fix: refactor get_graph_from_model to return nodes and edges correctly

cognee/api/v1/cognify/cognify_v2.py

@@ -81,13 +81,13 @@ async def run_cognify_pipeline(dataset: Dataset, user: User):
             Task(classify_documents),
             Task(check_permissions_on_documents, user = user, permissions = ["write"]),
             Task(extract_chunks_from_documents), # Extract text chunks based on the document type.
-            Task(add_data_points, task_config = { "batch_size": 10 }),
             Task(extract_graph_from_data, graph_model = KnowledgeGraph, task_config = { "batch_size": 10 }), # Generate knowledge graphs from the document chunks.
             Task(
                 summarize_text,
                 summarization_model = cognee_config.summarization_model,
                 task_config = { "batch_size": 10 }
             ),
+            Task(add_data_points, task_config = { "batch_size": 10 }),
         ]
 
         pipeline = run_tasks(tasks, data_documents, "cognify_pipeline")

cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py

@@ -29,7 +29,14 @@ def __init__(
         self.model = model
         self.dimensions = dimensions
 
+    MAX_RETRIES = 5
+    retry_count = 0
+
     async def embed_text(self, text: List[str]) -> List[List[float]]:
+        async def exponential_backoff(attempt):
+            wait_time = min(10 * (2 ** attempt), 60)  # Max 60 seconds
+            await asyncio.sleep(wait_time)
+
         try:
             response = await litellm.aembedding(
                 self.model,
@@ -38,23 +45,40 @@ async def embed_text(self, text: List[str]) -> List[List[float]]:
                 api_base = self.endpoint,
                 api_version = self.api_version
             )
+
+            self.retry_count = 0
+
             return [data["embedding"] for data in response.data]
 
         except litellm.exceptions.ContextWindowExceededError as error:
             if isinstance(text, list):
-                parts = [text[0:math.ceil(len(text)/2)], text[math.ceil(len(text)/2):]]
+                if len(text) == 1:
+                    parts = [text]
+                else:
+                    parts = [text[0:math.ceil(len(text)/2)], text[math.ceil(len(text)/2):]]
+
                 parts_futures = [self.embed_text(part) for part in parts]
                 embeddings = await asyncio.gather(*parts_futures)
 
                 all_embeddings = []
                 for embeddings_part in embeddings:
                     all_embeddings.extend(embeddings_part)
 
-                return [data["embedding"] for data in all_embeddings]
+                return all_embeddings
 
             logger.error("Context window exceeded for embedding text: %s", str(error))
             raise error
 
+        except litellm.exceptions.RateLimitError:
+            if self.retry_count >= self.MAX_RETRIES:
+                raise Exception(f"Rate limit exceeded and no more retries left.")
+
+            await exponential_backoff(self.retry_count)
+
+            self.retry_count += 1
+
+            return await self.embed_text(text)
+
         except Exception as error:
             logger.error("Error embedding text: %s", str(error))
             raise error

cognee/modules/chunking/TextChunker.py

@@ -35,6 +35,7 @@ def read(self):
                             is_part_of = self.document,
                             chunk_index = self.chunk_index,
                             cut_type = chunk_data["cut_type"],
+                            contains = [],
                             _metadata = {
                                 "index_fields": ["text"],
                                 "metadata_id": self.document.metadata_id
@@ -52,6 +53,7 @@ def read(self):
                                 is_part_of = self.document,
                                 chunk_index = self.chunk_index,
                                 cut_type = paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"],
+                                contains = [],
                                 _metadata = {
                                     "index_fields": ["text"],
                                     "metadata_id": self.document.metadata_id
@@ -73,6 +75,7 @@ def read(self):
                     is_part_of = self.document,
                     chunk_index = self.chunk_index,
                     cut_type = paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"],
+                    contains = [],
                     _metadata = {
                         "index_fields": ["text"],
                         "metadata_id": self.document.metadata_id

cognee/modules/chunking/models/DocumentChunk.py

@@ -1,6 +1,7 @@
-from typing import Optional
+from typing import List, Optional
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.data.processing.document_types import Document
+from cognee.modules.engine.models import Entity
 
 class DocumentChunk(DataPoint):
     __tablename__ = "document_chunk"
@@ -9,6 +10,7 @@ class DocumentChunk(DataPoint):
     chunk_index: int
     cut_type: str
     is_part_of: Document
+    contains: List[Entity] = None
 
     _metadata: Optional[dict] = {
         "index_fields": ["text"],

cognee/modules/chunking/models/__init__.py

@@ -0,0 +1 @@
+from .DocumentChunk import DocumentChunk

cognee/modules/engine/models/Entity.py

@@ -1,5 +1,4 @@
 from cognee.infrastructure.engine import DataPoint
-from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from cognee.modules.engine.models.EntityType import EntityType
 
 
@@ -8,7 +7,6 @@ class Entity(DataPoint):
     name: str
     is_a: EntityType
     description: str
-    mentioned_in: DocumentChunk
 
     _metadata: dict = {
         "index_fields": ["name"],

cognee/modules/engine/models/EntityType.py

@@ -1,13 +1,10 @@
 from cognee.infrastructure.engine import DataPoint
-from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
-
 
 class EntityType(DataPoint):
     __tablename__ = "entity_type"
     name: str
     type: str
     description: str
-    exists_in: DocumentChunk
 
     _metadata: dict = {
         "index_fields": ["name"],

cognee/modules/graph/utils/expand_with_nodes_and_edges.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from cognee.infrastructure.engine import DataPoint
+from cognee.modules.chunking.models import DocumentChunk
 from cognee.modules.engine.models import Entity, EntityType
 from cognee.modules.engine.utils import (
     generate_edge_name,
@@ -11,17 +11,19 @@
 
 
 def expand_with_nodes_and_edges(
-    graph_node_index: list[tuple[DataPoint, KnowledgeGraph]],
+    data_chunks: list[DocumentChunk],
+    chunk_graphs: list[KnowledgeGraph],
     existing_edges_map: Optional[dict[str, bool]] = None,
 ):
     if existing_edges_map is None:
         existing_edges_map = {}
 
     added_nodes_map = {}
     relationships = []
-    data_points = []
 
-    for graph_source, graph in graph_node_index:
+    for index, data_chunk in enumerate(data_chunks):
+        graph = chunk_graphs[index]
+
         if graph is None:
             continue
 
@@ -38,7 +40,6 @@ def expand_with_nodes_and_edges(
                     name = type_node_name,
                     type = type_node_name,
                     description = type_node_name,
-                    exists_in = graph_source,
                 )
                 added_nodes_map[f"{str(type_node_id)}_type"] = type_node
             else:
@@ -50,9 +51,13 @@ def expand_with_nodes_and_edges(
                     name = node_name,
                     is_a = type_node,
                     description = node.description,
-                    mentioned_in = graph_source,
                 )
-                data_points.append(entity_node)
+
+                if data_chunk.contains is None:
+                    data_chunk.contains = []
+
+                data_chunk.contains.append(entity_node)
+
                 added_nodes_map[f"{str(node_id)}_entity"] = entity_node
 
         # Add relationship that came from graphs.
@@ -80,4 +85,4 @@ def expand_with_nodes_and_edges(
                 )
                 existing_edges_map[edge_key] = True
 
-        return (data_points, relationships)
+    return (data_chunks, relationships)