From 8944e81c074ffad08aabb9b24734da686c57ad35 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Fri, 13 Feb 2026 12:35:05 +0100 Subject: [PATCH 1/9] Add Atomic GraphRAG --- pages/ai-ecosystem/_meta.ts | 1 + pages/ai-ecosystem/atomic-graphrag.mdx | 6 ++++++ 2 files changed, 7 insertions(+) create mode 100644 pages/ai-ecosystem/atomic-graphrag.mdx diff --git a/pages/ai-ecosystem/_meta.ts b/pages/ai-ecosystem/_meta.ts index fa3feae2e..297e2c661 100644 --- a/pages/ai-ecosystem/_meta.ts +++ b/pages/ai-ecosystem/_meta.ts @@ -1,5 +1,6 @@ export default { "graph-rag": "GraphRAG", + "atomic-graphrag": "Atomic GraphRAG", "integrations": "Integrations", "machine-learning": "Machine learning", "agents": "Agents", diff --git a/pages/ai-ecosystem/atomic-graphrag.mdx b/pages/ai-ecosystem/atomic-graphrag.mdx new file mode 100644 index 000000000..8eecd8f0e --- /dev/null +++ b/pages/ai-ecosystem/atomic-graphrag.mdx @@ -0,0 +1,6 @@ +--- +title: Atomic GraphRAG +description: +--- + +# Atomic GraphRAG From af9d6ae38ebe42a202f999c2d13626f3d258c0a6 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Wed, 18 Feb 2026 21:00:16 +0100 Subject: [PATCH 2/9] Add #1 proposal of the restructuring --- pages/ai-ecosystem/_meta.ts | 2 +- pages/ai-ecosystem/atomic-graphrag.mdx | 6 - pages/ai-ecosystem/graph-rag/_meta.ts | 3 +- .../graph-rag/atomic-graphrag.mdx | 31 + .../graph-rag/knowledge-graph-creation.mdx | 13 +- pages/ai-ecosystem/graph-rag/pipelines.mdx | 18 + .../ai-ecosystem/graph-rag/pipelines/_meta.ts | 5 + .../pipelines/query-focused-summarization.mdx | 26 + .../search.mdx} | 20 +- .../graph-rag/pipelines/text2cypher.mdx | 10 + .../ai-ecosystem/graph-rag/prerequisites.mdx | 2 +- pages/ai-ecosystem/skills.mdx | 29 + pages/querying/text-search.mdx | 4 + pages/querying/vector-search.mdx | 2 +- .../agentic-graphrag-pipeline.svg | 1 + .../pipelines/graphrag-pipelines.svg | 1 + .../search}/1-hop.jpg | Bin .../search}/community-detection.jpg | Bin .../search}/geospatial.jpg | Bin .../search}/medical-records-example.jpg | Bin .../search}/text-search.jpg | Bin .../search}/vector-search.jpg | Bin public/sitemap.xml | 647 +++++++++--------- 23 files changed, 476 insertions(+), 344 deletions(-) delete mode 100644 pages/ai-ecosystem/atomic-graphrag.mdx create mode 100644 pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx create mode 100644 pages/ai-ecosystem/graph-rag/pipelines.mdx create mode 100644 pages/ai-ecosystem/graph-rag/pipelines/_meta.ts create mode 100644 pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx rename pages/ai-ecosystem/graph-rag/{knowledge-retrieval.mdx => pipelines/search.mdx} (94%) create mode 100644 pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx create mode 100644 pages/ai-ecosystem/skills.mdx create mode 100644 public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg create mode 100644 public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/1-hop.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/community-detection.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/geospatial.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/medical-records-example.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/text-search.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{knowledge-retrieval => pipelines/search}/vector-search.jpg (100%) diff --git a/pages/ai-ecosystem/_meta.ts b/pages/ai-ecosystem/_meta.ts index 297e2c661..df6a4d100 100644 --- a/pages/ai-ecosystem/_meta.ts +++ b/pages/ai-ecosystem/_meta.ts @@ -1,6 +1,6 @@ export default { "graph-rag": "GraphRAG", - "atomic-graphrag": "Atomic GraphRAG", + "skills": "Skills", "integrations": "Integrations", "machine-learning": "Machine learning", "agents": "Agents", diff --git a/pages/ai-ecosystem/atomic-graphrag.mdx b/pages/ai-ecosystem/atomic-graphrag.mdx deleted file mode 100644 index 8eecd8f0e..000000000 --- a/pages/ai-ecosystem/atomic-graphrag.mdx +++ /dev/null @@ -1,6 +0,0 @@ ---- -title: Atomic GraphRAG -description: ---- - -# Atomic GraphRAG diff --git a/pages/ai-ecosystem/graph-rag/_meta.ts b/pages/ai-ecosystem/graph-rag/_meta.ts index 95da89010..511b7178c 100644 --- a/pages/ai-ecosystem/graph-rag/_meta.ts +++ b/pages/ai-ecosystem/graph-rag/_meta.ts @@ -1,6 +1,7 @@ export default { "prerequisites": "Prerequisites", "knowledge-graph-creation": "Knowledge graph creation", - "knowledge-retrieval": "Knowledge retrieval", + "pipelines": "Pipelines", + "atomic-graphrag": "Atomic GraphRAG", "examples-and-demos": "Examples & demos" } \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx b/pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx new file mode 100644 index 000000000..337aca050 --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx @@ -0,0 +1,31 @@ +--- +title: Atomic GraphRAG +description: +--- + +# Atomic GraphRAG + +A set of Memgraph tools or primitives that enables building GraphRAG pipelines +as single queries. The list of tools includes: +- [Real-time schema info (`SHOW SCHEMA INFO` query)](/querying/schema) +- Embedding procedures [`embeddings.text` and `embeddings.node_sentence`](/advanced-algorithms/available-algorithms/embeddings) +- Search primitives: + - [text search](/querying/text-search) + - [vector search](/querying/vector-search) +- Graph algorithms: + - [Deep path traversals](/advanced-algorithms/deep-path-traversal) + - [Community detection](/advanced-algorithms/available-algorithms/community_detection) + - [Page Rank](/advanced-algorithms/available-algorithms/pagerank) +- [`llm.complete`](/advanced-algorithms/available-algorithms/llm) +- Server-side parameters + +To get the basics of GraphRAG, take a look under the [GraphRAG +Pipelines](/ai-ecosystem/pipelines). + +Using the [GraphRAG +Skill](https://github.com/memgraph/skills/tree/main/memgraph-graph-rag), it's +possible to guide and agent to start building appropriate GraphRAG Pipelines +based on the user question/prompt. For more skills, visit [Memgraph Agent +Skills](https://github.com/memgraph/skills). + +![atomic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg) \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx b/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx index e6328f005..7079f1af4 100644 --- a/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx +++ b/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx @@ -72,11 +72,18 @@ Steps for structured data: ### Unstructured data -Working with **unstructured data** (e.g. text, images or logs) requires additional preprocessing to create a valid knowledge graph. The process involves balancing automation and human oversight. +Working with **unstructured data** (e.g. text, images or logs) requires +additional preprocessing to create a valid knowledge graph. The process involves +balancing automation and human oversight. #### Simplest approach -Transforming unstructured data like text, images or logs into a structured knowledge graph requires additional steps. Tools like [LangChain](/ai-ecosystem/integrations#langchain) and [LlamaIndex](/ai-ecosystem/integrations#llamaindex) simplify this process by bridging the gap between unstructured data and graph databases like Memgraph. +Transforming unstructured data like text, images or logs into a structured +knowledge graph requires additional steps. Tools +like [LangChain](/ai-ecosystem/integrations#langchain) and [LlamaIndex](/ai-ecosystem/integrations#llamaindex) simplify +this process by bridging the gap between unstructured data and graph databases +like Memgraph. There is also the packed solution under +[Unstructured2Graph](/ai-ecosystem/unstructured2graph). For more information, refer to the following: @@ -99,6 +106,6 @@ By following these steps, you can work towards making your knowledge graph more Here’s an example: [How to Extract Entities and Build a Knowledge Graph with Memgraph and SpaCy](https://memgraph.com/blog/extract-entities-build-knowledge-graph-memgraph-spacy). -To see how the concepts above can be applied in real-world scenarios, check out the [Knowledge retrieval](/ai-ecosystem/graph-rag/knowledge-retrieval) and [Examples and demos](/ai-ecosystem/graph-rag/examples-and-demos) pages as your next step. +To see how the concepts above can be applied in real-world scenarios, check out the [Pipelines](/ai-ecosystem/graph-rag/pipelines) and [Examples and demos](/ai-ecosystem/graph-rag/examples-and-demos) pages as your next step. diff --git a/pages/ai-ecosystem/graph-rag/pipelines.mdx b/pages/ai-ecosystem/graph-rag/pipelines.mdx new file mode 100644 index 000000000..4201775d3 --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/pipelines.mdx @@ -0,0 +1,18 @@ +--- +title: GraphRAG Pipelines +description: Overview of typical pipelines in a GraphRAG system, covering verious retrieval types. +--- + +# GraphRAG Pipelines + +There are a few main GraphRAG pipeline types: +- **Analytical**: When you need an exact database query to fetch specific data +(e.g., deterministic counting), use +[Text2Cypher](/ai-ecosystem/graph-rag/pipelines/text2cypher). +- **Local**: To retrieve a focused subset of your data for more contextual +answers, leverage [GraphRAG Search](/ai-ecosystem/graph-rag/pipelines/search). +- **Global**: For cases where the entire dataset requires advanced processing or +summarization, apply [Query-focused +Summarization](/ai-ecosystem/graph-rag/pipelines/query-focused-summarization). + +![graphrag_pipelines](/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg) \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/pipelines/_meta.ts b/pages/ai-ecosystem/graph-rag/pipelines/_meta.ts new file mode 100644 index 000000000..36ea46b58 --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/pipelines/_meta.ts @@ -0,0 +1,5 @@ +export default { + "text2cypher": "Text2Cypher", + "search": "GraphRAG Search", + "query-focused-summarization": "Query-focused summarization", +} \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx b/pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx new file mode 100644 index 000000000..bd0860110 --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx @@ -0,0 +1,26 @@ +--- +title: Query-Focused Summarization +description: Learn how to generate targeted summaries from graph-based retrieval to maximize answer relevance in GraphRAG pipelines. +--- + +# Query-focused Summarization + +Typical query to perform query-focused summarization follows: +```Cypher +WITH "... the question ..." AS USER_QUESTION +// 1. Identify relevant communities (thresholding) +MATCH (c:Community) WHERE c.nodes_count > 5 // only use significant communities +// 2. Map: Generate a partial answer for EACH community +WITH USER_QUESTION, c, + llm.complete("How does the following community summary: " + c.summary + + " relate to: " + USER_QUESTION) AS partial_answer + WHERE partial_answer IS NOT NULL AND partial_answer <> "" +WITH USER_QUESTION, collect(partial_answer) AS map_results +// 3. Reduce: Synthesize the final consolidated answer +WITH "The following are partial answers from different thematic clusters of " + + " a dataset. Synthesize them into a single, cohesive response to the " + + "original query: " + USER_QUESTION + + "\n\nPartial Answers:\n" + + reduce(s = "", res IN map_results | s + "- " + res + "\n") AS reduce_prompt +RETURN llm.complete(reduce_prompt) AS final_answer; +``` \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/knowledge-retrieval.mdx b/pages/ai-ecosystem/graph-rag/pipelines/search.mdx similarity index 94% rename from pages/ai-ecosystem/graph-rag/knowledge-retrieval.mdx rename to pages/ai-ecosystem/graph-rag/pipelines/search.mdx index e3981dc8f..cbc5f8a38 100644 --- a/pages/ai-ecosystem/graph-rag/knowledge-retrieval.mdx +++ b/pages/ai-ecosystem/graph-rag/pipelines/search.mdx @@ -1,13 +1,13 @@ --- -title: Knowledge retrieval -description: Knowledge graph retrieval involves querying a knowledge graph to extract the most relevant information for your use case. +title: GraphRAG Search +description: GraphRAG Search involves querying a knowledge graph to extract the most relevant information for your use case. --- import {CommunityLinks} from '/components/social-card/CommunityLinks' import { Callout } from 'nextra/components' import { Cards } from 'nextra/components' -# Knowledge retrieval +# GraphRAG Search Knowledge retrieval involves querying a knowledge graph to extract the most relevant information for your use case. In the context of GraphRAG, this step @@ -18,7 +18,7 @@ Choosing the best retrieval strategy depends on your data, the type of insights you’re seeking, and the complexity of your queries. Memgraph provides a suite of powerful tools and techniques to optimize retrieval for your knowledge graph. -![medical-records-example](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/medical-records-example.jpg) +![medical-records-example](/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg) ## Retrieval types @@ -57,7 +57,7 @@ similarity rather than exact matches. It’s ideal for scenarios where meaning, context, or relatedness is more important than specific terms (e.g., finding similar topics or concepts). -![vector-search](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/vector-search.jpg) +![vector-search](/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg) Perform vector search on your knowledge graph to extract semantically similar nodes, setting the foundation for more advanced retrieval. @@ -75,14 +75,14 @@ and “iconic landmarks”, leading to nodes like *Paris*. - [Simplify Data Retrieval with Memgraph’s Vector Search](https://memgraph.com/blog/simplify-data-retrieval-memgraph-vector-search) - [Building a Movie Similarity Search Engine with Vector Search in Memgraph](https://memgraph.com/blog/build-movie-similarity-search-vector-search-memgraph) -#### Text search (experimental) +#### Text search Currently, in Memgraph, text search is a retrieval technique that finds nodes with their properties based on exact text matches. It’s best used for queries involving specific terms, phrases, or entity names (e.g., “Find all nodes containing the keyword ‘climate change’”). -![text-search](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/text-search.jpg) +![text-search](/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg) Use text search as a direct approach to narrow down specific nodes before expanding context. @@ -103,7 +103,7 @@ types](/fundamentals/data-types#point) representing locations with latitude and longitude and on leveraging [point indexes](/fundamentals/indexes#point-index) to ensure optimized query performance, allowing for quick proximity and spatial queries. -![geospatial](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/geospatial.jpg) +![geospatial](/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg) Query example: @@ -148,7 +148,7 @@ Deep path traversals enable reasoning across your graph, providing insights that go beyond surface-level connections. This expanded information is sent to the LLM as part of the input prompt to provide better context. -![one-hop](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/1-hop.jpg) +![one-hop](/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg) Read more: @@ -169,7 +169,7 @@ node. This strategy allows you to get answers to global questions quickly, without the need to search across the whole graph and in that way provide meaningful insights for LLM. -![community-detection](/pages/ai-ecosystem/graph-rag/knowledge-retrieval/community-detection.jpg) +![community-detection](/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg) #### Centrality measures diff --git a/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx b/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx new file mode 100644 index 000000000..ef7af78de --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx @@ -0,0 +1,10 @@ +--- +title: Text-to-Cypher Generation +description: Learn how to convert natural language questions into precise Cypher queries for powerful knowledge retrieval in GraphRAG pipelines. +--- + +# Text2Cypher + +Text2Cypher is usually involves: +1. running the (`SHOW SCHEMA INFO` query](/querying/schema) +2. asking LLM to generate a Cypher query that would give the best data to answer the user question. \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/prerequisites.mdx b/pages/ai-ecosystem/graph-rag/prerequisites.mdx index b49cef138..d22faedcf 100644 --- a/pages/ai-ecosystem/graph-rag/prerequisites.mdx +++ b/pages/ai-ecosystem/graph-rag/prerequisites.mdx @@ -58,7 +58,7 @@ Once your environment is ready: 1. Proceed to [Knowledge graph creation](/ai-ecosystem/graph-rag/knowledge-graph-creation) to model and import your data into Memgraph. -2. Explore [GraphRAG features](/ai-ecosystem/graph-rag/knowledge-retrieval) to +2. Explore [GraphRAG Pipelines](/ai-ecosystem/graph-rag/pipelines) to understand how to retrieve and manage knowledge effectively. 3. Review [Examples and demos](/ai-ecosystem/graph-rag/examples-and-demos) for inspiration on building your own GraphRAG system. diff --git a/pages/ai-ecosystem/skills.mdx b/pages/ai-ecosystem/skills.mdx new file mode 100644 index 000000000..44b4a483f --- /dev/null +++ b/pages/ai-ecosystem/skills.mdx @@ -0,0 +1,29 @@ +--- +title: Agent Skills +description: Overview of the Agent Skills within Memgraph's ecosystem +--- + +# Agent Skills + +Agent skills are modular components that define specific abilities or behaviors +for AI agents. Rather than hardcoded instructions, a skill is like a "job +description", it describes what the agent can do, the kind of input it expects, +and the kinds of outputs or behaviors it can produce. Skills guide agents by +providing them with knowledge, context, and purpose, helping them to operate +effectively in non-deterministic scenarios where flexibility and judgment are +required. + +Key characteristics of agent skills: +- They enable agents to handle complex, open-ended, or ambiguous tasks—such as +understanding documentation or synthesizing information from large volumes of +text. +- Skills are not tools themselves, but rather reusable competency modules that +you can compose, mix, and match. +- Skills provide structure and guidance, making agents more reliable and +explainable. +- They serve as excellent documentation, clarifying what the agent is capable of +and how it should behave. +- Skills are defined in ways that LLMs (large language models) can understand. + +Visit [Memgraph Agent Skills repo](https://github.com/memgraph/skills), to start +using skills. \ No newline at end of file diff --git a/pages/querying/text-search.mdx b/pages/querying/text-search.mdx index f4c10cf21..e3a0a8afa 100644 --- a/pages/querying/text-search.mdx +++ b/pages/querying/text-search.mdx @@ -13,6 +13,10 @@ To make a node or edge searchable, you must first create a text index for it. Text indices and search are powered by the [Tantivy](https://github.com/quickwit-oss/tantivy) full-text search engine. +Text search is commonly used as a [retrieval +technique](/ai-ecosystem/graph-rag/pipelines/search#text-search) in RAG systems +to find entities based on exact and fuzzy matches. + Text search is no longer an experimental feature as of Memgraph version 3.6. You can use text search without any special configuration flags. diff --git a/pages/querying/vector-search.mdx b/pages/querying/vector-search.mdx index 305abce7c..c7e197303 100644 --- a/pages/querying/vector-search.mdx +++ b/pages/querying/vector-search.mdx @@ -29,7 +29,7 @@ To configure vector search as described in the example, please use the latest Me Vector search is commonly used as a [retrieval -technique](/ai-ecosystem/graph-rag/knowledge-retrieval#vector-search) in RAG +technique](/ai-ecosystem/graph-rag/pipelines/search#vector-search) in RAG systems to find entities based on semantic similarity rather than exact matches. ![vector-search-workflow](/pages/querying/vector-search/vector-search-workflow.png) diff --git a/public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg b/public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg new file mode 100644 index 000000000..0dae7f891 --- /dev/null +++ b/public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg b/public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg new file mode 100644 index 000000000..66936c66f --- /dev/null +++ b/public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/1-hop.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/1-hop.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/community-detection.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/community-detection.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/geospatial.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/geospatial.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/medical-records-example.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/medical-records-example.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/text-search.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/text-search.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/vector-search.jpg b/public/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/knowledge-retrieval/vector-search.jpg rename to public/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg diff --git a/public/sitemap.xml b/public/sitemap.xml index 76d16fdde..ba257d348 100644 --- a/public/sitemap.xml +++ b/public/sitemap.xml @@ -1,324 +1,329 @@ -https://memgraph.com/docs2026-02-12T11:39:47.901Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms2026-02-12T11:39:47.901Zdaily0.7 -https://memgraph.com/docs/advanced-algorithms/available-algorithms2026-02-12T11:39:47.901Zdaily0.7 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00:00:00 2001 From: Marko Budiselic Date: Fri, 20 Feb 2026 09:51:47 +0100 Subject: [PATCH 3/9] Add proper structure --- pages/ai-ecosystem/_meta.ts | 6 +- pages/ai-ecosystem/agents/_meta.ts | 5 + pages/ai-ecosystem/agents/index.mdx | 15 ++ .../sql2graph-agent.mdx} | 16 +- .../unstructured2graph-agent.mdx} | 6 +- pages/ai-ecosystem/graph-rag/_meta.ts | 4 +- ...omic-graphrag.mdx => agentic-graphrag.mdx} | 8 +- .../{pipelines.mdx => atomic-pipelines.mdx} | 8 +- .../{pipelines => atomic-pipelines}/_meta.ts | 2 +- .../local-graph-search.mdx} | 18 +- .../query-focused-summarization.mdx | 0 .../atomic-pipelines/text2cypher.mdx | 27 ++ .../graph-rag/knowledge-graph-creation.mdx | 6 +- .../graph-rag/pipelines/text2cypher.mdx | 10 - .../ai-ecosystem/graph-rag/prerequisites.mdx | 2 +- pages/ai-ecosystem/integrations.mdx | 235 +---------------- pages/ai-ecosystem/mcp.mdx | 239 ++++++++++++++++++ pages/querying/text-search.mdx | 2 +- pages/querying/vector-search.mdx | 2 +- .../agentic-graphrag-pipeline.svg | 0 .../atomic-graphrag-pipelines.svg} | 0 .../local-graph-search}/1-hop.jpg | Bin .../community-detection.jpg | Bin .../local-graph-search}/geospatial.jpg | Bin .../medical-records-example.jpg | Bin .../local-graph-search}/text-search.jpg | Bin .../local-graph-search}/vector-search.jpg | Bin public/sitemap.xml | 13 +- 28 files changed, 331 insertions(+), 293 deletions(-) create mode 100644 pages/ai-ecosystem/agents/_meta.ts create mode 100644 pages/ai-ecosystem/agents/index.mdx rename pages/ai-ecosystem/{agents.mdx => agents/sql2graph-agent.mdx} (94%) rename pages/ai-ecosystem/{unstructured2graph.mdx => agents/unstructured2graph-agent.mdx} (97%) rename pages/ai-ecosystem/graph-rag/{atomic-graphrag.mdx => agentic-graphrag.mdx} (84%) rename pages/ai-ecosystem/graph-rag/{pipelines.mdx => atomic-pipelines.mdx} (58%) rename pages/ai-ecosystem/graph-rag/{pipelines => atomic-pipelines}/_meta.ts (71%) rename pages/ai-ecosystem/graph-rag/{pipelines/search.mdx => atomic-pipelines/local-graph-search.mdx} (94%) rename pages/ai-ecosystem/graph-rag/{pipelines => atomic-pipelines}/query-focused-summarization.mdx (100%) create mode 100644 pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx delete mode 100644 pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx create mode 100644 pages/ai-ecosystem/mcp.mdx rename public/pages/ai-ecosystem/graph-rag/{atomic-graphrag => agentic-graphrag}/agentic-graphrag-pipeline.svg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/graphrag-pipelines.svg => atomic-pipelines/atomic-graphrag-pipelines.svg} (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/1-hop.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/community-detection.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/geospatial.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/medical-records-example.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/text-search.jpg (100%) rename public/pages/ai-ecosystem/graph-rag/{pipelines/search => atomic-pipelines/local-graph-search}/vector-search.jpg (100%) diff --git a/pages/ai-ecosystem/_meta.ts b/pages/ai-ecosystem/_meta.ts index df6a4d100..e75cf248c 100644 --- a/pages/ai-ecosystem/_meta.ts +++ b/pages/ai-ecosystem/_meta.ts @@ -1,8 +1,8 @@ export default { "graph-rag": "GraphRAG", + "mcp": "MCP", "skills": "Skills", - "integrations": "Integrations", - "machine-learning": "Machine learning", "agents": "Agents", - "unstructured2graph": "Unstructured2Graph" + "machine-learning": "Machine learning", + "integrations": "Integrations" } \ No newline at end of file diff --git a/pages/ai-ecosystem/agents/_meta.ts b/pages/ai-ecosystem/agents/_meta.ts new file mode 100644 index 000000000..2f2247c27 --- /dev/null +++ b/pages/ai-ecosystem/agents/_meta.ts @@ -0,0 +1,5 @@ +export default { + "index": "Agents", + "sql2graph-agent": "SQL2Graph Agent", + "unstructured2graph-agent": "Unstructured2Graph Agent", +} diff --git a/pages/ai-ecosystem/agents/index.mdx b/pages/ai-ecosystem/agents/index.mdx new file mode 100644 index 000000000..c6cccd607 --- /dev/null +++ b/pages/ai-ecosystem/agents/index.mdx @@ -0,0 +1,15 @@ +--- +title: Agents +description: Memgraph agents help you build graph applications faster by leveraging the power of AI. +--- + +# Memgraph Agents + +**Memgraph Agents** are specialized tools designed to streamline and enhance the +development of graph applications. These agents leverage **Large Language Models +(LLMs)** to provide intelligent solutions for various graph-related tasks. + +- [SQL2Graph Agent](/ai-ecosystem/agents/sql2graph-agent): AI-powered relational + to graph migration. +- [Unstructured2Graph Agent](/ai-ecosystem/agents/unstructured2graph-agent): + unstructured document ingestion and knowledge graph creation. diff --git a/pages/ai-ecosystem/agents.mdx b/pages/ai-ecosystem/agents/sql2graph-agent.mdx similarity index 94% rename from pages/ai-ecosystem/agents.mdx rename to pages/ai-ecosystem/agents/sql2graph-agent.mdx index 733d4dd15..b28212677 100644 --- a/pages/ai-ecosystem/agents.mdx +++ b/pages/ai-ecosystem/agents/sql2graph-agent.mdx @@ -1,19 +1,11 @@ --- -title: Agents -description: Memgraph agents are build to help you build graph applications faster by leveraging the power of AI. +title: SQL2Graph Agent +description: SQL2Graph Agent helps you migrate relational databases to Memgraph with deterministic or LLM-powered modeling. --- -import { Callout, Steps } from 'nextra/components' +import { Callout } from 'nextra/components' import {CommunityLinks} from '/components/social-card/CommunityLinks' -# Memgraph Agents - -**Memgraph Agents** are specialized tools designed to streamline and enhance the -development of Graph applications. These agents leverage **Large Language Models -(LLMs)** to provide intelligent solutions for various graph-related tasks. By the -nature of technology maturity, some agents may be experimental and are -continuously evolving to better serve your needs. - -## SQL2Graph Agent +# SQL2Graph Agent The **SQL2Graph Agent** is an intelligent database migration agent that transforms **relational databases** (MySQL, PostgreSQL) into **graph databases** using diff --git a/pages/ai-ecosystem/unstructured2graph.mdx b/pages/ai-ecosystem/agents/unstructured2graph-agent.mdx similarity index 97% rename from pages/ai-ecosystem/unstructured2graph.mdx rename to pages/ai-ecosystem/agents/unstructured2graph-agent.mdx index 1a72951c0..b29d8bd17 100644 --- a/pages/ai-ecosystem/unstructured2graph.mdx +++ b/pages/ai-ecosystem/agents/unstructured2graph-agent.mdx @@ -1,12 +1,12 @@ --- -title: Unstructured2Graph -description: Learn how to use Unstructured2Graph to transform unstructured data into structured graph data within Memgraph. +title: Unstructured2Graph Agent +description: Learn how to use the Unstructured2Graph Agent to transform unstructured data into structured graph data within Memgraph. --- import { Callout } from 'nextra/components' import { Steps, Tabs } from 'nextra/components' import { CommunityLinks } from '/components/social-card/CommunityLinks' -# Unstructured2Graph +# Unstructured2Graph Agent Every company sits on a pile of unstructured documents: reports, PDFs, research papers, policies, or meeting notes. They contain valuable knowledge, but little diff --git a/pages/ai-ecosystem/graph-rag/_meta.ts b/pages/ai-ecosystem/graph-rag/_meta.ts index 511b7178c..b1af5aeba 100644 --- a/pages/ai-ecosystem/graph-rag/_meta.ts +++ b/pages/ai-ecosystem/graph-rag/_meta.ts @@ -1,7 +1,7 @@ export default { "prerequisites": "Prerequisites", "knowledge-graph-creation": "Knowledge graph creation", - "pipelines": "Pipelines", - "atomic-graphrag": "Atomic GraphRAG", + "atomic-pipelines": "Atomic Pipelines", + "agentic-graphrag": "Agentic GraphRAG", "examples-and-demos": "Examples & demos" } \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx b/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx similarity index 84% rename from pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx rename to pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx index 337aca050..c79fb59c2 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-graphrag.mdx +++ b/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx @@ -1,9 +1,9 @@ --- -title: Atomic GraphRAG +title: Agentic GraphRAG description: --- -# Atomic GraphRAG +# Agentic GraphRAG A set of Memgraph tools or primitives that enables building GraphRAG pipelines as single queries. The list of tools includes: @@ -20,7 +20,7 @@ as single queries. The list of tools includes: - Server-side parameters To get the basics of GraphRAG, take a look under the [GraphRAG -Pipelines](/ai-ecosystem/pipelines). +Pipelines](/ai-ecosystem/graph-rag/atomic-pipelines). Using the [GraphRAG Skill](https://github.com/memgraph/skills/tree/main/memgraph-graph-rag), it's @@ -28,4 +28,4 @@ possible to guide and agent to start building appropriate GraphRAG Pipelines based on the user question/prompt. For more skills, visit [Memgraph Agent Skills](https://github.com/memgraph/skills). -![atomic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg) \ No newline at end of file +![agentic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/agentic-graphrag/agentic-graphrag-pipeline.svg) \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/pipelines.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx similarity index 58% rename from pages/ai-ecosystem/graph-rag/pipelines.mdx rename to pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx index 4201775d3..cfd58d191 100644 --- a/pages/ai-ecosystem/graph-rag/pipelines.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx @@ -8,11 +8,11 @@ description: Overview of typical pipelines in a GraphRAG system, covering veriou There are a few main GraphRAG pipeline types: - **Analytical**: When you need an exact database query to fetch specific data (e.g., deterministic counting), use -[Text2Cypher](/ai-ecosystem/graph-rag/pipelines/text2cypher). +[Text2Cypher](/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher). - **Local**: To retrieve a focused subset of your data for more contextual -answers, leverage [GraphRAG Search](/ai-ecosystem/graph-rag/pipelines/search). +answers, leverage [Local Graph Search](/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search). - **Global**: For cases where the entire dataset requires advanced processing or summarization, apply [Query-focused -Summarization](/ai-ecosystem/graph-rag/pipelines/query-focused-summarization). +Summarization](/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization). -![graphrag_pipelines](/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg) \ No newline at end of file +![atomic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/atomic-pipelines/atomic-graphrag-pipelines.svg) \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/pipelines/_meta.ts b/pages/ai-ecosystem/graph-rag/atomic-pipelines/_meta.ts similarity index 71% rename from pages/ai-ecosystem/graph-rag/pipelines/_meta.ts rename to pages/ai-ecosystem/graph-rag/atomic-pipelines/_meta.ts index 36ea46b58..05d3022a4 100644 --- a/pages/ai-ecosystem/graph-rag/pipelines/_meta.ts +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/_meta.ts @@ -1,5 +1,5 @@ export default { "text2cypher": "Text2Cypher", - "search": "GraphRAG Search", + "local-graph-search": "Local Graph Search", "query-focused-summarization": "Query-focused summarization", } \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/pipelines/search.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx similarity index 94% rename from pages/ai-ecosystem/graph-rag/pipelines/search.mdx rename to pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx index cbc5f8a38..b828ad741 100644 --- a/pages/ai-ecosystem/graph-rag/pipelines/search.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx @@ -1,13 +1,13 @@ --- -title: GraphRAG Search -description: GraphRAG Search involves querying a knowledge graph to extract the most relevant information for your use case. +title: Local Graph Search +description: Local Graph Search involves querying a knowledge graph to extract the most relevant information for your use case. --- import {CommunityLinks} from '/components/social-card/CommunityLinks' import { Callout } from 'nextra/components' import { Cards } from 'nextra/components' -# GraphRAG Search +# Local Graph Search Knowledge retrieval involves querying a knowledge graph to extract the most relevant information for your use case. In the context of GraphRAG, this step @@ -18,7 +18,7 @@ Choosing the best retrieval strategy depends on your data, the type of insights you’re seeking, and the complexity of your queries. Memgraph provides a suite of powerful tools and techniques to optimize retrieval for your knowledge graph. -![medical-records-example](/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg) +![medical-records-example](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/medical-records-example.jpg) ## Retrieval types @@ -57,7 +57,7 @@ similarity rather than exact matches. It’s ideal for scenarios where meaning, context, or relatedness is more important than specific terms (e.g., finding similar topics or concepts). -![vector-search](/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg) +![vector-search](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/vector-search.jpg) Perform vector search on your knowledge graph to extract semantically similar nodes, setting the foundation for more advanced retrieval. @@ -82,7 +82,7 @@ with their properties based on exact text matches. It’s best used for queries involving specific terms, phrases, or entity names (e.g., “Find all nodes containing the keyword ‘climate change’”). -![text-search](/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg) +![text-search](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/text-search.jpg) Use text search as a direct approach to narrow down specific nodes before expanding context. @@ -103,7 +103,7 @@ types](/fundamentals/data-types#point) representing locations with latitude and longitude and on leveraging [point indexes](/fundamentals/indexes#point-index) to ensure optimized query performance, allowing for quick proximity and spatial queries. -![geospatial](/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg) +![geospatial](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/geospatial.jpg) Query example: @@ -148,7 +148,7 @@ Deep path traversals enable reasoning across your graph, providing insights that go beyond surface-level connections. This expanded information is sent to the LLM as part of the input prompt to provide better context. -![one-hop](/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg) +![one-hop](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/1-hop.jpg) Read more: @@ -169,7 +169,7 @@ node. This strategy allows you to get answers to global questions quickly, without the need to search across the whole graph and in that way provide meaningful insights for LLM. -![community-detection](/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg) +![community-detection](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/community-detection.jpg) #### Centrality measures diff --git a/pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx similarity index 100% rename from pages/ai-ecosystem/graph-rag/pipelines/query-focused-summarization.mdx rename to pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx new file mode 100644 index 000000000..150b6c7ca --- /dev/null +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx @@ -0,0 +1,27 @@ +--- +title: Text-to-Cypher Generation +description: Learn how to convert natural language questions into precise Cypher queries for powerful knowledge retrieval in GraphRAG pipelines. +--- + +# Text2Cypher + +Text2Cypher is the process of translating plain language questions or commands +into Cypher queries (the language used to interact with graph databases like +Memgraph). This automation enables users to retrieve, manipulate, or analyze +graph data without needing to write Cypher themselves. By leveraging natural +language processing and language models, Text2Cypher bridges the gap between +human-friendly questions and database operations, making knowledge retrieval +from graph data both accessible and powerful. + +Text2Cypher is usually involves: +1. running the [`SHOW SCHEMA INFO` query](/querying/schema) +2. asking an LLM to generate a Cypher query that would based on the schema and +the question give the relevant data to answer the user question. + +This method is most effective when the user’s question requires a traditional +analytical database query, such as deterministic counting or extracting +statistical information. + +Perfect Text2Cypher use-cases are: +* [Memgraph Lab - GraphChat](/memgraph-lab/features/graphchat) +* generating query to run over [run_query MCP tool](/ai-ecosystem/mcp). \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx b/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx index 7079f1af4..df464846f 100644 --- a/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx +++ b/pages/ai-ecosystem/graph-rag/knowledge-graph-creation.mdx @@ -83,7 +83,7 @@ knowledge graph requires additional steps. Tools like [LangChain](/ai-ecosystem/integrations#langchain) and [LlamaIndex](/ai-ecosystem/integrations#llamaindex) simplify this process by bridging the gap between unstructured data and graph databases like Memgraph. There is also the packed solution under -[Unstructured2Graph](/ai-ecosystem/unstructured2graph). +[Unstructured2Graph Agent](/ai-ecosystem/agents/unstructured2graph-agent). For more information, refer to the following: @@ -106,6 +106,8 @@ By following these steps, you can work towards making your knowledge graph more Here’s an example: [How to Extract Entities and Build a Knowledge Graph with Memgraph and SpaCy](https://memgraph.com/blog/extract-entities-build-knowledge-graph-memgraph-spacy). -To see how the concepts above can be applied in real-world scenarios, check out the [Pipelines](/ai-ecosystem/graph-rag/pipelines) and [Examples and demos](/ai-ecosystem/graph-rag/examples-and-demos) pages as your next step. +To see how the concepts above can be applied in real-world scenarios, check out +the [Pipelines](/ai-ecosystem/graph-rag/atomic-pipelines) and [Examples and +demos](/ai-ecosystem/graph-rag/examples-and-demos) pages as your next step. diff --git a/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx b/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx deleted file mode 100644 index ef7af78de..000000000 --- a/pages/ai-ecosystem/graph-rag/pipelines/text2cypher.mdx +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Text-to-Cypher Generation -description: Learn how to convert natural language questions into precise Cypher queries for powerful knowledge retrieval in GraphRAG pipelines. ---- - -# Text2Cypher - -Text2Cypher is usually involves: -1. running the (`SHOW SCHEMA INFO` query](/querying/schema) -2. asking LLM to generate a Cypher query that would give the best data to answer the user question. \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/prerequisites.mdx b/pages/ai-ecosystem/graph-rag/prerequisites.mdx index d22faedcf..f797b7528 100644 --- a/pages/ai-ecosystem/graph-rag/prerequisites.mdx +++ b/pages/ai-ecosystem/graph-rag/prerequisites.mdx @@ -58,7 +58,7 @@ Once your environment is ready: 1. Proceed to [Knowledge graph creation](/ai-ecosystem/graph-rag/knowledge-graph-creation) to model and import your data into Memgraph. -2. Explore [GraphRAG Pipelines](/ai-ecosystem/graph-rag/pipelines) to +2. Explore [GraphRAG Pipelines](/ai-ecosystem/graph-rag/atomic-pipelines) to understand how to retrieve and manage knowledge effectively. 3. Review [Examples and demos](/ai-ecosystem/graph-rag/examples-and-demos) for inspiration on building your own GraphRAG system. diff --git a/pages/ai-ecosystem/integrations.mdx b/pages/ai-ecosystem/integrations.mdx index c53bee592..f3eec3e5f 100644 --- a/pages/ai-ecosystem/integrations.mdx +++ b/pages/ai-ecosystem/integrations.mdx @@ -12,7 +12,7 @@ Memgraph offers several integrations with popular AI frameworks to help you customize and build your own GenAI application from scratch. Here is a list of Memgraph's officially supported integrations: -- [Model Context Protocol](#model-context-protocol-mcp) +- [Model Context Protocol](/ai-ecosystem/mcp) - [LlamaIndex](#llamaindex) - [LangChain](#langchain) - [Cognee](#cognee) @@ -21,239 +21,6 @@ Memgraph's officially supported integrations: ![Memgraph AI Integrations](/pages/ai-ecosystem/integrations/memgraph-ai-integrations.png) - - -## Model Context Protocol (MCP) - - - } - title="Memgraph MCP Server" - href="https://github.com/memgraph/ai-toolkit/tree/main/integrations/mcp-memgraph" - /> - - -Memgraph offers Memgraph the [Memgraph MCP -Server](https://github.com/memgraph/ai-toolkit/tree/main/integrations/mcp-memgraph) -- a lightweight server implementation of the Model Context Protocol (MCP) -designed to connect Memgraph with LLMs. - -![mcp-server](/pages/ai-ecosystem/integrations/mcp-server.png) - - -### Run Memgraph MCP server - -1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) -2. Install [Claude for Desktop](https://claude.ai/download). -3. Add the Memgraph server to Claude config: - -Open the config file in your favorite text editor. The location of the config -file depends on your operating system: - -**MacOS/Linux** -``` -~/Library/Application\ Support/Claude/claude_desktop_config.json -``` - -**Windows** - -``` -$env:AppData\Claude\claude_desktop_config.json -``` - -Add the following config: -```json -{ - "mcpServers": { - "mpc-memgraph": { - "command": "uv", - "args": [ - "run", - "--with", - "mcp-memgraph", - "--python", - "3.13", - "mcp-memgraph" - ] - } - } -} -``` - - - You may need to put the full path to the uv executable in - the command field. You can get this by running `which uv` on MacOS/Linux or - `where uv` on Windows. Make sure you pass in the absolute path to your server. - - -

Chat with the database

- -1. Run Memgraph MAGE: - ```bash - docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True - ``` - - The `--schema-info-enabled` configuration setting is set to `True` to allow - LLM to run `SHOW SCHEMA INFO` query. -2. Open Claude Desktop and see the Memgraph tools and resources listed. Try it - out! (You can load dummy data from Memgraph Lab - [Datasets](/memgraph-lab/getting-started/data-migration#datasets)) - - -### Run Memgraph MCP server with Docker - - - -

Build the Docker image

- -To build the Docker image using your local `memgraph-toolbox` code, run from the -root of the monorepo: - -``` -cd /path/to/ai-toolkit -docker build -f integrations/mcp-memgraph/Dockerfile -t mcp-memgraph:latest . -``` - -This will install your local `memgraph-toolbox` into the image. - - -

Run the Docker image

- -**1. Streamable HTTP mode (recommended)** - -To expose the MCP HTTP server locally: - -``` -docker run --rm mcp-memgraph:latest -``` - -The server will be available at `http://localhost:8000/mcp/` - -**2. Stdio mode (for integration with MCP stdio clients)** - -Configure your MCP host to run the docker command and utilize stdio: - -``` -docker run --rm -i -e MCP_TRANSPORT=stdio mcp-memgraph:latest -``` - - - By default, the server will connect to a Memgraph instance running on - localhost docker network `bolt://host.docker.internal:7687`. If you have a - Memgraph instance running on a different host or port, you can specify it - using environment variables. - - -**3. Custom Memgraph connection (external instance, no host network)** - -To avoid using host networking, or to connect to an external Memgraph instance: - -``` -docker run --rm \ - -p 8000:8000 \ - -e MEMGRAPH_URL=bolt://memgraph:7687 \ - -e MEMGRAPH_USER=myuser \ - -e MEMGRAPH_PASSWORD=password \ - mcp-memgraph:latest -``` - -

Connect from developer tools

- -**Visual Studio Code (HTTP MCP extension)** - -If you are using VS Code MCP extension or similar, your configuration for an -HTTP server would look like: - -```json -{ - "servers": { - "mcp-memgraph-http": { - "url": "http://localhost:8000/mcp/" - } - } -} -``` - - - The URL must end in /mcp/ - - -**Visual Studio Code (stdio using Docker)** - -You can also run the server using stdio for integration with MCP stdio clients: - -1. Open Visual Studio Code, open Command Palette (Ctrl+Shift+P or Cmd+Shift+P on - Mac), and select `MCP: Add server...`. -2. Choose `Command (stdio)` -3. Enter `docker` as the command to run. -4. For Server ID enter `mcp-memgraph`. -5. Choose "User" (adds to user-space `settings.json`) or "Workspace" (adds to - `.vscode/mcp.json`). - -When the settings open, enhance the args as follows: - -```json -{ - "servers": { - "mcp-memgraph": { - "type": "stdio", - "command": "docker", - "args": [ - "run", - "--rm", - "-i", - "-e", "MCP_TRANSPORT=stdio", - "mcp-memgraph:latest" - ] - } - } -} -``` - -To connect to a remote Memgraph instance with authentication, add environment -variables to the `args` list: - -```json -{ - "servers": { - "mcp-memgraph": { - "type": "stdio", - "command": "docker", - "args": [ - "run", - "--rm", - "-i", - "-e", "MCP_TRANSPORT=stdio", - "-e", "MEMGRAPH_URL=bolt://memgraph:7687", - "-e", "MEMGRAPH_USER=myuser", - "-e", "MEMGRAPH_PASSWORD=mypassword", - "mcp-memgraph:latest" - ] - } - } -} -``` - -Then open GitHub Copilot in Agent mode, and interact with Memgraph using natural -language. - -
- -

Resources

- -- [Introducing the Memgraph MCP - Server](https://memgraph.com/blog/introducing-memgraph-mcp-server): A blog - post on how to run Memgraph MCP Server and what are the future plans. - -### MCP server in Lab - -Memgraph Lab has become an MCP Client and now has built-in support for MCP servers, including the Memgraph MCP Server. This means you can build your agentic applications powered by Memgraph MCP, Toolkit and Lab. - -![mcp-lab](/pages/ai-ecosystem/integrations/lab-mcp.png) - -More details about the MCP connection in Memgraph Lab can be found in the [Memgraph Lab](/memgraph-lab/getting-started/features/graphchat#mcp-servers) documentation. - - ## LlamaIndex diff --git a/pages/ai-ecosystem/mcp.mdx b/pages/ai-ecosystem/mcp.mdx new file mode 100644 index 000000000..d2c153c3f --- /dev/null +++ b/pages/ai-ecosystem/mcp.mdx @@ -0,0 +1,239 @@ +--- +title: "MCP" +description: +--- + +import { Cards } from 'nextra/components' +import { Callout } from 'nextra/components' +import { Steps } from 'nextra/components' +import GitHub from '/components/icons/GitHub' + +# Model Context Protocol (MCP) + + + } + title="Memgraph MCP Server" + href="https://github.com/memgraph/ai-toolkit/tree/main/integrations/mcp-memgraph" + /> + + +Memgraph offers Memgraph the [Memgraph MCP +Server](https://github.com/memgraph/ai-toolkit/tree/main/integrations/mcp-memgraph) +- a lightweight server implementation of the Model Context Protocol (MCP) +designed to connect Memgraph with LLMs. + +![mcp-server](/pages/ai-ecosystem/integrations/mcp-server.png) + + +### Run Memgraph MCP server + +1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) +2. Install [Claude for Desktop](https://claude.ai/download). +3. Add the Memgraph server to Claude config: + +Open the config file in your favorite text editor. The location of the config +file depends on your operating system: + +**MacOS/Linux** +``` +~/Library/Application\ Support/Claude/claude_desktop_config.json +``` + +**Windows** + +``` +$env:AppData\Claude\claude_desktop_config.json +``` + +Add the following config: +```json +{ + "mcpServers": { + "mpc-memgraph": { + "command": "uv", + "args": [ + "run", + "--with", + "mcp-memgraph", + "--python", + "3.13", + "mcp-memgraph" + ] + } + } +} +``` + + + You may need to put the full path to the uv executable in + the command field. You can get this by running `which uv` on MacOS/Linux or + `where uv` on Windows. Make sure you pass in the absolute path to your server. + + +

Chat with the database

+ +1. Run Memgraph MAGE: + ```bash + docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True + ``` + + The `--schema-info-enabled` configuration setting is set to `True` to allow + LLM to run `SHOW SCHEMA INFO` query. +2. Open Claude Desktop and see the Memgraph tools and resources listed. Try it + out! (You can load dummy data from Memgraph Lab + [Datasets](/memgraph-lab/getting-started/data-migration#datasets)) + + +### Run Memgraph MCP server with Docker + + + +

Build the Docker image

+ +To build the Docker image using your local `memgraph-toolbox` code, run from the +root of the monorepo: + +``` +cd /path/to/ai-toolkit +docker build -f integrations/mcp-memgraph/Dockerfile -t mcp-memgraph:latest . +``` + +This will install your local `memgraph-toolbox` into the image. + + +

Run the Docker image

+ +**1. Streamable HTTP mode (recommended)** + +To expose the MCP HTTP server locally: + +``` +docker run --rm mcp-memgraph:latest +``` + +The server will be available at `http://localhost:8000/mcp/` + +**2. Stdio mode (for integration with MCP stdio clients)** + +Configure your MCP host to run the docker command and utilize stdio: + +``` +docker run --rm -i -e MCP_TRANSPORT=stdio mcp-memgraph:latest +``` + + + By default, the server will connect to a Memgraph instance running on + localhost docker network `bolt://host.docker.internal:7687`. If you have a + Memgraph instance running on a different host or port, you can specify it + using environment variables. + + +**3. Custom Memgraph connection (external instance, no host network)** + +To avoid using host networking, or to connect to an external Memgraph instance: + +``` +docker run --rm \ + -p 8000:8000 \ + -e MEMGRAPH_URL=bolt://memgraph:7687 \ + -e MEMGRAPH_USER=myuser \ + -e MEMGRAPH_PASSWORD=password \ + mcp-memgraph:latest +``` + +

Connect from developer tools

+ +**Visual Studio Code (HTTP MCP extension)** + +If you are using VS Code MCP extension or similar, your configuration for an +HTTP server would look like: + +```json +{ + "servers": { + "mcp-memgraph-http": { + "url": "http://localhost:8000/mcp/" + } + } +} +``` + + + The URL must end in /mcp/ + + +**Visual Studio Code (stdio using Docker)** + +You can also run the server using stdio for integration with MCP stdio clients: + +1. Open Visual Studio Code, open Command Palette (Ctrl+Shift+P or Cmd+Shift+P on + Mac), and select `MCP: Add server...`. +2. Choose `Command (stdio)` +3. Enter `docker` as the command to run. +4. For Server ID enter `mcp-memgraph`. +5. Choose "User" (adds to user-space `settings.json`) or "Workspace" (adds to + `.vscode/mcp.json`). + +When the settings open, enhance the args as follows: + +```json +{ + "servers": { + "mcp-memgraph": { + "type": "stdio", + "command": "docker", + "args": [ + "run", + "--rm", + "-i", + "-e", "MCP_TRANSPORT=stdio", + "mcp-memgraph:latest" + ] + } + } +} +``` + +To connect to a remote Memgraph instance with authentication, add environment +variables to the `args` list: + +```json +{ + "servers": { + "mcp-memgraph": { + "type": "stdio", + "command": "docker", + "args": [ + "run", + "--rm", + "-i", + "-e", "MCP_TRANSPORT=stdio", + "-e", "MEMGRAPH_URL=bolt://memgraph:7687", + "-e", "MEMGRAPH_USER=myuser", + "-e", "MEMGRAPH_PASSWORD=mypassword", + "mcp-memgraph:latest" + ] + } + } +} +``` + +Then open GitHub Copilot in Agent mode, and interact with Memgraph using natural +language. + +
+ +

Resources

+ +- [Introducing the Memgraph MCP + Server](https://memgraph.com/blog/introducing-memgraph-mcp-server): A blog + post on how to run Memgraph MCP Server and what are the future plans. + +### MCP server in Lab + +Memgraph Lab has become an MCP Client and now has built-in support for MCP servers, including the Memgraph MCP Server. This means you can build your agentic applications powered by Memgraph MCP, Toolkit and Lab. + +![mcp-lab](/pages/ai-ecosystem/integrations/lab-mcp.png) + +More details about the MCP connection in Memgraph Lab can be found in the [Memgraph Lab](/memgraph-lab/getting-started/features/graphchat#mcp-servers) documentation. \ No newline at end of file diff --git a/pages/querying/text-search.mdx b/pages/querying/text-search.mdx index e3a0a8afa..08953d090 100644 --- a/pages/querying/text-search.mdx +++ b/pages/querying/text-search.mdx @@ -14,7 +14,7 @@ Text indices and search are powered by the [Tantivy](https://github.com/quickwit-oss/tantivy) full-text search engine. Text search is commonly used as a [retrieval -technique](/ai-ecosystem/graph-rag/pipelines/search#text-search) in RAG systems +technique](/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search#text-search) in RAG systems to find entities based on exact and fuzzy matches. diff --git a/pages/querying/vector-search.mdx b/pages/querying/vector-search.mdx index c7e197303..197c1812f 100644 --- a/pages/querying/vector-search.mdx +++ b/pages/querying/vector-search.mdx @@ -29,7 +29,7 @@ To configure vector search as described in the example, please use the latest Me Vector search is commonly used as a [retrieval -technique](/ai-ecosystem/graph-rag/pipelines/search#vector-search) in RAG +technique](/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search#vector-search) in RAG systems to find entities based on semantic similarity rather than exact matches. ![vector-search-workflow](/pages/querying/vector-search/vector-search-workflow.png) diff --git a/public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg b/public/pages/ai-ecosystem/graph-rag/agentic-graphrag/agentic-graphrag-pipeline.svg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/atomic-graphrag/agentic-graphrag-pipeline.svg rename to public/pages/ai-ecosystem/graph-rag/agentic-graphrag/agentic-graphrag-pipeline.svg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/atomic-graphrag-pipelines.svg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/graphrag-pipelines.svg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/atomic-graphrag-pipelines.svg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/1-hop.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/1-hop.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/1-hop.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/community-detection.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/community-detection.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/community-detection.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/geospatial.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/geospatial.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/geospatial.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/medical-records-example.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/medical-records-example.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/medical-records-example.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/text-search.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/text-search.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/text-search.jpg diff --git a/public/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg b/public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/vector-search.jpg similarity index 100% rename from public/pages/ai-ecosystem/graph-rag/pipelines/search/vector-search.jpg rename to public/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/vector-search.jpg diff --git a/public/sitemap.xml b/public/sitemap.xml index ba257d348..e97733585 100644 --- a/public/sitemap.xml +++ b/public/sitemap.xml @@ -80,18 +80,19 @@ https://memgraph.com/docs/ai-ecosystem2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/agents2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/graph-rag2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/graph-rag/atomic-graphrag2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/graph-rag/agentic-graphrag2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/graph-rag/examples-and-demos2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/graph-rag/knowledge-graph-creation2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/graph-rag/pipelines2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/graph-rag/pipelines/query-focused-summarization2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/graph-rag/pipelines/search2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/graph-rag/pipelines/text2cypher2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/graph-rag/atomic-pipelines2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/graph-rag/prerequisites2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/integrations2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/machine-learning2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/ai-ecosystem/skills2026-02-18T19:55:19.109Zdaily0.7 -https://memgraph.com/docs/ai-ecosystem/unstructured2graph2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/agents/sql2graph-agent2026-02-18T19:55:19.109Zdaily0.7 +https://memgraph.com/docs/ai-ecosystem/agents/unstructured2graph-agent2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/client-libraries2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/client-libraries/c-sharp2026-02-18T19:55:19.109Zdaily0.7 https://memgraph.com/docs/client-libraries/go2026-02-18T19:55:19.109Zdaily0.7 From a5b35d3556f0ae968b987eca38848b9a88d53f75 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Fri, 20 Feb 2026 09:58:03 +0100 Subject: [PATCH 4/9] Remove unnecessary index ref --- pages/ai-ecosystem/{agents/index.mdx => agents.mdx} | 0 pages/ai-ecosystem/agents/_meta.ts | 1 - 2 files changed, 1 deletion(-) rename pages/ai-ecosystem/{agents/index.mdx => agents.mdx} (100%) diff --git a/pages/ai-ecosystem/agents/index.mdx b/pages/ai-ecosystem/agents.mdx similarity index 100% rename from pages/ai-ecosystem/agents/index.mdx rename to pages/ai-ecosystem/agents.mdx diff --git a/pages/ai-ecosystem/agents/_meta.ts b/pages/ai-ecosystem/agents/_meta.ts index 2f2247c27..90213a5be 100644 --- a/pages/ai-ecosystem/agents/_meta.ts +++ b/pages/ai-ecosystem/agents/_meta.ts @@ -1,5 +1,4 @@ export default { - "index": "Agents", "sql2graph-agent": "SQL2Graph Agent", "unstructured2graph-agent": "Unstructured2Graph Agent", } From 412a6c5c4ea8a634c182cf99c81860670d4d79fd Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Fri, 20 Feb 2026 11:33:06 +0100 Subject: [PATCH 5/9] Remove the Agent from the navigation --- pages/ai-ecosystem/agents/_meta.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pages/ai-ecosystem/agents/_meta.ts b/pages/ai-ecosystem/agents/_meta.ts index 90213a5be..7712e4386 100644 --- a/pages/ai-ecosystem/agents/_meta.ts +++ b/pages/ai-ecosystem/agents/_meta.ts @@ -1,4 +1,4 @@ export default { - "sql2graph-agent": "SQL2Graph Agent", - "unstructured2graph-agent": "Unstructured2Graph Agent", + "sql2graph-agent": "SQL2Graph", + "unstructured2graph-agent": "Unstructured2Graph", } From 4f30682be09ca07a63b01970b32b34426d0a1c61 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Fri, 20 Feb 2026 14:51:13 +0100 Subject: [PATCH 6/9] Add more content and polish a bit (still WIP, more content + diagrams + polishment) required --- .../graph-rag/agentic-graphrag.mdx | 37 +++++++------- .../graph-rag/atomic-pipelines.mdx | 22 ++++++-- .../query-focused-summarization.mdx | 50 ++++++++++++++++++- 3 files changed, 86 insertions(+), 23 deletions(-) diff --git a/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx b/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx index c79fb59c2..1008a759e 100644 --- a/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx +++ b/pages/ai-ecosystem/graph-rag/agentic-graphrag.mdx @@ -5,27 +5,28 @@ description: # Agentic GraphRAG -A set of Memgraph tools or primitives that enables building GraphRAG pipelines -as single queries. The list of tools includes: -- [Real-time schema info (`SHOW SCHEMA INFO` query)](/querying/schema) -- Embedding procedures [`embeddings.text` and `embeddings.node_sentence`](/advanced-algorithms/available-algorithms/embeddings) -- Search primitives: - - [text search](/querying/text-search) - - [vector search](/querying/vector-search) -- Graph algorithms: - - [Deep path traversals](/advanced-algorithms/deep-path-traversal) - - [Community detection](/advanced-algorithms/available-algorithms/community_detection) - - [Page Rank](/advanced-algorithms/available-algorithms/pagerank) -- [`llm.complete`](/advanced-algorithms/available-algorithms/llm) -- Server-side parameters - -To get the basics of GraphRAG, take a look under the [GraphRAG +To learn the basics of GraphRAG, take a look under the [Atomic GraphRAG Pipelines](/ai-ecosystem/graph-rag/atomic-pipelines). +The combination of Atomic GraphRAG Pipelines and Agents adds another dimension +the the whole story because an agent can on it's own figure out the right set of +primitives to get the job done (answer a question or perform a task). + Using the [GraphRAG Skill](https://github.com/memgraph/skills/tree/main/memgraph-graph-rag), it's -possible to guide and agent to start building appropriate GraphRAG Pipelines -based on the user question/prompt. For more skills, visit [Memgraph Agent -Skills](https://github.com/memgraph/skills). +possible to guide and agent to start using appropriate GraphRAG Pipelines based +on the user question/prompt. For more skills, visit [Memgraph Agent +Skills](https://github.com/memgraph/skills). Also, make sure you have the +[MCP](/ai-ecosystem/mcp) properly configured (connected to the Memgraph +database), because any given agent will primarly use [MCP](/ai-ecosystem/mcp) to +executed the queries. If you use, [Memgraph Lab](/memgraph-lab) as the agentic +runtime / agent, everything should already be in-place. + +An example of a prompt under [Memgraph Lab GraphChat](/memgraph-lab/features/graphchat) follows: +``` +Using https://github.com/memgraph/skills/blob/main/memgraph-graph-rag/SKILL.md, +what's the most important person in dataset? +``` +Under the hood, you will end up with something like below image: ![agentic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/agentic-graphrag/agentic-graphrag-pipeline.svg) \ No newline at end of file diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx index cfd58d191..1fdad5081 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx @@ -3,14 +3,28 @@ title: GraphRAG Pipelines description: Overview of typical pipelines in a GraphRAG system, covering verious retrieval types. --- -# GraphRAG Pipelines +# Atomic GraphRAG Pipelines -There are a few main GraphRAG pipeline types: +Atomic GraphRAG is a set of Memgraph tools or primitives that enables building +GraphRAG pipelines as single queries. The list of tools includes: +- [Real-time schema info (`SHOW SCHEMA INFO` query)](/querying/schema) +- Embedding procedures [`embeddings.text` and `embeddings.node_sentence`](/advanced-algorithms/available-algorithms/embeddings) +- Search primitives: + - [text search](/querying/text-search) + - [vector search](/querying/vector-search) +- Graph algorithms: + - [Deep path traversals](/advanced-algorithms/deep-path-traversal) + - [Community detection](/advanced-algorithms/available-algorithms/community_detection) + - [Page Rank](/advanced-algorithms/available-algorithms/pagerank) +- [`llm.complete`](/advanced-algorithms/available-algorithms/llm) +- Server-side parameters. + +Based on the above primitives, there are a few main GraphRAG pipeline types: - **Analytical**: When you need an exact database query to fetch specific data (e.g., deterministic counting), use -[Text2Cypher](/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher). +[Text2Cypher](/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher) - **Local**: To retrieve a focused subset of your data for more contextual -answers, leverage [Local Graph Search](/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search). +answers, leverage [Local Graph Search](/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search) - **Global**: For cases where the entire dataset requires advanced processing or summarization, apply [Query-focused Summarization](/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization). diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx index bd0860110..212ef7b46 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/query-focused-summarization.mdx @@ -5,7 +5,55 @@ description: Learn how to generate targeted summaries from graph-based retrieval # Query-focused Summarization -Typical query to perform query-focused summarization follows: +Query-focused summarization (QFS) is a text analysis process that generates a +concise summary of documents specifically tailored to address a user's query or +topic of interest. Unlike generic summarization that covers the overall main +points, QFS filters, prioritizes, and compresses information relevant to the +user's specific information needs. + +## Prerequisites + +- homogeneous graph +- each node having the `text` property + +## How to do QFS? + +When working with a knowledge graph, you can create summaries based on its +underlying communities. Keep in mind: community detection works best on +homogeneous graphs, that is, graphs where nodes of the same type (with the same +label) are connected by edges. This structure ensures that the detected +communities are meaningful. Once your graph is structured this way, you can +detect and assign communities using queries such as the following ([multiple +community detection algorithms are +possible](/advanced-algorithms/available-algorithms)): +```Cypher +CALL community_detection.get() YIELD node, community_id +SET node.community_id = community_id; +``` + +After assigning community IDs, you can generate summaries for each community and +create corresponding `Community` nodes. These nodes are then connected to their +community members using the Cypher query below: +```Cypher +WITH "You are summarizing a community (cluster)." + + " Write a concise summary in 2–4 paragraphs that:" + + " - Captures the main themes and recurring topics." + " - Describes the kind of work or concerns this cluster represents." + + " - Does not list individual entities; synthesize into a coherent narrative." + AS SUMMARY_TEMPLATE +MATCH (n) WHERE n.community_id IS NOT NULL +WITH n.community_id AS c_id, count(n) AS c_count, collect(n) AS c_members, + llm.complete(reduce(s=SUMMARY_TEMPLATE, m IN collect(n) | s + m.text "; ")) AS c_summary +MERGE (community:Community {id: c_id, nodes_count: c_count}) +SET community.summary = c_summary +WITH community, c_members +UNWIND c_members AS c_member +MERGE (c_member)-[:BELONGS_TO]->(community) +RETURN community.id AS community_id, community.summary AS community_summary; +``` + +Once all the preprocessing is done, a typical query to perform query-focused +summarization follows: ```Cypher WITH "... the question ..." AS USER_QUESTION // 1. Identify relevant communities (thresholding) From 0f1c31e842059595be126f32fbbd6bdb85eb0210 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Sun, 22 Feb 2026 14:48:11 +0100 Subject: [PATCH 7/9] Further improvements --- pages/ai-ecosystem/graph-rag.mdx | 60 +---------- .../graph-rag/atomic-pipelines.mdx | 68 ++++++++++--- .../atomic-pipelines/local-graph-search.mdx | 91 ----------------- .../atomic-pipelines/text2cypher.mdx | 99 ++++++++++++++++++- pages/ai-ecosystem/skills.mdx | 4 + 5 files changed, 160 insertions(+), 162 deletions(-) diff --git a/pages/ai-ecosystem/graph-rag.mdx b/pages/ai-ecosystem/graph-rag.mdx index ab0ad4776..e57e60e9a 100644 --- a/pages/ai-ecosystem/graph-rag.mdx +++ b/pages/ai-ecosystem/graph-rag.mdx @@ -5,7 +5,6 @@ description: Learn how Memgraph fits into the architecture of your RAG system. import { Callout, Steps } from 'nextra/components' import {CommunityLinks} from '/components/social-card/CommunityLinks' - # GraphRAG with Memgraph ## Introduction @@ -42,7 +41,6 @@ to flat, vector-based systems. Here’s why they’re game-changing for RAG: - [How Memgraph Powers Real-Time Retrieval for LLMs](https://memgraph.com/white-paper/knowledge-graphs) - ## Memgraph's role in GraphRAG Memgraph is a high-performance graph database designed to handle the demands of @@ -62,7 +60,6 @@ GraphRAG enables developers to: - **Enhance AI applications.** Provide LLMs with context-rich, precise data for better answers and recommendations. - A GraphRAG application running in production needs to balance scalability, performance, and adaptability. Memgraph’s in-memory graph database provides: @@ -73,59 +70,12 @@ performance, and adaptability. Memgraph’s in-memory graph database provides: - **Durability.** Ensure data persistence for backup, recovery, and long-term analysis. +## Atomic GraphRAG Pipelines + +To learn more about what are the components of Memgraph's GraphRAG, please jump +to the [Atomic GraphRAG Pipelines](/ai-ecosystem/graph-rag/atomic-pipelines). -## Key features for GraphRAG - -Memgraph equips you with a powerful set of tools and algorithms that make -building a GraphRAG efficient and scalable. Here’s how each feature plays a role -in enhancing your Retrieval-Augmented Generation workflows: - -1. [Vector search](/querying/vector-search): A - pivotal first step for extracting relevant information during pivot - search. Vector search allows you to perform semantic searches, matching - context or meaning rather than exact terms. -2. [Deep-path - traversals](/advanced-algorithms/deep-path-traversal): - Enables rapid navigation through complex relationships in your graph, - supporting multi-hop queries and reasoning across interconnected data. -3. [Leiden community - detection](/advanced-algorithms/available-algorithms/leiden_community_detection): - A faster, more accurate algorithm than Louvain, Leiden ensures - well-connected communities, making it ideal for relevance expansion in - retrieval processes. -4. [Louvain](/advanced-algorithms/available-algorithms/community_detection) - and [dynamic community - detection](/advanced-algorithms/available-algorithms/community_detection_online): - Louvain is a foundational clustering algorithm for discovering - communities, while dynamic community detection adapts in real time, - accommodating graph updates without compromising cluster integrity. -5. [PageRank](/advanced-algorithms/available-algorithms/pagerank) - and [dynamic - PageRank](/advanced-algorithms/available-algorithms/pagerank_online): - PageRank ranks nodes based on their influence or importance. Its - dynamic counterpart adjusts these rankings as new data is ingested, - ensuring retrieval results remain relevant. -6. [Text search](/querying/text-search): - Facilitates keyword-based searches, allowing you to locate specific - entities or relationships within your graph quickly. This complements - vector search for hybrid retrieval strategies. -7. [Geospatial - search](/fundamentals/data-types#point): - Geospatial search enables you to integrate and retrieve location-based - insights, enhancing the relevance and precision of responses. -8. [Run-time schema - tracking](/querying/schema#run-time-schema-tracking): - Tracks schema changes as they happen, enabling seamless updates and - adaptations in your graph structure without manual intervention. This is - especially useful if you're including schema into the LLM context. -9. [Real-time data ingestion](/data-streams) - Memgraph supports seamless -integration with streaming platforms like Kafka, Redpanda, and Pulsar, allowing -you to continuously grow and update your knowledge graph. Combine this -with [Memgraph MAGE](/advanced-algorithms) or [custom -procedures](/custom-query-modules) to dynamically query and analyze incoming -data. - -![graphrag](/pages/ai-ecosystem/graph-rag/graphrag.png) +![atomic_graphrag_pipelines](/pages/ai-ecosystem/graph-rag/atomic-pipelines/atomic-graphrag-pipelines.svg)

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diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx index 1fdad5081..f09ceeb92 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines.mdx @@ -5,19 +5,61 @@ description: Overview of typical pipelines in a GraphRAG system, covering veriou # Atomic GraphRAG Pipelines -Atomic GraphRAG is a set of Memgraph tools or primitives that enables building -GraphRAG pipelines as single queries. The list of tools includes: -- [Real-time schema info (`SHOW SCHEMA INFO` query)](/querying/schema) -- Embedding procedures [`embeddings.text` and `embeddings.node_sentence`](/advanced-algorithms/available-algorithms/embeddings) -- Search primitives: - - [text search](/querying/text-search) - - [vector search](/querying/vector-search) -- Graph algorithms: - - [Deep path traversals](/advanced-algorithms/deep-path-traversal) - - [Community detection](/advanced-algorithms/available-algorithms/community_detection) - - [Page Rank](/advanced-algorithms/available-algorithms/pagerank) -- [`llm.complete`](/advanced-algorithms/available-algorithms/llm) -- Server-side parameters. +Memgraph provides a comprehensive suite of tools and algorithms that make +building GraphRAG systems both efficient and scalable. These tools, known as +"Atomic GraphRAG Pipelines," empower you (or an agent) to implement +sophisticated pipelines as atomic database queries. + +Here’s how each feature plays a role +in enhancing your Retrieval-Augmented Generation workflows: + +1. [Vector search](/querying/vector-search): A pivotal first step for extracting +relevant information during pivot search. Vector search allows you to perform +semantic searches, matching context or meaning rather than exact terms. +2. [Deep-path traversals](/advanced-algorithms/deep-path-traversal): Enables +rapid navigation through complex relationships in your graph, supporting +multi-hop queries and reasoning across interconnected data. +3. [Leiden community +detection](/advanced-algorithms/available-algorithms/leiden_community_detection): +A faster, more accurate algorithm than Louvain, Leiden ensures well-connected +communities, making it ideal for relevance expansion in retrieval processes. +4. [Louvain](/advanced-algorithms/available-algorithms/community_detection) and +[dynamic community +detection](/advanced-algorithms/available-algorithms/community_detection_online): +Louvain is a foundational clustering algorithm for discovering communities, +while dynamic community detection adapts in real time, accommodating graph +updates without compromising cluster integrity. +5. [PageRank](/advanced-algorithms/available-algorithms/pagerank) and [dynamic +PageRank](/advanced-algorithms/available-algorithms/pagerank_online): +PageRank ranks nodes based on their influence or importance. Its dynamic +counterpart adjusts these rankings as new data is ingested, ensuring retrieval +results remain relevant. +6. [Text search](/querying/text-search): Facilitates keyword-based searches, +allowing you to locate specific entities or relationships within your graph +quickly. This complements vector search for hybrid retrieval strategies. +7. [Geospatial search](/fundamentals/data-types#point): Geospatial search +enables you to integrate and retrieve location-based insights, enhancing the +relevance and precision of responses. +8. [Run-time schema tracking under `SHOW SCHEMA INFO` +query](/querying/schema#run-time-schema-tracking): Tracks schema changes as they +happen, enabling seamless updates and adaptations in your graph structure +without manual intervention. This is especially useful if you're including +schema into the LLM context. +9. [Real-time data ingestion](/data-streams): Memgraph supports seamless +integration with streaming platforms like Kafka, Redpanda, and Pulsar, allowing +you to continuously grow and update your knowledge graph. Combine this +with [Memgraph MAGE](/advanced-algorithms) or [custom +procedures](/custom-query-modules) to dynamically query and analyze incoming +data. +10. Embedding procedures [`embeddings.text` and +`embeddings.node_sentence`](/advanced-algorithms/available-algorithms/embeddings): +Offer the ability to compute embeddings during the preprocessing or retrieval +stages. +11. [`llm.complete`](/advanced-algorithms/available-algorithms/llm) function: +Allows you to call any LLM under any given Cypher query. +12. Server-side parameters: Could be used for many different things, but in this +context, the parameters help you with managing configuration under any given +query or pipeline. Based on the above primitives, there are a few main GraphRAG pipeline types: - **Analytical**: When you need an exact database query to fetch specific data diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx index b828ad741..1b68fcab1 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search.mdx @@ -39,12 +39,6 @@ the LLM to ground it and in that way avoid hallucinations. Additionally, to expand the search, you can perform [relevance expansion](#relevance-expansion) which includes advanced strategies to obtain relevant information. -Pivot search combined with relevance expansion is generally more reliable than -directly generating Cypher queries from natural language. These approaches can -leverage pre-built tools to ensure the resulting query is accurate and -contextually appropriate. To improve Cypher query generation from natural -language, [effective prompt engineering](#cypher-generation) is essential. - ### Pivot search Pivot search is the starting point of any retrieval process, identifying the @@ -150,7 +144,6 @@ LLM as part of the input prompt to provide better context. ![one-hop](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/1-hop.jpg) - Read more: - [Deep path traversal algorithms @@ -171,7 +164,6 @@ meaningful insights for LLM. ![community-detection](/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/community-detection.jpg) - #### Centrality measures By incorporating [PageRank](/advanced-algorithms/available-algorithms/pagerank) @@ -190,89 +182,6 @@ or topics, ensuring the retrieval covers diverse or connecting concepts. For example, if there are multiple nodes as potential answers, you can rank the nodes by their PageRank scores to get the most important ones. -### Cypher generation - -LLMs are trained on publicly available resources, including openCypher. Memgraph -supports openCypher but has also extended its Cypher implementation with -advanced constructs. While these enhancements improve usability, they can make -it harder for LLMs to generate optimal Cypher queries tailored for Memgraph. - -To address this, we're continually increasing compatibility with each release -and enhancing Memgraph's error messages. Here's a prompt that has been -particularly helpful in the process, and mostly used in the -[integrations](/ai-ecosystem/integrations) with other AI tools: - -``` -MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. -You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database. -Instructions: -- Use provided node and relationship labels and property names from the -schema which describes the database's structure. Upon receiving a user -question, synthesize the schema to craft a precise Cypher query that -directly corresponds to the user's intent. -- Generate valid executable Cypher queries on top of Memgraph database. -Any explanation, context, or additional information that is not a part -of the Cypher query syntax should be omitted entirely. -- Use Memgraph MAGE procedures instead of Neo4j APOC procedures. -- Do not include any explanations or apologies in your responses. -- Do not include any text except the generated Cypher statement. -- For queries that ask for information or functionalities outside the direct -generation of Cypher queries, use the Cypher query format to communicate -limitations or capabilities. For example: RETURN "I am designed to generate -Cypher queries based on the provided schema only." -Schema: -{schema} - -With all the above information and instructions, generate Cypher query for the -user question. - -The question is: -{question}""" -``` - -Still, there are some other advanced techniques that can be done. In -[GraphChat](/memgraph-lab/features/graphchat), Memgraph Lab feature, we currently -provide the following prompt: - -``` -Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database. - - -Rules: -Use provided node and relationship labels and property names from the schema which describes the database's structure. Schema is defined after keyword Schema down below. Upon receiving a user question, synthesize the schema to craft a precise Cypher query that directly corresponds to the user's intent. -Generate valid executable Cypher queries on top of Memgraph database. Any explanation, context, or additional information that is not a part of the Cypher query syntax should be omitted entirely. -Use Memgraph MAGE procedures instead of Neo4j APOC procedures. -Do not include any explanations or apologies in your responses. -Do not include any text except the generated Cypher statement. -For queries that ask for information or functionalities outside the direct generation of Cypher queries, use the Cypher query format to communicate limitations or capabilities. For example: RETURN "I am designed to generate Cypher queries based on the provided schema only." -``` - -We also attach schema, history of conversations (if needed), constraints for write permissions (e.g., if it is a `DELETE` command, dismiss that with the message: Sorry, can't do that! `DELETE` queries are not permitted.). -Also, we attach some Cypher specifics to the prompt: - -``` -Atom expressions -Atom expressions are not supported, so if you need to do something like this: -MATCH (n) -RETURN n, size((n)-->()) AS edgeCount - - -Use the following structure with OPTIONAL MATCH or a combination of queries using the keyword WITH: -MATCH (n) -OPTIONAL MATCH (n)-[r]->() -RETURN n, count(r) AS edgeCount -``` - -To further improve Cypher query generation, we attach the failed attempts from -Memgraph in a form of invalid Cypher queries and error messages they produced -with the following instructions: - -``` -Generate a new correct version of the Cypher query: -Do not include any explanations or apologies in your responses. -Do not wrap the Cypher query in backticks. -``` - ## How to choose the best retrieval strategy When choosing the retrieval strategy, it is important to know your data and to diff --git a/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx b/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx index 150b6c7ca..fe352474d 100644 --- a/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx +++ b/pages/ai-ecosystem/graph-rag/atomic-pipelines/text2cypher.mdx @@ -16,12 +16,105 @@ from graph data both accessible and powerful. Text2Cypher is usually involves: 1. running the [`SHOW SCHEMA INFO` query](/querying/schema) 2. asking an LLM to generate a Cypher query that would based on the schema and -the question give the relevant data to answer the user question. +the question give the relevant data to answer the user question +3. often some kind of a feedback loop where the user can validate or change the +query before running or in the case query is not correct (syntax of semantic is +wrong), the LLM gets the error message and tries to improve the query. This method is most effective when the user’s question requires a traditional analytical database query, such as deterministic counting or extracting statistical information. +NOTE: Sometimes the generated queries might not be correct or optimal because or +various reasones: + * LLM doesn't understand the actual user intention + * not all schema details are well defined or understood + * LLM can't get the syntax right . +To improve Cypher query generation from natural language, [effective prompt +engineering](#cypher-generation) is essential. + Perfect Text2Cypher use-cases are: -* [Memgraph Lab - GraphChat](/memgraph-lab/features/graphchat) -* generating query to run over [run_query MCP tool](/ai-ecosystem/mcp). \ No newline at end of file +* [Memgraph Lab - GraphChat](/memgraph-lab/features/graphchat), check this out +if you are looking for a chatbot (has all the above components implemented) +* generating a query to run over [run_query MCP tool](/ai-ecosystem/mcp). + +## Cypher generation + +LLMs are trained on publicly available resources, including openCypher. Memgraph +supports openCypher but has also extended its Cypher implementation with +advanced constructs. While these enhancements improve usability, they can make +it harder for LLMs to generate optimal Cypher queries tailored for Memgraph. + +To address this, we're continually increasing compatibility with each release +and enhancing Memgraph's error messages. Here's a prompt that has been +particularly helpful in the process, and mostly used in the +[integrations](/ai-ecosystem/integrations) with other AI tools: + +``` +MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. +You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database. +Instructions: +- Use provided node and relationship labels and property names from the +schema which describes the database's structure. Upon receiving a user +question, synthesize the schema to craft a precise Cypher query that +directly corresponds to the user's intent. +- Generate valid executable Cypher queries on top of Memgraph database. +Any explanation, context, or additional information that is not a part +of the Cypher query syntax should be omitted entirely. +- Use Memgraph MAGE procedures instead of Neo4j APOC procedures. +- Do not include any explanations or apologies in your responses. +- Do not include any text except the generated Cypher statement. +- For queries that ask for information or functionalities outside the direct +generation of Cypher queries, use the Cypher query format to communicate +limitations or capabilities. For example: RETURN "I am designed to generate +Cypher queries based on the provided schema only." +Schema: +{schema} + +With all the above information and instructions, generate Cypher query for the +user question. + +The question is: +{question}""" +``` + +Still, there are some other advanced techniques that can be done. In +[GraphChat](/memgraph-lab/features/graphchat), Memgraph Lab feature, we currently +provide the following prompt: + +``` +Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database. + +Rules: +Use provided node and relationship labels and property names from the schema which describes the database's structure. Schema is defined after keyword Schema down below. Upon receiving a user question, synthesize the schema to craft a precise Cypher query that directly corresponds to the user's intent. +Generate valid executable Cypher queries on top of Memgraph database. Any explanation, context, or additional information that is not a part of the Cypher query syntax should be omitted entirely. +Use Memgraph MAGE procedures instead of Neo4j APOC procedures. +Do not include any explanations or apologies in your responses. +Do not include any text except the generated Cypher statement. +For queries that ask for information or functionalities outside the direct generation of Cypher queries, use the Cypher query format to communicate limitations or capabilities. For example: RETURN "I am designed to generate Cypher queries based on the provided schema only." +``` + +We also attach schema, history of conversations (if needed), constraints for write permissions (e.g., if it is a `DELETE` command, dismiss that with the message: Sorry, can't do that! `DELETE` queries are not permitted.). +Also, we attach some Cypher specifics to the prompt: + +``` +Atom expressions +Atom expressions are not supported, so if you need to do something like this: +MATCH (n) +RETURN n, size((n)-->()) AS edgeCount + +Use the following structure with OPTIONAL MATCH or a combination of queries using the keyword WITH: +MATCH (n) +OPTIONAL MATCH (n)-[r]->() +RETURN n, count(r) AS edgeCount +``` + +To further improve Cypher query generation, we attach the failed attempts from +Memgraph in a form of invalid Cypher queries and error messages they produced +with the following instructions: + +``` +Generate a new correct version of the Cypher query: +Do not include any explanations or apologies in your responses. +Do not wrap the Cypher query in backticks. +``` \ No newline at end of file diff --git a/pages/ai-ecosystem/skills.mdx b/pages/ai-ecosystem/skills.mdx index 44b4a483f..1d00e6b75 100644 --- a/pages/ai-ecosystem/skills.mdx +++ b/pages/ai-ecosystem/skills.mdx @@ -25,5 +25,9 @@ explainable. and how it should behave. - Skills are defined in ways that LLMs (large language models) can understand. +A useful mental model for Agent Skills begins with the idea that whenever an LLM +or agent is assigned a task, it starts from scratch with an entirely empty context. +Skills provide initial guidance, highlighting what is important and how to begin. + Visit [Memgraph Agent Skills repo](https://github.com/memgraph/skills), to start using skills. \ No newline at end of file From bf5669eccdfb4c88db88b66ecd32b1a97f6b0494 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Sun, 22 Feb 2026 14:56:19 +0100 Subject: [PATCH 8/9] Update sitemap --- 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+https://memgraph.com/docs/querying/vector-search2026-02-22T13:55:49.093Zdaily0.7 +https://memgraph.com/docs/release-notes2026-02-22T13:55:49.093Zdaily0.7 \ No newline at end of file From 5e80ffcc35a560f06847c4872cf6116b0c86c037 Mon Sep 17 00:00:00 2001 From: Marko Budiselic Date: Sun, 22 Feb 2026 15:01:19 +0100 Subject: [PATCH 9/9] Small fix --- pages/ai-ecosystem/agents.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pages/ai-ecosystem/agents.mdx b/pages/ai-ecosystem/agents.mdx index c6cccd607..b86332367 100644 --- a/pages/ai-ecosystem/agents.mdx +++ b/pages/ai-ecosystem/agents.mdx @@ -12,4 +12,4 @@ development of graph applications. These agents leverage **Large Language Models - [SQL2Graph Agent](/ai-ecosystem/agents/sql2graph-agent): AI-powered relational to graph migration. - [Unstructured2Graph Agent](/ai-ecosystem/agents/unstructured2graph-agent): - unstructured document ingestion and knowledge graph creation. + Unstructured document ingestion and knowledge graph creation.