Merge branch 'main' into clean_dspy

cognee/infrastructure/databases/graph/graph_db_interface.py

@@ -62,3 +62,8 @@ class GraphDBInterface(Protocol):
     async def delete_graph(
         self,
     ): raise NotImplementedError
+
+    @abstractmethod
+    async def get_graph_data(
+        self
+    ): raise NotImplementedError

cognee/infrastructure/databases/graph/neo4j_driver/adapter.py

@@ -27,9 +27,6 @@ class Neo4jAdapter(GraphDBInterface):
             max_connection_lifetime = 120
         )
 
-    async def close(self) -> None:
-        await self.driver.close()
-
     @asynccontextmanager
     async def get_session(self) -> AsyncSession:
         async with self.driver.session() as session:

cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py

@@ -112,10 +112,18 @@ class LanceDBAdapter(VectorDBInterface):
                 for (data_point_index, data_point) in enumerate(data_points)
         ]
 
-        await collection.merge_insert("id") \
+        # TODO: This enables us to work with pydantic version but shouldn't
-            .when_matched_update_all() \
+        #  stay like this, existing rows should be updated
-            .when_not_matched_insert_all() \
+
-            .execute(lance_data_points)
+        await collection.delete("id IS NOT NULL")
+
+        original_size = await collection.count_rows()
+        await collection.add(lance_data_points)
+        new_size = await collection.count_rows()
+
+        if new_size <= original_size:
+            raise ValueError(
+                "LanceDB create_datapoints error: data points did not get added.")
         
 
     async def retrieve(self, collection_name: str, data_point_ids: list[str]):

cognee/modules/chunking/TextChunker.py

@@ -17,6 +17,7 @@ class TextChunker():
         self.get_text = get_text
 
     def read(self):
+        self.paragraph_chunks = []
         for content_text in self.get_text():
             for chunk_data in chunk_by_paragraph(
                 content_text,

cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py

@@ -0,0 +1,35 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Union
+from cognee.modules.graph.cognee_graph.CogneeGraphElements import Node, Edge
+from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
+
+class CogneeAbstractGraph(ABC):
+    """
+    Abstract base class for representing a graph structure.
+
+    """
+
+    @abstractmethod
+    def add_node(self, node: Node) -> None:
+        """Add a node to the graph."""
+        pass
+
+    @abstractmethod
+    def add_edge(self, edge: Edge) -> None:
+        """Add an edge to the graph."""
+        pass
+
+    @abstractmethod
+    def get_node(self, node_id: str) -> Node:
+        """Retrieve a node by its ID."""
+        pass
+
+    @abstractmethod
+    def get_edges(self, node_id: str) -> List[Edge]:
+        """Retrieve edges connected to a specific node."""
+        pass
+
+    @abstractmethod
+    async def project_graph_from_db(self, adapter: GraphDBInterface, directed: bool, dimension: int) -> None:
+        """Project the graph structure from a database using the provided adapter."""
+        pass

cognee/modules/graph/cognee_graph/CogneeGraph.py

@@ -0,0 +1,91 @@
+from typing import List, Dict, Union
+
+from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
+from cognee.modules.graph.cognee_graph.CogneeGraphElements import Node, Edge
+from cognee.modules.graph.cognee_graph.CogneeAbstractGraph import CogneeAbstractGraph
+from cognee.infrastructure.databases.graph import get_graph_engine
+
+class CogneeGraph(CogneeAbstractGraph):
+    """
+    Concrete implementation of the AbstractGraph class for Cognee.
+
+    This class provides the functionality to manage nodes and edges,
+    and project a graph from a database using adapters.
+    """
+
+    nodes: Dict[str, Node]
+    edges: List[Edge]
+    directed: bool
+
+    def __init__(self, directed: bool = True):
+        self.nodes = {}
+        self.edges = []
+        self.directed = directed
+
+    def add_node(self, node: Node) -> None:
+        if node.id not in self.nodes:
+            self.nodes[node.id] = node
+        else:
+            raise ValueError(f"Node with id {node.id} already exists.")
+
+    def add_edge(self, edge: Edge) -> None:
+        if edge not in self.edges:
+            self.edges.append(edge)
+            edge.node1.add_skeleton_edge(edge)
+            edge.node2.add_skeleton_edge(edge)
+        else:
+            raise ValueError(f"Edge {edge} already exists in the graph.")
+
+    def get_node(self, node_id: str) -> Node:
+        return self.nodes.get(node_id, None)
+
+    def get_edges(self, node_id: str) -> List[Edge]:
+        node = self.get_node(node_id)
+        if node:
+            return node.skeleton_edges
+        else:
+            raise ValueError(f"Node with id {node_id} does not exist.")
+
+    async def project_graph_from_db(self,
+                                    adapter: Union[GraphDBInterface],
+                                    node_properties_to_project: List[str],
+                                    edge_properties_to_project: List[str],
+                                    directed = True,
+                                    node_dimension = 1,
+                                    edge_dimension = 1) -> None:
+
+        if node_dimension < 1 or edge_dimension < 1:
+            raise ValueError("Dimensions must be positive integers")
+
+        try:
+            nodes_data, edges_data = await adapter.get_graph_data()
+
+            if not nodes_data:
+                raise ValueError("No node data retrieved from the database.")
+            if not edges_data:
+                raise ValueError("No edge data retrieved from the database.")
+
+            for node_id, properties in nodes_data:
+                node_attributes = {key: properties.get(key) for key in node_properties_to_project}
+                self.add_node(Node(str(node_id), node_attributes, dimension=node_dimension))
+
+            for source_id, target_id, relationship_type, properties in edges_data:
+                source_node = self.get_node(str(source_id))
+                target_node = self.get_node(str(target_id))
+                if source_node and target_node:
+                    edge_attributes = {key: properties.get(key) for key in edge_properties_to_project}
+                    edge_attributes['relationship_type'] = relationship_type
+
+                    edge = Edge(source_node, target_node, attributes=edge_attributes, directed=directed, dimension=edge_dimension)
+                    self.add_edge(edge)
+
+                    source_node.add_skeleton_edge(edge)
+                    target_node.add_skeleton_edge(edge)
+
+                else:
+                    raise ValueError(f"Edge references nonexistent nodes: {source_id} -> {target_id}")
+
+        except (ValueError, TypeError) as e:
+            print(f"Error projecting graph: {e}")
+        except Exception as ex:
+            print(f"Unexpected error: {ex}")

cognee/modules/graph/cognee_graph/CogneeGraphElements.py

@@ -0,0 +1,114 @@
+import numpy as np
+from typing import List, Dict, Optional, Any
+
+class Node:
+    """
+        Represents a node in a graph.
+        Attributes:
+            id (str): A unique identifier for the node.
+            attributes (Dict[str, Any]): A dictionary of attributes associated with the node.
+            neighbors (List[Node]): Represents the original nodes
+            skeleton_edges (List[Edge]): Represents the original edges
+        """
+    id: str
+    attributes: Dict[str, Any]
+    skeleton_neighbours: List["Node"]
+    skeleton_edges: List["Edge"]
+    status: np.ndarray
+
+    def __init__(self, node_id: str, attributes: Optional[Dict[str, Any]] = None, dimension: int = 1):
+        if dimension <= 0:
+            raise ValueError("Dimension must be a positive integer")
+        self.id = node_id
+        self.attributes = attributes if attributes is not None else {}
+        self.skeleton_neighbours = []
+        self.skeleton_edges = []
+        self.status = np.ones(dimension, dtype=int)
+
+    def add_skeleton_neighbor(self, neighbor: "Node") -> None:
+        if neighbor not in self.skeleton_neighbours:
+            self.skeleton_neighbours.append(neighbor)
+
+    def remove_skeleton_neighbor(self, neighbor: "Node") -> None:
+        if neighbor in self.skeleton_neighbours:
+            self.skeleton_neighbours.remove(neighbor)
+
+    def add_skeleton_edge(self, edge: "Edge") -> None:
+        if edge not in self.skeleton_edges:
+            self.skeleton_edges.append(edge)
+            # Add neighbor
+            if edge.node1 == self:
+                self.add_skeleton_neighbor(edge.node2)
+            elif edge.node2 == self:
+                self.add_skeleton_neighbor(edge.node1)
+
+    def remove_skeleton_edge(self, edge: "Edge") -> None:
+        if edge in self.skeleton_edges:
+            self.skeleton_edges.remove(edge)
+            # Remove neighbor if no other edge connects them
+            neighbor = edge.node2 if edge.node1 == self else edge.node1
+            if all(e.node1 != neighbor and e.node2 != neighbor for e in self.skeleton_edges):
+                self.remove_skeleton_neighbor(neighbor)
+
+    def is_node_alive_in_dimension(self, dimension: int) -> bool:
+        if dimension < 0 or dimension >= len(self.status):
+            raise ValueError(f"Dimension {dimension} is out of range. Valid range is 0 to {len(self.status) - 1}.")
+        return self.status[dimension] == 1
+
+    def __repr__(self) -> str:
+        return f"Node({self.id}, attributes={self.attributes})"
+
+    def __hash__(self) -> int:
+        return hash(self.id)
+
+    def __eq__(self, other: "Node") -> bool:
+        return isinstance(other, Node) and self.id == other.id
+
+
+class Edge:
+    """
+        Represents an edge in a graph, connecting two nodes.
+        Attributes:
+            node1 (Node): The starting node of the edge.
+            node2 (Node): The ending node of the edge.
+            attributes (Dict[str, Any]): A dictionary of attributes associated with the edge.
+            directed (bool): A flag indicating whether the edge is directed or undirected.
+    """
+
+    node1: "Node"
+    node2: "Node"
+    attributes: Dict[str, Any]
+    directed: bool
+    status: np.ndarray
+
+    def __init__(self, node1: "Node", node2: "Node", attributes: Optional[Dict[str, Any]] = None, directed: bool = True, dimension: int = 1):
+        if dimension <= 0:
+            raise ValueError("Dimensions must be a positive integer.")
+        self.node1 = node1
+        self.node2 = node2
+        self.attributes = attributes if attributes is not None else {}
+        self.directed = directed
+        self.status = np.ones(dimension, dtype=int)
+
+    def is_edge_alive_in_dimension(self, dimension: int) -> bool:
+        if dimension < 0 or dimension >= len(self.status):
+            raise ValueError(f"Dimension {dimension} is out of range. Valid range is 0 to {len(self.status) - 1}.")
+        return self.status[dimension] == 1
+
+    def __repr__(self) -> str:
+        direction = "->" if self.directed else "--"
+        return f"Edge({self.node1.id} {direction} {self.node2.id}, attributes={self.attributes})"
+
+    def __hash__(self) -> int:
+        if self.directed:
+            return hash((self.node1, self.node2))
+        else:
+            return hash(frozenset({self.node1, self.node2}))
+
+    def __eq__(self, other: "Edge") -> bool:
+        if not isinstance(other, Edge):
+            return False
+        if self.directed:
+            return self.node1 == other.node1 and self.node2 == other.node2
+        else:
+            return {self.node1, self.node2} == {other.node1, other.node2}

cognee/tests/unit/modules/graph/cognee_graph_elements_test.py

@@ -0,0 +1,144 @@
+import pytest
+import numpy as np
+
+from cognee.modules.graph.cognee_graph.CogneeGraphElements import Node, Edge
+
+
+def test_node_initialization():
+    """Test that a Node is initialized correctly."""
+    node = Node("node1", {"attr1": "value1"}, dimension=2)
+    assert node.id == "node1"
+    assert node.attributes == {"attr1": "value1"}
+    assert len(node.status) == 2
+    assert np.all(node.status == 1)
+
+def test_node_invalid_dimension():
+    """Test that initializing a Node with a non-positive dimension raises an error."""
+    with pytest.raises(ValueError, match="Dimension must be a positive integer"):
+        Node("node1", dimension=0)
+
+def test_add_skeleton_neighbor():
+    """Test adding a neighbor to a node."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    node1.add_skeleton_neighbor(node2)
+    assert node2 in node1.skeleton_neighbours
+
+def test_remove_skeleton_neighbor():
+    """Test removing a neighbor from a node."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    node1.add_skeleton_neighbor(node2)
+    node1.remove_skeleton_neighbor(node2)
+    assert node2 not in node1.skeleton_neighbours
+
+def test_add_skeleton_edge():
+    """Test adding an edge updates both skeleton_edges and skeleton_neighbours."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2)
+    node1.add_skeleton_edge(edge)
+    assert edge in node1.skeleton_edges
+    assert node2 in node1.skeleton_neighbours
+
+def test_remove_skeleton_edge():
+    """Test removing an edge updates both skeleton_edges and skeleton_neighbours."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2)
+    node1.add_skeleton_edge(edge)
+    node1.remove_skeleton_edge(edge)
+    assert edge not in node1.skeleton_edges
+    assert node2 not in node1.skeleton_neighbours
+
+def test_is_node_alive_in_dimension():
+    """Test checking node's alive status in a specific dimension."""
+    node = Node("node1", dimension=2)
+    assert node.is_node_alive_in_dimension(1)
+    node.status[1] = 0
+    assert not node.is_node_alive_in_dimension(1)
+
+def test_node_alive_invalid_dimension():
+    """Test that checking alive status with an invalid dimension raises an error."""
+    node = Node("node1", dimension=1)
+    with pytest.raises(ValueError, match="Dimension 1 is out of range"):
+        node.is_node_alive_in_dimension(1)
+
+def test_node_equality():
+    """Test equality between nodes."""
+    node1 = Node("node1")
+    node2 = Node("node1")
+    assert node1 == node2
+
+def test_node_hash():
+    """Test hashing for Node."""
+    node = Node("node1")
+    assert hash(node) == hash("node1")
+
+### Tests for Edge ###
+
+def test_edge_initialization():
+    """Test that an Edge is initialized correctly."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2, {"weight": 10}, directed=False, dimension=2)
+    assert edge.node1 == node1
+    assert edge.node2 == node2
+    assert edge.attributes == {"weight": 10}
+    assert edge.directed is False
+    assert len(edge.status) == 2
+    assert np.all(edge.status == 1)
+
+def test_edge_invalid_dimension():
+    """Test that initializing an Edge with a non-positive dimension raises an error."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    with pytest.raises(ValueError, match="Dimensions must be a positive integer."):
+        Edge(node1, node2, dimension=0)
+
+def test_is_edge_alive_in_dimension():
+    """Test checking edge's alive status in a specific dimension."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2, dimension=2)
+    assert edge.is_edge_alive_in_dimension(1)
+    edge.status[1] = 0
+    assert not edge.is_edge_alive_in_dimension(1)
+
+def test_edge_alive_invalid_dimension():
+    """Test that checking alive status with an invalid dimension raises an error."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2, dimension=1)
+    with pytest.raises(ValueError, match="Dimension 1 is out of range"):
+        edge.is_edge_alive_in_dimension(1)
+
+def test_edge_equality_directed():
+    """Test equality between directed edges."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge1 = Edge(node1, node2, directed=True)
+    edge2 = Edge(node1, node2, directed=True)
+    assert edge1 == edge2
+
+def test_edge_equality_undirected():
+    """Test equality between undirected edges."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge1 = Edge(node1, node2, directed=False)
+    edge2 = Edge(node2, node1, directed=False)
+    assert edge1 == edge2
+
+def test_edge_hash_directed():
+    """Test hashing for directed edges."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2, directed=True)
+    assert hash(edge) == hash((node1, node2))
+
+def test_edge_hash_undirected():
+    """Test hashing for undirected edges."""
+    node1 = Node("node1")
+    node2 = Node("node2")
+    edge = Edge(node1, node2, directed=False)
+    assert hash(edge) == hash(frozenset({node1, node2}))

cognee/tests/unit/modules/graph/cognee_graph_test.py

@@ -0,0 +1,79 @@
+import pytest
+
+from cognee.modules.graph.cognee_graph.CogneeGraphElements import Node, Edge
+from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
+
+
+@pytest.fixture
+def setup_graph():
+    """Fixture to initialize a CogneeGraph instance."""
+    return CogneeGraph()
+
+def test_add_node_success(setup_graph):
+    """Test successful addition of a node."""
+    graph = setup_graph
+    node = Node("node1")
+    graph.add_node(node)
+    assert graph.get_node("node1") == node
+
+def test_add_duplicate_node(setup_graph):
+    """Test adding a duplicate node raises an exception."""
+    graph = setup_graph
+    node = Node("node1")
+    graph.add_node(node)
+    with pytest.raises(ValueError, match="Node with id node1 already exists."):
+        graph.add_node(node)
+
+def test_add_edge_success(setup_graph):
+    """Test successful addition of an edge."""
+    graph = setup_graph
+    node1 = Node("node1")
+    node2 = Node("node2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+    edge = Edge(node1, node2)
+    graph.add_edge(edge)
+    assert edge in graph.edges
+    assert edge in node1.skeleton_edges
+    assert edge in node2.skeleton_edges
+
+def test_add_duplicate_edge(setup_graph):
+    """Test adding a duplicate edge raises an exception."""
+    graph = setup_graph
+    node1 = Node("node1")
+    node2 = Node("node2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+    edge = Edge(node1, node2)
+    graph.add_edge(edge)
+    with pytest.raises(ValueError, match="Edge .* already exists in the graph."):
+        graph.add_edge(edge)
+
+def test_get_node_success(setup_graph):
+    """Test retrieving an existing node."""
+    graph = setup_graph
+    node = Node("node1")
+    graph.add_node(node)
+    assert graph.get_node("node1") == node
+
+def test_get_node_nonexistent(setup_graph):
+    """Test retrieving a nonexistent node returns None."""
+    graph = setup_graph
+    assert graph.get_node("nonexistent") is None
+
+def test_get_edges_success(setup_graph):
+    """Test retrieving edges of a node."""
+    graph = setup_graph
+    node1 = Node("node1")
+    node2 = Node("node2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+    edge = Edge(node1, node2)
+    graph.add_edge(edge)
+    assert edge in graph.get_edges("node1")
+
+def test_get_edges_nonexistent_node(setup_graph):
+    """Test retrieving edges for a nonexistent node raises an exception."""
+    graph = setup_graph
+    with pytest.raises(ValueError, match="Node with id nonexistent does not exist."):
+        graph.get_edges("nonexistent")

examples/python/dynamic_steps_example.py

@@ -0,0 +1,229 @@
+import cognee
+import asyncio
+from cognee.api.v1.search import SearchType
+
+job_position = """0:Senior Data Scientist (Machine Learning)
+
+Company: TechNova Solutions
+Location: San Francisco, CA
+
+Job Description:
+
+TechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.
+
+Responsibilities:
+
+Develop and implement advanced machine learning algorithms and models.
+Analyze large, complex datasets to extract meaningful patterns and insights.
+Collaborate with cross-functional teams to integrate predictive models into products.
+Stay updated with the latest advancements in machine learning and data science.
+Mentor junior data scientists and provide technical guidance.
+Qualifications:
+
+Master’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.
+5+ years of experience in data science and machine learning.
+Proficient in Python, R, and SQL.
+Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
+Strong problem-solving skills and attention to detail.
+Candidate CVs
+"""
+
+job_1 = """
+CV 1: Relevant
+Name: Dr. Emily Carter
+Contact Information:
+
+Email: emily.carter@example.com
+Phone: (555) 123-4567
+Summary:
+
+Senior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.
+
+Education:
+
+Ph.D. in Computer Science, Stanford University (2014)
+B.S. in Mathematics, University of California, Berkeley (2010)
+Experience:
+
+Senior Data Scientist, InnovateAI Labs (2016 – Present)
+Led a team in developing machine learning models for natural language processing applications.
+Implemented deep learning algorithms that improved prediction accuracy by 25%.
+Collaborated with cross-functional teams to integrate models into cloud-based platforms.
+Data Scientist, DataWave Analytics (2014 – 2016)
+Developed predictive models for customer segmentation and churn analysis.
+Analyzed large datasets using Hadoop and Spark frameworks.
+Skills:
+
+Programming Languages: Python, R, SQL
+Machine Learning: TensorFlow, Keras, Scikit-Learn
+Big Data Technologies: Hadoop, Spark
+Data Visualization: Tableau, Matplotlib
+"""
+
+job_2 = """
+CV 2: Relevant
+Name: Michael Rodriguez
+Contact Information:
+
+Email: michael.rodriguez@example.com
+Phone: (555) 234-5678
+Summary:
+
+Data Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.
+
+Education:
+
+M.S. in Data Science, Carnegie Mellon University (2013)
+B.S. in Computer Science, University of Michigan (2011)
+Experience:
+
+Senior Data Scientist, Alpha Analytics (2017 – Present)
+Developed machine learning models to optimize marketing strategies.
+Reduced customer acquisition cost by 15% through predictive modeling.
+Data Scientist, TechInsights (2013 – 2017)
+Analyzed user behavior data to improve product features.
+Implemented A/B testing frameworks to evaluate product changes.
+Skills:
+
+Programming Languages: Python, Java, SQL
+Machine Learning: Scikit-Learn, XGBoost
+Data Visualization: Seaborn, Plotly
+Databases: MySQL, MongoDB
+"""
+
+
+job_3 = """
+CV 3: Relevant
+Name: Sarah Nguyen
+Contact Information:
+
+Email: sarah.nguyen@example.com
+Phone: (555) 345-6789
+Summary:
+
+Data Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.
+
+Education:
+
+M.S. in Statistics, University of Washington (2014)
+B.S. in Applied Mathematics, University of Texas at Austin (2012)
+Experience:
+
+Data Scientist, QuantumTech (2016 – Present)
+Designed and implemented machine learning algorithms for financial forecasting.
+Improved model efficiency by 20% through algorithm optimization.
+Junior Data Scientist, DataCore Solutions (2014 – 2016)
+Assisted in developing predictive models for supply chain optimization.
+Conducted data cleaning and preprocessing on large datasets.
+Skills:
+
+Programming Languages: Python, R
+Machine Learning Frameworks: PyTorch, Scikit-Learn
+Statistical Analysis: SAS, SPSS
+Cloud Platforms: AWS, Azure
+"""
+
+
+job_4 = """
+CV 4: Not Relevant
+Name: David Thompson
+Contact Information:
+
+Email: david.thompson@example.com
+Phone: (555) 456-7890
+Summary:
+
+Creative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.
+
+Education:
+
+B.F.A. in Graphic Design, Rhode Island School of Design (2012)
+Experience:
+
+Senior Graphic Designer, CreativeWorks Agency (2015 – Present)
+Led design projects for clients in various industries.
+Created branding materials that increased client engagement by 30%.
+Graphic Designer, Visual Innovations (2012 – 2015)
+Designed marketing collateral, including brochures, logos, and websites.
+Collaborated with the marketing team to develop cohesive brand strategies.
+Skills:
+
+Design Software: Adobe Photoshop, Illustrator, InDesign
+Web Design: HTML, CSS
+Specialties: Branding and Identity, Typography
+"""
+
+
+job_5 = """
+CV 5: Not Relevant
+Name: Jessica Miller
+Contact Information:
+
+Email: jessica.miller@example.com
+Phone: (555) 567-8901
+Summary:
+
+Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.
+
+Education:
+
+B.A. in Business Administration, University of Southern California (2010)
+Experience:
+
+Sales Manager, Global Enterprises (2015 – Present)
+Managed a sales team of 15 members, achieving a 20% increase in annual revenue.
+Developed sales strategies that expanded customer base by 25%.
+Sales Representative, Market Leaders Inc. (2010 – 2015)
+Consistently exceeded sales targets and received the 'Top Salesperson' award in 2013.
+Skills:
+
+Sales Strategy and Planning
+Team Leadership and Development
+CRM Software: Salesforce, Zoho
+Negotiation and Relationship Building
+"""
+
+async def main(enable_steps):
+    # Step 1: Reset data and system state
+    if enable_steps.get("prune_data"):
+        await cognee.prune.prune_data()
+        print("Data pruned.")
+
+    if enable_steps.get("prune_system"):
+        await cognee.prune.prune_system(metadata=True)
+        print("System pruned.")
+
+    # Step 2: Add text
+    if enable_steps.get("add_text"):
+        text_list = [job_position, job_1, job_2, job_3, job_4, job_5]
+        for text in text_list:
+            await cognee.add(text)
+            print(f"Added text: {text[:35]}...")
+
+    # Step 3: Create knowledge graph
+    if enable_steps.get("cognify"):
+        await cognee.cognify()
+        print("Knowledge graph created.")
+
+    # Step 4: Query insights
+    if enable_steps.get("search_insights"):
+        search_results = await cognee.search(
+            SearchType.INSIGHTS,
+            {'query': 'Which applicant has the most relevant experience in data science?'}
+        )
+        print("Search results:")
+        for result_text in search_results:
+            print(result_text)
+
+
+if __name__ == '__main__':
+    # Flags to enable/disable steps
+    steps_to_enable = {
+        "prune_data": True,
+        "prune_system": True,
+        "add_text": True,
+        "cognify": True,
+        "search_insights": True
+    }
+
+    asyncio.run(main(steps_to_enable))