Archives
/
langchain
mirror of https://github.com/hwchase17/langchain
2
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

add optimization notebook (#18155)

Browse Source
pull/18159/head
Harrison Chase 3 months ago committed by GitHub
parent 5ab69f907f
commit c673717c2b
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 1650 additions and 1 deletions
Show Stats Download Patch File Download Diff File

2
.github/workflows/codespell.yml vendored
Unescape Escape View File

@ -32,6 +32,6 @@ jobs:
- name: Codespell
uses: codespell-project/actions-codespell@v2
with:
skip: guide_imports.json,*.ambr
skip: guide_imports.json,*.ambr,./cookbook/data/imdb_top_1000.csv
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
exclude_file: libs/community/langchain_community/llms/yuan2.py

1001
cookbook/data/imdb_top_1000.csv
View File

File diff suppressed because it is too large Load Diff

648
cookbook/optimization.ipynb
Unescape Escape View File

@ -0,0 +1,648 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "c7fe38bc",
"metadata": {},
"source": [
"# Optimization\n",
"\n",
"This notebook goes over how to optimize chains using LangChain and [LangSmith](https://smith.langchain.com)."
]
},
{
"cell_type": "markdown",
"id": "2f87ccd5",
"metadata": {},
"source": [
"## Set up\n",
"\n",
"We will set an environment variable for LangSmith, and load the relevant data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "236bedc5",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"movie-qa\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a3fed0dd",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7cfff337",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"data/imdb_top_1000.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2d20fb9c",
"metadata": {},
"outputs": [],
"source": [
"df[\"Released_Year\"] = df[\"Released_Year\"].astype(int, errors=\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "09fc8fe2",
"metadata": {},
"source": [
"## Create the initial retrieval chain\n",
"\n",
"We will use a self-query retriever"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f71e24e2",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8881ea8e",
"metadata": {},
"outputs": [],
"source": [
"records = df.to_dict(\"records\")\n",
"documents = [Document(page_content=d[\"Overview\"], metadata=d) for d in records]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8f495423",
"metadata": {},
"outputs": [],
"source": [
"vectorstore = Chroma.from_documents(documents, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "31d33d62",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.query_constructor.base import AttributeInfo\n",
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"metadata_field_info = [\n",
" AttributeInfo(\n",
" name=\"Released_Year\",\n",
" description=\"The year the movie was released\",\n",
" type=\"int\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"Series_Title\",\n",
" description=\"The title of the movie\",\n",
" type=\"str\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"Genre\",\n",
" description=\"The genre of the movie\",\n",
" type=\"string\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"IMDB_Rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
" ),\n",
"]\n",
"document_content_description = \"Brief summary of a movie\"\n",
"llm = ChatOpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(\n",
" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a731533b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "05181849",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "feed4be6",
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate.from_template(\n",
" \"\"\"Answer the user's question based on the below information:\n",
"\n",
"Information:\n",
"\n",
"{info}\n",
"\n",
"Question: {question}\"\"\"\n",
")\n",
"generator = (prompt | ChatOpenAI() | StrOutputParser()).with_config(\n",
" run_name=\"generator\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "eb16cc9a",
"metadata": {},
"outputs": [],
"source": [
"chain = (\n",
" RunnablePassthrough.assign(info=(lambda x: x[\"question\"]) | retriever) | generator\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c70911cc",
"metadata": {},
"source": [
"## Run examples\n",
"\n",
"Run examples through the chain. This can either be manually, or using a list of examples, or production traffic"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "19a88d13",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'One of the horror movies released in the early 2000s is \"The Ring\" (2002), directed by Gore Verbinski.'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke({\"question\": \"what is a horror movie released in early 2000s\"})"
]
},
{
"cell_type": "markdown",
"id": "17f9cdae",
"metadata": {},
"source": [
"## Annotate\n",
"\n",
"Now, go to LangSmitha and annotate those examples as correct or incorrect"
]
},
{
"cell_type": "markdown",
"id": "5e211da6",
"metadata": {},
"source": [
"## Create Dataset\n",
"\n",
"We can now create a dataset from those runs.\n",
"\n",
"What we will do is find the runs marked as correct, then grab the sub-chains from them. Specifically, the query generator sub chain and the final generation step"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e4024267",
"metadata": {},
"outputs": [],
"source": [
"from langsmith import Client\n",
"\n",
"client = Client()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3814efc5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runs = list(\n",
" client.list_runs(\n",
" project_name=\"movie-qa\",\n",
" execution_order=1,\n",
" filter=\"and(eq(feedback_key, 'correctness'), eq(feedback_score, 1))\",\n",
" )\n",
")\n",
"\n",
"len(runs)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3eb123e0",
"metadata": {},
"outputs": [],
"source": [
"gen_runs = []\n",
"query_runs = []\n",
"for r in runs:\n",
" gen_runs.extend(\n",
" list(\n",
" client.list_runs(\n",
" project_name=\"movie-qa\",\n",
" filter=\"eq(name, 'generator')\",\n",
" trace_id=r.trace_id,\n",
" )\n",
" )\n",
" )\n",
" query_runs.extend(\n",
" list(\n",
" client.list_runs(\n",
" project_name=\"movie-qa\",\n",
" filter=\"eq(name, 'query_constructor')\",\n",
" trace_id=r.trace_id,\n",
" )\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a4397026",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'question': 'what is a high school comedy released in early 2000s'}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runs[0].inputs"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3fa6ad2a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output': 'One high school comedy released in the early 2000s is \"Mean Girls\" starring Lindsay Lohan, Rachel McAdams, and Tina Fey.'}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runs[0].outputs"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1fda5b4b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'query': 'what is a high school comedy released in early 2000s'}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_runs[0].inputs"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "1a1a51e6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output': {'query': 'high school comedy',\n",
" 'filter': {'operator': 'and',\n",
" 'arguments': [{'comparator': 'eq', 'attribute': 'Genre', 'value': 'comedy'},\n",
" {'operator': 'and',\n",
" 'arguments': [{'comparator': 'gte',\n",
" 'attribute': 'Released_Year',\n",
" 'value': 2000},\n",
" {'comparator': 'lt', 'attribute': 'Released_Year', 'value': 2010}]}]}}}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_runs[0].outputs"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e9d9966b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'question': 'what is a high school comedy released in early 2000s',\n",
" 'info': []}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen_runs[0].inputs"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "bc113f3d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output': 'One high school comedy released in the early 2000s is \"Mean Girls\" starring Lindsay Lohan, Rachel McAdams, and Tina Fey.'}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen_runs[0].outputs"
]
},
{
"cell_type": "markdown",
"id": "6cca74e5",
"metadata": {},
"source": [
"## Create datasets\n",
"\n",
"We can now create datasets for the query generation and final generation step.\n",
"We do this so that (1) we can inspect the datapoints, (2) we can edit them if needed, (3) we can add to them over time"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "69966f0e",
"metadata": {},
"outputs": [],
"source": [
"client.create_dataset(\"movie-query_constructor\")\n",
"\n",
"inputs = [r.inputs for r in query_runs]\n",
"outputs = [r.outputs for r in query_runs]\n",
"\n",
"client.create_examples(\n",
" inputs=inputs, outputs=outputs, dataset_name=\"movie-query_constructor\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7e15770e",
"metadata": {},
"outputs": [],
"source": [
"client.create_dataset(\"movie-generator\")\n",
"\n",
"inputs = [r.inputs for r in gen_runs]\n",
"outputs = [r.outputs for r in gen_runs]\n",
"\n",
"client.create_examples(inputs=inputs, outputs=outputs, dataset_name=\"movie-generator\")"
]
},
{
"cell_type": "markdown",
"id": "61cf9bcd",
"metadata": {},
"source": [
"## Use as few shot examples\n",
"\n",
"We can now pull down a dataset and use them as few shot examples in a future chain"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "d9c79173",
"metadata": {},
"outputs": [],
"source": [
"examples = list(client.list_examples(dataset_name=\"movie-query_constructor\"))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "a1771dd0",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"\n",
"def filter_to_string(_filter):\n",
" if \"operator\" in _filter:\n",
" args = [filter_to_string(f) for f in _filter[\"arguments\"]]\n",
" return f\"{_filter['operator']}({','.join(args)})\"\n",
" else:\n",
" comparator = _filter[\"comparator\"]\n",
" attribute = json.dumps(_filter[\"attribute\"])\n",
" value = json.dumps(_filter[\"value\"])\n",
" return f\"{comparator}({attribute}, {value})\""
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e67a3530",
"metadata": {},
"outputs": [],
"source": [
"model_examples = []\n",
"\n",
"for e in examples:\n",
" if \"filter\" in e.outputs[\"output\"]:\n",
" string_filter = filter_to_string(e.outputs[\"output\"][\"filter\"])\n",
" else:\n",
" string_filter = \"NO_FILTER\"\n",
" model_examples.append(\n",
" (\n",
" e.inputs[\"query\"],\n",
" {\"query\": e.outputs[\"output\"][\"query\"], \"filter\": string_filter},\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "84593135",
"metadata": {},
"outputs": [],
"source": [
"retriever1 = SelfQueryRetriever.from_llm(\n",
" llm,\n",
" vectorstore,\n",
" document_content_description,\n",
" metadata_field_info,\n",
" verbose=True,\n",
" chain_kwargs={\"examples\": model_examples},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4ec9bb92",
"metadata": {},
"outputs": [],
"source": [
"chain1 = (\n",
" RunnablePassthrough.assign(info=(lambda x: x[\"question\"]) | retriever1) | generator\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "64eb88e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1. \"Saving Private Ryan\" (1998) - Directed by Steven Spielberg, this war film follows a group of soldiers during World War II as they search for a missing paratrooper.\\n\\n2. \"The Matrix\" (1999) - Directed by the Wachowskis, this science fiction action film follows a computer hacker who discovers the truth about the reality he lives in.\\n\\n3. \"Lethal Weapon 4\" (1998) - Directed by Richard Donner, this action-comedy film follows two mismatched detectives as they investigate a Chinese immigrant smuggling ring.\\n\\n4. \"The Fifth Element\" (1997) - Directed by Luc Besson, this science fiction action film follows a cab driver who must protect a mysterious woman who holds the key to saving the world.\\n\\n5. \"The Rock\" (1996) - Directed by Michael Bay, this action thriller follows a group of rogue military men who take over Alcatraz and threaten to launch missiles at San Francisco.'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain1.invoke(\n",
" {\"question\": \"what are good action movies made before 2000 but after 1997?\"}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1ee8b55",
"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": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Write Preview
Loading…
Cancel
Save