refactor: Update corrective_rag to use Claude 4.5 Sonnet for LLM functionality

rag_tutorials/corrective_rag/README.md

@@ -4,10 +4,10 @@ A sophisticated Retrieval-Augmented Generation (RAG) system that implements a co
 ## Features
 
 - **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval
-- **Document Relevance Grading**: Employs Claude 3.5 sonnet to assess document relevance
+- **Document Relevance Grading**: Employs Claude 4.5 sonnet to assess document relevance
 - **Query Transformation**: Improves search results by optimizing queries when needed
 - **Web Search Fallback**: Uses Tavily API for web search when local documents aren't sufficient
-- **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3.5 sonnet for different tasks
+- **Multi-Model Approach**: Combines OpenAI embeddings and Claude 4.5 sonnet for different tasks
 - **Interactive UI**: Built with Streamlit for easy document upload and querying
 
 ## How to Run?
@@ -26,7 +26,7 @@ A sophisticated Retrieval-Augmented Generation (RAG) system that implements a co
 3. **Set Up API Keys**:
    You'll need to obtain the following API keys:
    - [OpenAI API key](https://platform.openai.com/api-keys) (for embeddings)
-   - [Anthropic API key](https://console.anthropic.com/settings/keys) (for Claude 3.5 sonnet as LLM)
+   - [Anthropic API key](https://console.anthropic.com/settings/keys) (for Claude 4.5 sonnet as LLM)
    - [Tavily API key](https://app.tavily.com/home) (for web search)
    - Qdrant Cloud Setup
       1. Visit [Qdrant Cloud](https://cloud.qdrant.io/)
@@ -52,7 +52,7 @@ A sophisticated Retrieval-Augmented Generation (RAG) system that implements a co
 - **LangChain**: For RAG orchestration and chains
 - **LangGraph**: For workflow management
 - **Qdrant**: Vector database for document storage
-- **Claude 3.5 sonnet**: Main language model for analysis and generation
+- **Claude 4.5 sonnet**: Main language model for analysis and generation
 - **OpenAI**: For document embeddings
 - **Tavily**: For web search capabilities
 - **Streamlit**: For the user interface

rag_tutorials/corrective_rag/corrective_rag.py

@@ -248,7 +248,7 @@ def generate(state):
             Context: {context}
             Question: {question}
             Answer:""", input_variables=["context", "question"])
-        llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", api_key=st.session_state.anthropic_api_key,
+        llm = ChatAnthropic(model="claude-sonnet-4-5", api_key=st.session_state.anthropic_api_key,
                            temperature=0, max_tokens=1000)
         context = "\n\n".join(doc.page_content for doc in documents)
 
@@ -284,7 +284,7 @@ def grade_documents(state):
     question = state_dict["question"]
     documents = state_dict["documents"]
 
-    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", api_key=st.session_state.anthropic_api_key,
+    llm = ChatAnthropic(model="claude-sonnet-4-5", api_key=st.session_state.anthropic_api_key,
                        temperature=0, max_tokens=1000)
 
     prompt = PromptTemplate(template="""You are grading the relevance of a retrieved document to a user question.
@@ -356,7 +356,7 @@ def transform_query(state):
 
     # Use Claude instead of Gemini
     llm = ChatAnthropic(
-        model="claude-3-5-sonnet-20240620",
+        model="claude-sonnet-4-5",
         anthropic_api_key=st.session_state.anthropic_api_key,
         temperature=0,
         max_tokens=1000