const axios = require('axios');

    class LocalAiEmbedder {
      constructor() {
        if (!process.env.EMBEDDING_BASE_PATH)
          throw new Error("No embedding base path was set.");
        if (!process.env.EMBEDDING_MODEL_PREF)
          throw new Error("No embedding model was set.");

        this.client = axios.create({
          baseURL: process.env.EMBEDDING_BASE_PATH,
        });

        // Arbitrary limit to ensure we stay within reasonable POST request size.
        this.embeddingChunkLimit = 1_000;
      }

      async embedTextInput(textInput) {
        const result = await this.embedChunks(textInput);
        return result?.[0] || [];
      }

      async embedChunks(textChunks = []) {
        // Because there is a hard POST limit on how many chunks can be sent at once to OpenAI (~8mb)
        // we concurrently execute each max batch of text chunks possible.
        // Refer to constructor embeddingChunkLimit for more info.
        const embeddingRequests = [];
        for (const chunk of toChunks(textChunks, this.embeddingChunkLimit)) {
          embeddingRequests.push(
            this.client.post('/createEmbedding', {
              model: process.env.EMBEDDING_MODEL_PREF,
              input: chunk,
            })
            .then((res) => {
              return { data: res.data?.data, error: null };
            })
            .catch((e) => {
              return { data: [], error: e?.response?.data?.error };
            })
          );
        }

        const { data = [], error = null } = await Promise.all(
          embeddingRequests
        ).then((results) => {
          // If any errors were returned from LocalAI abort the entire sequence because the embeddings
          // will be incomplete.
          const errors = results
            .filter((res) => !!res.error)
            .map((res) => res.error)
            .flat();
          if (errors.length > 0) {
            return {
              data: [],
              error: `(${errors.length}) Embedding Errors! ${errors
                .map((error) => `[${error.type}]: ${error.message}`)
                .join(", ")}`,
            };
          }
          return {
            data: results.map((res) => res?.data || []).flat(),
            error: null,
          };
        });

        if (!!error) throw new Error(`LocalAI Failed to embed: ${error}`);
        return data.length > 0 &&
        data.every((embd) => embd.hasOwnProperty("embedding"))
          ? data.map((embd) => embd.embedding)
          : null;
      }
    }

    module.exports = {
      LocalAiEmbedder,
    };

    async function fetchKeys() {
      const { EmbeddingEngine = "openai" } = await System.keys();
      setSettings(_settings);
      setEmbeddingChoice(EmbeddingEngine);
      setLoading(false);
    }
    fetchKeys();

    async function fetchKeys() {
      const { EmbeddingEngine = "openai" } = await System.keys();
      setSettings(_settings);
      setEmbeddingChoice(EmbeddingEngine);
      setLoading(false);
    }
    fetchKeys();