added evaluation module using llama_index for graph_rag

graph_rag/evalution/evaluation_llama_index.py

@@ -0,0 +1,26 @@
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.llama_dataset import LabelledRagDataset
+from llama_index.core.llama_dataset import download_llama_dataset
+from llama_index.core.llama_pack import download_llama_pack
+from llama_index.core import VectorStoreIndex
+from llama_index.llms.ollama import Ollama
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+RAG_DATASET="./data/rag_dataset.json"
+
+RagEvaluatorPack = download_llama_pack(
+    "RagEvaluatorPack", "./rag_evaluator_pack"
+)
+def evaluate(RAG_DATASET: str,query_engine: object,ollama_model: str="llama3",embedd_model: str="microsoft/codebert-base"):
+    rag_dataset = LabelledRagDataset.from_json(RAG_DATASET)
+    rag_evaluator_pack = RagEvaluatorPack(
+        rag_dataset=rag_dataset,
+        query_engine=query_engine,
+        judge_llm=Ollama(base_url="http://localhost:11434", model=ollama_model),
+        embed_model=HuggingFaceEmbedding(model_name=embedd_model),
+    )
+    benchmark_df = await rag_evaluator_pack.arun(
+        batch_size=5,  # batches the number of openai api calls to make
+        sleep_time_in_seconds=1,  # seconds to sleep before making an api call
+    )
+    return benchmark_df