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{
"cells": [
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{
"cell_type": "markdown",
"id": "d8a5c5d4",
"metadata": {},
"source": [
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"# LLM Chain\n",
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"\n",
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"This notebook showcases a simple LLM chain."
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]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"id": "835e6978",
"metadata": {},
"outputs": [],
"source": [
"from langchain import PromptTemplate, OpenAI, LLMChain"
]
},
{
"cell_type": "markdown",
"id": "06bcb078",
"metadata": {},
"source": [
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"## Single Input\n",
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"\n",
"First, lets go over an example using a single input"
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 2,
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"id": "51a54c4d",
"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
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"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
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"Prompt after formatting:\n",
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"\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
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"\n",
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"Answer: Let's think step by step.\u001B[0m\n",
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"\n",
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"\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
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]
},
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{
"data": {
"text/plain": [
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"' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys.'"
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]
},
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 2,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
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"\n",
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
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"llm_chain.predict(question=question)"
]
},
{
"cell_type": "markdown",
"id": "79c3ec4d",
"metadata": {},
"source": [
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"## Multiple Inputs\n",
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"Now lets go over an example using multiple inputs."
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]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 3,
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"id": "03dd6918",
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
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"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
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"Prompt after formatting:\n",
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"\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
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"\n",
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"\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
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]
},
{
"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.\""
]
},
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 3,
2022-12-07 16:40:08 +00:00
"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\")"
]
},
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{
"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\")"
]
},
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{
"cell_type": "code",
"execution_count": null,
2023-02-02 19:35:36 +00:00
"id": "9f0adbc7",
2022-12-07 16:40:08 +00:00
"metadata": {},
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"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"version": "3.10.9"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}