# May 15: Support for GraphRAG, OpenAI GPT-4o model, performance improvements and bug fixes ### New Features * :bulb: Graphlit now supports GraphRAG, where the extracted entities in the knowledge graph can be added as additional context to your RAG con,versation. Also, with GraphRAG, entities can be extracted from the user prompt, and used as additional content filters - or can be used to query related content sources, which are combined with the vector search results. This can be configured by specifying your `graphStrategy` in the Specification object. * :bulb: Graphlit now supports LLM revisions within RAG conversations, where the LLM can be prompted to revise its initial completion response. From our testing, this has been shown to provide 35% more output tokens with higher quality responses. This can be configured by specifying your `revisionStrategy`, and you can use our built-in revision prompt, or provide a custom one, and specify how many revisions you want the LLM to make. * :bulb: Graphlit now supports the new OpenAI [GPT-4o ](https://platform.openai.com/docs/models/gpt-4o)model for RAG conversations. * :zap: We have changed the default model for Conversations to be OpenAI GPT-4o, from Azure OpenAI GPT-3.5 16k. This provides faster performance and better quality output. * Added `graph` to `promptConversation` response, so you can visualize or leverage the nodes and edges of the knowledge graph, resulting from the content retrieval. For example, if a Person and Organization were observed in the cited content sources used by the RAG pipeline, you will get back those entities and their relationship (such as Person 'works-for' Organization). * Expanded the enriched data from WIkipedia to include the long description of an entity. * Added `getSharePointLibraries`, `getSharePointFolders`, and `getOneDriveFolders` queries to the API, which can be used to enumerate the storage services. This makes locating the SharePoint `libraryId` easier, for example. * Added `getTeams` and `getTeamsChannels` queries to the API for enumerating Microsoft Teams workspaces. * Added `extractedCount` to the entity extraction connector to limit the number of extracted entities, per entity type. I.e. if extracted count is 10, it will extract at most ten each of Persons, Organizations, etc. * :fire: We have improved performance in several areas: creation of observations after entity extraction, access to cloud storage, rendering the RAG context. * :fire: We have optimized the LLM entity extraction process to identify more properties, as well as entity-to-entity relationships. ### Bugs Fixed * GPLA-2652: Not extracting text from HTML in RSS post * GPLA-2627: Limit filter only returning half the results * GPLA-2613: Not properly extracting structured text from JSON/XML formats --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://changelog.graphlit.dev/may-2024/may-15-support-for-graphrag-openai-gpt-4o-model-performance-improvements-and-bug-fixes.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.