feat: langchain documents support for TestsetGenerator (#201)

Usage

```py
from langchain.document_loaders import PubMedLoader
from ragas.testset import TestsetGenerator

loader = PubMedLoader("liver", load_max_docs=10)
docs = loader.load()
len(docs)
# 10

testsetgenerator = TestsetGenerator.from_default()
test_size = 10
testset = testsetgenerator.generate(docs, test_size=test_size)
test_df = testset.to_pandas()
test_df.head()
```

src/ragas/testset/testset_generator.py

@@ -14,9 +14,10 @@
 from langchain.embeddings.base import Embeddings
 from langchain.llms.base import BaseLLM
 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.simple import SimpleNodeParser
-from llama_index.readers.schema import Document
+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
@@ -276,11 +277,26 @@ def _embed_nodes(self, nodes: t.List[BaseNode]) -> t.Dict[str, t.List[float]]:
 
         return embeddings
 
-    def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
+    def generate(
+        self,
+        documents: list[LlamaindexDocument] | list[LangchainDocument],
+        test_size: int,
+    ) -> TestDataset:
+        if isinstance(documents[0], LangchainDocument):
+            # cast to LangchainDocument since its the only case here
+            documents = t.cast(list[LangchainDocument], documents)
+            documents = [
+                LlamaindexDocument.from_langchain_format(doc) for doc in documents
+            ]
+        elif not isinstance(documents[0], LlamaindexDocument):
+            raise ValueError(
+                "Testset Generatation only supports LlamaindexDocuments or LangchainDocuments"  # noqa
+            )
         # Convert documents into nodes
         node_parser = SimpleNodeParser.from_defaults(
             chunk_size=self.chunk_size, chunk_overlap=0, include_metadata=True
         )
+        documents = t.cast(list[LlamaindexDocument], documents)
         document_nodes: t.List[BaseNode] = node_parser.get_nodes_from_documents(
             documents=documents
         )

src/ragas/testset/utils.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import json
 import re
 import warnings