Build a Knowledge Graph from a Python Repo: A Simple Guide
Build a Python repo knowledge graph with cognee to map dependencies and unlock better coding insights. Simplify AI integration for smarter development workflows.Read more
Fundraising in 2024
Discover how fundraising in 2024 in the AI infrastructure space looks like and how we went about securing 1.5m in fundingRead more
Memory Fragment Projection From Graph Databases
Discover how to enable the creation of personalized knowledge graph layers through memory fragment projection, enhancing retrieval processes and supporting GraphRAG pipelines for more refined data exploration.Read more
Improving LLM Accuracy: Graph-Based Retrieval and Chunking Methods
Discover how integrating graph-based retrieval methods and advanced chunking techniques can enhance the relevance and precision of responses generated by Large Language Models (LLMs).Read more
Structured vs Unstructured Data - Types, Differences, Examples
Explore the key differences between structured and unstructured data, their applications, and best practices. Learn more about data types in modern analytics.Read more
Big News: cognee raises €1.5 million to transform AI data management!
We’re excited to announce that cognee has raised €1.5 million in funding! 🎉 This achievement isn’t just a financial boost - it’s a vote of confidence in our mission to make AI data management simpler, more cost-effective, and highly scalable for you. Read more
cognee - Case study with Dynamo.fyi
Instead of developing in isolation, we chose to collaborate with several design partners to build a solid foundation of practical examples of what works and what doesn’t. Recently, we worked with our design partner Dynamo.fyi on one of the first production deployments of Cognee. We’ll summarize the results of this project in the following sections.Read more
Going beyond LangChain 4
In our quest for a robust RAG model, we delve into memory architecture and integrate with keepi.ai. Using human-inspired cognitive processes, we optimize data management with a focus on graph databases.Read more
Going beyond LangChain 3
Enhancing RAG applications involves testing adjustable parameters like document quantity and chunk size. Challenges include reliably linking memories and organizing memory elements for human-like understanding. We need to ensure robust AI development.Read more
Going beyond LangChain 2
At Level 2, our AI script advances with Memory Layer, FastAPI, Langchain, and Weaviate. Our Proof of Concept (POC) enables PDF upload and specific actions like translation. Attention modulators help data retrieval, mirroring cognitive science principles.Read more
Going beyond LangChain 1
In 2023, 7,000 new AI projects emerged, driven by model advancements and community collaboration. Despite this, many applications are rudimentary, prompting the need for a unified Large Language Model (LLM) platform.Read more