Spaces:
Sleeping
Sleeping
File size: 11,483 Bytes
cfd3735 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 |
{
"cells": [
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# Getting Started\n",
"\n",
"This notebook covers how to get started with chat models. The interface is based around messages rather than raw text."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "522686de",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain import PromptTemplate, LLMChain\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import (\n",
" AIMessage,\n",
" HumanMessage,\n",
" SystemMessage\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatOpenAI(temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "bbaec18e-3684-4eef-955f-c1cec8bf765d",
"metadata": {},
"source": [
"You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "76a6e7b0-e927-4bfb-a414-1332a4149106",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat([HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")])"
]
},
{
"cell_type": "markdown",
"id": "a62153d4-1211-411b-a493-3febfe446ae0",
"metadata": {},
"source": [
"OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"I love programming.\")\n",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "markdown",
"id": "36dc8d7e-bd25-47ac-8c1b-60e3422603d3",
"metadata": {},
"source": [
"You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2b21fc52-74b6-4950-ab78-45d12c68fb4d",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_messages = [\n",
" [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"I love programming.\")\n",
" ],\n",
" [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"I love artificial intelligence.\")\n",
" ],\n",
"]\n",
"result = chat.generate(batch_messages)\n",
"result"
]
},
{
"cell_type": "markdown",
"id": "2960f50f",
"metadata": {},
"source": [
"You can recover things like token usage from this LLMResult"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a6186bee",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'prompt_tokens': 57,\n",
" 'completion_tokens': 20,\n",
" 'total_tokens': 77}}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.llm_output"
]
},
{
"cell_type": "markdown",
"id": "b10b00ef-f373-4bc3-8302-2dfc28033734",
"metadata": {},
"source": [
"## PromptTemplates"
]
},
{
"cell_type": "markdown",
"id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
"metadata": {},
"source": [
"You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
"\n",
"For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "180c5cc8",
"metadata": {},
"outputs": [],
"source": [
"template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
"system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
"human_template=\"{text}\"\n",
"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fbb043e6",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={})"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
"\n",
"# get a chat completion from the formatted messages\n",
"chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())"
]
},
{
"cell_type": "markdown",
"id": "e28b98da",
"metadata": {},
"source": [
"If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d5b1ab1c",
"metadata": {},
"outputs": [],
"source": [
"prompt=PromptTemplate(\n",
" template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" input_variables=[\"input_language\", \"output_language\"],\n",
")\n",
"system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)"
]
},
{
"cell_type": "markdown",
"id": "92af0bba",
"metadata": {},
"source": [
"## LLMChain\n",
"You can use the existing LLMChain in a very similar way to before - provide a prompt and a model."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f2cbfe3d",
"metadata": {},
"outputs": [],
"source": [
"chain = LLMChain(llm=chat, prompt=chat_prompt)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "268543b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"J'adore la programmation.\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")"
]
},
{
"cell_type": "markdown",
"id": "eb779f3f",
"metadata": {},
"source": [
"## Streaming\n",
"\n",
"Streaming is supported for `ChatOpenAI` through callback handling."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "509181be",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Verse 1:\n",
"Bubbles rising to the top\n",
"A refreshing drink that never stops\n",
"Clear and crisp, it's pure delight\n",
"A taste that's sure to excite\n",
"\n",
"Chorus:\n",
"Sparkling water, oh so fine\n",
"A drink that's always on my mind\n",
"With every sip, I feel alive\n",
"Sparkling water, you're my vibe\n",
"\n",
"Verse 2:\n",
"No sugar, no calories, just pure bliss\n",
"A drink that's hard to resist\n",
"It's the perfect way to quench my thirst\n",
"A drink that always comes first\n",
"\n",
"Chorus:\n",
"Sparkling water, oh so fine\n",
"A drink that's always on my mind\n",
"With every sip, I feel alive\n",
"Sparkling water, you're my vibe\n",
"\n",
"Bridge:\n",
"From the mountains to the sea\n",
"Sparkling water, you're the key\n",
"To a healthy life, a happy soul\n",
"A drink that makes me feel whole\n",
"\n",
"Chorus:\n",
"Sparkling water, oh so fine\n",
"A drink that's always on my mind\n",
"With every sip, I feel alive\n",
"Sparkling water, you're my vibe\n",
"\n",
"Outro:\n",
"Sparkling water, you're the one\n",
"A drink that's always so much fun\n",
"I'll never let you go, my friend\n",
"Sparkling"
]
}
],
"source": [
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
"resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c095285d",
"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
}
|