testset generation: bug fixes (#185)

Fixes 

- [x] issues with multi-context question generation  
- [x] Error in doc filtering

src/ragas/testset/testset_generator.py

@@ -1,4 +1,6 @@
+import re
 import typing as t
+import warnings
 from collections import defaultdict, namedtuple
 from dataclasses import dataclass
 
@@ -33,10 +35,10 @@
 )
 
 DEFAULT_TEST_DISTRIBUTION = {
-    "simple": 0.5,
+    "simple": 0.4,
     "reasoning": 0.2,
     "multi_context": 0.2,
-    "conditional": 0.1,
+    "conditional": 0.2,
 }
 
 question_deep_map = {
@@ -106,7 +108,7 @@ def __init__(
         critic_llm: BaseLLM | BaseChatModel,
         embeddings_model: Embeddings,
         testset_distribution: t.Optional[t.Dict[str, float]] = None,
-        chat_qa: float = 0.3,
+        chat_qa: float = 0.0,
         chunk_size: int = 1024,
         seed: int = 42,
     ) -> None:
@@ -135,7 +137,7 @@ def from_default(
         openai_generator_llm: str = "gpt-3.5-turbo-16k",
         openai_filter_llm: str = "gpt-4",
         chat_qa: float = 0.3,
-        chunk_size: int = 1024,
+        chunk_size: int = 512,
     ):
         generator_llm = ChatOpenAI(model=openai_generator_llm)
         critic_llm = ChatOpenAI(model=openai_filter_llm)
@@ -173,14 +175,12 @@ def _filter_context(self, context: str) -> bool:
         prompt = ChatPromptTemplate.from_messages([human_prompt])
         results = generate(prompts=[prompt], llm=self.critic_llm)
         output = results.generations[0][0].text.strip()
-        score = eval(output)
-        if not isinstance(score, float | int):
-            index = output.lower().find("score:")
-            if index != -1:
-                index += len("score:")
-                score = eval(output[index:])
-            else:
-                score = 0.0
+        pattern = r"^[\d.]+$"
+        if not re.match(pattern, output):
+            score = 0.0
+        else:
+            score = eval(output)
+
         return score >= self.threshold
 
     def _seed_question(self, context: str) -> str:
@@ -241,22 +241,30 @@ def _generate_context(self, question: str, text_chunk: str) -> t.List[str]:
             for qstn in question.split("\n")
         ]
 
-    def _remove_index(self, available_indices: list, node_idx: list) -> t.List:
+    def _remove_nodes(self, available_indices: list, node_idx: list) -> t.List:
         for idx in node_idx:
             available_indices.remove(idx)
         return available_indices
 
-    def _generate_doc_node_map(
+    def _generate_doc_nodes_map(
         self, documenet_nodes: t.List[BaseNode]
-    ) -> t.Dict[str, list]:
-        doc_nodeidx = defaultdict(list)
-        for idx, node in enumerate(documenet_nodes):
-            doc_nodeidx[node.id_].append(idx)
-
-        return doc_nodeidx
-
-    def _get_neighbour_node(self, idx: int, node_indices: list) -> t.List[int]:
-        return [idx - 1, idx] if idx == node_indices[-1] else [idx, idx + 1]
+    ) -> t.Dict[str, BaseNode]:
+        doc_nodes_map: t.Dict[str, t.List[BaseNode]] = defaultdict(list[BaseNode])
+        for node in documenet_nodes:
+            if node.ref_doc_id:
+                doc_nodes_map[node.ref_doc_id].append(node)
+
+        return doc_nodes_map  # type: ignore
+
+    def _get_neighbour_node(
+        self, node: BaseNode, related_nodes: list[BaseNode]
+    ) -> t.List[BaseNode]:
+        if len(related_nodes) < 2:
+            warnings.warn("No neighbors exists")
+            return [node]
+        idx = related_nodes.index(node)
+        ids = [idx - 1, idx] if idx == (len(related_nodes) - 1) else [idx, idx + 1]
+        return [related_nodes[idx] for idx in ids]
 
     def _embed_nodes(self, nodes: t.List[BaseNode]) -> t.Dict[str, t.List[float]]:
         embeddings = {}
@@ -275,38 +283,38 @@ def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
         document_nodes: t.List[BaseNode] = node_parser.get_nodes_from_documents(
             documents=documents
         )
-
         # maximum 1 seed question per node
         if test_size > len(document_nodes):
             raise ValueError(
                 """Maximum possible number of samples exceeded, 
                              reduce test_size or add more documents"""
             )
 
-        available_indices = np.arange(0, len(document_nodes)).tolist()
-        doc_nodeidx = self._generate_doc_node_map(document_nodes)
+        available_nodes = document_nodes
+        doc_nodes_map = self._generate_doc_nodes_map(document_nodes)
+        count_neighbours = sum(len(val) > 1 for _, val in doc_nodes_map.items())
+        if count_neighbours < len(documents) // 2:
+            warnings.warn("Most documents are too short")
+
         count = 0
         samples = []
 
         pbar = tqdm(total=test_size)
-        while count < test_size and available_indices != []:
+        while count < test_size and available_nodes != []:
             evolve_type = self._get_evolve_type()
-            node_idx = self.rng.choice(available_indices, size=1)[0]
-            available_indices = self._remove_index(available_indices, [node_idx])
+            curr_node = self.rng.choice(available_nodes, size=1)[0]
+            available_nodes = self._remove_nodes(available_nodes, [curr_node])
 
-            neighbor_nodes = doc_nodeidx[
-                document_nodes[node_idx].node_id  # type: ignore
-            ]
+            neighbor_nodes = doc_nodes_map[curr_node.source_node.node_id]
 
             # Append multiple nodes randomly to remove chunking bias
             size = self.rng.integers(1, 3)
-            node_indices = (
-                self._get_neighbour_node(node_idx, neighbor_nodes)
+            nodes = (
+                self._get_neighbour_node(curr_node, neighbor_nodes)
                 if size > 1 and evolve_type != "multi_context"
-                else [node_idx]
+                else [curr_node]
             )
 
-            nodes = [document_nodes[node_idx] for node_idx in node_indices]
             text_chunk = " ".join([node.get_content() for node in nodes])
             score = self._filter_context(text_chunk)
             if not score:
@@ -316,14 +324,13 @@ def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
             if evolve_type == "multi_context":
                 # Find most similar chunk in same document
                 node_embedding = self._embed_nodes([nodes[-1]])
-                neighbor_nodes = self._remove_index(neighbor_nodes, node_indices)
-                neighbor_emb = self._embed_nodes(
-                    [document_nodes[idx][0] for idx in neighbor_nodes]
-                )
+                neighbor_nodes = self._remove_nodes(neighbor_nodes, nodes)
+                neighbor_emb = self._embed_nodes(neighbor_nodes)
+
                 _, indices = get_top_k_embeddings(
                     list(node_embedding.values())[0],
                     list(neighbor_emb.values()),
-                    similarity_cutoff=self.threshold,
+                    similarity_cutoff=self.threshold / 10,
                 )
                 if indices:
                     best_neighbor = neighbor_nodes[indices[0]]
@@ -332,7 +339,7 @@ def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
                         context1=text_chunk,
                         context2=best_neighbor.get_content(),
                     )
-                    text_chunk = "\n".join([text_chunk, best_neighbor.get_context()])
+                    text_chunk = "\n".join([text_chunk, best_neighbor.get_content()])
                 else:
                     continue