AI Shop: o poder da LangChain, OpenAI e MongoDB Atlas trabalhando juntos

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const schema = z.object({
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"shopping_list": z.array(z.object({
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"product": z.string().describe("The name of the product"),
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"quantity": z.number().describe("The quantity of the product"),
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"unit": z.string().optional(),
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"category": z.string().optional(),
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})),
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}).deepPartial();

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  // Initialize OpenAI instance
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    const llm = new OpenAI({ 
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        openAIApiKey: process.env.OPEN_AI_KEY,
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        modelName: "gpt-4",
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        temperature: 0
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    });
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    // Create a structured output parser using the Zod schema
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    const outputParser = StructuredOutputParser.fromZodSchema(schema);
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    const formatInstructions = outputParser.getFormatInstructions();
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    // Create a prompt template
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    const prompt = new PromptTemplate({
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    template: "Build a user grocery list in English as best as possible, if all the products does not fit the categories output empty list, however if some does add only those. \n{format_instructions}\n possible category {categories}\n{query}. Don't output the schema just the json of the list",
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        inputVariables: ["query", "categories"],
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        partialVariables: { format_instructions: formatInstructions },
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    });

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  // Fetch all categories from the database
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    const categories = await db.collection('categories').find({}, { "_id": 0 }).toArray();
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    const docs = categories.map((category) => category.categoryName);
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    // Format the input prompt
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    const input = await prompt.format({
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        query: query,
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        categories: docs
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    });
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    // Call the OpenAI model
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    const response = await llm.call(input);
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    const responseDoc = await outputParser.parse(response);
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    let shoppingList = responseDoc.shopping_list;
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    // Embed the shopping list
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    shoppingList = await placeEmbeddings(shoppingList);

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const placeEmbeddings = async (documents) => {
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      const embeddedDocuments = documents.map(async (document) => {
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                    const embeddedDocument = await embeddings.embedQuery(document.product);
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                      document.embeddings = embeddedDocument;
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                      return document;
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                      });
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      return Promise.all(embeddedDocuments);
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};

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[ {$vectorSearch: // product 1 (vector 3 alternatives)},
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  { $unionWith : { $search : //product 2...},
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  { $unionWith : { $search : //product 3...}]

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[ { searchTerm : "parmesan" ,
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   Products : [ //parmesan 1, //parmesan 2, // Mascarpone ]},
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 ...
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]

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const aggregationQuery = [
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        { "$vectorSearch": {
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                "index": "default",
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                "queryVector": shoppingList[0].embeddings,
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                "path": "embeddings",
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                "numCandidates": 20,
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                "limit": 3
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            }
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        },
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        { $addFields: { "searchTerm": shoppingList[0].product } },
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        ...shoppingList.slice(1).map((item) => ({
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            $unionWith: {
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                coll: "products",
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                pipeline: [
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                    {
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                        "$search": {
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                            "index": "default",
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                            "knnBeta": {
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                                "vector": item.embeddings,
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                                "path": "embeddings",
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                                "k": 20
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                            }
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                        }
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                    },
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                    {$limit: 3},
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                    { $addFields: { "searchTerm": item.product } }
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                ]
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            }
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        })),
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        { $group: { _id: "$searchTerm", products: { $push: "$$ROOT" } } },
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        { $project: { "_id": 0, "category": "$_id", "products.title": 1, "products.description": 1,"products.emoji" : 1, "products.imageUrl" : 1,"products.price": 1 } }
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    ]