add anthropic example (#1041)

Co-authored-by: Ivan Vendrov <ivendrov@gmail.com>
Co-authored-by: Sasmitha Manathunga <70096033+mmz-001@users.noreply.github.com>

docs/modules/llms/integrations.rst

@@ -19,6 +19,8 @@ The examples here are all "how-to" guides for how to integrate with various LLM
 
 `PromptLayer OpenAI <./integrations/promptlayer_openai.html>`_: Covers how to use `PromptLayer <https://promptlayer.com>`_ with Langchain.
 
+`Anthropic <./integrations/anthropic_example.html>`_: Covers how to use Anthropic models with Langchain.
+
 
 .. toctree::
    :maxdepth: 1

docs/modules/llms/integrations/anthropic_example.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Anthropic\n",
+    "This example goes over how to use LangChain to interact with Anthropic models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6fb585dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import Anthropic\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "035dea0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3f3458d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = Anthropic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a641dbd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f844993",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" Step 1: Justin Beiber was born on March 1, 1994\\nStep 2: The NFL season ends with the Super Bowl in January/February\\nStep 3: Therefore, the Super Bowl that occurred closest to Justin Beiber's birth would be Super Bowl XXIX in 1995\\nStep 4: The San Francisco 49ers won Super Bowl XXIX in 1995\\n\\nTherefore, the answer is the San Francisco 49ers won the Super Bowl in the year Justin Beiber was born.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4797d719",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/llms/integrations/petals_example.ipynb

@@ -136,15 +136,22 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('palm')",
+   "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",
-   "version": "3.9.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"

langchain/llms/anthropic.py

@@ -1,4 +1,5 @@
 """Wrapper around Anthropic APIs."""
+import re
 from typing import Any, Dict, Generator, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, root_validator
@@ -32,7 +33,7 @@ class Anthropic(LLM, BaseModel):
     """
 
     client: Any  #: :meta private:
-    model: Optional[str] = None
+    model: str = "claude-v1"
     """Model name to use."""
 
     max_tokens_to_sample: int = 256
@@ -99,10 +100,17 @@ class Anthropic(LLM, BaseModel):
     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.
-        else:
+
-            return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
+        # 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 _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
         if not self.HUMAN_PROMPT or not self.AI_PROMPT: