moved skills database to the new system

lollms/app.py

@@ -263,7 +263,7 @@ class LollmsApplication(LoLLMsCom):
         
 
     def _generate_text(self, prompt):
-        max_tokens = self.config.ctx_size - self.model.get_nb_tokens(prompt)
+        max_tokens = min(self.config.ctx_size - self.model.get_nb_tokens(prompt),self.config.max_n_predict)
         generated_text = self.model.generate(prompt, max_tokens)
         return generated_text.strip()
 

lollms/databases/skills_database.py

@@ -1,11 +1,15 @@
 import sqlite3
-from safe_store.text_vectorizer import TextVectorizer, VectorizationMethod, VisualizationMethod
+from lollmsvectordb import VectorDatabase, BERTVectorizer
+from lollmsvectordb.lollms_tokenizers.tiktoken_tokenizer import TikTokenTokenizer
 import numpy as np
+from ascii_colors import ASCIIColors
 class SkillsLibrary:
         
-    def __init__(self, db_path):
+    def __init__(self, db_path, model_name: str = 'bert-base-nli-mean-tokens', chunk_size:int=512, overlap:int=0, n_neighbors:int=5):
         self.db_path =db_path
         self._initialize_db()
+        self.vectorizer = VectorDatabase("", BERTVectorizer(), TikTokenTokenizer(),chunk_size, overlap, n_neighbors)
+        ASCIIColors.green("Vecorizer ready")
        
 
     def _initialize_db(self):
@@ -121,37 +125,38 @@ class SkillsLibrary:
         return res
 
     def query_vector_db(self, query_, top_k=3, max_dist=1000):
-        vectorizer = TextVectorizer(VectorizationMethod.TFIDF_VECTORIZER)
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
         # Use direct string concatenation for the MATCH expression.
         # Ensure text is safely escaped to avoid SQL injection.
-        query = "SELECT id, title FROM skills_library"
+        query = "SELECT id, title, content FROM skills_library"
         cursor.execute(query)
         res = cursor.fetchall()
         cursor.close()
         conn.close()
         skills = []
+        skill_titles = []        
         if len(res)>0:
             for entry in res:
-                vectorizer.add_document(entry[0],entry[1])
-            vectorizer.index()
+                self.vectorizer.add_document(entry[0],"Title:"+entry[1]+"\n"+entry[2])
+            self.vectorizer.build_index()
             
-            skill_titles, sorted_similarities, document_ids = vectorizer.recover_text(query_, top_k)
-            for skill_title, sim, id in zip(skill_titles, sorted_similarities, document_ids):
-                if  np.linalg.norm(sim[1])<max_dist:
-                    conn = sqlite3.connect(self.db_path)
-                    cursor = conn.cursor()
+            chunks = self.vectorizer.search(query_, top_k)
+            for chunk in chunks:
+                if  chunk.distance<max_dist:
+                    skills.append(chunk.text)
+                    skill_titles.append(chunk.doc.title)
+                    # conn = sqlite3.connect(self.db_path)
+                    # cursor = conn.cursor()
                     # Use direct string concatenation for the MATCH expression.
                     # Ensure text is safely escaped to avoid SQL injection.
-                    query = "SELECT content FROM skills_library WHERE id = ?"
-                    cursor.execute(query, (id,))
-                    res = cursor.fetchall()
-                    skills.append(res[0])
-                    cursor.close()
-                    conn.close()
-        else:
-            skill_titles = []
+                    #query = "SELECT content FROM skills_library WHERE id = ?"
+                    #cursor.execute(query, (chunk.chunk_id,))
+                    #res = cursor.fetchall()
+                    #skills.append(res[0])
+                    #cursor.close()
+                    #conn.close()
+            
         return skill_titles, skills