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+"""
+Minimal Retrieval‑Augmented Generation example using LangChain, Chroma and Ollama.
+
+The script demonstrates:
+1. Splitting a sample text into chunks.
+2. Creating embeddings with Ollama.
+3. Storing chunks in a local Chroma vector store.
+4. Querying the store and generating a response with an Ollama LLM.
+
+Prerequisites:
+- Ollama must be installed and a model (e.g. tinyllama) available.
+- Python dependencies from requirements.txt must be installed.
+"""
+
+from pathlib import Path
+
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain_ollama import OllamaEmbeddings, ChatOllama
+
+# 1. Sample text
+SAMPLE_TEXT = (
+    "LangChain is a framework for developing applications powered by language models. "
+    "It provides abstractions for prompt construction, chaining, and memory. "
+    "Chroma is a fast, lightweight vector database that can be used as a backend "
+    "for retrieval‑augmented generation. Ollama offers locally hosted LLMs that "
+    "can be used for embeddings and generation.")
+
+# 2. Split text into chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
+documents = text_splitter.split_text(SAMPLE_TEXT)
+
+# 3. Create embeddings
+embeddings = OllamaEmbeddings(model="tinyllama")
+
+# 4. Create a Chroma store in a temporary directory
+persist_dir = Path("./chroma_db")
+persist_dir.mkdir(exist_ok=True)
+
+vector_store = Chroma.from_texts(
+    texts=documents,
+    embedding=embeddings,
+    persist_directory=str(persist_dir),
+)
+vector_store.persist()
+
+# 5. Query the store
+query = "What is LangChain used for?"
+results = vector_store.similarity_search(query, k=1)
+retrieved_text = results[0].page_content
+
+# 6. Generate a response using Ollama LLM
+llm = ChatOllama(model="tinyllama")
+prompt = f"Answer the following question using the provided context:\n\nContext: {retrieved_text}\n\nQuestion: {query}\nAnswer:"
+response = llm.invoke(prompt)
+print("\n--- Generated Response ---\n")
+print(response)
+
+if __name__ == "__main__":
+    pass
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