Correct the wrong search return value in the Redis vector store. (#452)

Signed-off-by: SimFG <[email protected]>

gptcache/manager/vector_data/redis_vectorstore.py

@@ -126,7 +126,7 @@ def search(self, data: np.ndarray, top_k: int = -1):
             .search(query, query_params=query_params)
             .docs
         )
-        return [(result.score, result.id) for result in results]
+        return [(float(result.score), int(result.id[len(self.doc_prefix):])) for result in results]
 
     def rebuild(self, ids=None) -> bool:
         pass

gptcache/utils/dependency_control.py

@@ -8,11 +8,13 @@ def prompt_install(package: str, warn: bool = False):  # pragma: no cover
     """
     Function used to prompt user to install a package.
     """
-    cmd = f"pip install {package}"
+    cmd = f"pip install -q {package}"
     try:
         if warn and input(f"Install {package}? Y/n: ") != "Y":
             raise ModuleNotFoundError(f"No module named {package}")
+        print(f"start to install package: {package}")
         subprocess.check_call(cmd, shell=True)
+        print(f"successfully installed package: {package}")
         gptcache_log.info("%s installed successfully!", package)
     except subprocess.CalledProcessError as e:
         raise PipInstallError(package) from e

tests/unit_tests/manager/test_redis.py

@@ -1,11 +1,21 @@
+import random
+from unittest.mock import patch
+
 import numpy as np
 
-from gptcache.manager import VectorBase
+from gptcache import Cache
+from gptcache.adapter import openai
+from gptcache.adapter.api import init_similar_cache
+from gptcache.embedding import Onnx
+from gptcache.manager import VectorBase, manager_factory
 from gptcache.manager.vector_data.base import VectorData
+from gptcache.processor.pre import last_content
+from gptcache.utils.response import get_message_from_openai_answer
 
 
 def test_redis_vector_store():
-    dim = 10
+    encoder = Onnx()
+    dim = encoder.dimension
     vector_base = VectorBase("redis", dimension=dim)
     vector_base.mul_add([VectorData(id=i, data=np.random.rand(dim)) for i in range(10)])
 
@@ -21,4 +31,59 @@ def test_redis_vector_store():
 
     search_res = vector_base.search(np.random.rand(dim), top_k=10)
     print(search_res)
-    assert len(search_res) == 5
+    assert len(search_res) == 5
+
+
+def test_redis_sqlite():
+    redis_cache = Cache()
+    encoder = Onnx()
+    redis_data_manager = manager_factory(
+        "sqlite,redis",
+        data_dir=str(random.random()),
+        vector_params={"dimension": encoder.dimension},
+    )
+    init_similar_cache(
+        cache_obj=redis_cache,
+        pre_func=last_content,
+        embedding=encoder,
+        data_manager=redis_data_manager,
+    )
+    question = "what's github"
+    expect_answer = "GitHub is an online platform used primarily for version control and coding collaborations."
+    with patch("openai.ChatCompletion.create") as mock_create:
+        datas = {
+            "choices": [
+                {
+                    "message": {"content": expect_answer, "role": "assistant"},
+                    "finish_reason": "stop",
+                    "index": 0,
+                }
+            ],
+            "created": 1677825464,
+            "id": "chatcmpl-6ptKyqKOGXZT6iQnqiXAH8adNLUzD",
+            "model": "gpt-3.5-turbo-0301",
+            "object": "chat.completion.chunk",
+        }
+        mock_create.return_value = datas
+
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": question},
+            ],
+            cache_obj=redis_cache,
+        )
+
+        assert get_message_from_openai_answer(response) == expect_answer, response
+
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "can you explain what GitHub is"},
+        ],
+        cache_obj=redis_cache,
+    )
+    answer_text = get_message_from_openai_answer(response)
+    assert answer_text == expect_answer, answer_text