File size: 5,530 Bytes
acc4ffe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8a5c5d4",
   "metadata": {},
   "source": [
    "# LLM Chain\n",
    "\n",
    "This notebook showcases a simple LLM chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "835e6978",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import PromptTemplate, OpenAI, LLMChain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bcb078",
   "metadata": {},
   "source": [
    "## Single Input\n",
    "\n",
    "First, lets go over an example using a single input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51a54c4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
      "\n",
      "Answer: Let's think step by step.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys.'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\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": "markdown",
   "id": "79c3ec4d",
   "metadata": {},
   "source": [
    "## Multiple Inputs\n",
    "Now lets go over an example using multiple inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03dd6918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
    "\n",
    "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672f59d4",
   "metadata": {},
   "source": [
    "## From string\n",
    "You can also construct an LLMChain from a string template directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8bc262e",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb164a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f0adbc7",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}