Feature/refactor for added clarity

README.md

@@ -144,7 +144,7 @@ Examples of these classes are:
 Code example for creating an instance of 'SymbolCodeEmbedding':
 ```python
 import numpy as np
-from automata.core.symbol.base import SymbolCodeEmbedding
+from automata.core.symbol_embedding.base import SymbolCodeEmbedding
 from automata.core.symbol.parser import parse_symbol
 
 symbol_str = 'scip-python python automata 75482692a6fe30c72db516201a6f47d9fb4af065 `automata.core.agent.agent_enums`/ActionIndicator#'

automata/cli/scripts/run_code_embedding.py

@@ -6,13 +6,11 @@
 from automata.config.base import ConfigCategory
 from automata.core.base.database.vector import JSONEmbeddingVectorDatabase
 from automata.core.coding.py.module_loader import py_module_loader
-from automata.core.embedding.code_embedding import (
-    SymbolCodeEmbeddingBuilder,
-    SymbolCodeEmbeddingHandler,
-)
 from automata.core.llm.providers.openai import OpenAIEmbeddingProvider
+from automata.core.memory_store.symbol_code_embedding import SymbolCodeEmbeddingHandler
 from automata.core.symbol.graph import SymbolGraph
 from automata.core.symbol.symbol_utils import get_rankable_symbols
+from automata.core.symbol_embedding.embedding_builders import SymbolCodeEmbeddingBuilder
 from automata.core.utils import get_config_fpath
 
 logger = logging.getLogger(__name__)

automata/cli/scripts/run_doc_embedding_l2.py

@@ -10,24 +10,22 @@
     PyContextRetriever,
     PyContextRetrieverConfig,
 )
-from automata.core.embedding.code_embedding import (
-    SymbolCodeEmbeddingBuilder,
-    SymbolCodeEmbeddingHandler,
-)
-from automata.core.embedding.doc_embedding import (
-    SymbolDocEmbeddingBuilder,
-    SymbolDocEmbeddingHandler,
-)
-from automata.core.embedding.symbol_similarity import SymbolSimilarityCalculator
 from automata.core.llm.providers.openai import (
     OpenAIChatCompletionProvider,
     OpenAIEmbeddingProvider,
 )
+from automata.core.memory_store.symbol_code_embedding import SymbolCodeEmbeddingHandler
+from automata.core.memory_store.symbol_doc_embedding import SymbolDocEmbeddingHandler
 from automata.core.symbol.base import SymbolDescriptor
 from automata.core.symbol.graph import SymbolGraph
 from automata.core.symbol.search.rank import SymbolRankConfig
 from automata.core.symbol.search.symbol_search import SymbolSearch
 from automata.core.symbol.symbol_utils import get_rankable_symbols
+from automata.core.symbol_embedding.embedding_builders import (
+    SymbolCodeEmbeddingBuilder,
+    SymbolDocEmbeddingBuilder,
+)
+from automata.core.symbol_embedding.similarity import SymbolSimilarityCalculator
 from automata.core.utils import get_config_fpath
 
 logger = logging.getLogger(__name__)

automata/cli/scripts/run_doc_embedding_l3.py

@@ -10,24 +10,22 @@
     PyContextRetriever,
     PyContextRetrieverConfig,
 )
-from automata.core.embedding.code_embedding import (
-    SymbolCodeEmbeddingBuilder,
-    SymbolCodeEmbeddingHandler,
-)
-from automata.core.embedding.doc_embedding import (
-    SymbolDocEmbeddingBuilder,
-    SymbolDocEmbeddingHandler,
-)
-from automata.core.embedding.symbol_similarity import SymbolSimilarityCalculator
 from automata.core.llm.providers.openai import (
     OpenAIChatCompletionProvider,
     OpenAIEmbeddingProvider,
 )
+from automata.core.memory_store.symbol_code_embedding import SymbolCodeEmbeddingHandler
+from automata.core.memory_store.symbol_doc_embedding import SymbolDocEmbeddingHandler
 from automata.core.symbol.base import SymbolDescriptor
 from automata.core.symbol.graph import SymbolGraph
 from automata.core.symbol.search.rank import SymbolRankConfig
 from automata.core.symbol.search.symbol_search import SymbolSearch
 from automata.core.symbol.symbol_utils import get_rankable_symbols
+from automata.core.symbol_embedding.embedding_builders import (
+    SymbolCodeEmbeddingBuilder,
+    SymbolDocEmbeddingBuilder,
+)
+from automata.core.symbol_embedding.similarity import SymbolSimilarityCalculator
 from automata.core.utils import get_config_fpath
 
 logger = logging.getLogger(__name__)

automata/config/prompt/doc_generation.py

@@ -120,8 +120,8 @@
         │   └── vector.py
         ├── embedding
         │   ├── __init__.py
-        │   ├── code_embedding.py
-        │   ├── doc_embedding.py
+        │   ├── symbol_code_embedding.py
+        │   ├── symbol_doc_embedding.py
         │   ├── embedding_types.py
         │   └── symbol_similarity.py
         ├── symbol

automata/core/agent/providers.py

@@ -45,7 +45,7 @@ def __init__(self, instructions: str, config: AutomataOpenAIAgentConfig) -> None
         super().__init__(instructions)
         self.config = config
         self.iteration_count = 0
-        self.conversation = OpenAIConversation()
+        self.agent_conversations = OpenAIConversation()
         self.completed = False
         self._setup()
 
@@ -133,7 +133,7 @@ def run(self) -> str:
             except AgentStopIteration:
                 break
 
-        last_message = self.conversation.get_latest_message()
+        last_message = self.agent_conversations.get_latest_message()
         if self.iteration_count >= self.config.max_iterations:
             raise AgentMaxIterError("The agent exceeded the maximum number of iterations.")
         if not self.completed or not isinstance(last_message, OpenAIChatMessage):
@@ -148,7 +148,7 @@ def set_database_provider(self, provider: LLMConversationDatabaseProvider) -> No
         if self.database_provider:
             raise AgentDatabaseError("The database provider has already been set.")
         self.database_provider = provider
-        self.conversation.register_observer(provider)
+        self.agent_conversations.register_observer(provider)
 
     def _build_initial_messages(
         self, instruction_formatter: Dict[str, str]
@@ -217,21 +217,21 @@ def _setup(self) -> None:
             AgentError: If the agent fails to initialize.
         """
         logger.debug(f"Setting up agent with tools = {self.config.tools}")
-        self.conversation.add_message(
+        self.agent_conversations.add_message(
             OpenAIChatMessage(role="system", content=self.config.system_instruction)
         )
         for message in list(
             self._build_initial_messages({"user_input_instructions": self.instructions})
         ):
             logger.debug(f"Adding the following initial mesasge to the conversation {message}")
-            self.conversation.add_message(message)
+            self.agent_conversations.add_message(message)
             logging.debug(f"\n{('-' * 120)}")
 
         self.chat_provider = OpenAIChatCompletionProvider(
             model=self.config.model,
             temperature=self.config.temperature,
             stream=self.config.stream,
-            conversation=self.conversation,
+            conversation=self.agent_conversations,
             functions=self.functions,
         )
         self._initialized = True

automata/core/agent/tool/builder/context_oracle.py

@@ -7,8 +7,8 @@
 from automata.core.agent.tool.registry import AutomataOpenAIAgentToolBuilderRegistry
 from automata.core.base.agent import AgentToolBuilder, AgentToolProviders
 from automata.core.base.tool import Tool
-from automata.core.embedding.symbol_similarity import SymbolSimilarityCalculator
 from automata.core.llm.providers.openai import OpenAITool
+from automata.core.symbol_embedding.similarity import SymbolSimilarityCalculator
 
 logger = logging.getLogger(__name__)
 

automata/core/agent/tool/tool_utils.py

@@ -15,19 +15,17 @@
     PyContextRetriever,
     PyContextRetrieverConfig,
 )
-from automata.core.embedding.code_embedding import SymbolCodeEmbeddingHandler
-from automata.core.embedding.doc_embedding import (
-    SymbolDocEmbeddingBuilder,
-    SymbolDocEmbeddingHandler,
-)
-from automata.core.embedding.symbol_similarity import SymbolSimilarityCalculator
 from automata.core.llm.providers.openai import (
     OpenAIChatCompletionProvider,
     OpenAIEmbeddingProvider,
 )
+from automata.core.memory_store.symbol_code_embedding import SymbolCodeEmbeddingHandler
+from automata.core.memory_store.symbol_doc_embedding import SymbolDocEmbeddingHandler
 from automata.core.symbol.graph import SymbolGraph
 from automata.core.symbol.search.rank import SymbolRank, SymbolRankConfig
 from automata.core.symbol.search.symbol_search import SymbolSearch
+from automata.core.symbol_embedding.embedding_builders import SymbolDocEmbeddingBuilder
+from automata.core.symbol_embedding.similarity import SymbolSimilarityCalculator
 from automata.core.utils import get_config_fpath
 
 logger = logging.getLogger(__name__)

automata/core/base/database/vector.py

@@ -5,7 +5,7 @@
 
 import jsonpickle
 
-from automata.core.symbol.base import SymbolEmbedding
+from automata.core.symbol_embedding.base import SymbolEmbedding
 
 logger = logging.getLogger(__name__)
 

automata/core/coding/py/writer.py

@@ -17,7 +17,8 @@
     find_syntax_tree_node,
 )
 from automata.core.coding.py.reader import PyReader
-from automata.core.symbol.base import Symbol, SymbolDocEmbedding
+from automata.core.symbol.base import Symbol
+from automata.core.symbol_embedding.base import SymbolDocEmbedding
 
 logger = logging.getLogger(__name__)
 

automata/core/llm/providers/openai.py

@@ -218,7 +218,7 @@ def __init__(
         self.temperature = temperature
         self.stream = stream
         self.functions = functions
-        self.conversation = conversation
+        self.agent_conversations = conversation
         set_openai_api_key()
 
     def get_next_assistant_completion(self) -> OpenAIChatMessage:
@@ -229,15 +229,15 @@ def get_next_assistant_completion(self) -> OpenAIChatMessage:
         if functions:
             response = openai.ChatCompletion.create(
                 model=self.model,
-                messages=self.conversation.get_messages_for_next_completion(),
+                messages=self.agent_conversations.get_messages_for_next_completion(),
                 functions=functions,
                 function_call="auto",  # auto is default, but we'll be explicit
                 stream=self.stream,
             )
         else:
             response = openai.ChatCompletion.create(
                 model=self.model,
-                messages=self.conversation.get_messages_for_next_completion(),
+                messages=self.agent_conversations.get_messages_for_next_completion(),
                 stream=self.stream,
             )
         if self.stream:
@@ -249,11 +249,11 @@ def get_next_assistant_completion(self) -> OpenAIChatMessage:
         )
 
     def reset(self) -> None:
-        self.conversation.reset_conversation()
+        self.agent_conversations.reset_conversation()
 
     def standalone_call(self, prompt: str) -> str:
         """Return the completion message based on the provided prompt."""
-        if self.conversation.messages:
+        if self.agent_conversations.messages:
             raise ValueError(
                 "The conversation is not empty. Please call reset() before calling standalone_call()."
             )
@@ -266,11 +266,11 @@ def standalone_call(self, prompt: str) -> str:
 
     def add_message(self, message: LLMChatMessage) -> None:
         if not isinstance(message, OpenAIChatMessage):
-            self.conversation.add_message(
+            self.agent_conversations.add_message(
                 OpenAIChatMessage(role=message.role, content=message.content)
             )
         else:
-            self.conversation.add_message(message)
+            self.agent_conversations.add_message(message)
         logger.debug(
             f"Approximately {self.get_approximate_tokens_consumed()} tokens were after adding the latest message."
         )
@@ -361,7 +361,7 @@ def get_approximate_tokens_consumed(self) -> int:
                 "\n".join(
                     [
                         json.dumps(ele)
-                        for ele in self.conversation.get_messages_for_next_completion()
+                        for ele in self.agent_conversations.get_messages_for_next_completion()
                     ]
                 )
             )

automata/core/agent/conversation_database.py → automata/core/memory_store/agent_conversations.py

@@ -5,7 +5,7 @@
 from automata.core.llm.completion import LLMChatMessage, LLMConversationDatabaseProvider
 
 
-class AutomataAgentConversationDatabase(LLMConversationDatabaseProvider):
+class AgentConversationDatabase(LLMConversationDatabaseProvider):
     """A conversation database for an Automata agent."""
 
     def __init__(self, session_id: str, db_path: str = CONVERSATION_DB_PATH) -> None:

automata/core/embedding/code_embedding.py → automata/core/memory_store/symbol_code_embedding.py

@@ -1,30 +1,14 @@
 import logging
 
 from automata.core.base.database.vector import VectorDatabaseProvider
-from automata.core.llm.embedding import (
-    EmbeddingProvider,
-    SymbolEmbeddingBuilder,
-    SymbolEmbeddingHandler,
-)
-from automata.core.symbol.base import Symbol, SymbolCodeEmbedding
+from automata.core.llm.embedding import SymbolEmbeddingHandler
+from automata.core.symbol.base import Symbol
+from automata.core.symbol_embedding.base import SymbolCodeEmbedding
+from automata.core.symbol_embedding.embedding_builders import SymbolCodeEmbeddingBuilder
 
 logger = logging.getLogger(__name__)
 
 
-class SymbolCodeEmbeddingBuilder(SymbolEmbeddingBuilder):
-    """Builds `Symbol` source code embeddings."""
-
-    def __init__(
-        self,
-        embedding_provider: EmbeddingProvider,
-    ) -> None:
-        self.embedding_provider = embedding_provider
-
-    def build(self, source_code: str, symbol: Symbol) -> SymbolCodeEmbedding:
-        embedding_vector = self.embedding_provider.build_embedding_array(source_code)
-        return SymbolCodeEmbedding(symbol, source_code, embedding_vector)
-
-
 class SymbolCodeEmbeddingHandler(SymbolEmbeddingHandler):
     """Handles a database for `Symbol` source code embeddings."""
 

automata/core/memory_store/symbol_doc_embedding.py

@@ -0,0 +1,49 @@
+import logging
+
+from automata.core.base.database.vector import VectorDatabaseProvider
+from automata.core.llm.embedding import SymbolEmbeddingHandler
+from automata.core.symbol.base import Symbol
+from automata.core.symbol_embedding.base import SymbolDocEmbedding
+from automata.core.symbol_embedding.embedding_builders import SymbolDocEmbeddingBuilder
+
+logger = logging.getLogger(__name__)
+
+
+class SymbolDocEmbeddingHandler(SymbolEmbeddingHandler):
+    """A class to handle the embedding of symbols"""
+
+    def __init__(
+        self,
+        embedding_db: VectorDatabaseProvider,
+        embedding_builder: SymbolDocEmbeddingBuilder,
+    ) -> None:
+        self.embedding_db = embedding_db
+        self.embedding_builder = embedding_builder
+
+    def get_embedding(self, symbol: Symbol) -> SymbolDocEmbedding:
+        return self.embedding_db.get(symbol.dotpath)
+
+    def process_embedding(self, symbol: Symbol) -> None:
+        source_code = self.embedding_builder.fetch_embedding_context(symbol)
+
+        if not source_code:
+            raise ValueError(f"Symbol {symbol} has no source code")
+
+        if self.embedding_db.contains(symbol.dotpath):
+            self.update_existing_embedding(source_code, symbol)
+        else:
+            symbol_embedding = self.embedding_builder.build(source_code, symbol)
+            self.embedding_db.add(symbol_embedding)
+
+    def update_existing_embedding(self, source_code: str, symbol: Symbol) -> None:
+        existing_embedding = self.embedding_db.get(symbol.dotpath)
+        if existing_embedding.symbol != symbol or existing_embedding.source_code != source_code:
+            logger.debug(
+                f"Rolling forward the embedding for {existing_embedding.symbol} to {symbol}"
+            )
+            self.embedding_db.discard(symbol.dotpath)
+            existing_embedding.symbol = symbol
+            existing_embedding.source_code = source_code
+            self.embedding_db.add(existing_embedding)
+        else:
+            logger.debug("Passing for %s", symbol)