Docs: use LlmConfig instead of QueryConfig (#626)
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@@ -43,11 +43,11 @@ Dry Run is an option in the `add`, `query` and `chat` methods that allows the us
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- You can add config to your query method to stream responses like ChatGPT does. You would require a downstream handler to render the chunk in your desirable format. Supports both OpenAI model and OpenSourceApp. 📊
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- To use this, instantiate a `QueryConfig` or `ChatConfig` object with `stream=True`. Then pass it to the `.chat()` or `.query()` method. The following example iterates through the chunks and prints them as they appear.
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- To use this, instantiate a `LlmConfig` or `ChatConfig` object with `stream=True`. Then pass it to the `.chat()` or `.query()` method. The following example iterates through the chunks and prints them as they appear.
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```python
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app = App()
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query_config = QueryConfig(stream = True)
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query_config = LlmConfig(stream = True)
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resp = app.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config)
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for chunk in resp:
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@@ -63,21 +63,7 @@ class TestApp(unittest.TestCase):
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@patch("chromadb.api.models.Collection.Collection.add", MagicMock)
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def test_chat_with_where_in_params(self):
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"""
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This test checks the functionality of the 'chat' method in the App class.
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It simulates a scenario where the 'retrieve_from_database' method returns a context list based on
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a where filter and 'get_llm_model_answer' returns an expected answer string.
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The 'chat' method is expected to call 'retrieve_from_database' with the where filter and
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'get_llm_model_answer' methods appropriately and return the right answer.
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Key assumptions tested:
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- 'retrieve_from_database' method is called exactly once with arguments: "Test query" and an instance of
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QueryConfig.
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- 'get_llm_model_answer' is called exactly once. The specific arguments are not checked in this test.
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- 'chat' method returns the value it received from 'get_llm_model_answer'.
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The test isolates the 'chat' method behavior by mocking out 'retrieve_from_database' and
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'get_llm_model_answer' methods.
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Test where filter
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"""
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with patch.object(self.app, "retrieve_from_database") as mock_retrieve:
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mock_retrieve.return_value = ["Test context"]
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@@ -99,11 +85,11 @@ class TestApp(unittest.TestCase):
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a where filter and 'get_llm_model_answer' returns an expected answer string.
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The 'chat' method is expected to call 'retrieve_from_database' with the where filter specified
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in the QueryConfig and 'get_llm_model_answer' methods appropriately and return the right answer.
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in the LlmConfig and 'get_llm_model_answer' methods appropriately and return the right answer.
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Key assumptions tested:
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- 'retrieve_from_database' method is called exactly once with arguments: "Test query" and an instance of
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QueryConfig.
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LLmConfig.
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- 'get_llm_model_answer' is called exactly once. The specific arguments are not checked in this test.
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- 'chat' method returns the value it received from 'get_llm_model_answer'.
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@@ -114,8 +100,8 @@ class TestApp(unittest.TestCase):
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mock_answer.return_value = "Test answer"
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with patch.object(self.app.db, "query") as mock_database_query:
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mock_database_query.return_value = ["Test context"]
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queryConfig = BaseLlmConfig(where={"attribute": "value"})
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answer = self.app.chat("Test query", queryConfig)
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llm_config = BaseLlmConfig(where={"attribute": "value"})
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answer = self.app.chat("Test query", llm_config)
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self.assertEqual(answer, "Test answer")
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_args, kwargs = mock_database_query.call_args
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@@ -12,7 +12,7 @@ class TestGeneratePrompt(unittest.TestCase):
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def test_generate_prompt_with_template(self):
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"""
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Tests that the generate_prompt method correctly formats the prompt using
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a custom template provided in the QueryConfig instance.
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a custom template provided in the LlmConfig instance.
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This test sets up a scenario with an input query and a list of contexts,
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and a custom template, and then calls generate_prompt. It checks that the
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@@ -58,7 +58,7 @@ class TestGeneratePrompt(unittest.TestCase):
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def test_generate_prompt_with_history(self):
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"""
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Test the 'generate_prompt' method with QueryConfig containing a history attribute.
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Test the 'generate_prompt' method with LlmConfig containing a history attribute.
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"""
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config = BaseLlmConfig()
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config.template = Template("Context: $context | Query: $query | History: $history")
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@@ -24,7 +24,7 @@ class TestApp(unittest.TestCase):
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Key assumptions tested:
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- 'retrieve_from_database' method is called exactly once with arguments: "Test query" and an instance of
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QueryConfig.
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LlmConfig.
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- 'get_llm_model_answer' is called exactly once. The specific arguments are not checked in this test.
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- 'query' method returns the value it received from 'get_llm_model_answer'.
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@@ -94,7 +94,7 @@ class TestApp(unittest.TestCase):
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Key assumptions tested:
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- 'retrieve_from_database' method is called exactly once with arguments: "Test query" and an instance of
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QueryConfig.
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LlmConfig.
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- 'get_llm_model_answer' is called exactly once. The specific arguments are not checked in this test.
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- 'query' method returns the value it received from 'get_llm_model_answer'.
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@@ -125,7 +125,7 @@ class TestApp(unittest.TestCase):
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Key assumptions tested:
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- 'retrieve_from_database' method is called exactly once with arguments: "Test query" and an instance of
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QueryConfig.
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LlmConfig.
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- 'get_llm_model_answer' is called exactly once. The specific arguments are not checked in this test.
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- 'query' method returns the value it received from 'get_llm_model_answer'.
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@@ -137,8 +137,8 @@ class TestApp(unittest.TestCase):
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mock_answer.return_value = "Test answer"
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with patch.object(self.app.db, "query") as mock_database_query:
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mock_database_query.return_value = ["Test context"]
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queryConfig = BaseLlmConfig(where={"attribute": "value"})
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answer = self.app.query("Test query", queryConfig)
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llm_config = BaseLlmConfig(where={"attribute": "value"})
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answer = self.app.query("Test query", llm_config)
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self.assertEqual(answer, "Test answer")
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_args, kwargs = mock_database_query.call_args
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