chore: import upstream snapshot with attribution

apps/sim/tools/pinecone/fetch.ts

@@ -0,0 +1,111 @@
+import type { PineconeFetchParams, PineconeResponse, PineconeVector } from '@/tools/pinecone/types'
+import type { ToolConfig } from '@/tools/types'
+
+export const fetchTool: ToolConfig<PineconeFetchParams, PineconeResponse> = {
+  id: 'pinecone_fetch',
+  name: 'Pinecone Fetch',
+  description: 'Fetch vectors by ID from a Pinecone index',
+  version: '1.0',
+
+  params: {
+    indexHost: {
+      type: 'string',
+      required: true,
+      visibility: 'user-or-llm',
+      description: 'Full Pinecone index host URL (e.g., "https://my-index-abc123.svc.pinecone.io")',
+    },
+    ids: {
+      type: 'array',
+      required: true,
+      visibility: 'user-or-llm',
+      description: 'Array of vector IDs to fetch (e.g., ["vec-001", "vec-002"])',
+    },
+    namespace: {
+      type: 'string',
+      required: false,
+      visibility: 'user-or-llm',
+      description: 'Namespace to fetch vectors from (e.g., "documents", "embeddings")',
+    },
+    apiKey: {
+      type: 'string',
+      required: true,
+      visibility: 'user-only',
+      description: 'Pinecone API key',
+    },
+  },
+
+  request: {
+    method: 'GET',
+    url: (params) => {
+      const baseUrl = `${params.indexHost}/vectors/fetch`
+      const queryParams = new URLSearchParams()
+      queryParams.append('ids', params.ids.join(','))
+      if (params.namespace) {
+        queryParams.append('namespace', params.namespace)
+      }
+      return `${baseUrl}?${queryParams.toString()}`
+    },
+    headers: (params) => ({
+      'Api-Key': params.apiKey,
+      'Content-Type': 'application/json',
+    }),
+  },
+
+  transformResponse: async (response) => {
+    const data = await response.json()
+    const vectors = data.vectors as Record<string, PineconeVector>
+
+    return {
+      success: true,
+      output: {
+        matches: Object.entries(vectors).map(([id, vector]) => ({
+          id,
+          values: vector.values,
+          metadata: vector.metadata,
+          score: 1.0, // Fetch returns exact matches
+        })),
+        data: Object.values(vectors).map((vector) => ({
+          values: vector.values,
+          vector_type: 'dense' as const,
+        })),
+        usage: {
+          total_tokens: data.usage?.readUnits || 0,
+        },
+      },
+    }
+  },
+
+  outputs: {
+    matches: {
+      type: 'array',
+      description: 'Fetched vectors with ID, values, metadata, and score',
+      items: {
+        type: 'object',
+        properties: {
+          id: { type: 'string', description: 'Vector ID' },
+          values: { type: 'array', description: 'Vector values' },
+          metadata: { type: 'object', description: 'Associated metadata' },
+          score: { type: 'number', description: 'Match score (1.0 for exact matches)' },
+        },
+      },
+    },
+    data: {
+      type: 'array',
+      description: 'Vector data with values and vector type',
+      items: {
+        type: 'object',
+        properties: {
+          values: { type: 'array', description: 'Vector values' },
+          vector_type: { type: 'string', description: 'Vector type (dense/sparse)' },
+        },
+      },
+    },
+    usage: {
+      type: 'object',
+      description: 'Usage statistics including total read units',
+      properties: {
+        total_tokens: { type: 'number', description: 'Read units consumed' },
+      },
+    },
+  },
+}