typo and some formating fixes; embedding model change; removal of the llama 3 special tokens not needed for ollama

Author: jeffxtang

recipes/use_cases/langchain/README.md

@@ -4,48 +4,58 @@ LLM agents use [planning, memory, and tools](https://lilianweng.github.io/posts/
 
 LangChain offers several different ways to implement agents.
 
-(1) Use [agent executor](https://python.langchain.com/docs/modules/agents/quick_start/) with [tool-calling](https://python.langchain.com/docs/integrations/chat/) versions of llama3.
+(1) Use [AgentExecutor](https://python.langchain.com/docs/modules/agents/quick_start/) with [tool-calling](https://python.langchain.com/docs/integrations/chat/) versions of Llama 3.
 
-(2) Use [LangGraph](https://python.langchain.com/docs/langgraph), a library from LangChain that can be used to build reliable agents.
+(2) Use [LangGraph](https://python.langchain.com/docs/langgraph), a library from LangChain that can be used to build reliable agents with Llama 3.
 
 ---
 
-### Agent Executor
+### AgentExecutor Agent
 
-Our first notebook, `tool-calling-agent`, shows how to build a [tool calling agent](https://python.langchain.com/docs/modules/agents/agent_types/tool_calling/) with agent executor.
+AgentExecutor is the runtime for an agent. AgentExecutor calls the agent, executes the actions it chooses, passes the action outputs back to the agent, and repeats.
 
-This show how to build an agent that uses web search and retrieval tools.
+Our first notebook, `tool-calling-agent`, shows how to build a [tool calling agent](https://python.langchain.com/docs/modules/agents/agent_types/tool_calling/) with AgentExecutor and Llama 3.
+
+This shows how to build an agent that uses web search and retrieval tools.
 
 --- 
 
-### LangGraph
+### LangGraph Agent
 
 [LangGraph](https://python.langchain.com/docs/langgraph) is a library from LangChain that can be used to build reliable agents.
 
 LangGraph can be used to build agents with a few pieces:
 - **Planning:** Define a control flow of steps that you want the agent to take (a graph)
 - **Memory:** Persist information (graph state) across these steps
-- **Tool use:** Tools can be used at any step to modify state
+- **Tool use:** Modify state at any step
+
+Our second notebook, `langgraph-agent`, shows how to build a Llama 3 powered agent that uses web search and retrieval tool in LangGraph.
 
-Our second notebook, `langgraph-agent`, shows how to build an agent that uses web search and retrieval tool in LangGraph.
+It discusses some of the trade-offs between AgentExecutor and LangGraph.
+
+--- 
 
-It discusses some of the trade-offs between agent executor and LangGraph.
+### LangGraph RAG Agent
 
-Our third notebook, `langgraph-rag-agent`, shows how to apply LangGraph to build advanced RAG agents that use ideas from 3 papers:
+Our third notebook, `langgraph-rag-agent`, shows how to apply LangGraph to build advanced Llama 3 powered RAG agents that use ideas from 3 papers:
 
 * Corrective-RAG (CRAG) [paper](https://arxiv.org/pdf/2401.15884.pdf) uses self-grading on retrieved documents and web-search fallback if documents are not relevant.
 * Self-RAG [paper](https://arxiv.org/abs/2310.11511) adds self-grading on generations for hallucinations and for ability to answer the question.
 * Adaptive RAG [paper](https://arxiv.org/abs/2403.14403) routes queries between different RAG approaches based on their complexity.
 
 We implement each approach as a control flow in LangGraph:
-- **Planning:** The sequence of RAG steps (e.g., retrieval, grading, generation) that we want the agent to take
+- **Planning:** The sequence of RAG steps (e.g., retrieval, grading, and generation) that we want the agent to take
 - **Memory:** All the RAG-related information (input question, retrieved documents, etc) that we want to pass between steps
 - **Tool use:** All the tools needed for RAG (e.g., decide web search or vectorstore retrieval based on the question)
 
 We will build from CRAG (blue, below) to Self-RAG (green) and finally to Adaptive RAG (red):
 
 ![Screenshot 2024-05-03 at 10 50 02 AM](https://github.com/rlancemartin/llama-recipes/assets/122662504/ec4aa1cd-3c7e-4cd1-a1e7-7deddc4033a8)
 
-Our fouth notebook, `langgraph-rag-agent-local`, shows how to apply LangGraph to build advanced RAG agents that run locally and reliable.
+--- 
+
+### Local LangGraph RAG Agent
+
+Our fouth notebook, `langgraph-rag-agent-local`, shows how to apply LangGraph to build advanced RAG agents using Llama 3 that run locally and reliably.
 
 See this [video overview](https://www.youtube.com/watch?v=sgnrL7yo1TE) for more detail.