// convex/schema.ts

export default defineSchema({
  document: defineTable({
    text: v.string(),
    embeddings: v.array(v.float64()),
    metadata: v.object({
      chatbotId: v.id("chatbot"),
    }),
  })
    .vectorIndex("byEmbeddings", {
      vectorField: "embeddings",
      filterFields: ["metadata.chatbotId"],
      dimensions: 1024,
    })
    .index("by_chatbot", ["metadata.chatbotId"])
})

// convex/schema.ts

export default defineSchema({
  document: defineTable({
    text: v.string(),
    embeddings: v.array(v.float64()),
    metadata: v.object({
      chatbotId: v.id("chatbot"),
    }),
  })
    .vectorIndex("byEmbeddings", {
      vectorField: "embeddings",
      filterFields: ["metadata.chatbotId"],
      dimensions: 1024,
    })
    .index("by_chatbot", ["metadata.chatbotId"])
})

// convex/ai.ts

export const search = internalAction({
  args: {
    query: v.string(),
    chatbotId: v.id("chatbot"),
  },
  handler: async (ctx, args) => {
    const embeddings = await cohereClient.embed({
      texts: [args.query],
      model: DEFAULT_EMBEDDING_MODEL,
      inputType: "search_query",
    });
    const [vector] = embeddings.embeddings as number[][];

    const results = await ctx.vectorSearch("document", "byEmbeddings", {
      vector,
      filter: (q) => q.eq("metadata.chatbotId", args.chatbotId),
      limit: 5,
    });

    return results;
  },
});

// convex/ai.ts

export const search = internalAction({
  args: {
    query: v.string(),
    chatbotId: v.id("chatbot"),
  },
  handler: async (ctx, args) => {
    const embeddings = await cohereClient.embed({
      texts: [args.query],
      model: DEFAULT_EMBEDDING_MODEL,
      inputType: "search_query",
    });
    const [vector] = embeddings.embeddings as number[][];

    const results = await ctx.vectorSearch("document", "byEmbeddings", {
      vector,
      filter: (q) => q.eq("metadata.chatbotId", args.chatbotId),
      limit: 5,
    });

    return results;
  },
});