このガイドで学ぶこと
このガイドでは、次の方法を説明します。- ナレッジベースを構築する
- 関連ドキュメントを検索する取得ステップを備えた RAG アプリケーションを作成する
- Weave で取得ステップをトラッキングする
- LLM ジャッジモデルを使用して RAG アプリケーションを評価し、コンテキストの適合率を測定する
- カスタムのスコアリング関数を定義する
前提条件
- CoreWeave Forge アカウント
- Python 3.10 以降または Node.js 18 以降
- 必須パッケージがインストールされていること:
- Python:
pip install weave openai - TypeScript:
npm install weave openai
- Python:
- OpenAI APIキーが環境変数として設定されていること。
ナレッジベースを構築する
ナレッジベースは、RAG アプリケーションがクエリ時に検索するコーパスです。このセクションでは、少数の記事のベクトル埋め込みを計算し、後で質問に対して最も関連性の高い記事を取得できるようにします。 まず、記事の埋め込みを計算します。通常この処理は一度だけ行い、埋め込みとメタデータをデータベースに保存しておきます。ここでは簡略化のため、スクリプトを実行するたびに計算します。- Python
- TypeScript
from openai import OpenAI
import weave
from weave import Model
import numpy as np
import json
import asyncio
articles = [
"Novo Nordisk and Eli Lilly rival soars 32 percent after promising weight loss drug results Shares of Denmarks Zealand Pharma shot 32 percent higher in morning trade, after results showed success in its liver disease treatment survodutide, which is also on trial as a drug to treat obesity. The trial “tells us that the 6mg dose is safe, which is the top dose used in the ongoing [Phase 3] obesity trial too,” one analyst said in a note. The results come amid feverish investor interest in drugs that can be used for weight loss.",
"Berkshire shares jump after big profit gain as Buffetts conglomerate nears $1 trillion valuation Berkshire Hathaway shares rose on Monday after Warren Buffetts conglomerate posted strong earnings for the fourth quarter over the weekend. Berkshires Class A and B shares jumped more than 1.5%, each. Class A shares are higher by more than 17% this year, while Class B has gained more than 18%. Berkshire was last valued at $930.1 billion, up from $905.5 billion where it closed on Friday, according to FactSet. Berkshire on Saturday posted fourth-quarter operating earnings of $8.481 billion, about 28 percent higher than the $6.625 billion from the year-ago period, driven by big gains in its insurance business. Operating earnings refers to profits from businesses across insurance, railroads and utilities. Meanwhile, Berkshires cash levels also swelled to record levels. The conglomerate held $167.6 billion in cash in the fourth quarter, surpassing the $157.2 billion record the conglomerate held in the prior quarter.",
"Highmark Health says its combining tech from Google and Epic to give doctors easier access to information Highmark Health announced it is integrating technology from Google Cloud and the health-care software company Epic Systems. The integration aims to make it easier for both payers and providers to access key information they need, even if its stored across multiple points and formats, the company said. Highmark is the parent company of a health plan with 7 million members, a provider network of 14 hospitals and other entities",
"Rivian and Lucid shares plunge after weak EV earnings reports Shares of electric vehicle makers Rivian and Lucid fell Thursday after the companies reported stagnant production in their fourth-quarter earnings after the bell Wednesday. Rivian shares sank about 25 percent, and Lucids stock dropped around 17 percent. Rivian forecast it will make 57,000 vehicles in 2024, slightly less than the 57,232 vehicles it produced in 2023. Lucid said it expects to make 9,000 vehicles in 2024, more than the 8,428 vehicles it made in 2023.",
"Mauritius blocks Norwegian cruise ship over fears of a potential cholera outbreak Local authorities on Sunday denied permission for the Norwegian Dawn ship, which has 2,184 passengers and 1,026 crew on board, to access the Mauritius capital of Port Louis, citing “potential health risks.” The Mauritius Ports Authority said Sunday that samples were taken from at least 15 passengers on board the cruise ship. A spokesperson for the U.S.-headquartered Norwegian Cruise Line Holdings said Sunday that 'a small number of guests experienced mild symptoms of a stomach-related illness' during Norwegian Dawns South Africa voyage.",
"Intuitive Machines lands on the moon in historic first for a U.S. company Intuitive Machines Nova-C cargo lander, named Odysseus after the mythological Greek hero, is the first U.S. spacecraft to soft land on the lunar surface since 1972. Intuitive Machines is the first company to pull off a moon landing — government agencies have carried out all previously successful missions. The company's stock surged in extended trading Thursday, after falling 11 percent in regular trading.",
"Lunar landing photos: Intuitive Machines Odysseus sends back first images from the moon Intuitive Machines cargo moon lander Odysseus returned its first images from the surface. Company executives believe the lander caught its landing gear sideways on the moon's surface while touching down and tipped over. Despite resting on its side, the company's historic IM-1 mission is still operating on the moon.",
]
def docs_to_embeddings(docs: list) -> list:
openai = OpenAI()
document_embeddings = []
for doc in docs:
response = (
openai.embeddings.create(input=doc, model="text-embedding-3-small")
.data[0]
.embedding
)
document_embeddings.append(response)
return document_embeddings
article_embeddings = docs_to_embeddings(articles) # 注: 通常、この処理は対象の記事に対して一度だけ実行し、埋め込みとメタデータはデータベースに格納しておきます
require('dotenv').config();
import { OpenAI } from 'openai';
import * as weave from 'weave';
interface Article {
text: string;
embedding?: number[];
}
const articles: Article[] = [
{
text: `Novo Nordisk and Eli Lilly rival soars 32 percent after promising weight loss drug results Shares of Denmarks Zealand Pharma shot 32 percent higher in morning trade, after results showed success in its liver disease treatment survodutide, which is also on trial as a drug to treat obesity. The trial tells us that the 6mg dose is safe, which is the top dose used in the ongoing [Phase 3] obesity trial too, one analyst said in a note. The results come amid feverish investor interest in drugs that can be used for weight loss.`
},
{
text: `Berkshire shares jump after big profit gain as Buffetts conglomerate nears $1 trillion valuation Berkshire Hathaway shares rose on Monday after Warren Buffetts conglomerate posted strong earnings for the fourth quarter over the weekend. Berkshires Class A and B shares jumped more than 1.5%, each. Class A shares are higher by more than 17% this year, while Class B has gained more than 18%. Berkshire was last valued at $930.1 billion, up from $905.5 billion where it closed on Friday, according to FactSet. Berkshire on Saturday posted fourth-quarter operating earnings of $8.481 billion, about 28 percent higher than the $6.625 billion from the year-ago period, driven by big gains in its insurance business. Operating earnings refers to profits from businesses across insurance, railroads and utilities. Meanwhile, Berkshires cash levels also swelled to record levels. The conglomerate held $167.6 billion in cash in the fourth quarter, surpassing the $157.2 billion record the conglomerate held in the prior quarter.`
},
{
text: `Highmark Health says its combining tech from Google and Epic to give doctors easier access to information Highmark Health announced it is integrating technology from Google Cloud and the health-care software company Epic Systems. The integration aims to make it easier for both payers and providers to access key information they need, even if its stored across multiple points and formats, the company said. Highmark is the parent company of a health plan with 7 million members, a provider network of 14 hospitals and other entities`
}
];
function cosineSimilarity(a: number[], b: number[]): number {
const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
return dotProduct / (magnitudeA * magnitudeB);
}
const docsToEmbeddings = weave.op(async function(docs: Article[]): Promise<Article[]> {
const openai = new OpenAI();
const enrichedDocs = await Promise.all(docs.map(async (doc) => {
const response = await openai.embeddings.create({
input: doc.text,
model: "text-embedding-3-small"
});
return {
...doc,
embedding: response.data[0].embedding
};
}));
return enrichedDocs;
});
RAG アプリを作成する
ナレッジベースの準備ができたので、RAG アプリケーション本体を構築します。このセクションでは、取得ステップと LLM Call を組み合わせ、両方を Weave でラップすることで、すべての入力と出力を自動的に追跡します。 次に、取得関数get_most_relevant_document を weave.op() デコレーターでラップし、Model クラスを作成します。取得関数を weave.op() でラップすると、Weave は Call のたびにその入力と出力を取得します。これにより、後から取得ステップを検査できるようになります。weave.init('<team-name>/rag-quickstart') を Call すると、関数のすべての入力と出力のトラッキングが開始され、後から検査できるようになります。チーム名を指定しない場合、Weave は出力を W&B のデフォルトのチームまたは entity に記録します。
- Python
- TypeScript
from openai import OpenAI
import weave
from weave import Model
import numpy as np
import asyncio
@weave.op()
def get_most_relevant_document(query):
openai = OpenAI()
query_embedding = (
openai.embeddings.create(input=query, model="text-embedding-3-small")
.data[0]
.embedding
)
similarities = [
np.dot(query_embedding, doc_emb)
/ (np.linalg.norm(query_embedding) * np.linalg.norm(doc_emb))
for doc_emb in article_embeddings
]
# 最も類似度の高いドキュメントのインデックスを取得する
most_relevant_doc_index = np.argmax(similarities)
return articles[most_relevant_doc_index]
class RAGModel(Model):
system_message: str
model_name: str = "gpt-3.5-turbo-1106"
@weave.op()
def predict(self, question: str) -> dict: # 注: `question` は後で評価の行からデータを選択する際に使用します
from openai import OpenAI
context = get_most_relevant_document(question)
client = OpenAI()
query = f"""Use the following information to answer the subsequent question. If the answer cannot be found, write "I don't know."
Context:
\"\"\"
{context}
\"\"\"
Question: {question}"""
response = client.chat.completions.create(
model=self.model_name,
messages=[
{"role": "system", "content": self.system_message},
{"role": "user", "content": query},
],
temperature=0.0,
response_format={"type": "text"},
)
answer = response.choices[0].message.content
return {'answer': answer, 'context': context}
# チーム名とプロジェクト名を設定する
weave.init('<team-name>/rag-quickstart')
model = RAGModel(
system_message="You are an expert in finance and answer questions related to finance, financial services, and financial markets. When responding based on provided information, be sure to cite the source."
)
model.predict("What significant result was reported about Zealand Pharma's obesity trial?")
class RAGModel {
private openai: OpenAI;
private systemMessage: string;
private modelName: string;
private articleEmbeddings: Article[];
constructor(config: {
systemMessage: string;
modelName?: string;
articleEmbeddings: Article[];
}) {
this.openai = new OpenAI();
this.systemMessage = config.systemMessage;
this.modelName = config.modelName || "gpt-3.5-turbo-1106";
this.articleEmbeddings = config.articleEmbeddings;
this.predict = weave.op(this, this.predict);
}
async predict(question: string): Promise<{
answer: string;
context: string;
}> {
const context = await this.getMostRelevantDocument(question);
const response = await this.openai.chat.completions.create({
model: this.modelName,
messages: [
{ role: "system", content: this.systemMessage },
{ role: "user", content: `Use the following information to answer the subsequent question. If the answer cannot be found, write "I don't know."
Context:
"""
${context}
"""
Question: ${question}` }
],
temperature: 0
});
return {
answer: response.choices[0].message.content || "",
context
};
}
}
LLM ジャッジモデルで評価する
RAG アプリケーションが稼働し、Weave で追跡できるようになったら、次のステップはアプリケーションが質問にどの程度的確に回答できているかを評価することです。このセクションでは、LLM を自動ジャッジとして使用し、手作業でラベルを付けることなくアプリケーションの応答をスコアリングする方法を説明します。 アプリケーションを簡単に評価する方法がない場合は、LLM を使用してアプリケーションのさまざまな側面を評価するという方法があります。以下は、LLM ジャッジモデルを使用してコンテキストの適合率を測定する例です。ここでは、与えられた回答を導き出すうえでコンテキストが役立ったかどうかを検証するよう、プロンプトで LLM に指示しています。このプロンプトは、広く使われている RAGAS フレームワーク のプロンプトを拡張したものです。スコアリング関数を定義する
評価パイプラインの構築チュートリアルと同様に、アプリのテストに使うサンプル行のセットと、スコアリング関数を定義します。スコアリング関数は 1 つの行を受け取って評価します。入力引数は行内の対応するキーと一致させる必要があるため、ここでのquestion は行の辞書から取得されます。output はモデルの出力です。モデルへの入力も入力引数に基づいてサンプルから取得されるため、こちらも question になります。この例では、並列実行できるように async 関数を使用しています。async の概要については、Python asyncio のドキュメントを参照してください。
- Python
- TypeScript
from openai import OpenAI
import weave
import asyncio
@weave.op()
async def context_precision_score(question, output):
context_precision_prompt = """Given question, answer and context verify if the context was useful in arriving at the given answer. Give verdict as "1" if useful and "0" if not with json output.
Output in only valid JSON format.
question: {question}
context: {context}
answer: {answer}
verdict: """
client = OpenAI()
prompt = context_precision_prompt.format(
question=question,
context=output['context'],
answer=output['answer'],
)
response = client.chat.completions.create(
model="gpt-4-turbo-preview",
messages=[{"role": "user", "content": prompt}],
response_format={ "type": "json_object" }
)
response_message = response.choices[0].message
response = json.loads(response_message.content)
return {
"verdict": int(response["verdict"]) == 1,
}
questions = [
{"question": "What significant result was reported about Zealand Pharma's obesity trial?"},
{"question": "How much did Berkshire Hathaway's cash levels increase in the fourth quarter?"},
{"question": "What is the goal of Highmark Health's integration of Google Cloud and Epic Systems technology?"},
{"question": "What were Rivian and Lucid's vehicle production forecasts for 2024?"},
{"question": "Why was the Norwegian Dawn cruise ship denied access to Mauritius?"},
{"question": "Which company achieved the first U.S. moon landing since 1972?"},
{"question": "What issue did Intuitive Machines' lunar lander encounter upon landing on the moon?"}
]
evaluation = weave.Evaluation(dataset=questions, scorers=[context_precision_score])
asyncio.run(evaluation.evaluate(model)) # 注: 評価対象のモデルを定義する必要があります
const contextPrecisionScore = weave.op(async function(args: {
datasetRow: QuestionRow;
modelOutput: { answer: string; context: string; }
}): Promise<ScorerResult> {
const openai = new OpenAI();
const prompt = `Given question, answer and context verify if the context was useful...`;
const response = await openai.chat.completions.create({
model: "gpt-4-turbo-preview",
messages: [{ role: "user", content: prompt }],
response_format: { type: "json_object" }
});
const result = JSON.parse(response.choices[0].message.content || "{}");
return {
verdict: parseInt(result.verdict) === 1
};
});
const evaluation = new weave.Evaluation({
dataset: createQuestionDataset(),
scorers: [contextPrecisionScore]
});
await evaluation.evaluate({
model: weave.op((args: { datasetRow: QuestionRow }) =>
model.predict(args.datasetRow.question)
)
});
オプション: Scorer クラスを定義する
前のセクションのスコアリング関数は単純なケースには十分ですが、複数の評価で同じ judge を再利用したい場合や、スコアの集計方法をカスタマイズしたい場合には Scorer クラスが便利です。以下の手順では、どのような場合にどのように定義するかを説明します。
アプリケーションによっては、カスタムの評価クラス (たとえば、チャットモデルやプロンプトなどのパラメーターを持つ標準化された LLMJudge クラス) や、行ごとのカスタムスコアリング、集計スコアの独自の計算方法を作成したい場合があります。Weave にはすぐに使用できる Scorer クラスが多数用意されており、カスタムの Scorer も簡単に作成できます。次の例では、カスタムの class CorrectnessLLMJudge(Scorer) を作成する方法を示します。
カスタム Scorer を作成する大まかな手順は次のとおりです。
weave.flow.scorer.Scorerを継承するカスタムクラスを定義します。score関数をオーバーライドします。関数の各呼び出しをトラッキングしたい場合は@weave.op()を追加します。- この関数では、モデルの予測が渡される
output引数を定義する必要があります。モデルが “None” を返す場合に備えて、タイプはOptional[dict]として定義してください。 - 残りの引数には、汎用的な
Anyやdictを指定するか、weave.Evaluateクラスでモデルを評価する際に使用するデータセットから特定の列を選択して指定できます。引数名は、列名と完全に一致させる必要があります。preprocess_model_inputを使用する場合は、それに渡された後の単一行のキーと一致させてください。
- この関数では、モデルの予測が渡される
- オプション:
summarize関数をオーバーライドして、集計スコアの計算方法をカスタマイズします。カスタム関数を定義しない場合、Weave はデフォルトでweave.flow.scorer.auto_summarize関数を使用します。- この関数には
@weave.op()デコレーターを付ける必要があります。
- この関数には
- Python
- TypeScript
from weave import Scorer
class CorrectnessLLMJudge(Scorer):
prompt: str
model_name: str
device: str
@weave.op()
async def score(self, output: Optional[dict], query: str, answer: str) -> Any:
"""pred、query、target を比較して予測の正確性をスコアリングします。
引数:
- output: 評価対象のモデルから提供される dict
- query: 質問 (データセットで定義)
- answer: 正解の回答 (データセットで定義)
戻り値:
- 単一の dict {メトリクス名: 単一の評価値}"""
# get_model は、指定された params に基づいてモデルを取得する汎用関数として定義されています (OpenAI、HF など)
eval_model = get_model(
model_name = self.model_name,
prompt = self.prompt
device = self.device,
)
# 評価を高速化するための非同期評価 (非同期でなくてもかまいません)
grade = await eval_model.async_predict(
{
"query": query,
"answer": answer,
"result": output.get("result"),
}
)
# 出力の解析 - pydantic を使用するとより堅牢に実装できます
evaluation = "incorrect" not in grade["text"].strip().lower()
# Weave に表示される列名
return {"correct": evaluation}
@weave.op()
def summarize(self, score_rows: list) -> Optional[dict]:
"""スコアリング関数によって各行で計算されたすべてのスコアを集計します。
引数:
- score_rows: dict のリスト。各 dict にはメトリクスとスコアが含まれます
戻り値:
- 入力と同じ構造を持つネストされた dict"""
# 何も指定しない場合は weave.flow.scorer.auto_summarize 関数が使用されます
# return auto_summarize(score_rows)
valid_data = [x.get("correct") for x in score_rows if x.get("correct") is not None]
count_true = list(valid_data).count(True)
int_data = [int(x) for x in valid_data]
sample_mean = np.mean(int_data) if int_data else 0
sample_variance = np.var(int_data) if int_data else 0
sample_error = np.sqrt(sample_variance / len(int_data)) if int_data else 0
# 追加の "correct" 階層は必須ではありませんが、UI 上の表示が構造化されます
return {
"correct": {
"true_count": count_true,
"true_fraction": sample_mean,
"stderr": sample_error,
}
}
この機能は TypeScript ではまだ利用できません。
Evaluation の scorers 引数に渡します。
- Python
- TypeScript
evaluation = weave.Evaluation(dataset=questions, scorers=[CorrectnessLLMJudge()])
This feature is not available in TypeScript yet.
すべてをまとめる
このセクションでは、これまでのステップの内容を 1 つのエンドツーエンドのサンプルにまとめます。各要素がどのように組み合わさるのかを確認しながら、ご自身の RAG アプリケーションに合わせて調整してください。 ご自身の RAG アプリで同じ結果を得るには、次の手順を実行します。- LLM Call と取得ステップの関数を
weave.op()でラップします。 - オプション:
predict関数とアプリの詳細を含むModelサブクラスを作成します。 - 評価用のサンプルを収集します。
- 1 つのサンプルをスコアリングするスコアリング関数を作成します。
Evaluationクラスを使用して、サンプルに対して評価を実行します。
WEAVE_PARALLELISM=3) 。
コード全体は次のとおりです。
- Python
- TypeScript
from openai import OpenAI
import weave
from weave import Model
import numpy as np
import json
import asyncio
# 評価に使用するサンプル
articles = [
"Novo Nordisk and Eli Lilly rival soars 32 percent after promising weight loss drug results Shares of Denmarks Zealand Pharma shot 32 percent higher in morning trade, after results showed success in its liver disease treatment survodutide, which is also on trial as a drug to treat obesity. The trial “tells us that the 6mg dose is safe, which is the top dose used in the ongoing [Phase 3] obesity trial too,” one analyst said in a note. The results come amid feverish investor interest in drugs that can be used for weight loss.",
"Berkshire shares jump after big profit gain as Buffetts conglomerate nears $1 trillion valuation Berkshire Hathaway shares rose on Monday after Warren Buffetts conglomerate posted strong earnings for the fourth quarter over the weekend. Berkshires Class A and B shares jumped more than 1.5%, each. Class A shares are higher by more than 17% this year, while Class B has gained more than 18%. Berkshire was last valued at $930.1 billion, up from $905.5 billion where it closed on Friday, according to FactSet. Berkshire on Saturday posted fourth-quarter operating earnings of $8.481 billion, about 28 percent higher than the $6.625 billion from the year-ago period, driven by big gains in its insurance business. Operating earnings refers to profits from businesses across insurance, railroads and utilities. Meanwhile, Berkshires cash levels also swelled to record levels. The conglomerate held $167.6 billion in cash in the fourth quarter, surpassing the $157.2 billion record the conglomerate held in the prior quarter.",
"Highmark Health says its combining tech from Google and Epic to give doctors easier access to information Highmark Health announced it is integrating technology from Google Cloud and the health-care software company Epic Systems. The integration aims to make it easier for both payers and providers to access key information they need, even if it's stored across multiple points and formats, the company said. Highmark is the parent company of a health plan with 7 million members, a provider network of 14 hospitals and other entities",
"Rivian and Lucid shares plunge after weak EV earnings reports Shares of electric vehicle makers Rivian and Lucid fell Thursday after the companies reported stagnant production in their fourth-quarter earnings after the bell Wednesday. Rivian shares sank about 25 percent, and Lucids stock dropped around 17 percent. Rivian forecast it will make 57,000 vehicles in 2024, slightly less than the 57,232 vehicles it produced in 2023. Lucid said it expects to make 9,000 vehicles in 2024, more than the 8,428 vehicles it made in 2023.",
"Mauritius blocks Norwegian cruise ship over fears of a potential cholera outbreak Local authorities on Sunday denied permission for the Norwegian Dawn ship, which has 2,184 passengers and 1,026 crew on board, to access the Mauritius capital of Port Louis, citing “potential health risks.” The Mauritius Ports Authority said Sunday that samples were taken from at least 15 passengers on board the cruise ship. A spokesperson for the U.S.-headquartered Norwegian Cruise Line Holdings said Sunday that 'a small number of guests experienced mild symptoms of a stomach-related illness' during Norwegian Dawns South Africa voyage.",
"Intuitive Machines lands on the moon in historic first for a U.S. company Intuitive Machines Nova-C cargo lander, named Odysseus after the mythological Greek hero, is the first U.S. spacecraft to soft land on the lunar surface since 1972. Intuitive Machines is the first company to pull off a moon landing — government agencies have carried out all previously successful missions. The company's stock surged in extended trading Thursday, after falling 11 percent in regular trading.",
"Lunar landing photos: Intuitive Machines Odysseus sends back first images from the moon Intuitive Machines cargo moon lander Odysseus returned its first images from the surface. Company executives believe the lander caught its landing gear sideways on the surface of the moon while touching down and tipped over. Despite resting on its side, the company's historic IM-1 mission is still operating on the moon.",
]
def docs_to_embeddings(docs: list) -> list:
openai = OpenAI()
document_embeddings = []
for doc in docs:
response = (
openai.embeddings.create(input=doc, model="text-embedding-3-small")
.data[0]
.embedding
)
document_embeddings.append(response)
return document_embeddings
article_embeddings = docs_to_embeddings(articles) # 注: 通常、この処理は記事に対して一度だけ実行し、埋め込みとメタデータをデータベースに保存します
# 取得ステップにデコレーターを追加します
@weave.op()
def get_most_relevant_document(query):
openai = OpenAI()
query_embedding = (
openai.embeddings.create(input=query, model="text-embedding-3-small")
.data[0]
.embedding
)
similarities = [
np.dot(query_embedding, doc_emb)
/ (np.linalg.norm(query_embedding) * np.linalg.norm(doc_emb))
for doc_emb in article_embeddings
]
# 最も類似度の高いドキュメントのインデックスを取得します
most_relevant_doc_index = np.argmax(similarities)
return articles[most_relevant_doc_index]
# アプリの詳細と、応答を生成する predict 関数を持つ Model サブクラスを作成します
class RAGModel(Model):
system_message: str
model_name: str = "gpt-3.5-turbo-1106"
@weave.op()
def predict(self, question: str) -> dict: # 注: `question` は後で評価用の行からデータを選択する際に使用されます
from openai import OpenAI
context = get_most_relevant_document(question)
client = OpenAI()
query = f"""Use the following information to answer the subsequent question. If the answer cannot be found, write "I don't know."
Context:
\"\"\"
{context}
\"\"\"
Question: {question}"""
response = client.chat.completions.create(
model=self.model_name,
messages=[
{"role": "system", "content": self.system_message},
{"role": "user", "content": query},
],
temperature=0.0,
response_format={"type": "text"},
)
answer = response.choices[0].message.content
return {'answer': answer, 'context': context}
# チーム名と project 名を設定します
weave.init('<team-name>/rag-quickstart')
model = RAGModel(
system_message="You are an expert in finance and answer questions related to finance, financial services, and financial markets. When responding based on provided information, be sure to cite the source."
)
# 質問と出力からスコアを算出するスコアリング関数
@weave.op()
async def context_precision_score(question, output):
context_precision_prompt = """Given question, answer and context verify if the context was useful in arriving at the given answer. Give verdict as "1" if useful and "0" if not with json output.
Output in only valid JSON format.
question: {question}
context: {context}
answer: {answer}
verdict: """
client = OpenAI()
prompt = context_precision_prompt.format(
question=question,
context=output['context'],
answer=output['answer'],
)
response = client.chat.completions.create(
model="gpt-4-turbo-preview",
messages=[{"role": "user", "content": prompt}],
response_format={ "type": "json_object" }
)
response_message = response.choices[0].message
response = json.loads(response_message.content)
return {
"verdict": int(response["verdict"]) == 1,
}
questions = [
{"question": "What significant result was reported about Zealand Pharma's obesity trial?"},
{"question": "How much did Berkshire Hathaway's cash levels increase in the fourth quarter?"},
{"question": "What is the goal of Highmark Health's integration of Google Cloud and Epic Systems technology?"},
{"question": "What were Rivian and Lucid's vehicle production forecasts for 2024?"},
{"question": "Why was the Norwegian Dawn cruise ship denied access to Mauritius?"},
{"question": "Which company achieved the first U.S. moon landing since 1972?"},
{"question": "What issue did Intuitive Machines' lunar lander encounter upon landing on the moon?"}
]
# Evaluation オブジェクトを定義し、サンプルの質問とスコアリング関数を渡します
evaluation = weave.Evaluation(dataset=questions, scorers=[context_precision_score])
asyncio.run(evaluation.evaluate(model))
require('dotenv').config();
import { OpenAI } from 'openai';
import * as weave from 'weave';
interface Article {
text: string;
embedding?: number[];
}
const articles: Article[] = [
{
text: `Novo Nordisk and Eli Lilly rival soars 32 percent after promising weight loss drug results Shares of Denmarks Zealand Pharma shot 32 percent higher in morning trade, after results showed success in its liver disease treatment survodutide, which is also on trial as a drug to treat obesity. The trial tells us that the 6mg dose is safe, which is the top dose used in the ongoing [Phase 3] obesity trial too, one analyst said in a note. The results come amid feverish investor interest in drugs that can be used for weight loss.`
},
{
text: `Berkshire shares jump after big profit gain as Buffetts conglomerate nears $1 trillion valuation Berkshire Hathaway shares rose on Monday after Warren Buffetts conglomerate posted strong earnings for the fourth quarter over the weekend. Berkshires Class A and B shares jumped more than 1.5%, each. Class A shares are higher by more than 17% this year, while Class B has gained more than 18%. Berkshire was last valued at $930.1 billion, up from $905.5 billion where it closed on Friday, according to FactSet. Berkshire on Saturday posted fourth-quarter operating earnings of $8.481 billion, about 28 percent higher than the $6.625 billion from the year-ago period, driven by big gains in its insurance business. Operating earnings refers to profits from businesses across insurance, railroads and utilities. Meanwhile, Berkshires cash levels also swelled to record levels. The conglomerate held $167.6 billion in cash in the fourth quarter, surpassing the $157.2 billion record the conglomerate held in the prior quarter.`
},
{
text: `Highmark Health says its combining tech from Google and Epic to give doctors easier access to information Highmark Health announced it is integrating technology from Google Cloud and the health-care software company Epic Systems. The integration aims to make it easier for both payers and providers to access key information they need, even if its stored across multiple points and formats, the company said. Highmark is the parent company of a health plan with 7 million members, a provider network of 14 hospitals and other entities`
}
];
function cosineSimilarity(a: number[], b: number[]): number {
const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
return dotProduct / (magnitudeA * magnitudeB);
}
const docsToEmbeddings = weave.op(async function(docs: Article[]): Promise<Article[]> {
const openai = new OpenAI();
const enrichedDocs = await Promise.all(docs.map(async (doc) => {
const response = await openai.embeddings.create({
input: doc.text,
model: "text-embedding-3-small"
});
return {
...doc,
embedding: response.data[0].embedding
};
}));
return enrichedDocs;
});
class RAGModel {
private openai: OpenAI;
private systemMessage: string;
private modelName: string;
private articleEmbeddings: Article[];
constructor(config: {
systemMessage: string;
modelName?: string;
articleEmbeddings: Article[];
}) {
this.openai = new OpenAI();
this.systemMessage = config.systemMessage;
this.modelName = config.modelName || "gpt-3.5-turbo-1106";
this.articleEmbeddings = config.articleEmbeddings;
this.predict = weave.op(this, this.predict);
}
private async getMostRelevantDocument(query: string): Promise<string> {
const queryEmbedding = await this.openai.embeddings.create({
input: query,
model: "text-embedding-3-small"
});
const similarities = this.articleEmbeddings.map(doc => {
if (!doc.embedding) return 0;
return cosineSimilarity(queryEmbedding.data[0].embedding, doc.embedding);
});
const mostRelevantIndex = similarities.indexOf(Math.max(...similarities));
return this.articleEmbeddings[mostRelevantIndex].text;
}
async predict(question: string): Promise<{
answer: string;
context: string;
}> {
const context = await this.getMostRelevantDocument(question);
const response = await this.openai.chat.completions.create({
model: this.modelName,
messages: [
{ role: "system", content: this.systemMessage },
{
role: "user",
content: `Use the following information to answer the subsequent question. If the answer cannot be found, write "I don't know."
Context:
"""
${context}
"""
Question: ${question}`
}
],
temperature: 0
});
return {
answer: response.choices[0].message.content || "",
context
};
}
}
interface ScorerResult {
verdict: boolean;
}
interface QuestionRow {
question: string;
}
function createQuestionDataset(): weave.Dataset<QuestionRow> {
return new weave.Dataset<QuestionRow>({
id: 'rag-questions',
rows: [
{ question: "What significant result was reported about Zealand Pharma's obesity trial?" },
{ question: "How much did Berkshire Hathaway's cash levels increase in the fourth quarter?" },
{ question: "What is the goal of Highmark Health's integration of Google Cloud and Epic Systems technology?" }
]
});
}
const contextPrecisionScore = weave.op(async function(args: {
datasetRow: QuestionRow;
modelOutput: { answer: string; context: string; }
}): Promise<ScorerResult> {
const openai = new OpenAI();
const prompt = `Given question, answer and context verify if the context was useful in arriving at the given answer. Give verdict as "1" if useful and "0" if not with json output.
Output in only valid JSON format.
question: ${args.datasetRow.question}
context: ${args.modelOutput.context}
answer: ${args.modelOutput.answer}
verdict: `;
const response = await openai.chat.completions.create({
model: "gpt-4-turbo-preview",
messages: [{ role: "user", content: prompt }],
response_format: { type: "json_object" }
});
const result = JSON.parse(response.choices[0].message.content || "{}");
return {
verdict: parseInt(result.verdict) === 1
};
});
async function main() {
# チーム名とプロジェクト名を設定してください
await weave.init('<team-name>/rag-quickstart');
const articleEmbeddings = await docsToEmbeddings(articles);
const model = new RAGModel({
systemMessage: "You are an expert in finance and answer questions related to finance, financial services, and financial markets. When responding based on provided information, be sure to cite the source.",
articleEmbeddings
});
const evaluation = new weave.Evaluation({
dataset: createQuestionDataset(),
scorers: [contextPrecisionScore]
});
const results = await evaluation.evaluate({
model: weave.op((args: { datasetRow: QuestionRow }) =>
model.predict(args.datasetRow.question)
)
});
console.log('Evaluation results:', results);
}
if (require.main === module) {
main().catch(console.error);
}