File size: 16,037 Bytes
6b62834 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 | {
"entities": {
"text_c-ec4611a61b53": {
"id": "text_c-ec4611a61b53",
"name": "Retrieval-Augmented Generation (RAG) is a techniqu…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models by retrieving relevant documents from a knowledge base before generating answers. RAG was introduced by Lewis et al. in 2020 and has become a standard approach for grounding LLM outputs in external knowledge. The key advantage of RAG is that it significantly reduces hallucinations by providing the model with verified source material.",
"_item_text": "Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models by retrieving relevant documents from a knowledge base before generating answers. RAG was introduced by Lewis et al. in 2020 and has become a standard approach for grounding LLM outputs in external knowledge. The key advantage of RAG is that it significantly reduces hallucinations by providing the model with verified source material.",
"_item_page_idx": 0
},
"doc-4c5673cd0cc9": {
"id": "doc-4c5673cd0cc9",
"name": "rag_intro.txt",
"type": "document",
"properties": {
"doc_id": "b72c4759-f6b6-45fb-a01b-cb086ba472dd"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-55153b8f9577": {
"id": "text_c-55153b8f9577",
"name": "The RAG pipeline consists of five core components:…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "The RAG pipeline consists of five core components: 1.Document Ingestion — parsing and chunking source documents into manageable pieces.2.Embedding — converting text chunks into dense vector representations using models like text-embedding-3-small.3.Vector Storage — storing embeddings in a vector database such as Milvus, Pinecone, or ChromaDB.4.Retrieval — finding the most semantically similar documents to a user query using cosine similarity search.",
"_item_text": "The RAG pipeline consists of five core components: 1.Document Ingestion — parsing and chunking source documents into manageable pieces.2.Embedding — converting text chunks into dense vector representations using models like text-embedding-3-small.3.Vector Storage — storing embeddings in a vector database such as Milvus, Pinecone, or ChromaDB.4.Retrieval — finding the most semantically similar documents to a user query using cosine similarity search.",
"_item_page_idx": 0
},
"text_c-c3c3cde6071f": {
"id": "text_c-c3c3cde6071f",
"name": "5.Generation — passing retrieved context along wit…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 1
},
"content_type": "text",
"content_text": "5.Generation — passing retrieved context along with the query to an LLM to produce an accurate, cited response.",
"_item_text": "5.Generation — passing retrieved context along with the query to an LLM to produce an accurate, cited response.",
"_item_page_idx": 0
},
"doc-f4b87e7b51cb": {
"id": "doc-f4b87e7b51cb",
"name": "rag_components.txt",
"type": "document",
"properties": {
"doc_id": "cb8cc41d-4fb5-4760-902a-0a1b2f7b8ecf"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-ef467a62127a": {
"id": "text_c-ef467a62127a",
"name": "Milvus is an open-source vector database designed …",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "Milvus is an open-source vector database designed for similarity search at scale. It supports multiple index types including IVF_FLAT, HNSW, and DiskANN. Vector databases store embeddings as high-dimensional vectors and enable efficient approximate nearest neighbor (ANN) search. Key metrics include cosine similarity, Euclidean distance, and inner product.",
"_item_text": "Milvus is an open-source vector database designed for similarity search at scale. It supports multiple index types including IVF_FLAT, HNSW, and DiskANN. Vector databases store embeddings as high-dimensional vectors and enable efficient approximate nearest neighbor (ANN) search. Key metrics include cosine similarity, Euclidean distance, and inner product.",
"_item_page_idx": 0
},
"doc-1b2e2373a460": {
"id": "doc-1b2e2373a460",
"name": "vector_databases.txt",
"type": "document",
"properties": {
"doc_id": "1d971110-0404-499a-a3fd-902221b95293"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-7bd94cd4393c": {
"id": "text_c-7bd94cd4393c",
"name": "Text embedding models convert natural language int…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "Text embedding models convert natural language into fixed-length vector representations. OpenAI's text-embedding-3-small produces 1536-dimensional vectors optimized for semantic search. Alternative models include BGE from BAAI, E5 from Microsoft, and multilingual models for cross-lingual retrieval. Embedding quality directly impacts RAG retrieval accuracy.",
"_item_text": "Text embedding models convert natural language into fixed-length vector representations. OpenAI's text-embedding-3-small produces 1536-dimensional vectors optimized for semantic search. Alternative models include BGE from BAAI, E5 from Microsoft, and multilingual models for cross-lingual retrieval. Embedding quality directly impacts RAG retrieval accuracy.",
"_item_page_idx": 0
},
"doc-a97956d2e621": {
"id": "doc-a97956d2e621",
"name": "embedding_models.txt",
"type": "document",
"properties": {
"doc_id": "898fbb98-1b40-4229-99c2-542a8f400d47"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-676838941010": {
"id": "text_c-676838941010",
"name": "The ReAct (Reasoning + Acting) framework enables A…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "The ReAct (Reasoning + Acting) framework enables AI agents to interleave reasoning steps with tool-calling actions. ReAct agents follow a Think → Act → Observe loop, where each iteration involves the agent reasoning about what to do next, selecting and invoking a tool, and observing the result. This pattern is fundamental to building reliable AI agents.",
"_item_text": "The ReAct (Reasoning + Acting) framework enables AI agents to interleave reasoning steps with tool-calling actions. ReAct agents follow a Think → Act → Observe loop, where each iteration involves the agent reasoning about what to do next, selecting and invoking a tool, and observing the result. This pattern is fundamental to building reliable AI agents.",
"_item_page_idx": 0
},
"doc-19e2c95f250b": {
"id": "doc-19e2c95f250b",
"name": "react_agent.txt",
"type": "document",
"properties": {
"doc_id": "bbbe6cb6-d79b-4d8f-9629-d3579d4c4239"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-1926476d68ac": {
"id": "text_c-1926476d68ac",
"name": "RAG System Architecture Diagram — showing the flow…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "RAG System Architecture Diagram — showing the flow from user query to final answer.",
"_item_text": "RAG System Architecture Diagram — showing the flow from user query to final answer.",
"_item_page_idx": 0
},
"image-d107b040969f": {
"id": "image-d107b040969f",
"name": "image_1",
"type": "image",
"properties": {
"page_idx": 0,
"content_type": "image",
"has_caption": false,
"index": 1
},
"content_type": "image",
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"doc-621766721b8c": {
"id": "doc-621766721b8c",
"name": "rag_architecture_diagram",
"type": "document",
"properties": {
"doc_id": "86e6838c-1570-45d1-9531-60f8048e5c13"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-723213cefe2f": {
"id": "text_c-723213cefe2f",
"name": "RAG performance benchmarks across different config…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "RAG performance benchmarks across different configurations.",
"_item_text": "RAG performance benchmarks across different configurations.",
"_item_page_idx": 0
},
"table-31e9e6015b3d": {
"id": "table-31e9e6015b3d",
"name": "| Configuration | Accuracy | Latency |\n| Naive RAG",
"type": "table",
"properties": {
"page_idx": 0,
"content_type": "table",
"has_caption": true,
"index": 1
},
"content_type": "table",
"content_text": "| Configuration | Accuracy | Latency |\n| Naive RAG | 78% | 200ms |\n| Hybrid RAG | 94% | 350ms |\n| Graph RAG | 96% | 600ms |\n\n[Table]: RAG Performance Comparison\n| Configuration | Accuracy | Latency |\n| Naive RAG | 78% | 200ms |\n| Hybrid RAG | 94% | 350ms |\n| Graph RAG | 96% | 600ms |",
"_item_text": "| Configuration | Accuracy | Latency |\n| Naive RAG | 78% | 200ms |\n| Hybrid RAG | 94% | 350ms |\n| Graph RAG | 96% | 600ms |",
"_item_page_idx": 0
},
"doc-63f5b9638e1c": {
"id": "doc-63f5b9638e1c",
"name": "performance_benchmark",
"type": "document",
"properties": {
"doc_id": "b6908504-9061-41b7-bda6-da3819cdf256"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-c531165a9bda": {
"id": "text_c-c531165a9bda",
"name": "HyDE (Hypothetical Document Embeddings) improves r…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "HyDE (Hypothetical Document Embeddings) improves retrieval by generating a hypothetical answer first.",
"_item_text": "HyDE (Hypothetical Document Embeddings) improves retrieval by generating a hypothetical answer first.",
"_item_page_idx": 0
},
"doc-16aa639a33f8": {
"id": "doc-16aa639a33f8",
"name": "test_hyde",
"type": "document",
"properties": {
"doc_id": "be3bc86f-6a10-431c-9c31-71666b2feb23"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"text_c-9a5155983e92": {
"id": "text_c-9a5155983e92",
"name": "CLIP model bridges vision and language for cross-m…",
"type": "text_chunk",
"properties": {
"page_idx": 0,
"content_type": "text",
"has_caption": false,
"index": 0
},
"content_type": "text",
"content_text": "CLIP model bridges vision and language for cross-modal retrieval.",
"_item_text": "CLIP model bridges vision and language for cross-modal retrieval.",
"_item_page_idx": 0
},
"image-dfc8f45882f2": {
"id": "image-dfc8f45882f2",
"name": "image_1",
"type": "image",
"properties": {
"page_idx": 0,
"content_type": "image",
"has_caption": false,
"index": 1
},
"content_type": "image",
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
},
"doc-4d768d8694fc": {
"id": "doc-4d768d8694fc",
"name": "api_multimodal",
"type": "document",
"properties": {
"doc_id": "24101de9-b27a-4b1a-a63f-96aa92329107"
},
"content_type": null,
"content_text": "",
"_item_text": "",
"_item_page_idx": 0
}
},
"relations": [
{
"source": "text_c-ec4611a61b53",
"target": "doc-4c5673cd0cc9",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-55153b8f9577",
"target": "doc-f4b87e7b51cb",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-c3c3cde6071f",
"target": "doc-f4b87e7b51cb",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-55153b8f9577",
"target": "text_c-c3c3cde6071f",
"type": "nearby",
"weight": 0.8
},
{
"source": "text_c-ef467a62127a",
"target": "doc-1b2e2373a460",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-7bd94cd4393c",
"target": "doc-a97956d2e621",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-676838941010",
"target": "doc-19e2c95f250b",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-1926476d68ac",
"target": "doc-621766721b8c",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "image-d107b040969f",
"target": "doc-621766721b8c",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-1926476d68ac",
"target": "image-d107b040969f",
"type": "nearby",
"weight": 0.8
},
{
"source": "image-d107b040969f",
"target": "text_c-1926476d68ac",
"type": "describes",
"weight": 0.6
},
{
"source": "text_c-723213cefe2f",
"target": "doc-63f5b9638e1c",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "table-31e9e6015b3d",
"target": "doc-63f5b9638e1c",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-723213cefe2f",
"target": "table-31e9e6015b3d",
"type": "nearby",
"weight": 0.8
},
{
"source": "table-31e9e6015b3d",
"target": "text_c-723213cefe2f",
"type": "describes",
"weight": 0.6
},
{
"source": "text_c-c531165a9bda",
"target": "doc-16aa639a33f8",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-9a5155983e92",
"target": "doc-4d768d8694fc",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "image-dfc8f45882f2",
"target": "doc-4d768d8694fc",
"type": "belongs_to",
"weight": 1.0
},
{
"source": "text_c-9a5155983e92",
"target": "image-dfc8f45882f2",
"type": "nearby",
"weight": 0.8
},
{
"source": "image-dfc8f45882f2",
"target": "text_c-9a5155983e92",
"type": "describes",
"weight": 0.6
}
]
} |