout.close()
+        sys.stdout = self._original_stdout
+
+
 class SerpAPIChain(Chain, BaseModel):
     """Chain that calls SerpAPI."""
 
@@ -60,9 +75,9 @@ def _run(self, inputs: Dict[str, Any]) -> Dict[str, str]:
             "gl": "us",
             "hl": "en",
         }
-
-        search = self.search_engine(params)
-        res = search.get_dict()
+        with HiddenPrints():
+            search = self.search_engine(params)
+            res = search.get_dict()
 
         if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
             toret = res["answer_box"]["answer"]

langchain/llms/__init__.py

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

setup.py

@@ -8,7 +8,7 @@
     name="langchain",
     version_format="0.0.0",
     packages=find_packages(),
-    description="Building LLM empowered applications",
+    description="Building applications with LLMs through composability",
     install_requires=["pydantic"],
     long_description=long_description,
     license="MIT",