added hippocampus query api in get_text_from_vectorsQuery function

config.py

@@ -58,4 +58,7 @@ class Config:
     DOCSTAR_ORG_ID = os.getenv('DOCSTAR_ORG_ID')
     DOCSTAR_COLLECTION_ID = os.getenv('DOCSTAR_COLLECTION_ID')
     AI_ML_APIKEY = os.getenv('AI_ML_APIKEY')
-    AI_MIDDLEWARE_PAUTH_KEY = os.getenv('AI_MIDDLEWARE_PAUTH_KEY')
+    AI_MIDDLEWARE_PAUTH_KEY = os.getenv('AI_MIDDLEWARE_PAUTH_KEY')
+
+    HIPPOCAMPUS_API_KEY = os.getenv('HIPPOCAMPUS_API_KEY')
+    HIPPOCAMPUS_API_URL = os.getenv('HIPPOCAMPUS_API_URL')

src/controllers/rag_controller.py

@@ -14,6 +14,7 @@
 from ..services.utils.rag_utils import extract_pdf_text, extract_csv_text, extract_docx_text
 import traceback
 from ..services.utils.rag_utils import get_csv_query_type
+from ...utils.apiservice import fetch
 
 rag_model = db["rag_datas"]
 rag_parent_model = db["rag_parent_datas"]
@@ -251,97 +252,144 @@ async def delete_doc(request):
         raise HTTPException(status_code=500, detail = error)
 
 
-async def get_text_from_vectorsQuery(args, Flag = True, score = 0.1):
-    try:
-        doc_id = args.get('Document_id')
-        query = args.get('query')
-        org_id = args.get('org_id')
-        top_k = args.get('top_k', 3)
-        additional_query = {}
+# async def get_text_from_vectorsQuery(args, Flag = True, score = 0.1):
+#     try:
+#         doc_id = args.get('Document_id')
+#         query = args.get('query')
+#         org_id = args.get('org_id')
+#         top_k = args.get('top_k', 3)
+#         additional_query = {}
 
-        if query is None:
-            raise HTTPException(status_code=400, detail="Query is required.")
+#         if query is None:
+#             raise HTTPException(status_code=400, detail="Query is required.")
 
-        doc_data = await rag_parent_model.find_one({
-            '_id' : ObjectId(doc_id)
-        })
+#         doc_data = await rag_parent_model.find_one({
+#             '_id' : ObjectId(doc_id)
+#         })
 
-        if not doc_data: 
-            raise Exception("Invalid document id provided.")
+#         if not doc_data: 
+#             raise Exception("Invalid document id provided.")
 
-        if doc_data['source']['fileFormat'] == 'csv': 
-            to_search_for = await get_csv_query_type(doc_data, query)
-            additional_query['chunkType'] = to_search_for
+#         if doc_data['source']['fileFormat'] == 'csv': 
+#             to_search_for = await get_csv_query_type(doc_data, query)
+#             additional_query['chunkType'] = to_search_for
 
-        embedding = OpenAIEmbeddings(api_key=Config.OPENAI_API_KEY, model="text-embedding-3-small").embed_documents([query])
+#         embedding = OpenAIEmbeddings(api_key=Config.OPENAI_API_KEY, model="text-embedding-3-small").embed_documents([query])
 
-        # Query Pinecone with timing
-        start_time = time.time()
-        query_response = index.query(
-            vector=embedding[0] if isinstance(embedding, list) and len(embedding) == 1 else list(map(float, embedding)),
-            namespace=org_id,
-            filter={ "docId": doc_id, **additional_query },
-            top_k = top_k  # Adjust the number of results as needed
-        )
+#         # Query Pinecone with timing
+#         start_time = time.time()
+#         query_response = index.query(
+#             vector=embedding[0] if isinstance(embedding, list) and len(embedding) == 1 else list(map(float, embedding)),
+#             namespace=org_id,
+#             filter={ "docId": doc_id, **additional_query },
+#             top_k = top_k  # Adjust the number of results as needed
+#         )
 
-        # Filter results based on score threshold - only include results with score >= threshold
-        # Skip filtering if Flag is True
-        if Flag:
-            filtered_matches = query_response['matches']
-        else:
-            filtered_matches = []
+#         # Filter results based on score threshold - only include results with score >= threshold
+#         # Skip filtering if Flag is True
+#         if Flag:
+#             filtered_matches = query_response['matches']
+#         else:
+#             filtered_matches = []
 
-            for result in query_response['matches']:
-                if result['score'] >= score:
-                    filtered_matches.append(result)
+#             for result in query_response['matches']:
+#                 if result['score'] >= score:
+#                     filtered_matches.append(result)
 
-        query_response_ids = [result['id'] for result in filtered_matches]
+#         query_response_ids = [result['id'] for result in filtered_matches]
 
-        # Create a dictionary to map id to score and position
-        id_to_score = {result['id']: result['score'] for result in filtered_matches}
-        id_to_position = {result['id']: pos for pos, result in enumerate(filtered_matches)}
+#         # Create a dictionary to map id to score and position
+#         id_to_score = {result['id']: result['score'] for result in filtered_matches}
+#         id_to_position = {result['id']: pos for pos, result in enumerate(filtered_matches)}
 
 
-        # Query MongoDB using query_response_ids
-        mongo_query = {"_id": {"$in": [ObjectId(id) for id in query_response_ids] }}
-        cursor = rag_model.find(mongo_query)
+#         # Query MongoDB using query_response_ids
+#         mongo_query = {"_id": {"$in": [ObjectId(id) for id in query_response_ids] }}
+#         cursor = rag_model.find(mongo_query)
 
-        mongo_results = await cursor.to_list(length=None)
-        mongo_results.sort(key=lambda x: id_to_position.get(str(x.get('_id')), float('inf')))
+#         mongo_results = await cursor.to_list(length=None)
+#         mongo_results.sort(key=lambda x: id_to_position.get(str(x.get('_id')), float('inf')))
 
-        text = ""
-        results_with_scores = []
-        for result in mongo_results:
-            text += result.get('data', '')
-            result_id = str(result.get('_id'))
-            result_data = {
-                'id': result_id,
-                'data': result.get('data', '')
-            }
-            # Only add score if Flag is False
-            if not Flag:
-                result_data['score'] = id_to_score.get(result_id, 0.0)
-            results_with_scores.append(result_data)
+#         text = ""
+#         results_with_scores = []
+#         for result in mongo_results:
+#             text += result.get('data', '')
+#             result_id = str(result.get('_id'))
+#             result_data = {
+#                 'id': result_id,
+#                 'data': result.get('data', '')
+#             }
+#             # Only add score if Flag is False
+#             if not Flag:
+#                 result_data['score'] = id_to_score.get(result_id, 0.0)
+#             results_with_scores.append(result_data)
 
-        # Build response metadata based on Flag
-        metadata = {"type": "RAG"}
-        if not Flag:
-            metadata["results_with_scores"] = results_with_scores
-            metadata["similarity_scores"] = [{'id': result['id'], 'score': result['score']} for result in filtered_matches]
-        else:
-            metadata["results"] = [{'id': item['id'], 'data': item['data']} for item in results_with_scores]
+#         # Build response metadata based on Flag
+#         metadata = {"type": "RAG"}
+#         if not Flag:
+#             metadata["results_with_scores"] = results_with_scores
+#             metadata["similarity_scores"] = [{'id': result['id'], 'score': result['score']} for result in filtered_matches]
+#         else:
+#             metadata["results"] = [{'id': item['id'], 'data': item['data']} for item in results_with_scores]
+
+#         return {
+#             'response': text,
+#             'metadata': metadata,
+#             'status': 1
+#         }
 
+#     except Exception as error:
+#         return {
+#             'response': str(error),
+#             'metadata': {
+#                 "type": "RAG"
+#             },
+#             'status': 0  # 0 indicates error/failure
+#         }
+
+
+
+
+
+async def get_text_from_vectorsQuery(args):
+    try:
+        query = args.get("query")
+        collection_id = args.get("collectionId")
+        resource_id = args.get("resourceId")
+        owner_id = args.get("ownerId")
+        is_review = args.get("isReview", True)
+
+        if not query:
+            raise ValueError("Query is required")
+
+        payload = {
+            "query": query,
+            "collectionId": collection_id,
+            "resourceId": resource_id,
+            "ownerId": owner_id,
+            "isReview": is_review
+        }
+
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": Config.HIPPOCAMPUS_API_KEY
+        }
+
+        response_data, response_headers = await fetch(
+            url=f"{Config.HIPPOCAMPUS_BASE_URL}/search",
+            method="POST",
+            headers=headers,
+            json_body=payload
+        )
+
         return {
-            'response': text,
-            'metadata': metadata,
-            'status': 1
+            "success": True,
+            "response": response_data,
+            "headers": response_headers
         }
-        
+
     except Exception as error:
         return {
-            'response': str(error),
-            'metadata': {
-                "type": "RAG"
-            },
-            'status': 0  # 0 indicates error/failure
-        }
+            "success": False,
+            "error": str(error)
+        }