mirror of
https://github.com/hwchase17/langchain
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361 lines
9.0 KiB
Plaintext
361 lines
9.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "da7d0df7-f07c-462f-bd46-d0426f11f311",
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"metadata": {},
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"source": [
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"## LLM Chain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a55e9a1-becf-4357-889e-f365d23362ff",
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"metadata": {},
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"source": [
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"`LLMChain` is perhaps one of the most popular ways of querying an LLM object. It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. Below we show additional functionalities of `LLMChain` class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0e720e34-a0f0-4f1a-9732-43bc1460053a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'product': 'colorful socks', 'text': '\\n\\nSocktastic!'}"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain import PromptTemplate, OpenAI, LLMChain\n",
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"\n",
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"prompt_template = \"What is a good name for a company that makes {product}?\"\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"llm_chain = LLMChain(\n",
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" llm=llm,\n",
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" prompt=PromptTemplate.from_template(prompt_template)\n",
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")\n",
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"llm_chain(\"colorful socks\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94304332-6398-4280-a61e-005ba29b5e1e",
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"metadata": {},
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"source": [
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"## Additional ways of running LLM Chain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e51981f-cde9-4c05-99e1-446c27994e99",
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"metadata": {},
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"source": [
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"Aside from `__call__` and `run` methods shared by all `Chain` object (see [Getting Started](../getting_started.ipynb) to learn more), `LLMChain` offers a few more ways of calling the chain logic:"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c08d2356-412d-4327-b8a0-233dcc443e30",
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"metadata": {},
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"source": [
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"- `apply` allows you run the chain against a list of inputs:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "cf519eb6-2358-4db7-a28a-27433435181e",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'text': '\\n\\nSocktastic!'},\n",
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" {'text': '\\n\\nTechCore Solutions.'},\n",
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" {'text': '\\n\\nFootwear Factory.'}]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"input_list = [\n",
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" {\"product\": \"socks\"},\n",
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" {\"product\": \"computer\"},\n",
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" {\"product\": \"shoes\"}\n",
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"]\n",
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"\n",
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"llm_chain.apply(input_list)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "add442fb-baf6-40d9-ae8e-4ac1d8251ad0",
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"metadata": {
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"tags": []
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},
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"source": [
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"- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "85cbff83-a5cc-40b7-823c-47274ae4117d",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[Generation(text='\\n\\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_chain.generate(input_list)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "a178173b-b183-432a-a517-250fe3191173",
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"metadata": {},
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"source": [
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"- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "787d9f55-b080-4123-bed2-0598a9cb0466",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nSocktastic!'"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Single input example\n",
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"llm_chain.predict(product=\"colorful socks\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "092a769f-9661-42a0-9da1-19d09ccbc4a7",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Multiple inputs example\n",
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"\n",
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"template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))\n",
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"\n",
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"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b72ad22-0a5d-4ca7-9e3f-8c46dc17f722",
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"metadata": {},
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"source": [
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"## Parsing the outputs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "85a77662-d028-4048-be4b-aa496e2dde22",
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"metadata": {},
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"source": [
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"By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "b83977f1-847c-45de-b840-f1aff6725f83",
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"metadata": {},
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"source": [
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"With `predict`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "5feb5177-c20b-4909-890b-a64d7e551f55",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nRed, orange, yellow, green, blue, indigo, violet'"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.output_parsers import CommaSeparatedListOutputParser\n",
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"\n",
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"output_parser = CommaSeparatedListOutputParser()\n",
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"template = \"\"\"List all the colors in a rainbow\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"\n",
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"llm_chain.predict()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b931615-804b-4f34-8086-7bbc2f96b3b2",
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"metadata": {},
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"source": [
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"With `predict_and_parser`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "43a374cd-a179-43e5-9aa0-62f3cbdf510d",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_chain.predict_and_parse()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8176f619-4e5c-4a02-91ba-e96ebe2aabda",
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"metadata": {},
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"source": [
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"## Initialize from string"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9813ac87-e118-413b-b448-2fefdf2319b8",
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"metadata": {},
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"source": [
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"You can also construct an LLMChain from a string template directly."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "ca88ccb1-974e-41c1-81ce-753e3f1234fa",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n",
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"llm_chain = LLMChain.from_string(llm=llm, template=template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "4703d1bc-f4fc-44bc-9ea1-b4498835833d",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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