Fix search

cognitive_architecture/api/v1/search/search.py

@@ -25,7 +25,7 @@ async def complex_search(graph, query_params: Dict[SearchType, Dict[str, Any]])
         SearchType.NEIGHBOR: search_neighbour,
     }
 
-    results = set()
+    results = []
 
     # Create a list to hold all the coroutine objects
     search_tasks = []
@@ -34,7 +34,8 @@ async def complex_search(graph, query_params: Dict[SearchType, Dict[str, Any]])
         search_func = search_functions.get(search_type)
         if search_func:
             # Schedule the coroutine for execution and store the task
-            task = search_func(graph, **params)
+            full_params = {**params, 'graph': graph}
+            task = search_func(**full_params)
             search_tasks.append(task)
 
     # Use asyncio.gather to run all scheduled tasks concurrently
@@ -42,9 +43,9 @@ async def complex_search(graph, query_params: Dict[SearchType, Dict[str, Any]])
 
     # Update the results set with the results from all tasks
     for search_result in search_results:
-        results.update(search_result)
+        results.append(search_result)
 
-    return list(results)
+    return results
 
 if __name__ == "__main__":
     import asyncio

cognitive_architecture/infrastructure/databases/vector/qdrant/QDrantAdapter.py

@@ -65,7 +65,8 @@ class QDrantAdapter(VectorDBInterface):
 
         return await client.search(
             collection_name = collection_name,
-            query_vector = query_vector,
+            query_vector = (
+            "content", query_vector),
             limit = limit,
             with_vectors = with_vector
         )

cognitive_architecture/infrastructure/llm/openai/adapter.py

@@ -28,12 +28,12 @@ class OpenAIAdapter(LLMInterface):
         return await openai.chat.completions.acreate(**kwargs)
 
     @retry(stop = stop_after_attempt(5))
-    async def acreate_embedding_with_backoff(self, input: List[str], model: str = "text-embedding-ada-002"):
+    async def acreate_embedding_with_backoff(self, input: List[str], model: str = "text-embedding-3-large"):
         """Wrapper around Embedding.acreate w/ backoff"""
 
         return await self.aclient.embeddings.create(input=input, model=model)
 
-    async def async_get_embedding_with_backoff(self, text, model="text-embedding-ada-002"):
+    async def async_get_embedding_with_backoff(self, text, model="text-embedding-3-large"):
         """To get text embeddings, import/call this function
         It specifies defaults + handles rate-limiting + is async"""
         text = text.replace("\n", " ")
@@ -46,7 +46,7 @@ class OpenAIAdapter(LLMInterface):
         """Wrapper around Embedding.create w/ backoff"""
         return openai.embeddings.create(**kwargs)
 
-    def get_embedding_with_backoff(self, text: str, model: str = "text-embedding-ada-002"):
+    def get_embedding_with_backoff(self, text: str, model: str = "text-embedding-3-large"):
         """To get text embeddings, import/call this function
         It specifies defaults + handles rate-limiting
         :param text: str

cognitive_architecture/modules/search/vector/search_similarity.py

@@ -15,7 +15,12 @@ async def search_similarity(query:str ,graph):
     query = await client.async_get_embedding_with_backoff(query)
     # print(query)
     for id in unique_layer_uuids:
-        result = client.search(id, query[0])
+        from cognitive_architecture.infrastructure.databases.vector.get_vector_database import get_vector_database
+        vector_client = get_vector_database()
+
+        print(query)
+
+        result = await vector_client.search(id, query,10)
 
         if result:
             result_ = [ result_.id for result_ in result]
@@ -23,4 +28,17 @@ async def search_similarity(query:str ,graph):
 
             out.append([result_, score_])
 
-    return out
+    relevant_context = []
+
+    for proposition_id in out[0][0]:
+        print(proposition_id)
+        for n,attr in graph.nodes(data=True):
+            if proposition_id in n:
+                for n_, attr_ in graph.nodes(data=True):
+                    relevant_layer = attr['layer_uuid']
+
+                    if attr_.get('layer_uuid') == relevant_layer:
+                        print(attr_['description'])
+                        relevant_context.append(attr_['description'])
+
+    return relevant_context