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LLM Paper Study: GraphRAG's Alternative to LightRAG

Popularity:513 ℃/2024-10-30 14:41:53

1. contexts

Recently there is a very hot open source projectLightRAGGithub6.4K+Star*, a joint production of Beipiao and HKU, is a MicrosoftGraphRAGexcellent alternatives to the current one, so thisqiang~When I had some free time, I read the papers and ran the source code, which led to this article.

2. LightRAGorganizing plan

2.1 already existingRAGLimitations of the system

1) Many systemsRelies only on flat data representations(e.g. plain text), limiting the ability to understand and retrieve information based on the complex relationships between entities in a text.

2) Many systemsLack of contextual awareness required for consistency between various entities and their relationships, resulting in the possibility that the user's problem may not be fully resolved.

2.2  LightRAGcompetitive edge

1) Introducing graph structure: Introducing graph structure into text indexing and related information retrieval links, graph structure can effectively represent entities and their relationships, which is conducive to the coherence and richness of the context.

2) Integrated Information Retrieval: Extract the complete context of interdependent entities from all documents to ensure comprehensive information retrieval. As opposed to the traditionalRAGIt is possible to focus only on theChunkAfter the localized text, there is a lack of global integrated information.

3) Enhanced search efficiency: Improving the efficiency of knowledge retrieval based on graph structures to significantly reduce response time.

4) Rapid adaptation of new data: The ability to quickly adapt to new data updates ensures that the system remains relevant in a dynamic environment.

5) Reduced retrieval overhead: as opposed toGraphRAGIn a community traversal approach, theLightRAGFocus on entity and relationship retrieval, which in turn reduces overhead.

2.3 LightRAGframeworks

 

 

LightRAGIndexing text based on graph structures(graph-based text indexing)Seamless integration into a two-tier search framework(dual-level retrieval framework)in the response, thus enabling the extraction of complex internal relationships between entities and improving the richness and coherence of the response.

The two-tier search strategy consists of a low-level search and a high-level search, where the low-level search focuses on accurate information about specific entities and their relationships, and the high-level search contains a wide range of subject information.

Furthermore, by combining the graph structure with the vector characterizationLightRAGFacilitates efficient retrieval of relevant entities and relationships while enhancing the comprehensiveness of the results based on relevant information in a structured knowledge graph.

LightRAGNo need to rebuild the entire index, reducing computational costs and accelerating adaptation, and its incremental update algorithm ensures timely integration of new data.

2.3.1 Graph-based text indexing

1) Entity and relationship extractionLightRAGFirst cut the large text into smaller text and then use theLLMidentifying and extracting various entities and their relationships in small texts, a move that can facilitate the creation of comprehensive knowledge graphs.promptExamples are shown below:

 

 

2) utilizationLLMPerformance analysis function generates key-value pairs: UseLLMProvides performance analysis functions that generate a text key-value pair for each entity and each relationship(K, V)whichKis a word or phrase that facilitates efficient retrieval.Vis a text paragraph for a summary of the text fragment

3) De-weighting to optimize graph operations: Identical entities and relationships from different passages are recognized and merged through a de-duplication function. Effectively reduces the overhead associated with graph operations by minimizing the size of the graph, thus enabling more efficient data processing

2.3.2 Two-tier search mechanism

1) Generate query keys at the detail level and abstraction level respectivelyConcrete queries are detail-oriented, allowing precise retrieval of information related to characteristic nodes or edges; abstract queries are more conceptual, covering a wider range of topics, abstracts, which are not associated with specific entities.

2) Two-tier search mechanism: Low-level retrieval focuses on retrieving information about specific entities and their attributes or relationships, aiming to retrieve precise information about specified nodes or edges in the graph; high-level retrieval deals with a broader range of topics, aggregating information about multiple related entities and relationships to provide insights into high-level concepts and summaries.

3) Integration of graphs and vectors for efficient retrieval: The graph structure and vector representation enable the retrieval algorithm to effectively utilize local and global keywords, simplify the search process and improve the relevance of the results. It is divided into the following steps:

a. Query Keyword Extraction: In response to the given question.LightRAGThe retrieval algorithm first extracts the local query keywords and all query keywords respectively

keyword extractedpromptBelow:

 

 

b. keyword matching: The retrieval algorithm uses a vector database to match local query keywords with candidate entities, and global query keywords with candidate relationships(Associated with global keywords)

c. Enhancing higher-order relevance: LightRAGFurther collection of local subgraphs of retrieved entities or relations, such as one-hop neighboring nodes of entities or relations

2.3.3 Search Enhanced Answer Generation

1) Using Retrieved Information: Utilizing retrieved information, including entity names, entity descriptions, relationship descriptions, and original text fragments.LightRAGUse the genericLLMto generate responses.

2) Context integration and answer generation: Integrate the query string with the context by calling theLLMGenerate answers.

2.3.4 Example of overall process

 

 

3. test

3.1 data sources

through (a gap)UltraDomainThe benchmark was selected from the4The datasets, which include agriculture, computer science, law, and mixed sets, each contain60W-500Wclassifier for individual things or people, general, catch-all classifiertoken

 

 

3.2 Question generation

In order to assessLightRAGThe performance of the first byLLMgenerating5classifier for individual things or people, general, catch-all classifierRAGusers, and for each user generates5Tasks. Each user has descriptive information detailing their expertise and characteristics to trigger them to ask relevant questions. Each user task also has descriptive information that emphasizes that one of the users lies in theRAGunderlying intent at the time of interaction. For each combination of user tasks, theLLMgenerating5a problem that requires understanding the entire dataset. Thus, each dataset generates a total of125A question.

Question GeneratedpromptBelow:

 

 

3.3 baseline model

selected4The baseline models includeNaive RAG, RQ-RAG, HyDE, GraphRAG

3.4Evaluation dimensions and details

In the experiments, vector retrieval was performed usingnano Vector library.LLMoptionGPT-4o-mini, the chunk size of each dataset is1200In addition, the collection of parameters(gleaning parameterThe purpose of the program is to provide a comprehensive overview of the program by means of only1diskLLMIt is not possible to fully extract the corresponding entity or relationship, so this parameter is intended to add multiple calls to theLLM)set to1

The evaluation criteria were adopted based onLLMThe multidimensional comparison method usingGPT-4o-minibe directed againstLightRAGwith each baseline response being ranked. The main components are as follows4one dimension:comprehensiveness(How well the answer addresses all aspects and details of the question)Diversity(Different perspectives related to the question, how diverse and rich are the answers)Acceptance(Whether the answer is effective in enabling the reader to understand the topic and make clear judgments)Overall evaluation(Cumulative evaluations assessing the first three criteria)

estimationpromptBelow:

 

 

3.5 Results

3.5.1 vs. baselineRAGComparison of methods

 

 

3.5.2 Bi-level search and graph structure-based indexing to enhance ablation results

 

 

3.5.3 Specific case studies

 

 

3.5.4 together withGraphRAGComparison of the cost of

 

 

4. Overall workflow

Pictures are recommended to be enlarged for a better view~

 

 

 

LightGraphThe source code is very readable, and it is recommended that you debug it step-by-step, based on the flowchart above.LightGraphto understand the specific details of its two modules, Retrieval and Generation.

If there are problems at the source code level, you can communicate further in private messages or comments~

5.summarize

One word is enough.~

This paper addresses the open sourceLightRAGThesis research as well as rationale analysis, including core modules, overall workflow of the framework, etc.

If you want to get free access toGPT-4o-mini(used form a nominal expression)apiinterface, as well as the principle or source code is not clear to the watchers, can be private mail or comments to communicate.

6.consultation

1) LightGraphpaper address: /pdf/2410.05779v1

2) LightGraphSource code address:/HKUDS/LightRAG