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Comment backend code for the workshop
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Signed-off-by: Andreia Ocănoaia <[email protected]>
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@andreia-oca
andreia-oca committed Jun 12, 2024
1 parent 5c64177 commit ec8fea9
Showing 1 changed file with 92 additions and 92 deletions.
184 changes: 92 additions & 92 deletions server/backend.ts
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Expand Up @@ -30,99 +30,99 @@ export class BackendService {
// I am a fullstack software engineer interested in: open source, generative ai, backend technologies, cloud, cloud native, deployment, dev tools.
// I am a product engineer interested in leadership, defining clear scopes, user experience, getting feedback
async ask(user: UserDescription): Promise<Recommendation[]> {
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
if (!OPENAI_API_KEY) {
throw new Error(
"You need to provide an OpenAI API key. Go to https://platform.openai.com/account/api-keys and save it in a `.env` file.",
);
}

// Connect to the database
const db = await connect(DATABASE_PATH);

// Open the table
const table = await db.openTable("vectors");

// Initialize the vector store object with the OpenAI embeddings and the table
const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table });

// Debugging: Retrieve the most similar context to the input question
const result = await vectorStore.similaritySearch(user.description, CONTEXT_DOCS_NUMBER);
for (const item of result) {
console.log("Context metadata: ", item.metadata);
console.log("Context content: ", item.pageContent.slice(0, 10));
}

// Retrieve the most similar context to the input question
const retriever = vectorStore.asRetriever(
{
vectorStore: vectorStore,
k: CONTEXT_DOCS_NUMBER,
searchType: "similarity",
filter: {},
},
{
verbose: true
},
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
if (!OPENAI_API_KEY) {
throw new Error(
"You need to provide an OpenAI API key. Go to https://platform.openai.com/account/api-keys and save it in a `.env` file.",
);
}

// Create a pipeline that will feed the input question and the database retrieved context to the model
const setupAndRetrieval = RunnableMap.from({
context: new RunnableLambda({
func: (input: string) => {
return retriever.invoke(input).then((response) => response.map(item => item.pageContent).join(' ')
)
}
}).withConfig({ runName: "context" }),
question: new RunnablePassthrough(),
});

// Define the prompt that will be fed to the model
const prompt = ChatPromptTemplate.fromMessages([
[
"ai",
`Your task is to advise me on the top 3 speakers I should see at a conference.
Based on the provided user description select the top 3 speakers you would recommend to the user.
You must also mention why you selected these speakers.
You must respond as a json object with the following structure: a list of speakers with the following fields: speaker, reason.
Do not add any additional information to the response.
Respond only based on the context provided below - do not use any external information:
Context: {context}`,
],
[
"human",
`User description: {question}`,],
]);

// Define the OpenAI model
const model = new OpenAI({
modelName: "gpt-4o",
openAIApiKey: OPENAI_API_KEY,
temperature: 0.9,
verbose: true
});

// Create an output parser that will convert the model's response to a string
const outputParser = new StringOutputParser();

// Feed the input question and the database retrieved context to the model
const chain = setupAndRetrieval.pipe(prompt).pipe(model).pipe(outputParser);

// Invoke the model to answer the question
const rawResponse = await chain.invoke(
user.description,
);

const response = rawResponse.replace('```json', '').replace('```', '');
const recommendationList = JSON.parse(response) as Recommendation[];

console.log("Recommendation list: ", recommendationList);

return recommendationList;
// // Connect to the database
// const db = await connect(DATABASE_PATH);

// // Open the table
// const table = await db.openTable("vectors");

// // Initialize the vector store object with the OpenAI embeddings and the table
// const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table });

// // Debugging: Retrieve the most similar context to the input question
// const result = await vectorStore.similaritySearch(user.description, CONTEXT_DOCS_NUMBER);
// for (const item of result) {
// console.log("Context metadata: ", item.metadata);
// console.log("Context content: ", item.pageContent.slice(0, 10));
// }

// // Retrieve the most similar context to the input question
// const retriever = vectorStore.asRetriever(
// {
// vectorStore: vectorStore,
// k: CONTEXT_DOCS_NUMBER,
// searchType: "similarity",
// filter: {},
// },
// {
// verbose: true
// },
// );

// // Create a pipeline that will feed the input question and the database retrieved context to the model
// const setupAndRetrieval = RunnableMap.from({
// context: new RunnableLambda({
// func: (input: string) => {
// return retriever.invoke(input).then((response) => response.map(item => item.pageContent).join(' ')
// )
// }
// }).withConfig({ runName: "context" }),
// question: new RunnablePassthrough(),
// });

// // Define the prompt that will be fed to the model
// const prompt = ChatPromptTemplate.fromMessages([
// [
// "ai",
// `Your task is to advise me on the top 3 speakers I should see at a conference.

// Based on the provided user description select the top 3 speakers you would recommend to the user.
// You must also mention why you selected these speakers.

// You must respond as a json object with the following structure: a list of speakers with the following fields: speaker, reason.

// Do not add any additional information to the response.

// Respond only based on the context provided below - do not use any external information:

// Context: {context}`,
// ],
// [
// "human",
// `User description: {question}`,],
// ]);

// // Define the OpenAI model
// const model = new OpenAI({
// modelName: "gpt-4o",
// openAIApiKey: OPENAI_API_KEY,
// temperature: 0.9,
// verbose: true
// });

// // Create an output parser that will convert the model's response to a string
// const outputParser = new StringOutputParser();

// // Feed the input question and the database retrieved context to the model
// const chain = setupAndRetrieval.pipe(prompt).pipe(model).pipe(outputParser);

// // Invoke the model to answer the question
// const rawResponse = await chain.invoke(
// user.description,
// );

// const response = rawResponse.replace('```json', '').replace('```', '');
// const recommendationList = JSON.parse(response) as Recommendation[];

// console.log("Recommendation list: ", recommendationList);

return [];
}
}

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