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"filename: README.md\ncategory: codebase\n\nContext\nLLMs are a phenomenal piece of technology for knowledge generation and reasoning.\nThey are pre-trained on large amounts of publicly available data.\nHow do we best augment LLMs with our own private data?\nWe need a comprehensive toolkit to help perform this data augmentation for LLMs.\n\nProposed Solution\nThat's where LlamaIndex comes in. LlamaIndex is a \"data framework\" to help\nyou build LLM apps. It provides the following tools:\n\nOffers data connectors to ingest your existing data sources and data formats\n(APIs, PDFs, docs, SQL, etc.)\nProvides ways to structure your data (indices, graphs) so that this data can be\neasily used with LLMs.\nProvides an advanced retrieval/query interface over your data:\nFeed in any LLM input prompt, get back retrieved context and knowledge-augmented output.\nAllows easy integrations with your outer application framework\n(e.g. with LangChain, Flask, Docker, ChatGPT, anything else).\nLlamaIndex provides tools for both beginner users and advanced users.\nOur high-level API allows beginner users to use LlamaIndex to ingest and\nquery their data in 5 lines of code. Our lower-level APIs allow advanced users to\ncustomize and extend any module (data connectors, indices, retrievers, query engines,\nreranking modules), to fit their needs."
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181c33df-b9b1-4c14-87c6-051215b43009 | OpenAIEmbedding._get_query_embedding-94de3ab6-9348-48c5-861a-07e7d427b9e5 | BaseEmbedding.get_query_embedding-3bd9285f-6777-435a-9005-2d0b4a7070d7 | null | 1,731,757,793.313799 | 1,731,757,793.977827 | 0.664028 | null | null | null | null | null | null | null |
181c33df-b9b1-4c14-87c6-051215b43009 | BaseSynthesizer.synthesize-603ab2c7-ebfe-440e-874e-2fbde2605c31 | RetrieverQueryEngine._query-bf3f214f-a341-4607-a557-f1fc62dac92d | null | 1,731,757,793.980527 | 1,731,757,794.802716 | 0.822189 | null | null | null | null | null | null | null |
181c33df-b9b1-4c14-87c6-051215b43009 | CompactAndRefine.get_response-ae356ec4-6d56-4dea-9fe4-fb763cf74e58 | BaseSynthesizer.synthesize-603ab2c7-ebfe-440e-874e-2fbde2605c31 | null | 1,731,757,793.980707 | 1,731,757,794.802549 | 0.821842 | null | null | null | null | null | null | null |
181c33df-b9b1-4c14-87c6-051215b43009 | TokenTextSplitter.split_text-644347ff-734e-4c0d-a9bb-3ecf8d5e2c03 | CompactAndRefine.get_response-ae356ec4-6d56-4dea-9fe4-fb763cf74e58 | null | 1,731,757,793.983777 | 1,731,757,793.984429 | 0.000652 | null | null | null | null | null | null | null |
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181c33df-b9b1-4c14-87c6-051215b43009 | DefaultRefineProgram.__call__-bcb3fe41-2b27-4264-95ff-8105441f9c77 | Refine.get_response-51cc755e-04c3-435b-9b28-1997ab5a0c20 | null | 1,731,757,793.985457 | 1,731,757,794.802327 | 0.81687 | null | null | null | null | null | null | null |
181c33df-b9b1-4c14-87c6-051215b43009 | LLM.predict-c77889b0-5322-4d69-9243-13a903849832 | DefaultRefineProgram.__call__-bcb3fe41-2b27-4264-95ff-8105441f9c77 | null | 1,731,757,793.985505 | 1,731,757,794.802272 | 0.816767 | null | d5684daa-151a-4cbf-bef0-d5c69caaf651 | LLMPredictStartEvent | LLM.predict-c77889b0-5322-4d69-9243-13a903849832 | 1,731,757,793.985517 | null | {
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181c33df-b9b1-4c14-87c6-051215b43009 | LLM.predict-c77889b0-5322-4d69-9243-13a903849832 | DefaultRefineProgram.__call__-bcb3fe41-2b27-4264-95ff-8105441f9c77 | null | 1,731,757,793.985505 | 1,731,757,794.802272 | 0.816767 | null | f5a22458-a707-4365-892e-b5f451d2e959 | LLMPredictEndEvent | LLM.predict-c77889b0-5322-4d69-9243-13a903849832 | 1,731,757,794.802117 | null | {
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181c33df-b9b1-4c14-87c6-051215b43009 | OpenAI.chat-f9d05c7f-d53c-4aea-8838-cc22be71edde | LLM.predict-c77889b0-5322-4d69-9243-13a903849832 | null | 1,731,757,793.985888 | 1,731,757,794.802042 | 0.816154 | null | 2ec37de3-4dff-4165-b55a-b13be1f664ce | LLMChatStartEvent | OpenAI.chat-f9d05c7f-d53c-4aea-8838-cc22be71edde | 1,731,757,793.985924 | null | {
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"messages": "[ChatMessage(role=<MessageRole.SYSTEM: 'system'>, content=\"You are an expert Q&A system that is trusted around the world.\\nAlways answer the query using the provided context information, and not prior knowledge.\\nSome rules to follow:\\n1. Never directly reference the given context in your answer.\\n2. Avoid statements like 'Based on the context, ...' or 'The context information ...' or anything along those lines.\", additional_kwargs={}), ChatMessage(role=<MessageRole.USER: 'user'>, content='Context information is below.\\n---------------------\\nfilename: README.md\\ncategory: codebase\\n\\nContext\\nLLMs are a phenomenal piece of technology for knowledge generation and reasoning.\\nThey are pre-trained on large amounts of publicly available data.\\nHow do we best augment LLMs with our own private data?\\nWe need a comprehensive toolkit to help perform this data augmentation for LLMs.\\n\\nProposed Solution\\nThat\\'s where LlamaIndex comes in. LlamaIndex is a \"data framework\" to help\\nyou build LLM apps. It provides the following tools:\\n\\nOffers data connectors to ingest your existing data sources and data formats\\n(APIs, PDFs, docs, SQL, etc.)\\nProvides ways to structure your data (indices, graphs) so that this data can be\\neasily used with LLMs.\\nProvides an advanced retrieval/query interface over your data:\\nFeed in any LLM input prompt, get back retrieved context and knowledge-augmented output.\\nAllows easy integrations with your outer application framework\\n(e.g. with LangChain, Flask, Docker, ChatGPT, anything else).\\nLlamaIndex provides tools for both beginner users and advanced users.\\nOur high-level API allows beginner users to use LlamaIndex to ingest and\\nquery their data in 5 lines of code. Our lower-level APIs allow advanced users to\\ncustomize and extend any module (data connectors, indices, retrievers, query engines,\\nreranking modules), to fit their needs.\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: Tell me about LLMs?\\nAnswer: ', additional_kwargs={})]",
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} |
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