From fbefc32319bccec27cf8d1b27e71273ec433f838 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:25:42 +0200 Subject: [PATCH 1/7] added new blog post --- docs/blog/index.md | 3 +- docs/blog/posts/Shiny_new_LLMOps.md | 90 ++++++++++++++++++ docs/blog/posts/Shiny_new_LLMOps/Untitled.png | Bin 0 -> 263664 bytes 3 files changed, 91 insertions(+), 2 deletions(-) create mode 100644 docs/blog/posts/Shiny_new_LLMOps.md create mode 100644 docs/blog/posts/Shiny_new_LLMOps/Untitled.png diff --git a/docs/blog/index.md b/docs/blog/index.md index 0b0320ad..2674a9c2 100644 --- a/docs/blog/index.md +++ b/docs/blog/index.md @@ -5,9 +5,8 @@ The goal of the blog is to discuss broader topics around the cognee project, inc ## knowledge graphs + rags -In progress -[//]: # (1. [LLMOps stack + Graphs](posts/llmops-and-knowledge-graphs.md)) +1. [LLMOps stack + Graphs](posts/Shiny_new_LLMOps.md) [//]: # (2. [Where do knowledge graphs fit, and where do they not? A case study with dynamo.fyi](posts/where-do-knowledge-graphs-fit.md)) diff --git a/docs/blog/posts/Shiny_new_LLMOps.md b/docs/blog/posts/Shiny_new_LLMOps.md new file mode 100644 index 00000000..7c6af4f6 --- /dev/null +++ b/docs/blog/posts/Shiny_new_LLMOps.md @@ -0,0 +1,90 @@ +# LLMOps stack + Graphs + +### The past: Berlin startup scene + +Machine learning has had its place in the business models of companies for several years, but due to high labor costs, lack of generalizability, and long development cycles, it often did not meet the early days' expectations. With the rise of ChatGPT, however, foundation models and LLMs are reemerging as the next step in the evolution in the Machine Learning stack, democratizing it for the end users. + +As a consultant and operator in the Berlin startup scene over the past ten years, I have seen the vocation of “Data Scientist” reflect this trend as well. Its initial popularity, decline, and resurgence have mirrored the rise and fall of the Roman Empire. + +In my early years as a data analyst in tier-II e-commerce businesses, data science was considered a prestigious, cutting-edge title. However,  most of these ventures lacked the experience or maturity to properly productionize their business models. + +Often, I would see a data scientist building tons of features for their company’s LLM models to only slightly outperform their basic KPIs. They were often stuck in the limbo of demoware, and only the businesses in which data was a key operational element would successfully deploy and run data science systems at scale. + +--- + +### Pandemic and fall out of grace + +Over the years, this low impact-high drain dynamic led to data science falling out of favor. The COVID pandemic seemed to deliver a death blow to the Berlin IT community, with many data scientists having been made redundant. + +This played out differently in larger markets and companies, where I saw more mature setups heavily relying on machine learning. However, from the perspective of most software *Mittelstand* (a German term for medium-sized enterprises), the technology was seen as a nice-to-have, not a must-have. + +Suddenly, with the release of ChatGPT, most knowledge previously required to operate machine-learning became obsolete,  with the only thing now needed being an API key. This dropped the barrier to entry to the floor and created a need for new tools to be built around these APIs. + +Tools like Langchain met this need perfectly, enabling everyone to interact with their data. + +### **ML to LLMOps** + +A question arises about how we should approach LLM tooling. Ignoring previous knowledge and inferring new paradigms(Agents come to mind)  as if in a vacuum can be counterproductive. Re-inventing categories should be done cautiously; history shows that overdoing it can lead to failure. + +A recently published article by angel investor and Professor of Neuroscience at U.C. London, Peter Zhegin, has effectively mapped out the elements of the MLOps system ripe for disruption, suggesting which ones might be impacted: + +1. **Vector Stores**: The authors argue that data storage and vector stores will be acquired by large players, but that differentiation still may be possible in the data space. They state that "A realistic way to differentiate might be to push for real-time vectorization while finding the best way to use traditional databases and feature stores (relational/NoSQL) together with vector DBs." +2. **Feature Storage**: The authors note that "Prompt engineering does not involve traditional training but allows one to change the model's behavior during inference by creating appropriate instructions or queries. This ‘training without training’ presents an interesting opportunity outside the MLOps pipeline." + +### **Feature Stores and the Next Steps** + +The evolution of the MLOps stack signals a need for a new type of feature store that enables in-context learning. + +Since fine-tuning LLMs will start happening at inference time, we need a system to interact with and manage data points fed to the LLM at scale. This implies a need for a feature store that provides more determinism to the LLM outputs, enabling the integration of business processes and practices into data that captures the context of an enterprise or organization. + +An example of such a use case would be using a set of documents from different departments, enabling the LLM to understand the relationships between these documents and their individual parts. + +This effort often requires humans to provide the base rules for how the LLM should interact with the information, leading to the creation of what is commonly referred to as a RAG (Retrieval Augmented Generation) pipeline. + +--- + +Since recently, we’ve been able to combine graphs and vector data stores to create a semantic layer on top of naive RAGs. This layer has been a major step towards encoding rules into an in-context learning pipeline. + +In [his recent blog post](https://medium.com/enterprise-rag/understanding-the-knowledge-graph-rag-opportunity-694b61261a9c), co-founder of WhyHow.AI, Chia Jeng Yang, explained what a typical RAG pipeline looks like. He also introduced Graph Ops and Vector Ops as new elements of the RAG stack which can lead to more stable retrieval patterns. + +![Shiny%20new%20LLMOps%209964a0e749ec4842b7c5d363a5e90a77/Untitled.png](Shiny%20new%20LLMOps%209964a0e749ec4842b7c5d363a5e90a77/Untitled.png) + +The argument Zhegin [made a few months ago is now taking shape](https://investingbyapproximation.substack.com/p/the-stunning-rise-of-llmops-navigating?r=2jos7&utm_campaign=post&utm_medium=web&triedRedirect=true). We are seeing feature stores evolve into tools that manage vector and graph stores. + +We are still in the early stages, though. As Jon Turrow of Madrona Ventures  [**suggests**](https://www.madrona.com/the-rise-of-ai-agent-infrastructure/), the next generation of AI agent infrastructure—what Chia refers to as Graph Ops—will be a personalization layer. + +--- + +I believe that these terms are interchangeable and that a new in-context Feature Store, Vector and Graph Ops, and personalization layers are essentially the same thing. Moreover, it’s my belief that Vector and Graph Ops are not differentiation categories in and of themselves. + +The challenge is, thus, not connecting Vector and Graph stores or giving a RAG system a 10% performance boost. + +The challenge and solution lie in creating a new type of probabilistic data engine—one with an interface as simple as SQL, but which can retrieve and structure information in real time, optimizing what we feed the LLM based on solid evaluations. + +--- + +Striving to make sense of the best computing engine we know of—our mind—cognitive sciences may offer us clues on how to move forward.. + +After all, we process, store, and retrieve data from our mental lexicon with ease, with inherent personalization and dynamic data enrichment. + +I believe that understanding the way our mind carries out these processes may allow us to replicate them in machine learning. + +With human language as the new SQL and cognitive theories as inspiration, the next generation of tooling is still on the horizon. + +The argument Zhegin made a few months ago is now taking shape. We see feature stores evolving into tools that manage vector and graph stores. + +We are still in the early stages. In a recent article, Jon Turrow from Madrona suggests that for the next generation of AI agent infrastructure, what Jia refers to as Graph Ops will become a personalization layer. + +--- + +I believe these terms are interchangeable and that a new in-context feature store, graph ops, and personalization are essentially the same thing. + +Moreover, vector and graph ops are not differentiation categories by themselves. The challenge is not connecting vector and graph stores or giving a RAG system a 10% performance boost. + +The challenge and solution lie in creating a new type of probabilistic data engine, with an interface as simple as SQL, that can retrieve and structure information in real time and optimize what we give to the LLM based on solid evaluations. + +I often refer to cognitive sciences and the best computing engine we know of—our mind. + +We process, store, and retrieve data from our mental lexicon with ease, where personalization is inherent, and data enrichment happens dynamically. + +With human language as the new SQL and cognitive theories as inspiration, the next generation of tooling is still on the horizon. \ No newline at end of file diff --git a/docs/blog/posts/Shiny_new_LLMOps/Untitled.png b/docs/blog/posts/Shiny_new_LLMOps/Untitled.png new file mode 100644 index 0000000000000000000000000000000000000000..89c0600492221601793134d7ce7be8ab287cd1ee GIT binary patch literal 263664 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zC@$7)%`_L+)32GQUx@In%Qq;=x1bj5ozd*YNLjB0NTU`npszGTr4rv`%*TQ=7MOZ3 zREwlCA_=_sP*VW)@M{ri$B!M#$w=?)Q{!mwHFpQEsk%@pORuqxT~~mqsvJ7> za?o^rU}UU73`}U*>-Jc$RGiV+?xSz+!2EAe9f`2To@17$MTDnwG>?I`XIdk=!ec2# zK@?jd>-8#0(C%|)_lnfXrL}X1EjwTeXl0IpVyFq@X%q`nv^NZTs_aV*kcgq~Dgu*YerTcPN=&=oE`{7$Maqa+8Emjw24 zJ3k)2I&IhPoJS;dt}85={Q$<|+z+;2h}k&kC_`Rwi>B*DydgSmZLz+VRq&LvhS7)6r}rOC;#{Y6A3S^ouAOn|7G)AfZ_1uU$Xy|od1&jALKLh@xRjkx2MRz zhs@uS1vrTQJ!JmjH2(LH`S+0d*OK`MlkEQ|OJ>#-cjh9xA102v1pGZz*1BJG&&>b- E07D2=Bme*a literal 0 HcmV?d00001 From 9f698cabe2c5a5bd56a38e8a1dee06b2895c873d Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:27:24 +0200 Subject: [PATCH 2/7] Shiny new LLMOps --- docs/blog/posts/Shiny_new_LLMOps.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/Shiny_new_LLMOps.md b/docs/blog/posts/Shiny_new_LLMOps.md index 7c6af4f6..1523cccc 100644 --- a/docs/blog/posts/Shiny_new_LLMOps.md +++ b/docs/blog/posts/Shiny_new_LLMOps.md @@ -47,7 +47,7 @@ Since recently, we’ve been able to combine graphs and vector data stores to cr In [his recent blog post](https://medium.com/enterprise-rag/understanding-the-knowledge-graph-rag-opportunity-694b61261a9c), co-founder of WhyHow.AI, Chia Jeng Yang, explained what a typical RAG pipeline looks like. He also introduced Graph Ops and Vector Ops as new elements of the RAG stack which can lead to more stable retrieval patterns. -![Shiny%20new%20LLMOps%209964a0e749ec4842b7c5d363a5e90a77/Untitled.png](Shiny%20new%20LLMOps%209964a0e749ec4842b7c5d363a5e90a77/Untitled.png) +![Shiny_new_LLMOps/Untitled.png](Shiny_new_LLMOps/Untitled.png) The argument Zhegin [made a few months ago is now taking shape](https://investingbyapproximation.substack.com/p/the-stunning-rise-of-llmops-navigating?r=2jos7&utm_campaign=post&utm_medium=web&triedRedirect=true). We are seeing feature stores evolve into tools that manage vector and graph stores. From 229eb878760f3d3f6155516c00b8af0b8664d9aa Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:29:55 +0200 Subject: [PATCH 3/7] Shiny new LLMOps --- docs/blog/posts/Shiny_new_LLMOps.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/docs/blog/posts/Shiny_new_LLMOps.md b/docs/blog/posts/Shiny_new_LLMOps.md index 1523cccc..abb2fbf4 100644 --- a/docs/blog/posts/Shiny_new_LLMOps.md +++ b/docs/blog/posts/Shiny_new_LLMOps.md @@ -1,3 +1,15 @@ +--- +draft: False +date: 2024-06-14 +tags: + - LLMOps + - Graphs + - Vector Stores + - Feature Storage +authors: + - tricalt +--- + # LLMOps stack + Graphs ### The past: Berlin startup scene From d23f55115f2cfe901f776c26af8b43e07bb6dc84 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:34:36 +0200 Subject: [PATCH 4/7] added new blog post --- docs/blog/posts/Shiny_new_LLMOps.md | 3 +++ .../posts/Shiny_new_LLMOps/llm_problems.png | Bin 0 -> 57384 bytes 2 files changed, 3 insertions(+) create mode 100644 docs/blog/posts/Shiny_new_LLMOps/llm_problems.png diff --git a/docs/blog/posts/Shiny_new_LLMOps.md b/docs/blog/posts/Shiny_new_LLMOps.md index abb2fbf4..a5c5e3fd 100644 --- a/docs/blog/posts/Shiny_new_LLMOps.md +++ b/docs/blog/posts/Shiny_new_LLMOps.md @@ -71,6 +71,9 @@ I believe that these terms are interchangeable and that a new in-context Feature The challenge is, thus, not connecting Vector and Graph stores or giving a RAG system a 10% performance boost. +The main issues still remain +![Shiny_new_LLMOps/llm_problems.png](Shiny_new_LLMOps/llm_problems.png) + The challenge and solution lie in creating a new type of probabilistic data engine—one with an interface as simple as SQL, but which can retrieve and structure information in real time, optimizing what we feed the LLM based on solid evaluations. --- diff --git a/docs/blog/posts/Shiny_new_LLMOps/llm_problems.png 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From 2aafcf36c482994c5d1149fdfe7f6b38c43512c0 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:35:36 +0200 Subject: [PATCH 5/7] added new blog post --- docs/blog/index.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/blog/index.md b/docs/blog/index.md index 2674a9c2..95bf8628 100644 --- a/docs/blog/index.md +++ b/docs/blog/index.md @@ -20,8 +20,10 @@ The goal of the blog is to discuss broader topics around the cognee project, inc This section covers the release notes for the cognee library. It includes the new features, bug fixes, and improvements in each release. 1. [Cognee - library release](posts/cognee-library-release.md) -2. [Cognee - v0.1.11 announcement](posts/cognee-v0.1.1.md) -3. [New website for cognee](posts/new-website-for-cognee.md) + +[//]: # (2. [Cognee - v0.1.11 announcement](posts/cognee-v0.1.1.md)) + +[//]: # (3. [New website for cognee](posts/new-website-for-cognee.md)) From c8361f75e952126c60ce431e135cd7d80c9087d5 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 11:43:42 +0200 Subject: [PATCH 6/7] added new blog post --- docs/blog/index.md | 4 ++-- docs/blog/posts/Shiny_new_LLMOps.md | 4 ++-- docs/blog/posts/cognee-new-website.md | 17 +++++++++++++++++ 3 files changed, 21 insertions(+), 4 deletions(-) create mode 100644 docs/blog/posts/cognee-new-website.md diff --git a/docs/blog/index.md b/docs/blog/index.md index 95bf8628..527bfcfb 100644 --- a/docs/blog/index.md +++ b/docs/blog/index.md @@ -21,9 +21,9 @@ This section covers the release notes for the cognee library. It includes the ne 1. [Cognee - library release](posts/cognee-library-release.md) -[//]: # (2. [Cognee - v0.1.11 announcement](posts/cognee-v0.1.1.md)) +[//]: # (2. [Cognee - v0.1.12 announcement](posts/cognee-v0.1.12.md)) -[//]: # (3. [New website for cognee](posts/new-website-for-cognee.md)) +3. [New website for cognee](posts/cognee-new-website.md) diff --git a/docs/blog/posts/Shiny_new_LLMOps.md b/docs/blog/posts/Shiny_new_LLMOps.md index a5c5e3fd..d7c07f2f 100644 --- a/docs/blog/posts/Shiny_new_LLMOps.md +++ b/docs/blog/posts/Shiny_new_LLMOps.md @@ -12,7 +12,7 @@ authors: # LLMOps stack + Graphs -### The past: Berlin startup scene +### **The past: Berlin startup scene** Machine learning has had its place in the business models of companies for several years, but due to high labor costs, lack of generalizability, and long development cycles, it often did not meet the early days' expectations. With the rise of ChatGPT, however, foundation models and LLMs are reemerging as the next step in the evolution in the Machine Learning stack, democratizing it for the end users. @@ -24,7 +24,7 @@ Often, I would see a data scientist building tons of features for their company --- -### Pandemic and fall out of grace +### **Pandemic and fall out of grace** Over the years, this low impact-high drain dynamic led to data science falling out of favor. The COVID pandemic seemed to deliver a death blow to the Berlin IT community, with many data scientists having been made redundant. diff --git a/docs/blog/posts/cognee-new-website.md b/docs/blog/posts/cognee-new-website.md new file mode 100644 index 00000000..eafae2e2 --- /dev/null +++ b/docs/blog/posts/cognee-new-website.md @@ -0,0 +1,17 @@ +--- +draft: False +date: 2024-06-14 +tags: + - graphs + - GraphRAG +authors: + - tricalt +--- + +We are excited to announce the launch of Cognee.ai. + +Highlights: +Book a Discussion: Schedule a consultation directly through our website. +Check it out at: www.cognee.ai and book your discussion with our experts. + +We'd be happy to hear your feedback and discuss GraphRAGs, and more. \ No newline at end of file From a2836d7cfc25113ab734416db7c6685cdee3cbfd Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 14 Jun 2024 12:12:05 +0200 Subject: [PATCH 7/7] Shiny new LLMOps --- docs/research.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/research.md b/docs/research.md index 15f06d44..1439dab5 100644 --- a/docs/research.md +++ b/docs/research.md @@ -5,6 +5,7 @@ The page is dedicated to collecting all research that was collected in the past This is not an exhaustive list, and any PRs would be welcome ### Research Papers +- [2024/06/04] [Symbolic reasoning](https://arxiv.org/abs/2402.01817) - [2024/06/04] [Transformers and episodic memory](https://arxiv.org/abs/2405.14992) - [2024/03/24] [Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs](https://arxiv.org/abs/2404.07103) - [2024/03/24] [Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention](https://arxiv.org/abs/2404.07143)