Infranodus Knowledge Graphs & Text Analysis

- generate_knowledge_graph: Generate a knowledge graph with main topics, topical clusters, concepts, concepts (nodes) relations (edges) and structural gaps. Only use when explicitly asked to analyze a text or generate a knowledge graph. Do not use for short clarifying questions that you already have an answer to from the context of the conversation.
- create_knowledge_graph: Create a knowledge graph in InfraNodus from text or from a URL, save it, and provide its name and a link to it for future use.
- generate_ontology_graph: Use AI to generate a reasoning ontology knowledge graph (entities and the relations between them) for a topic, prompt, or text, and optionally save it as a InfraNodus graph. Use to get a rich overview or to produce a reasoning map of a topic for expert workflows.
- memory_add_relations: Add relations to the InfraNodus memory from text, save it, and provide its name and a link to it for future use.
- memory_get_relations: Provide a list of relations from the InfraNodus memory for a given concept or entity
- analyze_existing_graph_by_name: Extract and analyze the content of an existing InfraNodus graph from your account.
- analyze_text: Extract and analyze a graph from text, URL, YouTube video transcript, or an existing InfraNodus graph.
- generate_content_gaps: Generate content gaps from text, URL, or an existing graph using knowledge graph analysis.
- generate_topical_clusters: Generate topics and clusters of keywords from text, URL, or an existing graph using knowledge graph analysis.
- generate_research_questions: Analyze text or an existing graph and generate innovative research questions based on the content gaps identified between the topical clusters. Provide either text, url, or graphName. Can be used to improve the text and the discourse it relates to
- generate_research_ideas: Analyze text or an existing graph and generate innovative research ideas based on the content gaps identified between the topical clusters inside the text that can be used to improve the text and the discourse it relates to.
- generate_responses_from_graph: Use text, URL, or an existing InfraNodus knowledge graph and generate responses and expert advice based on a prompt provided.
- generate_contextual_hint: Generate information about the main topics and concepts in a text to augment RAG retrieval and text analysis.
- retrieve_from_knowledge_base: Retrieve the statements and general overview of an existing InfraNodus knowledge graph based on the user's prompt for GraphRAG based retrieval.
- develop_conceptual_bridges: Analyze text or an existing graph and get ideas on how to develop conceptual bridges in this text to link it to a broader discourse. Provide either text, url, or graphName.
- develop_latent_topics: Analyze text or an existing graph, extract underdeveloped topics and get an idea on how to develop them. Provide either text, url, or graphName.
- optimize_text_structure: Analyze the level of bias and coherence in text. If it's too biased, develop the represented topics, if it's focused or diversified, develop the content gaps. If it's dispersed, focus the most common gap topics.
- optimize_reasoning: Analyze the structure of the model's current reasoning or chat with the user using knowledge graph analysis, and steer it toward optimal diversity and coherence at the same time to optimize balance. Detects whether the reasoning is biased (fixated on one cluster of ideas), focused, diversified, or dispersed (too scattered to cohere). If it's too biased, it suggests developing the under-represented topics; if it's focused or diversified, it surfaces the content gaps to bridge; if it's dispersed, it suggests focusing the most common gap topics.
- develop_text_tool: Analyze text or an existing graph to extract research questions, develop latent topics, and identify content gaps in a single workflow with progress tracking. Provide either text, url, or graphName.
- list_graphs: List all graphs (contexts) for the currently logged in user with optional filtering by name, type, date, language, or favorite status. Use this to discover available graphs before analyzing or searching them.
- search: Find the concepts and terms in existing InfraNodus graphs
- fetch: Fetch a specific search result for an InfraNodus knowledge graph
- overlap_between_texts: Extract the common relationships and similarities between texts and generate an overlap graph
- merged_graph_from_texts: Build a graph of all the texts, URLs, and existing InfraNodus graphs provided, providing topical clusters and gaps present in the merged graph generated from all the texts.
- difference_between_texts: Extract the conceptial relations that are missing in the first text, url, or InfraNodus graph but are present in the other texts
- analyze_google_search_results: Generate a knowledge graph and topical clusters from Google search results for provided search queries
- analyze_youtube_results: Generate a knowledge graph and topical clusters from YouTube results — search results, a channel's or playlist's videos, video comments, or transcribed subtitles — to reveal the main topics, clusters, and content gaps in the discourse
- analyze_related_search_queries: Generate a knowledge graph and identifymain topical clusters in the search requests related to the search queries provided
- search_queries_vs_search_results: Find the combinations of keywords and topics people search for that don't appear in the search results for the same queries
- analyze_llm_results: Ask an LLM to describe a topic, then turn its response into a knowledge graph that reveals how the model frames it — main concepts, clusters, content gaps, and the relations between them. Useful for probing model bias, surfacing the implicit structure of an LLM's view on a subject, or comparing how different models describe the same topic.
- generate_seo_report: Analyze content for SEO optimization by comparing its knowledge graph with the graphs of Google search results and search queries to identify content gaps and opportunities based on the differences
- get_more_tools: Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.