feat: add vector database interface

Dockerfile

@@ -17,7 +17,6 @@ COPY pyproject.toml poetry.lock /app/
 
 # Install the dependencies
 RUN poetry config virtualenvs.create false && \
-    poetry lock --no-update && \
     poetry install --no-root --no-dev
 
 RUN apt-get update -q && \

cognitive_architecture/classifiers/classify_documents.py

@@ -1,26 +1,24 @@
 """ This module contains the classifiers for the documents. """
+
+import json
 import logging
 
 from langchain.prompts import ChatPromptTemplate
-import json
 from langchain.document_loaders import TextLoader
 from langchain.document_loaders import DirectoryLoader
 from langchain.chains import create_extraction_chain
 from langchain.chat_models import ChatOpenAI
 
 from ..config import Config
-from ..database.vectordb.loaders.loaders import _document_loader
 
 config = Config()
 config.load()
 OPENAI_API_KEY = config.openai_key
 
-
-
 async def classify_documents(query: str, document_id: str, content: str):
     """Classify the documents based on the query and content."""
     document_context = content
-    logging.info("This is the document context", document_context)
+    logging.info("This is the document context %s", document_context)
 
     llm = ChatOpenAI(temperature=0, model=config.model)
     prompt_classify = ChatPromptTemplate.from_template(

cognitive_architecture/classifiers/classify_summary.py

@@ -1,8 +1,9 @@
 """ This module contains the function to classify a summary of a document. """
+
+import json
 import logging
 
 from langchain.prompts import ChatPromptTemplate
-import json
 from langchain.chains import create_extraction_chain
 from langchain.chat_models import ChatOpenAI
 
@@ -12,11 +13,6 @@ config = Config()
 config.load()
 OPENAI_API_KEY = config.openai_key
 
-
-
-
-
-
 async def classify_summary(query, document_summaries):
     """Classify the documents based on the query and content."""
     llm = ChatOpenAI(temperature=0, model=config.model)

cognitive_architecture/classifiers/classify_user_input.py

@@ -1,16 +1,13 @@
 """ This module contains the classifiers for the documents. """
 
+import json
 import logging
 
 from langchain.prompts import ChatPromptTemplate
-import json
-
-
 from langchain.chains import create_extraction_chain
 from langchain.chat_models import ChatOpenAI
 
 from ..config import Config
-from ..database.vectordb.loaders.loaders import _document_loader
 
 config = Config()
 config.load()
@@ -20,7 +17,7 @@ async def classify_user_input(query, input_type):
     """ Classify the user input based on the query and input type."""
     llm = ChatOpenAI(temperature=0, model=config.model)
     prompt_classify = ChatPromptTemplate.from_template(
-        """You are a  classifier. 
+        """You are a classifier. 
         Determine with a True or False if the following input: {query}, 
         is relevant for the following memory category: {input_type}"""
     )

cognitive_architecture/classifiers/classify_user_query.py

@@ -1,19 +1,19 @@
 """ This module contains the function to classify the user query. """
-from langchain.prompts import ChatPromptTemplate
+
 import json
+
+from langchain.prompts import ChatPromptTemplate
 from langchain.chains import create_extraction_chain
 from langchain.chat_models import ChatOpenAI
 from langchain.document_loaders import TextLoader
 from langchain.document_loaders import DirectoryLoader
 
 from ..config import Config
-from ..database.vectordb.loaders.loaders import _document_loader
 
 config = Config()
 config.load()
 OPENAI_API_KEY = config.openai_key
 
-
 async def classify_user_query(query, context, document_types):
     """Classify the user query based on the context and document types."""
     llm = ChatOpenAI(temperature=0, model=config.model)

cognitive_architecture/infrastructure/databases/vector/qdrant/adapter.py

@@ -0,0 +1,13 @@
+from vector.vector_db_interface import VectorDBInterface
+from qdrant_client import AsyncQdrantClient
+
+class QDrantAdapter(VectorDBInterface):
+    def __init__(self, qdrant_url, qdrant_api_key):
+        self.qdrant_client = AsyncQdrantClient(qdrant_url, qdrant_api_key)
+  
+    async def create_collection(
+      self,
+      collection_name: str,
+      collection_config: object
+    ):
+        return await self.qdrant_client.create_collection(collection_name, collection_config)

cognitive_architecture/infrastructure/databases/vector/vector_db_interface.py

@@ -0,0 +1,68 @@
+from abc import abstractmethod
+from typing import Protocol
+
+class VectorDBInterface(Protocol):
+    """ Collections """
+    @abstractmethod
+    async def create_collection(
+        self,
+        collection_name: str,
+        collection_config: object
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def update_collection(
+        self,
+        collection_name: str,
+        collection_config: object
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def delete_collection(
+        self,
+        collection_name: str
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def create_vector_index(
+        self,
+        collection_name: str,
+        vector_index_config: object
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def create_data_index(
+        self,
+        collection_name: str,
+        vector_index_config: object
+    ): raise NotImplementedError
+
+    """ Data points """
+    @abstractmethod
+    async def create_data_point(
+        self,
+        collection_name: str,
+        payload: object
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def get_data_point(
+        self,
+        collection_name: str,
+        data_point_id: str
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def update_data_point(
+        self,
+        collection_name: str,
+        data_point_id: str,
+        payload: object
+    ): raise NotImplementedError
+
+    @abstractmethod
+    async def delete_data_point(
+        self,
+        collection_name: str,
+        data_point_id: str
+    ): raise NotImplementedError