fixes

gui/pages/Content/Agents/Details.js

@@ -164,7 +164,7 @@ export default function Details({agentDetails, runCount, goals, instructions, ag
       </div>
       <div className={styles.agent_info_box}>
         <div><Image width={15} height={15} src="/images/books.svg" alt="book-icon"/></div>
-        <div style={info_text}>knowledge name</div>
+        <div style={info_text}>{agentDetails?.knowledge_name || 'Not Found' }</div>
       </div>
       <div className={styles.agent_info_box}>
         <div><Image width={15} height={15} src="/images/deployed_code.svg" alt="model-icon"/></div>
@@ -174,10 +174,10 @@ export default function Details({agentDetails, runCount, goals, instructions, ag
       {/*  <div><Image width={15} height={15} src="/images/cancel_presentation.svg" alt="exit-icon"/></div>*/}
       {/*  <div style={info_text}>{exit}</div>*/}
       {/*</div>*/}
-      <div className={styles.agent_info_box}>
+      {/* <div className={styles.agent_info_box}>
         <div><Image width={15} height={15} src="/images/overview.svg" alt="window-icon"/></div>
         <div style={info_text}>{agentDetails?.memory_window || 0} milliseconds</div>
-      </div>
+      </div> */}
       <div className={styles.agent_info_box}>
         <div><Image width={15} height={15} src="/images/key.svg" alt="permission-type-icon"/></div>
         <div style={info_text}>{agentDetails?.permission_type.replace(/\s*\([^)]*\)/g, '') || ''}</div>

superagi/controllers/agent.py

@@ -30,6 +30,7 @@
 import json
 
 from superagi.models.toolkit import Toolkit
+from superagi.models.knowledges import Knowledges
 
 from sqlalchemy import func
 # from superagi.types.db import AgentOut, AgentIn
@@ -480,13 +481,16 @@ def get_agent_configuration(agent_id: int,
         AgentExecution.agent_id == agent_id).scalar()
     total_tokens = db.session.query(func.sum(AgentExecution.num_of_tokens)).filter(
         AgentExecution.agent_id == agent_id).scalar()
+
 
     # Construct the JSON response
     response = {result.key: result.value for result in results}
+    knowledge = db.session.query(Knowledges).filter(Knowledges.id == response['knowledge']).first()
     response = merge(response, {"name": agent.name, "description": agent.description,
                                 # Query the AgentConfiguration table for the speci
                                 "goal": eval(response["goal"]),
                                 "instruction": eval(response.get("instruction", '[]')),
+                                "knowledge_name": knowledge.name,
                                 "calls": total_calls,
                                 "tokens": total_tokens,
                                 "constraints": eval(response.get("constraints")),

superagi/controllers/knowledges.py

@@ -126,7 +126,7 @@ def install_selected_knowledge(knowledge_name: str, vector_db_index_id: int, org
     file_chunks = S3Helper().get_json_file(selected_knowledge_config["file_path"])
     vector = Vectordbs.get_vector_db_from_id(db.session, vector_db_index.vector_db_id)
     db_creds = VectordbConfigs.get_vector_db_config_from_db_id(db.session, vector.id)
-    upsert_data = VectorEmbeddingFactory.build_vector_storge(vector.db_type).get_vector_embeddings_from_chunks(file_chunks)
+    upsert_data = VectorEmbeddingFactory.build_vector_storage(vector.db_type, file_chunks).get_vector_embeddings_from_chunks()
     try:
         vector_db_storage = VectorFactory.build_vector_storage(vector.db_type, vector_db_index.name, **db_creds)
         vector_db_storage.add_embeddings_to_vector_db(upsert_data)

superagi/models/agent.py

@@ -106,9 +106,9 @@ def eval_agent_config(cls, key, value):
 
         """
 
-        if key in ["name", "description", "agent_type", "exit", "model", "permission_type", "LTM_DB", "resource_summary"]:
+        if key in ["name", "description", "agent_type", "exit", "model", "permission_type", "LTM_DB", "resource_summary", "knowledge"]:
             return value
-        elif key in ["project_id", "memory_window", "max_iterations", "iteration_interval", "knowledge"]:
+        elif key in ["project_id", "memory_window", "max_iterations", "iteration_interval"]:
             return int(value)
         elif key in ["goal", "constraints", "instruction", "is_deleted"]:
             return eval(value)

superagi/vector_embeddings/vector_embedding_factory.py

@@ -9,7 +9,7 @@
 class VectorEmbeddingFactory:
 
     @classmethod
-    def build_vector_storge(cls, vector_store: VectorStoreType, chunk_json: Optional[dict] = None):
+    def build_vector_storage(cls, vector_store: VectorStoreType, chunk_json: Optional[dict] = None):
         """
         Get the vector embeddings from final chunks.
         Args:
@@ -18,14 +18,14 @@ def build_vector_storge(cls, vector_store: VectorStoreType, chunk_json: Optional
             The vector storage object
         """
         final_chunks = []
+        uuid = []
+        embeds = []
+        metadata = []
         vector_store = VectorStoreType.get_vector_store_type(vector_store)
         if chunk_json is not None:
             for key in chunk_json.keys():
                 final_chunks.append(chunk_json[key])
 
-            uuid = []
-            embeds = []
-            metadata = []
             for i in range(0, len(final_chunks)):
                 uuid.append(final_chunks[i]["id"])
                 embeds.append(final_chunks[i]["embeds"])
@@ -35,6 +35,7 @@ def build_vector_storge(cls, vector_store: VectorStoreType, chunk_json: Optional
                     'knowledge_name': final_chunks[i]['knowledge_name']
                 }
                 metadata.append(data)
+
         if vector_store == VectorStoreType.PINECONE:
             return Pinecone(uuid, embeds, metadata)
 

tests/unit_tests/vector_embeddings/test_vector_embedding_factory.py

@@ -12,13 +12,13 @@ def test_build_vector_storge(self, mock_qdrant, mock_pinecone):
             "2": {"id": 2, "embeds": [4,5,6], "text": "test2", "chunk": "chunk2", "knowledge_name": "knowledge2"},
         }
 
-        vector_storge = VectorEmbeddingFactory.build_vector_storge('Pinecone', test_data)
+        vector_storge = VectorEmbeddingFactory.build_vector_storage('Pinecone', test_data)
 
         mock_pinecone.assert_called_once_with(
             [1,2], [[1,2,3],[4,5,6]], [{"text": "test", "chunk": "chunk", "knowledge_name": "knowledge"}, {"text": "test2", "chunk": "chunk2", "knowledge_name": "knowledge2"}]
         )
 
-        vector_storge = VectorEmbeddingFactory.build_vector_storge('Qdrant', test_data)
+        vector_storge = VectorEmbeddingFactory.build_vector_storage('Qdrant', test_data)
 
         mock_qdrant.assert_called_once_with(
             [1,2], [[1,2,3],[4,5,6]], [{"text": "test", "chunk": "chunk", "knowledge_name": "knowledge"}, {"text": "test2", "chunk": "chunk2", "knowledge_name": "knowledge2"}]