1import type { NextApiRequest, NextApiResponse } from "next";
2import weaviate from "weaviate-client";
3import { NearTextType } from "types";
4
5export default async function handler(
6  req: NextApiRequest,
7  res: NextApiResponse<Object>
8) {
9  console.log("Received request", req.body);
10  try {
11    const bookPreference = req.body.query;
12    const userInterests = req.body.userInterests;
13    const returnLimits = req.body.returnLimits;
14    console.log(
15      "bookPreference:",
16      bookPreference,
17      "userInterests:",
18      userInterests
19    );
20    const WEAVIATE_CLUSTER_URL = process.env.WEAVIATE_CLUSTER_URL;
21    const WEAVIATE_API_KEY = process.env.WEAVIATE_API_KEY;
22
23    if (!WEAVIATE_CLUSTER_URL || !WEAVIATE_API_KEY) {
24      res
25        .status(500)
26        .json({ error: "Weaviate cluster URL or API key is missing" });
27      return;
28    }
29
30    let headers: { [key: string]: string } = {};
31    if (process.env.COHERE_API_KEY) {
32      headers["X-Cohere-Api-Key"] = process.env.COHERE_API_KEY;
33    }
34
35    const client = await weaviate.connectToWeaviateCloud(WEAVIATE_CLUSTER_URL, {
36      authCredentials: new weaviate.ApiKey(WEAVIATE_API_KEY),
37      headers: {
38        "X-Cohere-Api-Key": process.env.COHERE_API_KEY || "",
39        "X-OpenAI-Api-Key": process.env.OPENAI_API_KEY || "",
40      },
41    });
42
43    let nearText: NearTextType = {
44      concepts: bookPreference,
45    };
46
47    let generatePrompt =
48      "Briefly describe why this book might be interesting to someone who has interests or hobbies in " +
49      userInterests +
50      ". The book's title is {title}, with a description: {description}, 
51      and is in the genre: {categories}. Don't make up anything that wasn't given in this prompt and don't ask how 	
52      you can help.";
53
54    const myCollection = client.collections.get("Book");
55
56    // step 1: vector search
57    let searchResult = await myCollection.query.nearText(nearText.concepts, {
58      limit: returnLimits,
59      returnProperties: [
60        "title",
61        "isbn10",
62        "isbn13",
63        "categories",
64        "thumbnail",
65        "description",
66        "num_pages",
67        "average_rating",
68        "published_year",
69        "authors",
70      ],
71      returnMetadata: ["distance"],
72    });
73
74    // step 2: generative query
75    const generativeResult = await myCollection.generate.nearText(
76      nearText.concepts,
77      {
78        singlePrompt: generatePrompt,
79      },
80      {
81        limit: returnLimits,
82      }
83    );
84
85    // merge the results
86    const books = searchResult.objects.map((item, index) => ({
87      properties: item.properties,
88      distance: item.metadata?.distance,
89      generatedPrompt: generativeResult.objects[index].generated,
90    }));
91
92    console.log("Books found:", books);
93    res.status(200).json({ books });
94  } catch (err) {
95    console.error("Error handling request:", err);
96    res.status(500).json({ error: "Internal server error" });
97  }
98}
99

1import os
2import weaviate
3import json
4
5from dotenv import load_dotenv
6
7load_dotenv()
8
9WEAVIATE_CLUSTER_URL = os.getenv('WEAVIATE_CLUSTER_URL')
10
11
12WEAVIATE_API_KEY = os.getenv('WEAVIATE_API_KEY') 
13
14print("theWEAVIATE_API_KEY is ",WEAVIATE_API_KEY)
15
16OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
17print("the open ai key is ",OPENAI_API_KEY)
18COHERE_API_KEY = os.getenv('COHERE_API_KEY')
19print("the cohere key is ",COHERE_API_KEY)
20
21client = weaviate.Client(
22    url=WEAVIATE_CLUSTER_URL,
23    auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
24    additional_headers={"X-OpenAI-Api-Key": OPENAI_API_KEY, "X-Cohere-Api-Key": COHERE_API_KEY})
25
26nearText = {
27    "concepts":
28    ["technology", "data structures and algorithms", "distributed systems"]
29}
30generate_prompt = "Explain why this book might be interesting to someone who likes playing the violin, 
31rock climbing, and doing yoga. the book's title is {title}, with a description: {description}, and is 
32in the genre: {categories}."
33response = (client.query.get("Book", [
34    "title",
35    "isbn10",
36    "isbn13",
37    "categories",
38    "thumbnail",
39    "description",
40    "num_pages",
41    "average_rating",
42    "published_year",
43    "authors",
44]) .with_generate(single_prompt=generate_prompt).with_near_text(nearText).with_limit(10).do())
45
46print(json.dumps(response, indent=4))
47