test

babyagi/classesa/diamond.py

@@ -80,6 +80,59 @@ class ProductDatabase:
             results = cursor.fetchall()
             return results            
 
+def calculate(x,y,z,c):
+    # データベース接続情報
+    DATABASE_URL = "postgresql://miyataken999:[email protected]/neondb?sslmode=require"
+    
+    # ProductDatabaseクラスのインスタンスを作成
+    db = ProductDatabase(DATABASE_URL)
+    
+    # データベースに接続
+    db.connect()
+    
+    try:
+        # pgvector拡張機能のインストールとカラムの追加
+        db.setup_vector_extension_and_column()
+        print("Vector extension installed and column added successfully.")
+        query_text="1"
+        results = db.search_similar_all(query_text)
+        print("Search results:")
+        DEBUG=0
+        if DEBUG==1:
+            for result in results:
+                print(result) 
+                id = result[0]
+                sample_text = str(result[1])+str(result[2])+str(result[3])+str(result[4])+str(result[5])+str(result[6])+str(result[7])+str(result[8])+str(result[9])
+                print(sample_text)
+                db.insert_vector(id, sample_text) 
+        #return
+        # サンプルデータの挿入
+        #sample_text = """"""
+        #sample_product_id = 1  # 実際の製品IDを使用
+        #db.insert_vector(sample_product_id, sample_text)
+        #db.insert_vector(2, sample_text)
+
+        #print(f"Vector inserted for product ID {sample_product_id}.")
+
+        
+        # ベクトル検索
+        query_text = "2.03Very GoodJSI262.058.08.068.125.05"
+
+        query_text = "2.03Very GoodJSI2"
+
+        #query_text = "2.03-Very Good-J-SI2-62.2-58.0-7.27-7.33-4.55"
+        results = db.search_similar_vectors(query_text)
+        res_all = ""
+        print("Search results:")
+        for result in results:
+            print(result)
+            res_all += result+""
+    
+    finally:
+        # 接続を閉じる
+        db.close()
+
+
 def main():
     # データベース接続情報
     DATABASE_URL = "postgresql://miyataken999:[email protected]/neondb?sslmode=require"

controllers/gpt_enginner20240623055745

@@ -0,0 +1 @@
+Subproject commit 35fa17068f94587089db2c188dfebe760e97c074

controllers/gpt_enginner20240623060021

@@ -0,0 +1 @@
+Subproject commit 33283e43c1fb61111147c97ecf3587ade44b4c4e

controllers/gpt_enginner20240623060323

@@ -0,0 +1 @@
+Subproject commit e48e80a8f35468c3d3965d84ddc03745878db6f3

controllers/gpt_enginner20240623062247

@@ -0,0 +1 @@
+Subproject commit b14eca59be89da0baf0648650a8a86c6e155572a

controllers/gra_02_openInterpreter/OpenInterpreter.py

@@ -3,7 +3,7 @@ from mysite.libs.utilities import chat_with_interpreter, completion, process_fil
 from interpreter import interpreter
 import mysite.interpreter.interpreter_config  # インポートするだけで設定が適用されます
 import duckdb
-from logger import logger
+#from logger import logger
 
 def format_response(chunk, full_response):
     # Message

mysite/routers/diamond.py

@@ -0,0 +1,138 @@
+# prompt: fastapi route 処理作成　引数は　calat wehth state x
+
+from fastapi import APIRouter, HTTPException
+from babyagi.classesa import da
+
+import psycopg2
+from sentence_transformers import SentenceTransformer
+from fastapi import APIRouter, HTTPException
+
+router = APIRouter(prefix="/leaning", tags=["leaning"])
+@router.get("/route/{calat}/{wehth}/{state}/{x}")
+async def route(calat: float, wehth: float, state: str, x: int):
+    # Validate input parameters
+    if not (0.0 <= calat <= 90.0):
+        raise HTTPException(status_code=400, detail="Invalid calat value.")
+    if not (0.0 <= wehth <= 180.0):
+        raise HTTPException(status_code=400, detail="Invalid wehth value.")
+    if state not in ["AC", "AL", "AP", ..., "TO"]:
+        raise HTTPException(status_code=400, detail="Invalid state value.")
+    if not (0 <= x <= 100):
+        raise HTTPException(status_code=400, detail="Invalid x value.")
+
+    # Process the request and return a response
+    # ...
+
+    return {"result": "OK"}
+
+class ProductDatabase:
+    def __init__(self, database_url):
+        self.database_url = database_url
+        self.conn = None
+        self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+    
+    def connect(self):
+        self.conn = psycopg2.connect(self.database_url)
+    
+    def close(self):
+        if self.conn:
+            self.conn.close()
+    
+    def setup_vector_extension_and_column(self):
+        with self.conn.cursor() as cursor:
+            # pgvector拡張機能のインストール
+            cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+            
+            # ベクトルカラムの追加
+            cursor.execute("ALTER TABLE products ADD COLUMN IF NOT EXISTS vector_col vector(384);")
+            
+            self.conn.commit()
+
+    def get_embedding(self, text):
+        embedding = self.model.encode(text)
+        return embedding
+
+    def insert_vector(self, product_id, text):
+        vector = self.get_embedding(text).tolist()  # ndarray をリストに変換
+        with self.conn.cursor() as cursor:
+            cursor.execute("UPDATE diamondprice SET vector_col = %s WHERE id = %s", (vector, product_id))
+            self.conn.commit()
+
+    def search_similar_vectors(self, query_text, top_k=50):
+        query_vector = self.get_embedding(query_text).tolist()  # ndarray をリストに変換
+        with self.conn.cursor() as cursor:
+            cursor.execute("""
+                SELECT id,price,carat, cut, color, clarity, depth, diamondprice.table, x, y, z, vector_col <=> %s::vector AS distance
+                FROM diamondprice
+                WHERE vector_col IS NOT NULL
+                ORDER BY distance asc
+                LIMIT %s;
+            """, (query_vector, top_k))
+            results = cursor.fetchall()
+            return results
+
+    def search_similar_all(self, query_text, top_k=5):
+        query_vector = self.get_embedding(query_text).tolist()  # ndarray をリストに変換
+        with self.conn.cursor() as cursor:
+            cursor.execute("""
+                SELECT id,carat, cut, color, clarity, depth, diamondprice.table, x, y, z
+                FROM diamondprice
+                order by id asc
+                limit 10000000
+            """, (query_vector, top_k))
+            results = cursor.fetchall()
+            return results            
+
+def calculate(x,y,z,c):
+    # データベース接続情報
+    DATABASE_URL = "postgresql://miyataken999:[email protected]/neondb?sslmode=require"
+    
+    # ProductDatabaseクラスのインスタンスを作成
+    db = ProductDatabase(DATABASE_URL)
+    
+    # データベースに接続
+    db.connect()
+    
+    try:
+        # pgvector拡張機能のインストールとカラムの追加
+        db.setup_vector_extension_and_column()
+        print("Vector extension installed and column added successfully.")
+        query_text="1"
+        results = db.search_similar_all(query_text)
+        print("Search results:")
+        DEBUG=0
+        if DEBUG==1:
+            for result in results:
+                print(result) 
+                id = result[0]
+                sample_text = str(result[1])+str(result[2])+str(result[3])+str(result[4])+str(result[5])+str(result[6])+str(result[7])+str(result[8])+str(result[9])
+                print(sample_text)
+                db.insert_vector(id, sample_text) 
+        #return
+        # サンプルデータの挿入
+        #sample_text = """"""
+        #sample_product_id = 1  # 実際の製品IDを使用
+        #db.insert_vector(sample_product_id, sample_text)
+        #db.insert_vector(2, sample_text)
+
+        #print(f"Vector inserted for product ID {sample_product_id}.")
+
+        
+        # ベクトル検索
+        query_text = "2.03Very GoodJSI262.058.08.068.125.05"
+
+        query_text = "2.03Very GoodJSI2"
+
+        #query_text = "2.03-Very Good-J-SI2-62.2-58.0-7.27-7.33-4.55"
+        results = db.search_similar_vectors(query_text)
+        res_all = ""
+        print("Search results:")
+        for result in results:
+            print(result)
+            res_all += result+""
+    
+    finally:
+        # 接続を閉じる
+        db.close()
+#router = APIRouter()
+

workspace/product_info.json

@@ -0,0 +1,12 @@
+{
+    "ブランド名": "",
+    "モデル名": "",
+    "型番や品番": "",
+    "購入店": "",
+    "購入時期": "",
+    "購入金額": "",
+    "付属品": "",
+    "コンディション": "",
+    "貴金属品位": "",
+    "貴金属重量": ""
+}