import llama_index gracefully, solves #318 (#319)

I added informative message for import errors regarding llama_index, and
changed the existing messages to match each other.

Author: ceferisbarov

src/ragas/llama_index/evaluation.py

@@ -77,7 +77,7 @@ def evaluate(
     except ImportError:
         raise ImportError(
             "llama_index must be installed to use this function. "
-            "Install it with `pip install llama_index`."
+            "Please, install it with `pip install llama_index`."
         )
 
     # TODO: rate limit, error handling, retries

src/ragas/llms/llamaindex.py

@@ -10,7 +10,13 @@
 if t.TYPE_CHECKING:
     from langchain.callbacks.base import Callbacks
     from langchain.prompts import ChatPromptTemplate
-    from llama_index.llms.base import LLM as LiLLM
+    try:
+        from llama_index.llms.base import LLM as LiLLM
+    except ImportError:
+        raise ImportError(
+            "llama_index must be installed to use this function. "
+            "Please, install it with `pip install llama_index`."
+        )
 
 
 class LlamaIndexLLM(RagasLLM):

src/ragas/testset/testset_generator.py

@@ -5,17 +5,24 @@
 from collections import defaultdict, namedtuple
 from dataclasses import dataclass
 
+try:
+    from llama_index.indices.query.embedding_utils import get_top_k_embeddings
+    from llama_index.node_parser import SimpleNodeParser
+    from llama_index.readers.schema import Document as LlamaindexDocument
+    from llama_index.schema import BaseNode
+except ImportError:
+    raise ImportError(
+        "llama_index must be installed to use this function. "
+        "Please, install it with `pip install llama_index`."
+    )
+
 import numpy as np
 import numpy.testing as npt
 import pandas as pd
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.embeddings.base import Embeddings
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.document import Document as LangchainDocument
-from llama_index.indices.query.embedding_utils import get_top_k_embeddings
-from llama_index.node_parser import SimpleNodeParser
-from llama_index.readers.schema import Document as LlamaindexDocument
-from llama_index.schema import BaseNode
 from numpy.random import default_rng
 from tqdm import tqdm