Add LangChain recipies

recipes/use_cases/langchain/README.md

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+# LangChain <> Llama3 Cookbooks
+
+LLM agents use [planning, memory, and tools](https://lilianweng.github.io/posts/2023-06-23-agent/) to accomplish tasks.
+
+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.
+
+(2) Use [LangGraph](https://python.langchain.com/docs/langgraph), a library from LangChain that can be used to build reliable agents.
+
+---
+
+### Agent Executor
+
+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.
+
+This show how to build an agent that uses web search and retrieval tools.
+
+--- 
+
+### LangGraph
+
+[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
+
+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 agent executor and LangGraph.
+
+Our third notebook, `langgraph-rag-agent`, shows how to apply LangGraph to build advanced 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
+- **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):
+
+![RAG](./img/langgraph_adaptive_rag.png "RAG control flow")
+
+Our fouth notebook, `langgraph-rag-agent-local`, shows how to apply LangGraph to build advanced RAG agents that run locally and reliable.
+
+See this [video overview](https://www.youtube.com/watch?v=sgnrL7yo1TE) for more detail.