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JoseRFJuniorLLMs · Jul 24, 2024 · a2a6181 · a2a6181
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diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json
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     "AI frameworks.md",
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     "RAG/RAG Re-Ranking.md",
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     "FineTuning",
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diff --git a/Correlation vs Covariance.md b/Correlation vs Covariance.md
@@ -1,3 +1,3 @@
-![[Pasted image 20240722183124.png]]
+![[Correlation_vs_Covariance.png]]
 
 [[Correlation]] vs [[Covariance]]
diff --git a/Cross Encoders.md b/Cross Encoders.md
@@ -1,3 +1,3 @@
-![[Pasted image 20240722183028.png]]
+![[CrossEncoders.png]]
 
 [[Cross Encoders]]
diff --git a/LLM-Generated Content.md → Fine-tuning.md b/LLM-Generated Content.md → Fine-tuning.md
diff --git a/Improving search.md b/Improving search.md
diff --git a/Integration.md b/Integration.md
diff --git a/LLMs/Agent LLMs Calls.md b/LLMs/Agent LLMs Calls.md
@@ -1,4 +1,4 @@
-![[20240715222340.png]]LLM Agents are a capable framework bringing LLM performance to the next level.  
+![[Agent_Framework_Executing_LLM.png]]LLM Agents are a capable framework bringing LLM performance to the next level.  
 
 Agent framework leverages a Large Language Model to act as a decision engine capable of solving complex tasks by multi-stage reasoning.  
 

diff --git a/LLMs/Developing an LLMs.md b/LLMs/Developing an LLMs.md
@@ -1,4 +1,4 @@
-![[20240715221155.png]]
+![[Developing_LLM.png]]
 
 Stage 1: Building  
 Stage 2: Pre-training  

diff --git a/LLMs/LLM models.md b/LLMs/LLM models.md
diff --git a/LLMs/LLM-Generated Content.md b/LLMs/LLM-Generated Content.md
diff --git a/LLMs/LLMs APP.md b/LLMs/LLMs APP.md
@@ -1 +1 @@
-![[20240716000730.png]]
+![[first_llm.png]]
diff --git a/LLMs Road Map.md → LLMs/LLMs Road Map.md b/LLMs Road Map.md → LLMs/LLMs Road Map.md
diff --git a/LLMs/LLMs, chunking strategies.md b/LLMs/LLMs, chunking strategies.md
@@ -1,4 +1,4 @@
-![[20240715234606.png]]
+![[LLM_Chunking_Strategies.png]]
 
 [[LLMs, chunking strategies]]
 
diff --git a/LLMs/LLMs.md b/LLMs/LLMs.md
@@ -13,4 +13,4 @@ Chunking
 Embedding
 Vector Database
 
-![[20240715210016.png]]
+![[Gen_AI.png]]
diff --git a/LLMs/Self-Learning LLMs.md b/LLMs/Self-Learning LLMs.md
@@ -1,4 +1,4 @@
-![[20240715215838.png]]
+![[Self_Learning_LLMs.png]]
 
 Self-learning LLM framework enables an LLM to independently learn previously unknown knowledge through self-assessment of their own hallucinations.  
 

diff --git a/Layer-Wise Training.md b/Layer-Wise Training.md
diff --git a/ML.md b/ML.md
diff --git a/ML/ML.md b/ML/ML.md
@@ -0,0 +1 @@
+![[Choosing_The_Right.png]]
diff --git a/Multi-Scale Transformer (MST).md b/Multi-Scale Transformer (MST).md
@@ -0,0 +1,11 @@
+A MST é um tipo de transformador que processa o texto em diferentes escalas e níveis de detalhe. Isso permite ao modelo
+capturar informações relevantes a diferentes níveis, desde as palavras individuais até as frases e textos inteiros.
+
+Os autores também utilizaram uma técnica chamada "[[Layer-Wise Training]]" para treinar os embeddings do modelo em camadas
+separadas. Isso ajuda a evitar o [[Overfitting]] e permite ao modelo aprender relações mais complexas entre as palavras e textos.
+
+A combinação dessas técnicas permitiu ao GPT-4 gerar embeddings de alta qualidade que podem capturar informações relevantes sobre o contexto em que as palavras e textos são utilizados.
+
+Além disso, o GPT-4 também utiliza uma técnica chamada "Knowledge Distillation" para transferir conhecimento do modelo treinado para modelos mais pequenos. Isso permite ao modelo gerar embeddings de alta qualidade mesmo em situações onde não há acesso a grande quantidade de dados de treinamento.
+
+É importante notar que o GPT-4 é uma variação do GPT-3 e utiliza as mesmas técnicas de embedding que o modelo original, com algumas ajustes para melhorar a performance e a eficiência.
diff --git a/NLP/Semantic Search.md b/NLP/Semantic Search.md
@@ -1,4 +1,4 @@
-![[20240715221527.png]]
+![[Semantic_Search.png]]
 
 
 ➤ [[Text Embedding]]: The first step is to convert the text data (documents, queries, etc.) into high-dimensional vector representations using a pre-trained language model like BERT, GPT, or custom models. These vectors capture the semantic meaning of the text in a dense numerical form.  

diff --git a/Overfitting.md b/Overfitting.md
diff --git a/Query Guardrails.md b/Query Guardrails.md
diff --git a/RAG/AI Agents Use Case.md b/RAG/AI Agents Use Case.md
@@ -1 +1 @@
-![[20240715222611.png]]
+![[AI_Real_World.png]]
diff --git a/RAG/Advanced RAG LimaIndex & Claude 3.md b/RAG/Advanced RAG LimaIndex & Claude 3.md
@@ -1 +1 @@
-![[20240716000446.png]]
+![[Advanced_RAG_LIamaIndex_Claude3.png]]
diff --git a/RAG/Advanced RAG Techniques.md b/RAG/Advanced RAG Techniques.md
@@ -1 +1 @@
-![[20240715220423.png]]
+![[Advanced_RAG_Tecniques.png]]
diff --git a/RAG/Advanced RAG.md b/RAG/Advanced RAG.md
@@ -1 +1 @@
-![[20240715214731.png]]
+![[Advanced_RAG.png]]
diff --git a/RAG/Improving RAG Pipeline.md b/RAG/Improving RAG Pipeline.md
@@ -1,2 +1,2 @@
 
-![[improving_rag_pipeline.png]]
+![[improving_rag_pipeline.png]]
diff --git a/RAG/Knowledge Graph RAG with SingleStore.md b/RAG/Knowledge Graph RAG with SingleStore.md
@@ -1 +1,2 @@
-![[20240715221947.png]]
+![[Knowledge_Graph_RAG_Single_Store.png]]
+
diff --git a/RAG/Modular RAG Framework.md b/RAG/Modular RAG Framework.md
@@ -1 +1 @@
-![[20240715220117.png]] 
+![[Modular_RAG_Framework.png]] 
diff --git a/RAG/RAG Query Guardrails vs Response Guardrails.md b/RAG/RAG Query Guardrails vs Response Guardrails.md
@@ -0,0 +1,3 @@
+![[rag_query_response.png]]
+
+[[Query Guardrails]] vs [[Response Guardrails]]
diff --git a/RAG/RAG Re-Ranking.md b/RAG/RAG Re-Ranking.md
@@ -1,8 +1,8 @@
 ![[rag_reranking.png]]
 
 LLMs can acquire new information in at least two ways:  
-1. Fine-tuning  
-2. RAG (retrieval augmented generation)  
+1. [[Fine-tuning ]] 
+2. [[RAG]] (retrieval augmented generation)  
 
 Retrieval-augmented generation (RAG) is the practice of extending the “memory” or knowledge of LLM by providing access to information from an external data source.  
 

diff --git a/RAG/RAG Retrieval Sources.md b/RAG/RAG Retrieval Sources.md
@@ -1,4 +1,4 @@
-![[20240715235013.png]]
+![[RAG_Retrieval_Sources.png]]
 
 ⮕ [[Unstructured Data]] (Text): This includes plain text documents, web pages, and other free-form textual sources.  
 

diff --git a/RAG/RAG Stack.md b/RAG/RAG Stack.md
@@ -1,4 +1,4 @@
-![[20240716000053.png]]
+![[RAG_Stack.png]]
 
 But to make RAG work perfectly, here are some key points to consider:  
 1. Quality of External Knowledge Source  
@@ -7,12 +7,12 @@ But to make RAG work perfectly, here are some key points to consider:
 
 3. [[Chunking]] Size & Retrieval Strategy: Experiment with different chunk sizes to find the optimal length for context retrieval.  
 
-4. Integration with Language Model: The way the retrieved information is integrated with the language model's generation process is crucial.  
+4. [[Integration]] with Language Model: The way the retrieved information is integrated with the language model's generation process is crucial.  
 
-5. Evaluation & Fine-tuning: Evaluating the performance of the RAG model on relevant datasets and tasks is important to identify areas for improvement.  
+5. [[Evaluation]] & Fine-tuning: Evaluating the performance of the RAG model on relevant datasets and tasks is important to identify areas for improvement.  
 
-6. Ethical Considerations: Ensure that the external knowledge source is unbiased and does not contain offensive or misleading information.  
+6.[[ Ethical Considerations]]: Ensure that the external knowledge source is unbiased and does not contain offensive or misleading information.  
 
-7. Vector database: Having a vector database that supports fast ingestion, retrieval performance, hybrid search is utmost important.  
+7. [[Vector Database]]: Having a vector database that supports fast ingestion, retrieval performance, hybrid search is utmost important.  
 
-8. LLM models: Consider LLM models that are robust and fast enough to build your RAG application.
+8.[[ LLM models]]: Consider LLM models that are robust and fast enough to build your RAG application.
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		@@ -1 +1 @@
		![[20240716000446.png]]
		![[Advanced_RAG_LIamaIndex_Claude3.png]]
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		![[20240715220423.png]]
		![[Advanced_RAG_Tecniques.png]]
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		@@ -1,2 +1,2 @@

		![[improving_rag_pipeline.png]]
		![[improving_rag_pipeline.png]]
Original file line number	Diff line number	Diff line change
		@@ -1 +1,2 @@
		![[20240715221947.png]]
		![[Knowledge_Graph_RAG_Single_Store.png]]
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		![[20240715220117.png]]
		![[Modular_RAG_Framework.png]]
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,3 @@
		![[rag_query_response.png]]

		[[Query Guardrails]] vs [[Response Guardrails]]