Merge branch 'Significant-Gravitas:master' into master

.env.template

@@ -42,7 +42,7 @@ FAST_LLM_MODEL=gpt-3.5-turbo
 ### LLM MODEL SETTINGS
 # FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
 # SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
-# When using --gpt3onlythis needs to be set to 4000.
+# When using --gpt3only this needs to be set to 4000.
 FAST_TOKEN_LIMIT=4000
 SMART_TOKEN_LIMIT=8000
 

autogpt/memory/__init__.py

@@ -22,7 +22,7 @@
     PineconeMemory = None
 
 try:
-    from memory.milvus import MilvusMemory
+    from autogpt.memory.milvus import MilvusMemory
 except ImportError:
     print("pymilvus not installed. Skipping import.")
     MilvusMemory = None
@@ -48,14 +48,14 @@ def get_memory(cfg, init=False):
             )
         else:
             memory = RedisMemory(cfg)
-    elif cfg.memory_backend == "no_memory":
-        memory = NoMemory(cfg)
     elif cfg.memory_backend == "milvus":
         if not MilvusMemory:
             print("Error: Milvus sdk is not installed."
                   "Please install pymilvus to use Milvus as memory backend.")
         else:
             memory = MilvusMemory(cfg)
+    elif cfg.memory_backend == "no_memory":
+        memory = NoMemory(cfg)
 
     if memory is None:
         memory = LocalCache(cfg)

scripts/memory/milvus.py → autogpt/memory/milvus.py

@@ -6,7 +6,7 @@
     Collection,
 )
 
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
 
 
 class MilvusMemory(MemoryProviderSingleton):
@@ -28,15 +28,16 @@ def __init__(self, cfg):
         ]
 
         # create collection if not exist and load it.
-        schema = CollectionSchema(fields, "auto-gpt memory storage")
-        self.collection = Collection(cfg.milvus_collection, schema)
+        self.milvus_collection = cfg.milvus_collection
+        self.schema = CollectionSchema(fields, "auto-gpt memory storage")
+        self.collection = Collection(self.milvus_collection, self.schema)
         # create index if not exist.
-        if not self.collection.has_index(index_name="embeddings"):
+        if not self.collection.has_index():
             self.collection.release()
             self.collection.create_index("embeddings", {
-                "index_type": "IVF_FLAT",
                 "metric_type": "IP",
-                "params": {"nlist": 128},
+                "index_type": "HNSW",
+                "params": {"M": 8, "efConstruction": 64},
             }, index_name="embeddings")
         self.collection.load()
 
@@ -65,6 +66,13 @@ def clear(self):
         """ Drop the index in memory.
         """
         self.collection.drop()
+        self.collection = Collection(self.milvus_collection, self.schema)
+        self.collection.create_index("embeddings", {
+            "metric_type": "IP",
+            "index_type": "HNSW",
+            "params": {"M": 8, "efConstruction": 64},
+        }, index_name="embeddings")
+        self.collection.load()
         return "Obliviated"
 
     def get_relevant(self, data, num_relevant=5):

tests/integration/milvus_memory_tests.py

@@ -0,0 +1,48 @@
+import random
+import string
+import unittest
+
+from autogpt.config import Config
+from autogpt.memory.milvus import MilvusMemory
+
+
+class TestMilvusMemory(unittest.TestCase):
+    def random_string(self, length):
+        return "".join(random.choice(string.ascii_letters) for _ in range(length))
+
+    def setUp(self):
+        cfg = Config()
+        cfg.milvus_addr = "localhost:19530"
+        self.memory = MilvusMemory(cfg)
+        self.memory.clear()
+
+        # Add example texts to the cache
+        self.example_texts = [
+            "The quick brown fox jumps over the lazy dog",
+            "I love machine learning and natural language processing",
+            "The cake is a lie, but the pie is always true",
+            "ChatGPT is an advanced AI model for conversation",
+        ]
+
+        for text in self.example_texts:
+            self.memory.add(text)
+
+        # Add some random strings to test noise
+        for _ in range(5):
+            self.memory.add(self.random_string(10))
+
+    def test_get_relevant(self):
+        query = "I'm interested in artificial intelligence and NLP"
+        k = 3
+        relevant_texts = self.memory.get_relevant(query, k)
+
+        print(f"Top {k} relevant texts for the query '{query}':")
+        for i, text in enumerate(relevant_texts, start=1):
+            print(f"{i}. {text}")
+
+        self.assertEqual(len(relevant_texts), k)
+        self.assertIn(self.example_texts[1], relevant_texts)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/milvus_memory_test.py

@@ -0,0 +1,64 @@
+import os
+import sys
+import unittest
+
+from autogpt.memory.milvus import MilvusMemory
+
+
+def MockConfig():
+    return type(
+        "MockConfig",
+        (object,),
+        {
+            "debug_mode": False,
+            "continuous_mode": False,
+            "speak_mode": False,
+            "milvus_collection": "autogpt",
+            "milvus_addr": "localhost:19530",
+
+        },
+    )
+
+
+class TestMilvusMemory(unittest.TestCase):
+    def setUp(self):
+        self.cfg = MockConfig()
+        self.memory = MilvusMemory(self.cfg)
+
+    def test_add(self):
+        text = "Sample text"
+        self.memory.clear()
+        self.memory.add(text)
+        result = self.memory.get(text)
+        self.assertEqual([text], result)
+
+    def test_clear(self):
+        self.memory.clear()
+        self.assertEqual(self.memory.collection.num_entities, 0)
+
+    def test_get(self):
+        text = "Sample text"
+        self.memory.clear()
+        self.memory.add(text)
+        result = self.memory.get(text)
+        self.assertEqual(result, [text])
+
+    def test_get_relevant(self):
+        text1 = "Sample text 1"
+        text2 = "Sample text 2"
+        self.memory.clear()
+        self.memory.add(text1)
+        self.memory.add(text2)
+        result = self.memory.get_relevant(text1, 1)
+        self.assertEqual(result, [text1])
+
+    def test_get_stats(self):
+        text = "Sample text"
+        self.memory.clear()
+        self.memory.add(text)
+        stats = self.memory.get_stats()
+        self.assertEqual(15, len(stats))
+
+
+if __name__ == "__main__":
+    unittest.main()