rough working recommend endpoint

requirements.txt

@@ -1,3 +1,5 @@
+annotated-types==0.7.0
+anyio==4.8.0
 blinker==1.9.0
 certifi==2025.1.31
 charset-normalizer==3.4.1
@@ -5,7 +7,15 @@ click==8.1.8
 filelock==3.17.0
 Flask==3.1.0
 fsspec==2025.3.0
+grpcio==1.71.0
+grpcio-tools==1.71.0
+h11==0.14.0
+h2==4.2.0
+hpack==4.1.0
+httpcore==1.0.7
+httpx==0.28.1
 huggingface-hub==0.29.2
+hyperframe==6.1.0
 idna==3.10
 itsdangerous==2.2.0
 Jinja2==3.1.6
@@ -29,12 +39,18 @@ nvidia-nccl-cu12==2.21.5
 nvidia-nvjitlink-cu12==12.4.127
 nvidia-nvtx-cu12==12.4.127
 packaging==24.2
+portalocker==2.10.1
+protobuf==5.29.3
+pydantic==2.10.6
+pydantic_core==2.27.2
 python-dotenv==1.0.1
 PyYAML==6.0.2
+qdrant-client==1.13.3
 regex==2024.11.6
 requests==2.32.3
 safetensors==0.5.3
 setuptools==76.0.0
+sniffio==1.3.1
 sympy==1.13.1
 tokenizers==0.21.0
 torch==2.6.0

src/app.py

@@ -1,7 +1,11 @@
 from flask import Flask, jsonify, request
 from dotenv import load_dotenv
 from services.embedding_service import Embedding
+import logging
+import json
+from qdrant_client.http import models
 
+logger = logging.getLogger(__name__)
 
 load_dotenv()
 app = Flask(__name__)
@@ -15,6 +19,51 @@ def hello():
     return jsonify({"message":"Hello, World!"})
 
 
+@app.get("/recommend")
+def recommend():
+    try:
+        
+        from services.recommend_service import Recommend
+        clause = request.args.get("clause", '')
+        # filter shall be a json array. each element is a dict with following keys:
+        # key: field name
+        # value: field value
+        # type :  operator type, default (and the only supported one for now) is "match"
+        # example: [{"key": "meta.source", "value": "km", "type": "match"}]
+        # please note we limit the number of filters to 1 for now
+
+        filter_ = request.args.get("filter", None)
+        logger.info(f"recommend request received: [{clause}] with filter [{filter_}]")
+
+        updated_clause = clause.replace('"', '')
+         
+
+        if filter_:
+            filter_ = json.loads(filter_)
+            filter_ = filter_[0]
+            if filter_['type'] != 'match':
+                raise ValueError('unsupported filter type')
+            f = models.Filter(
+                must=[
+                    models.FieldCondition(
+                        key=filter_['key'], match=models.MatchValue(value=filter_['value'])
+                    ),
+                ],
+            )
+            filter_ = f
+
+        result = Recommend.call(updated_clause, filter=filter_)
+        if result is None:
+            result = {"data": []}
+        else:
+            result = {"data": [i.dict() for i in result]}
+        logger.info("recommender returns %d results" % len(result["data"]))
+        return jsonify(result), 200
+    except Exception as e:
+        logger.error('recommend error: %s', type(e).__name__, exc_info=True)
+        return jsonify({"error":"Internal Server Error"}), 500
+
+
 @app.post("/get_embedding")
 def get_embedding():
     # Get the query text from request JSON
@@ -29,7 +78,7 @@ def get_embedding():
     
     return jsonify({"query": query, "number of embedding": len(vector), "one embedding":len(vector[0])})
 
-print(__name__)
+
 
 if __name__=="__main__":
     app.run(debug=True, port=8000)

src/qdrant_services/qdrant.py

@@ -5,7 +5,7 @@ from functools import wraps
 
 from qdrant_client import QdrantClient
 
-from qdrant_services.settings import settings
+from qdrant_services.settings import QDRANT_HOST, QDRANT_PORT, QDRANT_HTTPS, QDRANT_SSL_VERIFY,QDRANT_API_KEY, QDRANT_CLUSTER, QDRANT_COLLECTION_NAME
 from services.service import Singleton
 
 from qdrant_services.policy import QdrantClientSelectionPolicy
@@ -25,16 +25,16 @@ class Qdrant(metaclass=Singleton):
                  ):
         self.collection_name = collection_name or self.__class__.get_default_collection_name()
 
-        if settings.QDRANT_HTTPS is not None and kwargs.get('https') is None:
-            kwargs['https'] = settings.QDRANT_HTTPS
-        if settings.QDRANT_SSL_VERIFY is not None and kwargs.get('verify') is None:
-            kwargs['verify'] = settings.QDRANT_SSL_VERIFY
-        if settings.QDRANT_API_KEY is not None and kwargs.get('api_key') is None:
-            kwargs['api_key'] = settings.QDRANT_API_KEY
+        if QDRANT_HTTPS is not None and kwargs.get('https') is None:
+            kwargs['https'] = QDRANT_HTTPS
+        if QDRANT_SSL_VERIFY is not None and kwargs.get('verify') is None:
+            kwargs['verify'] = QDRANT_SSL_VERIFY
+        if QDRANT_API_KEY is not None and kwargs.get('api_key') is None:
+            kwargs['api_key'] = QDRANT_API_KEY
 
-        host = host or settings.QDRANT_HOST
-        port = port or settings.QDRANT_PORT
-        cluster = cluster or settings.QDRANT_CLUSTER
+        host = host or QDRANT_HOST
+        port = port or QDRANT_PORT
+        cluster = cluster or QDRANT_CLUSTER
         if cluster:
             self.logger.info(
                 "Connecting to qdrant cluster: cluster: %s, collection_name: %s" % (cluster, self.collection_name))
@@ -82,13 +82,16 @@ class Qdrant(metaclass=Singleton):
 
     @classmethod
     def get_default_collection_name(cls) -> str:
-        return settings.QDRANT_COLLECTION_NAME
+        return QDRANT_COLLECTION_NAME
 
     class QdrantClientProxy(QdrantClient):
 
         def __init__(self, outer_instance, *args, **kwargs):
             self.outer_instance = outer_instance
-            super().__init__(*args, **kwargs)
+            super().__init__(url="https://192.168.99.122:6333",
+    api_key="NS00TXlKUUIweUhWaGFuUUpUVTk6bWNVWXI1VXRSN2VWcFRtaEZ6NmdCUQ==",
+    https=True,
+     verify=False)
 
         _excluded_methods = ['_dynamic_call_decorator']
 

src/qdrant_services/settings.py

@@ -1,2 +1,8 @@
 QDRANT_HOST = "192.168.99.122"
-QDRANT_PORT = 6333 
+QDRANT_PORT = 6333 
+QDRANT_HTTPS=''
+QDRANT_SSL_VERIFY=''
+QDRANT_API_KEY=''
+QDRANT_CLUSTER=''
+QDRANT_COLLECTION_NAME='titan-2502110203'
+

src/services/embedding_service.py

@@ -80,11 +80,7 @@ class Embedding(Component):
                 clause_segments[-1] = clause_segments[-1] + ' ' + temp_str
             else:
                 clause_segments.append(temp_str)
-        # print("in embedding_service line 82, clause_segments: ", clause_segments)
-        # print("clause_segments len: ", len(clause_segments))
-        # print(len(clause_segments[0]))
-        # print(len(clause_segments[1]))
-        # print(len(clause_segments[2]))
+        
         clause_vectors = self.embedding_sentences(clause_segments)
         for i in range(len(clause_segments)):
             if len(clause_segments[i]) < segment_threshold:

src/services/recommend_service.py

@@ -0,0 +1,66 @@
+import logging
+
+import numpy as np
+from qdrant_client.http import models
+
+from services.service import Component
+from services.embedding_service import Embedding
+from qdrant_services.qdrant import Qdrant
+
+logger = logging.getLogger(__name__)
+
+
+class Recommend(Component):
+    def process(self, *args, **kwargs):
+        return self.do_query_multiplier_new(*args, **kwargs)
+
+    def do_query_multiplier_new(self, query_clause, filter = None):
+        top = 10
+
+        input_query_clause = query_clause.replace('"', '')
+        input_query_clause = clean_recommended_clause(input_query_clause)
+        # can perform any times of queries
+        ###########################################
+        # note: even set top_k = 10 but system may return < 10, because some clauses have more than 1 vectors
+        # top_k = 10 : imply return 10 vectors. if a clause has 2 vectors, 1 clause returned only
+
+        return recommend_clauses(input_query_clause, top_k=top * 7, filter=filter);
+
+
+def clean_recommended_clause(clause):
+    m_clause = clause.replace('\t', ' ')
+    m_clause = m_clause.strip()
+    m_clause_list = m_clause.split(' ')
+    result = ""
+    for clause_part in m_clause_list:
+        if len(clause_part) == 0:
+            continue
+        result += clause_part + " "
+    result = result.strip()
+    return result
+
+
+def recommend_clauses(query_str, top_k=10, filter=None):
+    # query_vector = embedding_a_clause(query_str, is_query=True)
+    query_vector = Embedding.call(query_str, is_query=True)
+    query_vector = np.array(query_vector).astype(np.float32)
+
+    search_queries = [
+        models.SearchRequest(
+            vector=vector.tolist(),
+            filter = filter,
+            limit=top_k,
+            with_payload=True,
+        )
+        for vector in query_vector
+    ]
+    logger.info(f"search_queries: {search_queries}")
+    results = Qdrant.get_client().search_batch(requests=search_queries)
+    results = [i for j in results for i in j]  # flatten
+    results = sorted(results, key=lambda x: x.score)  # sort by score, ascending
+    # as this is a batch search, same record may appear in different search results
+    # remove duplicates by id (keep the highest score for each record)
+    results = list({v.id: v for v in results}.values())
+    results.reverse()  # sort by score, descending
+    results = results[:top_k]
+    return results