examples

1
{
2
  "cells": [
3
    {
4
      "attachments": {},
5
      "cell_type": "markdown",
6
      "metadata": {
7
        "id": "AWGzucuFfbBn"
8
      },
9
      "source": [
10
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/00-langchain-intro.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/00-langchain-intro.ipynb)\n",
11
        "\n",
12
        "#### [LangChain Handbook](https://github.com/pinecone-io/examples/tree/master/generation/langchain/handbook)\n",
13
        "\n",
14
        "# Intro to LangChain\n",
15
        "\n",
16
        "LangChain is a popular framework that allow users to quickly build apps and pipelines around **L**arge **L**anguage **M**odels. It can be used to for chatbots, **G**enerative **Q**uestion-**A**nwering (GQA), summarization, and much more.\n",
17
        "\n",
18
        "The core idea of the library is that we can _\"chain\"_ together different components to create more advanced use-cases around LLMs. Chains may consist of multiple components from several modules:\n",
19
        "\n",
20
        "* **Prompt templates**: Prompt templates are, well, templates for different types of prompts. Like \"chatbot\" style templates, ELI5 question-answering, etc\n",
21
        "\n",
22
        "* **LLMs**: Large language models like GPT-3, BLOOM, etc\n",
23
        "\n",
24
        "* **Agents**: Agents use LLMs to decide what actions should be taken, tools like web search or calculators can be used, and all packaged into logical loop of operations.\n",
25
        "\n",
26
        "* **Memory**: Short-term memory, long-term memory."
27
      ]
28
    },
29
    {
30
      "cell_type": "code",
31
      "execution_count": 4,
32
      "metadata": {
33
        "id": "r-ryCeG_f_GC"
34
      },
35
      "outputs": [],
36
      "source": [
37
        "!pip install -qU langchain"
38
      ]
39
    },
40
    {
41
      "cell_type": "markdown",
42
      "metadata": {
43
        "id": "mNaXrEPOhbuL"
44
      },
45
      "source": [
46
        "# Using LLMs in LangChain\n",
47
        "\n",
48
        "LangChain supports several LLM providers, like Hugging Face and OpenAI.\n",
49
        "\n",
50
        "Let's start our exploration of LangChain by learning how to use a few of these different LLM integrations.\n",
51
        "\n",
52
        "## Hugging Face\n",
53
        "\n",
54
        "We first need to install additional prerequisite libraries:"
55
      ]
56
    },
57
    {
58
      "cell_type": "code",
59
      "execution_count": 5,
60
      "metadata": {
61
        "colab": {
62
          "base_uri": "https://localhost:8080/"
63
        },
64
        "id": "LWA15ZkVjg80",
65
        "outputId": "b38a4c6a-9d98-44b4-eb71-6e96398c647a"
66
      },
67
      "outputs": [
68
        {
69
          "name": "stdout",
70
          "output_type": "stream",
71
          "text": [
72
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
73
            "Collecting huggingface_hub\n",
74
            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
75
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/182.4 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
76
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n",
77
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (21.3)\n",
78
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n",
79
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.25.1)\n",
80
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.9.0)\n",
81
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.4.0)\n",
82
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
83
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n",
84
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.24.3)\n",
85
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
86
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (4.0.0)\n",
87
            "Installing collected packages: huggingface_hub\n",
88
            "Successfully installed huggingface_hub-0.11.1\n"
89
          ]
90
        }
91
      ],
92
      "source": [
93
        "!pip install -qU huggingface_hub"
94
      ]
95
    },
96
    {
97
      "cell_type": "markdown",
98
      "metadata": {
99
        "id": "m-whfR5Tjf1O"
100
      },
101
      "source": [
102
        "For Hugging Face models we need a Hugging Face Hub API token. We can find this by first getting an account at [HuggingFace.co](https://huggingface.co/) and clicking on our profile in the top-right corner > click *Settings* > click *Access Tokens* > click *New Token* > set *Role* to *write* > *Generate* > copy and paste the token below:"
103
      ]
104
    },
105
    {
106
      "cell_type": "code",
107
      "execution_count": 1,
108
      "metadata": {
109
        "id": "sRGTytxCjKaW"
110
      },
111
      "outputs": [],
112
      "source": [
113
        "import os\n",
114
        "\n",
115
        "os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'HF_API_KEY'"
116
      ]
117
    },
118
    {
119
      "cell_type": "markdown",
120
      "metadata": {
121
        "id": "exAl3iQgnAra"
122
      },
123
      "source": [
124
        "We can then generate text using a HF Hub model (we'll use `google/flan-t5-x1`) using the Inference API built into Hugging Face Hub.\n",
125
        "\n",
126
        "_(The default Inference API doesn't use specialized hardware and so can be slow and cannot run larger models like `bigscience/bloom-560m` or `google/flan-t5-xxl`)_"
127
      ]
128
    },
129
    {
130
      "cell_type": "code",
131
      "execution_count": 6,
132
      "metadata": {
133
        "colab": {
134
          "base_uri": "https://localhost:8080/"
135
        },
136
        "id": "l7yubiSJhIfs",
137
        "outputId": "39f9bb8b-c116-46a3-e9be-c3b3549789a9"
138
      },
139
      "outputs": [
140
        {
141
          "name": "stdout",
142
          "output_type": "stream",
143
          "text": [
144
            "green bay packers\n"
145
          ]
146
        }
147
      ],
148
      "source": [
149
        "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
150
        "\n",
151
        "# initialize HF LLM\n",
152
        "flan_t5 = HuggingFaceHub(\n",
153
        "    repo_id=\"google/flan-t5-xl\",\n",
154
        "    model_kwargs={\"temperature\":1e-10}\n",
155
        ")\n",
156
        "\n",
157
        "# build prompt template for simple question-answering\n",
158
        "template = \"\"\"Question: {question}\n",
159
        "\n",
160
        "Answer: \"\"\"\n",
161
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
162
        "\n",
163
        "llm_chain = LLMChain(\n",
164
        "    prompt=prompt,\n",
165
        "    llm=flan_t5\n",
166
        ")\n",
167
        "\n",
168
        "question = \"Which NFL team won the Super Bowl in the 2010 season?\"\n",
169
        "\n",
170
        "print(llm_chain.run(question))"
171
      ]
172
    },
173
    {
174
      "cell_type": "markdown",
175
      "metadata": {
176
        "id": "DXroZSjCKxa2"
177
      },
178
      "source": [
179
        "If we'd like to ask multiple questions we can by passing a list of dictionary objects, where the dictionaries must contain the input variable set in our prompt template (`\"question\"`) that is mapped to the question we'd like to ask."
180
      ]
181
    },
182
    {
183
      "cell_type": "code",
184
      "execution_count": 7,
185
      "metadata": {
186
        "colab": {
187
          "base_uri": "https://localhost:8080/"
188
        },
189
        "id": "4jNZgxSIJsXj",
190
        "outputId": "f5711d89-de5a-48d0-e815-9f186a84e807"
191
      },
192
      "outputs": [
193
        {
194
          "data": {
195
            "text/plain": [
196
              "LLMResult(generations=[[Generation(text='green bay packers', generation_info=None)], [Generation(text='184', generation_info=None)], [Generation(text='john glenn', generation_info=None)], [Generation(text='one', generation_info=None)]], llm_output=None)"
197
            ]
198
          },
199
          "execution_count": 7,
200
          "metadata": {},
201
          "output_type": "execute_result"
202
        }
203
      ],
204
      "source": [
205
        "qs = [\n",
206
        "    {'question': \"Which NFL team won the Super Bowl in the 2010 season?\"},\n",
207
        "    {'question': \"If I am 6 ft 4 inches, how tall am I in centimeters?\"},\n",
208
        "    {'question': \"Who was the 12th person on the moon?\"},\n",
209
        "    {'question': \"How many eyes does a blade of grass have?\"}\n",
210
        "]\n",
211
        "res = llm_chain.generate(qs)\n",
212
        "res"
213
      ]
214
    },
215
    {
216
      "cell_type": "markdown",
217
      "metadata": {
218
        "id": "7zoxlXHYLQix"
219
      },
220
      "source": [
221
        "It is a LLM, so we can try feeding in all questions at once:"
222
      ]
223
    },
224
    {
225
      "cell_type": "code",
226
      "execution_count": 8,
227
      "metadata": {
228
        "colab": {
229
          "base_uri": "https://localhost:8080/"
230
        },
231
        "id": "b96WIvouLQ-7",
232
        "outputId": "c9ff1c1f-2991-4832-d57c-b46cc346ca64"
233
      },
234
      "outputs": [
235
        {
236
          "name": "stdout",
237
          "output_type": "stream",
238
          "text": [
239
            "six\n"
240
          ]
241
        }
242
      ],
243
      "source": [
244
        "multi_template = \"\"\"Answer the following questions one at a time.\n",
245
        "\n",
246
        "Questions:\n",
247
        "{questions}\n",
248
        "\n",
249
        "Answers:\n",
250
        "\"\"\"\n",
251
        "long_prompt = PromptTemplate(\n",
252
        "    template=multi_template,\n",
253
        "    input_variables=[\"questions\"]\n",
254
        ")\n",
255
        "\n",
256
        "llm_chain = LLMChain(\n",
257
        "    prompt=long_prompt,\n",
258
        "    llm=flan_t5\n",
259
        ")\n",
260
        "\n",
261
        "qs_str = (\n",
262
        "    \"Which NFL team won the Super Bowl in the 2010 season?\\n\" +\n",
263
        "    \"If I am 6 ft 4 inches, how tall am I in centimeters?\\n\" +\n",
264
        "    \"Who was the 12th person on the moon?\" +\n",
265
        "    \"How many eyes does a blade of grass have?\"\n",
266
        ")\n",
267
        "\n",
268
        "print(llm_chain.run(qs_str))"
269
      ]
270
    },
271
    {
272
      "cell_type": "markdown",
273
      "metadata": {
274
        "id": "y99CMKSbOqBy"
275
      },
276
      "source": [
277
        "But with this model it doesn't work too well, we'll see this approach works better with different models soon."
278
      ]
279
    },
280
    {
281
      "cell_type": "markdown",
282
      "metadata": {
283
        "id": "YpdXG9YtzrLJ"
284
      },
285
      "source": [
286
        "## OpenAI\n",
287
        "\n",
288
        "Start by installing additional prerequisites:"
289
      ]
290
    },
291
    {
292
      "cell_type": "code",
293
      "execution_count": 9,
294
      "metadata": {
295
        "colab": {
296
          "base_uri": "https://localhost:8080/"
297
        },
298
        "id": "FHo2YRHPDgHH",
299
        "outputId": "c8fd417b-d6b4-4f8e-a8a0-a95e3e1e676f"
300
      },
301
      "outputs": [
302
        {
303
          "name": "stdout",
304
          "output_type": "stream",
305
          "text": [
306
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
307
            "Collecting openai\n",
308
            "  Downloading openai-0.26.1.tar.gz (55 kB)\n",
309
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.3/55.3 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
310
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
311
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
312
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
313
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from openai) (3.8.3)\n",
314
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from openai) (4.64.1)\n",
315
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.8/dist-packages (from openai) (2.25.1)\n",
316
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
317
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.20->openai) (4.0.0)\n",
318
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.20->openai) (2.10)\n",
319
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.20->openai) (2022.12.7)\n",
320
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (6.0.4)\n",
321
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (22.2.0)\n",
322
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (2.1.1)\n",
323
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.3.1)\n",
324
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (4.0.2)\n",
325
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.3.3)\n",
326
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.8.2)\n",
327
            "Building wheels for collected packages: openai\n",
328
            "  Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
329
            "  Created wheel for openai: filename=openai-0.26.1-py3-none-any.whl size=67316 sha256=3f271293eb83a4726bba3b95ac96b32dabbf776235bd73bdf63d9fef623ce735\n",
330
            "  Stored in directory: /root/.cache/pip/wheels/2f/9c/55/95d3609ccfc463eeffb96d50c756f1f1899453b85e92021a0a\n",
331
            "Successfully built openai\n",
332
            "Installing collected packages: openai\n",
333
            "Successfully installed openai-0.26.1\n"
334
          ]
335
        }
336
      ],
337
      "source": [
338
        "!pip install -qU openai"
339
      ]
340
    },
341
    {
342
      "cell_type": "markdown",
343
      "metadata": {
344
        "id": "0fOo9qQvDgkz"
345
      },
346
      "source": [
347
        "We can also use OpenAI's generative models. The process is similar, we need to\n",
348
        "give our API key which can be retrieved by signing up for an account on the\n",
349
        "[OpenAI website](https://openai.com/api/) (see top-right of page). We then pass the API key below:"
350
      ]
351
    },
352
    {
353
      "cell_type": "code",
354
      "execution_count": 2,
355
      "metadata": {
356
        "id": "deWmOJecfbBr"
357
      },
358
      "outputs": [],
359
      "source": [
360
        "import os\n",
361
        "\n",
362
        "os.environ['OPENAI_API_KEY'] = 'OPENAI_API_KEY'"
363
      ]
364
    },
365
    {
366
      "cell_type": "markdown",
367
      "metadata": {
368
        "id": "CU4xirWX-Ds4"
369
      },
370
      "source": [
371
        "If using OpenAI via Azure you should also set:\n",
372
        "\n",
373
        "```python\n",
374
        "os.environ['OPENAI_API_TYPE'] = 'azure'\n",
375
        "# API version to use (Azure has several)\n",
376
        "os.environ['OPENAI_API_VERSION'] = '2022-12-01'\n",
377
        "# base URL for your Azure OpenAI resource\n",
378
        "os.environ['OPENAI_API_BASE'] = 'https://your-resource-name.openai.azure.com'\n",
379
        "```"
380
      ]
381
    },
382
    {
383
      "cell_type": "markdown",
384
      "metadata": {
385
        "id": "2AWnaTCP0Ryg"
386
      },
387
      "source": [
388
        "Then we decide on which model we'd like to use, there are several options but we will go with `text-davinci-003`:"
389
      ]
390
    },
391
    {
392
      "cell_type": "code",
393
      "execution_count": 10,
394
      "metadata": {
395
        "id": "ZhQSDoYe0ly4"
396
      },
397
      "outputs": [],
398
      "source": [
399
        "from langchain.llms import OpenAI\n",
400
        "\n",
401
        "davinci = OpenAI(model_name='text-davinci-003')"
402
      ]
403
    },
404
    {
405
      "cell_type": "markdown",
406
      "metadata": {
407
        "id": "_NvK4o6SDrs0"
408
      },
409
      "source": [
410
        "Alternatively if using Azure OpenAI we do:\n",
411
        "\n",
412
        "```python\n",
413
        "from langchain.llms import AzureOpenAI\n",
414
        "\n",
415
        "llm = AzureOpenAI(\n",
416
        "    deployment_name=\"your-azure-deployment\", \n",
417
        "    model_name=\"text-davinci-003\"\n",
418
        ")\n",
419
        "```"
420
      ]
421
    },
422
    {
423
      "cell_type": "markdown",
424
      "metadata": {
425
        "id": "SGL2zs3uEVj6"
426
      },
427
      "source": [
428
        "We'll use the same simple question-answer prompt template as before with the Hugging Face example. The only change is that we now pass our OpenAI LLM `davinci`:"
429
      ]
430
    },
431
    {
432
      "cell_type": "code",
433
      "execution_count": 11,
434
      "metadata": {
435
        "colab": {
436
          "base_uri": "https://localhost:8080/"
437
        },
438
        "id": "gVSsC3iGEPAp",
439
        "outputId": "1d562f8d-2fbf-4cc4-84cd-cce998720eb3"
440
      },
441
      "outputs": [
442
        {
443
          "name": "stdout",
444
          "output_type": "stream",
445
          "text": [
446
            " The Green Bay Packers won the Super Bowl in the 2010 season.\n"
447
          ]
448
        }
449
      ],
450
      "source": [
451
        "llm_chain = LLMChain(\n",
452
        "    prompt=prompt,\n",
453
        "    llm=davinci\n",
454
        ")\n",
455
        "\n",
456
        "print(llm_chain.run(question))"
457
      ]
458
    },
459
    {
460
      "cell_type": "markdown",
461
      "metadata": {
462
        "id": "DL-buasOKpKs"
463
      },
464
      "source": [
465
        "The same works again for multiple questions using `generate`:"
466
      ]
467
    },
468
    {
469
      "cell_type": "code",
470
      "execution_count": 12,
471
      "metadata": {
472
        "colab": {
473
          "base_uri": "https://localhost:8080/"
474
        },
475
        "id": "vMua1MWcKtSx",
476
        "outputId": "55efeae1-8c30-4069-a2a8-fb78212f8523"
477
      },
478
      "outputs": [
479
        {
480
          "data": {
481
            "text/plain": [
482
              "LLMResult(generations=[[Generation(text=' The Green Bay Packers won the Super Bowl in the 2010 season.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=' 193.04 centimeters', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=' Eugene A. Cernan was the 12th person to walk on the moon. He was part of the Apollo 17 mission in December 1972.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=' A blade of grass does not have any eyes.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'total_tokens': 131, 'prompt_tokens': 75, 'completion_tokens': 56}})"
483
            ]
484
          },
485
          "execution_count": 12,
486
          "metadata": {},
487
          "output_type": "execute_result"
488
        }
489
      ],
490
      "source": [
491
        "qs = [\n",
492
        "    {'question': \"Which NFL team won the Super Bowl in the 2010 season?\"},\n",
493
        "    {'question': \"If I am 6 ft 4 inches, how tall am I in centimeters?\"},\n",
494
        "    {'question': \"Who was the 12th person on the moon?\"},\n",
495
        "    {'question': \"How many eyes does a blade of grass have?\"}\n",
496
        "]\n",
497
        "llm_chain.generate(qs)"
498
      ]
499
    },
500
    {
501
      "attachments": {},
502
      "cell_type": "markdown",
503
      "metadata": {},
504
      "source": [
505
        "Note that the below format doesn't feed the questions in iteratively but instead all in one chunk."
506
      ]
507
    },
508
    {
509
      "cell_type": "code",
510
      "execution_count": 15,
511
      "metadata": {
512
        "colab": {
513
          "base_uri": "https://localhost:8080/"
514
        },
515
        "id": "w2-es7SgFddS",
516
        "outputId": "d16b7afc-f0d1-4f6a-9d89-f6811839bb02"
517
      },
518
      "outputs": [
519
        {
520
          "name": "stdout",
521
          "output_type": "stream",
522
          "text": [
523
            "\n",
524
            "1. The New Orleans Saints \n",
525
            "2. 193 centimeters \n",
526
            "3. Harrison Schmitt \n",
527
            "4. Zero.\n"
528
          ]
529
        }
530
      ],
531
      "source": [
532
        "qs = [\n",
533
        "    \"Which NFL team won the Super Bowl in the 2010 season?\",\n",
534
        "    \"If I am 6 ft 4 inches, how tall am I in centimeters?\",\n",
535
        "    \"Who was the 12th person on the moon?\",\n",
536
        "    \"How many eyes does a blade of grass have?\"\n",
537
        "]\n",
538
        "print(llm_chain.run(qs))"
539
      ]
540
    },
541
    {
542
      "attachments": {},
543
      "cell_type": "markdown",
544
      "metadata": {},
545
      "source": [
546
        "Now we can try to answer all question in one go, as mentioned, more powerful LLMs like `text-davinci-003` will be more likely to handle these more complex queries."
547
      ]
548
    },
549
    {
550
      "cell_type": "code",
551
      "execution_count": 17,
552
      "metadata": {
553
        "colab": {
554
          "base_uri": "https://localhost:8080/"
555
        },
556
        "id": "xbjxnnVzA47s",
557
        "outputId": "cf5397ca-9e06-4221-eb45-17b1346f6f06"
558
      },
559
      "outputs": [
560
        {
561
          "name": "stdout",
562
          "output_type": "stream",
563
          "text": [
564
            "The New Orleans Saints won the Super Bowl in the 2010 season.\n",
565
            "If you are 6 ft 4 inches, you are 193.04 centimeters tall.\n",
566
            "The 12th person on the moon was Harrison Schmitt.\n",
567
            "A blade of grass does not have any eyes."
568
          ]
569
        }
570
      ],
571
      "source": [
572
        "multi_template = \"\"\"Answer the following questions one at a time.\n",
573
        "\n",
574
        "Questions:\n",
575
        "{questions}\n",
576
        "\n",
577
        "Answers:\n",
578
        "\"\"\"\n",
579
        "long_prompt = PromptTemplate(\n",
580
        "    template=multi_template,\n",
581
        "    input_variables=[\"questions\"]\n",
582
        ")\n",
583
        "\n",
584
        "llm_chain = LLMChain(\n",
585
        "    prompt=long_prompt,\n",
586
        "    llm=davinci\n",
587
        ")\n",
588
        "\n",
589
        "qs_str = (\n",
590
        "    \"Which NFL team won the Super Bowl in the 2010 season?\\n\" +\n",
591
        "    \"If I am 6 ft 4 inches, how tall am I in centimeters?\\n\" +\n",
592
        "    \"Who was the 12th person on the moon?\" +\n",
593
        "    \"How many eyes does a blade of grass have?\"\n",
594
        ")\n",
595
        "\n",
596
        "print(llm_chain.run(qs_str))"
597
      ]
598
    },
599
    {
600
      "cell_type": "markdown",
601
      "metadata": {
602
        "id": "ybMkI18xfbBr"
603
      },
604
      "source": [
605
        "---"
606
      ]
607
    }
608
  ],
609
  "metadata": {
610
    "colab": {
611
      "provenance": []
612
    },
613
    "gpuClass": "standard",
614
    "kernelspec": {
615
      "display_name": "ml",
616
      "language": "python",
617
      "name": "python3"
618
    },
619
    "language_info": {
620
      "name": "python",
621
      "version": "3.9.12 (main, Apr  5 2022, 01:52:34) \n[Clang 12.0.0 ]"
622
    },
623
    "orig_nbformat": 4,
624
    "vscode": {
625
      "interpreter": {
626
        "hash": "b8e7999f96e1b425e2d542f21b571f5a4be3e97158b0b46ea1b2500df63956ce"
627
      }
628
    }
629
  },
630
  "nbformat": 4,
631
  "nbformat_minor": 0
632
}
633