Fixes

cognee/api/v1/cognify/cognify.py

@@ -178,7 +178,12 @@ async def process_text(input_text: str, file_metadata: dict):
 
     results = await resolve_cross_graph_references(nodes_by_layer)
 
+
     relationships = graph_ready_output(results)
+    # print(relationships)
+    await graph_client.load_graph_from_file()
+
+    graph = graph_client.graph
 
     connect_nodes_in_graph(graph, relationships)
 

cognee/api/v1/search/search.py

@@ -2,7 +2,7 @@
 import asyncio
 from enum import Enum, auto
 from typing import Dict, Any, Callable, List
-from pydantic import BaseModel, validator
+from pydantic import BaseModel, field_validator
 from cognee.modules.search.graph.search_adjacent import search_adjacent
 from cognee.modules.search.vector.search_similarity import search_similarity
 from cognee.modules.search.graph.search_categories import search_categories
@@ -28,7 +28,7 @@ class SearchParameters(BaseModel):
     search_type: SearchType
     params: Dict[str, Any]
 
-    @validator('search_type', pre=True)
+    @field_validator('search_type', mode='before')
     def convert_string_to_enum(cls, value):
         if isinstance(value, str):
             return SearchType.from_str(value)
@@ -80,8 +80,8 @@ if __name__ == "__main__":
         await graph_client.load_graph_from_file()
         graph = graph_client.graph
         # Assuming 'graph' is your graph object, obtained from somewhere
-        search_type = 'ADJACENT'
+        search_type = 'CATEGORIES'
-        params = {'query': 'example query', 'other_param': {"node_id": "LLM_LAYER_SUMMARY:DOCUMENT:881ecb36-2819-54c3-8147-ed80293084d6"}}
+        params = {'query': 'Ministarstvo', 'other_param': {"node_id": "LLM_LAYER_SUMMARY:DOCUMENT:881ecb36-2819-54c3-8147-ed80293084d6"}}
 
         results = await search(graph, search_type, params)
         print(results)

cognee/infrastructure/databases/vector/models/ScoredResult.py

@@ -3,6 +3,6 @@ from typing import Any, Dict
 from pydantic import BaseModel
 
 class ScoredResult(BaseModel):
-    id: UUID
+    id: str
-    score: int
+    score: float
     payload: Dict[str, Any]

cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py

@@ -49,17 +49,19 @@ class WeaviateAdapter(VectorDBInterface):
         return self.get_collection(collection_name).data.insert_many(objects)
 
     async def search(self, collection_name: str, query_text: str, limit: int, with_vector: bool = False):
-        search_result = self.get_collection(collection_name).query.bm25(
+        search_result = self.get_collection(collection_name).query.hybrid(
             query = query_text,
             limit = limit,
             include_vector = with_vector,
             return_metadata = wvc.query.MetadataQuery(score = True),
         )
 
+        # print(search_result.objects)
+
         return list(map(lambda result: ScoredResult(
-            id = result.uuid,
+            id = str(result.uuid),
             payload = result.properties,
-            score = str(result.metadata.score)
+            score = float(result.metadata.score)
         ), search_result.objects))
 
     async def batch_search(self, collection_name: str, query_texts: List[str], limit: int,  with_vectors: bool = False):

cognee/modules/cognify/graph/add_node_connections.py

@@ -31,7 +31,7 @@ async def group_nodes_by_layer(node_descriptions):
 
     return grouped_data
 
-def connect_nodes_in_graph(graph: Graph, relationship_dict: dict) -> Graph:
+def connect_nodes_in_graph(graph: Graph, relationship_dict: dict, score_treshold:float=None) -> Graph:
     """
     For each relationship in relationship_dict, check if both nodes exist in the graph based on node attributes.
     If they do, create a connection (edge) between them.
@@ -39,37 +39,43 @@ def connect_nodes_in_graph(graph: Graph, relationship_dict: dict) -> Graph:
     :param graph: A NetworkX graph object
     :param relationship_dict: A dictionary containing relationships between nodes
     """
+    if score_treshold is None:
+        score_treshold = 0.9
     for id, relationships in relationship_dict.items():
         for relationship in relationships:
             searched_node_attr_id = relationship["searched_node_id"]
             score_attr_id = relationship["original_id_for_search"]
             score = relationship["score"]
 
-            # Initialize node keys for both searched_node and score_node
+            if score> score_treshold:
-            searched_node_key, score_node_key = None, None
+                # Initialize node keys for both searched_node and score_node
+                searched_node_key, score_node_key = None, None
 
-            # Find nodes in the graph that match the searched_node_id and score_id from their attributes
+                # Find nodes in the graph that match the searched_node_id and score_id from their attributes
-            for node, attrs in graph.nodes(data = True):
+                for node, attrs in graph.nodes(data = True):
-                if "unique_id" in attrs:  # Ensure there is an "id" attribute
+                    if "unique_id" in attrs:  # Ensure there is an "id" attribute
-                    if attrs["unique_id"] == searched_node_attr_id:
+                        if attrs["unique_id"] == searched_node_attr_id:
-                        searched_node_key = node
+                            searched_node_key = node
-                    elif attrs["unique_id"] == score_attr_id:
+                        elif attrs["unique_id"] == score_attr_id:
-                        score_node_key = node
+                            score_node_key = node
 
-                # If both nodes are found, no need to continue checking other nodes
+                    # If both nodes are found, no need to continue checking other nodes
-                if searched_node_key and score_node_key:
+                    if searched_node_key and score_node_key:
-                    break
+                        break
 
-            # Check if both nodes were found in the graph
+                # Check if both nodes were found in the graph
-            if searched_node_key is not None and score_node_key is not None:
+                if searched_node_key is not None and score_node_key is not None:
-                # If both nodes exist, create an edge between them
+                    # print(f"Connecting {searched_node_key} to {score_node_key}")
-                # You can customize the edge attributes as needed, here we use "score" as an attribute
+                    # If both nodes exist, create an edge between them
-                graph.add_edge(
+                    # You can customize the edge attributes as needed, here we use "score" as an attribute
-                    searched_node_key,
+                    graph.add_edge(
-                    score_node_key,
+                        searched_node_key,
-                    weight = score,
+                        score_node_key,
-                    score_metadata = relationship.get("score_metadata")
+                        weight = score,
-                )
+                        score_metadata = relationship.get("score_metadata")
+                    )
+            else:
+                pass
 
     return graph
 
@@ -97,3 +103,32 @@ def graph_ready_output(results):
                 })
 
     return relationship_dict
+
+
+if __name__ == "__main__":
+
+    async def main():
+        graph_client = get_graph_client(GraphDBType.NETWORKX)
+
+        await graph_client.load_graph_from_file()
+
+
+        graph = graph_client.graph
+
+        # for nodes, attr in graph.nodes(data=True):
+        #     if 'd0bd0f6a-09e5-4308-89f6-400d66895126' in nodes:
+        #         print(nodes)
+
+
+        relationships = {'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah': [{'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'd0bd0f6a-09e5-4308-89f6-400d66895126', 'score': 1.0, 'score_metadata': {'text': 'Pravilnik o izmenama i dopunama Pravilnika o sadržini, načinu i postupku izrade i način vršenja kontrole tehničke dokumentacije prema klasi i nameni objekata'}, 'original_id_for_search': '2801f7b5-55bf-499b-9843-97d48f8e067a'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'd0bd0f6a-09e5-4308-89f6-400d66895126', 'score': 0.1648828387260437, 'score_metadata': {'text': 'Zakon o planiranju i izgradnji'}, 'original_id_for_search': '57966b55-33e2-4eae-a7fa-2f0237643bbe'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'd0bd0f6a-09e5-4308-89f6-400d66895126', 'score': 0.12986786663532257, 'score_metadata': {'text': 'Službeni glasnik RS, broj 77/2015'}, 'original_id_for_search': '0f626d48-4441-43c1-9060-ea7e54f6d8e2'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'c9b9a460-c64a-4e2e-a4d6-aa5b3769274b', 'score': 1.0, 'score_metadata': {'text': 'Službeni glasnik RS, broj 77/2015'}, 'original_id_for_search': '0f626d48-4441-43c1-9060-ea7e54f6d8e2'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'c9b9a460-c64a-4e2e-a4d6-aa5b3769274b', 'score': 0.07603412866592407, 'score_metadata': {'text': 'Prof. dr Zorana Mihajlović'}, 'original_id_for_search': '5d064a62-3cd6-4895-9f60-1a0d8bc299e8'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'c9b9a460-c64a-4e2e-a4d6-aa5b3769274b', 'score': 0.07226034998893738, 'score_metadata': {'text': 'Ministar građevinarstva, saobraćaja i infrastrukture'}, 'original_id_for_search': 'f5d052ca-c4a0-490e-a3ac-d8ad522dea83'}, {'collection_id': 'SuaGeKyKWKWyaSeiqWeWaSyuSKqieSamiyah', 'searched_node_id': 'bbd6d2d6-e673-4b59-a50c-516972a9d0de', 'score': 0.5, 'score_metadata': {'text': 'Pravilnik o izmenama i dopunama Pravilnika o sadržini, načinu i postupku izrade i način vršenja kontrole tehničke dokumentacije prema klasi i nameni objekata'}, 'original_id_for_search': '2801f7b5-55bf-499b-9843-97d48f8e067a'}]}
+
+        connect_nodes_in_graph(graph, relationships)
+
+        from cognitive_architecture.utils import render_graph
+
+        graph_url = await render_graph(graph, graph_type="networkx")
+
+        print(graph_url)
+
+    import asyncio
+    asyncio.run(main())

cognee/modules/search/vector/search_similarity.py

@@ -28,11 +28,18 @@ async def search_similarity(query: str, graph, other_param: str = None):
 
     for proposition_id in out[0][0]:
         for n, attr in graph.nodes(data = True):
-            if proposition_id in n:
+            if str(proposition_id) in str(n):
                 for n_, attr_ in graph.nodes(data=True):
                     relevant_layer = attr["layer_uuid"]
 
                     if attr_.get("layer_uuid") == relevant_layer:
                         relevant_context.append(attr_["description"])
 
+    def deduplicate_list(original_list):
+        seen = set()
+        deduplicated_list = [x for x in original_list if not (x in seen or seen.add(x))]
+        return deduplicated_list
+
+    relevant_context = deduplicate_list(relevant_context)
+
     return relevant_context

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cognee"
-version = "0.1.0"
+version = "0.1.1"
 description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
 authors = ["Vasilije Markovic"]
 readme = "README.md"
@@ -18,7 +18,6 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = "~3.10"
-langchain = "^0.0.338"
 openai = "1.12.0"
 python-dotenv = "1.0.1"
 fastapi = "^0.109.2"