--- a
+++ b/eval_rag.py
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+
+#RAG evaluation is quite hard for me , I refer some documentation online
+
+eval_dataset = Dataset.from_csv("haa_develAdmittimes.csv")
+
+eval_dataset
+
+!pip install llama-index -qU
+
+from ragas.metrics import (
+    answer_relevancy,
+    faithfulness,
+    context_recall,
+    context_precision,
+)
+from ragas.metrics.critique import harmfulness
+from ragas import evaluate
+
+subject_id	hadm_id	timestamp	observations
+
+def create_ragas_dataset(rag_pipeline, eval_dataset):
+  rag_dataset = []
+  for row in tqdm(eval_dataset):
+    answer = rag_pipeline({"query" : row["timestamp"]})
+    rag_dataset.append(
+        {"subject_id" : row["subject_id"],
+         "answer" : answer["hadm_id"],
+         "contexts" : [context.page_content for context in answer haa_develAdmittimes['hadm_id']],
+         "observations" : [row["observations"]]
+         }
+    )
+
+haa_develAdmittimes['combined'] = haa_develAdmittimes['hadm_id'].astype(str) + " at " + haa_develAdmittimes['admittime'].astype(str)
+
+
+
+  rag_df = haa_develAdmittimes['combined']
+  rag_eval_dataset = Dataset.from_pandas(haa_develAdmittimes['combined'])
+  return rag_eval_dataset
+
+def evaluate_ragas_dataset(ragas_dataset):
+  result = evaluate(
+    ragas_dataset,
+    metrics=[
+        context_precision,
+        faithfulness,
+        answer_relevancy,
+        context_recall,
+    ],
+  )
+  return result
+
+"""Lets create our dataset first:"""
+
+from tqdm import tqdm
+import pandas as pd
+
+basic_qa_ragas_dataset = create_ragas_dataset(qa_chain, eval_dataset)
+
+"""Save it for later:"""
+
+basic_qa_ragas_dataset.to_csv("basic_qa_ragas_dataset.csv")
+
+"""And finally - evaluate how it did!"""
+
+basic_qa_result = evaluate_ragas_dataset(basic_qa_ragas_dataset)
+
+basic_qa_result
+
+"""### Testing Other Retrievers
+
+Now we can test our how changing our Retriever impacts our RAGAS evaluation!
+"""
+
+def create_qa_chain(medical_retriever):
+  primary_qa_llm = llm
+
+  created_qa_chain = RetrievalQA.from_chain_type(
+      primary_qa_llm,
+      medical_retriever=medical_retriever,
+      return_source_documents=True
+  )
+
+  return created_qa_chain
+
+"""#### Parent Document Retriever
+
+One of the easier ways we can imagine improving a retriever is to embed our documents into small chunks, and then retrieve a significant amount of additional context that "surrounds" the found context.
+
+You can read more about this method [here](https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever)!
+"""
+
+!pip install chromadb -qU
+
+from langchain.retrievers import ParentDocumentRetriever
+from langchain.storage import InMemoryStore
+from langchain.vectorstores import Chroma
+
+parent_splitter = RecursiveCharacterTextSplitter(chunk_size=750)
+child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)
+
+vectorstore = Chroma(collection_name="split_parents", embedding_function=embeddings_model)
+
+store = InMemoryStore()
+
+parent_document_retriever = ParentDocumentRetriever(
+    vectorstore=vectorstore,
+    docstore=store,
+    child_splitter=child_splitter,
+    parent_splitter=parent_splitter,
+)
+
+parent_document_retriever.add_documents(base_docs)
+
+"""Let's create, test, and then evaluate our new chain!"""
+
+parent_document_retriever_qa_chain = create_qa_chain(parent_document_retriever)
+
+parent_document_retriever_qa_chain({"query" : "What is RAG?"})["result"]
+
+pdr_qa_ragas_dataset = create_ragas_dataset(parent_document_retriever_qa_chain, eval_dataset)
+
+pdr_qa_ragas_dataset.to_csv("pdr_qa_ragas_dataset.csv")
+
+pdr_qa_result = evaluate_ragas_dataset(pdr_qa_ragas_dataset)
+
+pdr_qa_result
+
+!pip install -q -U rank_bm25
+
+from langchain.retrievers import BM25Retriever, EnsembleRetriever
+
+text_splitter = RecursiveCharacterTextSplitter()
+docs = text_splitter.split_documents(base_docs)
+
+bm25_retriever = BM25Retriever.from_documents(docs)
+bm25_retriever.k = 1
+
+embedding = OpenAIEmbeddings()
+vectorstore = Chroma.from_documents(docs, embedding)
+chroma_retriever = vectorstore.as_retriever(search_kwargs={"k": 1})
+
+ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5])
+
+ensemble_retriever_qa_chain = create_qa_chain(ensemble_retriever)
+
+ensemble_retriever_qa_chain({"query" : "What subject id here ?"})["result"]
+
+ensemble_qa_ragas_dataset = create_ragas_dataset(ensemble_retriever_qa_chain, eval_dataset)
+
+ensemble_qa_ragas_dataset.to_csv("ensemble_qa_ragas_dataset.csv")
+
+ensemble_qa_result = evaluate_ragas_dataset(ensemble_qa_ragas_dataset)
+
+ensemble_qa_result
+
+
+from rouge_score import rouge_scorer
+
+def calculate_rouge_scores(references, predictions):
+    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+    scores = []
+    for ref, pred in zip(references, predictions):
+        score = scorer.score(ref, pred)
+        scores.append(score)
+    return scores
+
+# Example usage with dummy data
+references = ["subject_id  hadm_id   timestamp observations 0          12   112213  2104-08-05  C0392747 C0684224 C3273238 C3812171 C0700287 C... 1          12   112213  2104-08-07  C0392747 C0684224 C3273238 C1523018 C0700287  12   112213  2104-08-08  C0181904 C1552822 C0015392 C0450429 C0150369 C..." ]
+
+
+predictions = ["2          12   112213  2104-08-08  C0181904 C1552822 C0015392 C0450429 C0150369 C...3          12   112213  2104-08-08  C0392747 C0684224 C3273238 C0202059 C4050465 C.."]
+
+rouge_scores = calculate_rouge_scores(references, predictions)
+for score in rouge_scores:
+    print(score)