update vector search to use 'annoy'

content.py

@@ -1044,38 +1044,6 @@ def txt_clean_index():
 
 
 
-def search_embeddings():
-    model = SentenceTransformer('all-MiniLM-L6-v2')
-    save_embeds = pickle.load( open( "cache/embeddings.p", "rb" ) )
-    columns = list(zip(*save_embeds))
-    files = columns[0]
-    sentences = columns[1]
-    embeddings = columns[2]
-
-    print(files[:20])
-    print(sentences[:20])
-    print(embeddings[:20])
-
-    s = ''
-    while s != 'q':
-        s = input("search or 'q' to quit: ")
-        if s == 'q':
-            return
-        query_embedding = model.encode(s)
-
-        # Compute the cosine similarity between the query embedding and the sentence embeddings
-        cosine_scores = util.cos_sim(query_embedding, embeddings)
-
-        # Sort the sentences by their cosine similarity to the query sentence
-        results = sorted(zip(sentences, cosine_scores, files), key=lambda x: x[1], reverse=True)
-
-        print(results[:5])
-
-        # Print the top 5 results
-        for i, (sentence, score, file) in enumerate(results[:5]):
-            print(f'Top {i+1}: {file} - {sentence} - (Score: {score})')
-
-
 
 from whoosh import fields, columns
 from whoosh.index import create_in, open_dir
@@ -1207,27 +1175,77 @@ def create_search_index():
     writer.commit()
 
 
+
+from annoy import AnnoyIndex
+import random
+
+def test_embed():
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    sample = "What is this world coming to? What happens in the data and the research?"
+    embed = model.encode(sample)
+
+    print("\nSample sentence:", sample)
+    print("\nEmbedding:", embed)
+    print("\nEmbedding size:", len(embed))
+
+
 def create_embeddings():
     model = SentenceTransformer('all-MiniLM-L6-v2')
+    vecsize = 384   # sentence transformer embedding size
+    t = AnnoyIndex(vecsize, 'angular')
     files = os.listdir('cache/crawl')
-    output = []
-    save_embeds = []    #  ['file','sentence','embedding']
+    output = []    #  ['index', 'file','sentence']
+    index = 0
+    save_embeds = []  
     files.sort()
     for f in files:
+        print(f)
         m = re.match(r'https?..www\.gavilan\.edu\+(.*)\.\w\w\w\w?\.txt$',f)
         if m:
             lines = displayfile(f,1)
             embeddings = model.encode(lines)
 
-            print("\n-----", f)
+            print("\n-----", index, f)
 
-            #Print the embeddings
             for sentence, embedding in zip(lines, embeddings):
-                print("Sentence:", sentence)
-                #print("Embedding:", embedding)
+                if len(sentence.split(' ')) > 5:
+                    print(index, "Sentence:", sentence)
+                    print(embedding[:8])
+                    t.add_item(index, embedding)
+                    output.append( [index,f,sentence] )
+                    index += 1
+        if index > 500:
+            break
+    t.build(30) # 30 trees
+    t.save('cache/sentences.ann')
+    pickle.dump( output, open( "cache/embedding_index.p", "wb" ) )
 
-                save_embeds.append([f,sentence,embedding])
-    pickle.dump( save_embeds, open( "cache/embeddings.p", "wb" ) )
+
+
+
+def search_embeddings():
+    f = 384   # sentence transformer embedding size
+    n = 10    # how many results
+
+    u = AnnoyIndex(f, 'angular')
+    u.load('cache/sentences.ann') # super fast, will just mmap the file
+    print(u.get_n_items(), "items in index")
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    search_index = pickle.load( open( "cache/embedding_index.p", "rb" ) )
+    print(search_index)
+
+
+    s = ''
+    while s != 'q':
+        s = input("search or 'q' to quit: ")
+        if s == 'q':
+            return
+        query_embedding = model.encode(s)
+        results = u.get_nns_by_vector(query_embedding, n)
+
+        # Print the top 5 results
+        for i, r in enumerate(results):
+            print(f'Top {i+1}: {r}, {search_index[r]}')     #{file} - {sentence} - (Score: {score})')
 
 
 if __name__ == "__main__":
@@ -1248,6 +1266,7 @@ if __name__ == "__main__":
                13: ['do an index search', search_index],
                14: ['do a vector search', search_embeddings],
                15: ['test priority', test_priority], 
+               16: ['test embed', test_embed], 
               }
     
     if len(sys.argv) > 1 and re.search(r'^\d+',sys.argv[1]):