Create rag_basic_example.py

ai_research/LangChain/rag_basic_example.py

@@ -0,0 +1,54 @@
+from langchain.document_loaders import WebBaseLoader
+from langchain.document_transformers import ChunkTransformer
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.retrievers import SemanticRetriever
+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+# Step 1: Load documents
+loader = WebBaseLoader("https://example.com")
+documents = loader.load()
+
+# Step 2: Transform documents
+transformer = ChunkTransformer(chunk_size=512)
+transformed_documents = transformer.transform(documents)
+
+# Step 3: Create embeddings
+embedding_model = OpenAIEmbeddings()
+embeddings = embedding_model.embed(transformed_documents)
+
+# Step 4: Store embeddings in a vector store
+vector_store = FAISS.from_embeddings(embeddings)
+
+# Step 5: Create a retriever
+retriever = SemanticRetriever(vector_store)
+
+# Step 6: Define the prompt template
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# Step 7: Create the language model
+model = ChatOpenAI()
+
+# Step 8: Define the output parser
+output_parser = StrOutputParser()
+
+# Step 9: Define the RAG pipeline
+pipeline = {
+    "context": retriever,
+    "question": RunnablePassthrough(),
+} | prompt | model | output_parser
+
+# Step 10: Invoke the RAG pipeline with a question
+question = "What is the capital of France?"
+answer = pipeline.invoke({"question": question})
+
+# Step 11: Print the answer
+print(answer)