Quickstart - Contextual Agent

npm install @alchemystai/sdk

import AlchemystAI from '@alchemystai/sdk';

const client = new AlchemystAI({
    apiKey: process.env.ALCHEMYST_AI_API_KEY,
});

interface AlchemystDocument {
content: string;
metadata?: { //optional
  file_name?: string;
  file_type?: string;
  group_name?: string[];
};
}

const docs_array: AlchemystDocument[] = [];

docs_array.push({
content: "files content",
metadata: {
  file_name: "file_name",
  file_type: "pdf/txt/json",
  group_name: ["group1", "group2"],
},
});

await client.v1.context.add({
  documents:docs_array,
  context_type: 'resource',  // one of: resource | conversation | instruction
  source: 'web-upload',
  scope: 'internal',     // one of: internal | external
    // [OPTIONAL] metadata can be null. Use for filtering later.
  metadata: {
    fileName: 'notes.txt',
    fileType: 'text/plain',
    lastModified: new Date().toISOString(),
    fileSize: 1024,
    groupName: ['group1', 'group2'],
  },
});
console.log("Context successfully added.")

  const { contexts } = await client.v1.context.search({
      query: "user_query",
      similarity_threshold: 0.8,
      minimum_similarity_threshold: 0.5,
      scope: 'internal',
      metadata: null,
  });

const promptText = contexts?.length ? `Use the context below to answer the question. If the context is insufficient, say so.
      ${contexts.map((c, i) => `Context ${i + 1}: ${c.content ?? JSON.stringify(c)}`).join('\n\n')}
      Question: ${userQuestion}`;

const result = await model.generateContent(promptText);