RAG

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     "LangChain",
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LLMs/Agent LLMs Calls.md

@@ -1,4 +1,4 @@
-![[Pasted image 20240715222340.png]]LLM Agents are a capable framework bringing LLM performance to the next level.  
+![[20240715222340.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.  
 

LLMs/Developing an LLMs.md

@@ -1,4 +1,4 @@
-![[Pasted image 20240715221155.png]]
+![[20240715221155.png]]
 
 Stage 1: Building  
 Stage 2: Pre-training  

LLMs/LLMs APP.md

@@ -1 +1 @@
-![[Pasted image 20240716000730.png]]
+![[20240716000730.png]]

LLMs/LLMs, chunking strategies.md

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-![[Pasted image 20240715234606.png]]
+![[20240715234606.png]]
 
 [[LLMs, chunking strategies]]
 

LLMs/LLMs.md

@@ -13,4 +13,4 @@ Chunking
 Embedding
 Vector Database
 
-![[Pasted image 20240715210016.png]]
+![[20240715210016.png]]

LLMs/Self-Learning LLMs.md

@@ -1,4 +1,4 @@
-![[Pasted image 20240715215838.png]]
+![[20240715215838.png]]
 
 Self-learning LLM framework enables an LLM to independently learn previously unknown knowledge through self-assessment of their own hallucinations.  
 

NLP/Semantic Search.md

@@ -1,4 +1,4 @@
-![[Pasted image 20240715221527.png]]
+![[20240715221527.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.  

RAG/AI Agents Use Case.md

@@ -1 +1 @@
-![[Pasted image 20240715222611.png]]
+![[20240715222611.png]]

RAG/Advanced RAG LimaIndex & Claude 3.md

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-![[Pasted image 20240716000446.png]]
+![[20240716000446.png]]

RAG/Advanced RAG Techniques.md

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-![[Pasted image 20240715220423.png]]
+![[20240715220423.png]]

RAG/Advanced RAG.md

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-![[Pasted image 20240715214731.png]]
+![[20240715214731.png]]

RAG/Knowledge Graph RAG with SingleStore.md

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-![[Pasted image 20240715221947.png]]
+![[20240715221947.png]]

RAG/Modular RAG Framework.md

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-![[Pasted image 20240715220117.png]] 
+![[20240715220117.png]] 

RAG/RAG Retrieval Sources.md

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-![[Pasted image 20240715235013.png]]
+![[20240715235013.png]]
 
 ⮕ [[Unstructured Data]] (Text): This includes plain text documents, web pages, and other free-form textual sources.  
 

RAG/RAG Stack.md

@@ -1,4 +1,4 @@
-![[Pasted image 20240716000053.png]]
+![[20240716000053.png]]
 
 But to make RAG work perfectly, here are some key points to consider:  
 1. Quality of External Knowledge Source  

RAG/RAG Using Llama 3.md

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-![[Pasted image 20240715222653.png]]
+![[20240715222653.png]]

RAG/RAG implementing.md

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-![[Pasted image 20240715235811.png]]
+![[20240715235811.png]]
 
 Retrieval component, Augmentation component, Generation component.  
 

RAG/RAG vs FineTuning.md

@@ -1 +1 @@
-![[Pasted image 20240716000624.png]]
+![[20240716000624.png]]

RAG/Systematic RAG Workflow.md

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-![[Pasted image 20240715222528.png]]
+![[20240715222528.png]]

RAG/Text & Knowledge Graph Embeddings.md

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+![[20240715222256.png]]

RAG/Text Splitting RAG.md

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-![[Pasted image 20240716000406.png]]
+![[20240716000406.png]]

RAG/Two-Stage Rretrieval System.md

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+![[20240715222219.png]]

RAG/Types of Embedding Models for RAG.md

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