From 7d0ee16d46264c38d6be793304f7301dfc5e09cd Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Fri, 15 Dec 2023 18:12:02 +0100 Subject: [PATCH] Update summarization logic, --- level_4/api.py | 17 +- .../classifiers/classifier.py | 4 +- level_4/cognitive_architecture/config.py | 3 + .../database/postgres/models/docs.py | 3 +- .../database/vectordb/basevectordb.py | 64 +- .../database/vectordb/vectordb.py | 43 +- level_4/cognitive_architecture/utils.py | 135 ++ level_4/docker-compose.yml | 1 + level_4/main.py | 162 +- level_4/poetry.lock | 2046 ++++++++++------- level_4/pyproject.toml | 3 +- 11 files changed, 1507 insertions(+), 974 deletions(-) diff --git a/level_4/api.py b/level_4/api.py index 8fb5c918a..bb2e06d97 100644 --- a/level_4/api.py +++ b/level_4/api.py @@ -79,7 +79,17 @@ async def add_memory( async with session_scope(session=AsyncSessionLocal()) as session: from main import load_documents_to_vectorstore - output = await load_documents_to_vectorstore(session, decoded_payload['user_id'], loader_settings=decoded_payload['settings']) + if 'settings' in decoded_payload and decoded_payload['settings'] is not None: + settings_for_loader = decoded_payload['settings'] + else: + settings_for_loader = None + + if 'content' in decoded_payload and decoded_payload['content'] is not None: + content = decoded_payload['content'] + else: + content = None + + output = await load_documents_to_vectorstore(session, decoded_payload['user_id'], content=content, loader_settings=settings_for_loader) return JSONResponse(content={"response": output}, status_code=200) except Exception as e: @@ -107,11 +117,8 @@ async def user_query_to_graph(payload: Payload): async def document_to_graph_db(payload: Payload): try: decoded_payload = payload.payload - - # Execute the query - replace this with the actual execution method async with session_scope(session=AsyncSessionLocal()) as session: - # Assuming you have a method in Neo4jGraphDB to execute the query - result = await add_documents_to_graph_db(postgres_session =session, user_id = decoded_payload['user_id'], loader_settins =decoded_payload['settings']) + result = await add_documents_to_graph_db(session =session, user_id = decoded_payload['user_id'], loader_settings =decoded_payload['settings']) return result except Exception as e: diff --git a/level_4/cognitive_architecture/classifiers/classifier.py b/level_4/cognitive_architecture/classifiers/classifier.py index 580207c6e..bd337b7b0 100644 --- a/level_4/cognitive_architecture/classifiers/classifier.py +++ b/level_4/cognitive_architecture/classifiers/classifier.py @@ -23,9 +23,9 @@ from langchain.document_loaders import TextLoader from langchain.document_loaders import DirectoryLoader -async def classify_documents(query:str, document_id:str, loader_settings:dict): +async def classify_documents(query:str, document_id:str, content:str): - document_context = await _document_loader(query, loader_settings) + document_context = content logging.info("This is the document context", document_context) llm = ChatOpenAI(temperature=0, model=config.model) diff --git a/level_4/cognitive_architecture/config.py b/level_4/cognitive_architecture/config.py index 841ea9ad0..c2694f637 100644 --- a/level_4/cognitive_architecture/config.py +++ b/level_4/cognitive_architecture/config.py @@ -34,6 +34,9 @@ class Config: graph_database_url: str = os.getenv('GRAPH_DB_URL') graph_database_username: str = os.getenv('GRAPH_DB_USER') graph_database_password: str = os.getenv('GRAPH_DB_PW') + weaviate_url: str = os.getenv('WEAVIATE_URL') + weaviate_api_key: str = os.getenv('WEAVIATE_API_KEY') + # Client ID diff --git a/level_4/cognitive_architecture/database/postgres/models/docs.py b/level_4/cognitive_architecture/database/postgres/models/docs.py index 64b06b4f9..490c687b7 100644 --- a/level_4/cognitive_architecture/database/postgres/models/docs.py +++ b/level_4/cognitive_architecture/database/postgres/models/docs.py @@ -1,6 +1,6 @@ from datetime import datetime -from sqlalchemy import Column, String, DateTime, ForeignKey +from sqlalchemy import Column, String, DateTime, ForeignKey, Boolean from sqlalchemy.orm import relationship import os import sys @@ -11,6 +11,7 @@ class DocsModel(Base): id = Column(String, primary_key=True) operation_id = Column(String, ForeignKey('operations.id'), index=True) doc_name = Column(String, nullable=True) + graph_summary = Column(Boolean, nullable=True) created_at = Column(DateTime, default=datetime.utcnow) updated_at = Column(DateTime, onupdate=datetime.utcnow) diff --git a/level_4/cognitive_architecture/database/vectordb/basevectordb.py b/level_4/cognitive_architecture/database/vectordb/basevectordb.py index 230e78c18..8d6d73ddb 100644 --- a/level_4/cognitive_architecture/database/vectordb/basevectordb.py +++ b/level_4/cognitive_architecture/database/vectordb/basevectordb.py @@ -231,41 +231,41 @@ class BaseMemory: embeddings: Optional[str] = None, ): - from ast import literal_eval - class DynamicSchema(Schema): - pass - - default_version = 'current_timestamp' - version_in_params = params.get("version", default_version) - - # Check and update metadata version in DB. - schema_fields = params - - def create_field(field_type, **kwargs): - field_mapping = { - "Str": fields.Str, - "Int": fields.Int, - "Float": fields.Float, - "Bool": fields.Bool, - } - return field_mapping[field_type](**kwargs) - - # Dynamic Schema Creation - params['user_id'] = self.user_id - - - schema_instance = self.create_dynamic_schema(params) # Always creating Str field, adjust as needed - - logging.info(f"params : {params}") - - # Schema Validation - schema_instance = schema_instance - print("Schema fields: ", [field for field in schema_instance._declared_fields]) - loaded_params = schema_instance.load(params) + # from ast import literal_eval + # class DynamicSchema(Schema): + # pass + # + # default_version = 'current_timestamp' + # version_in_params = params.get("version", default_version) + # + # # Check and update metadata version in DB. + # schema_fields = params + # + # def create_field(field_type, **kwargs): + # field_mapping = { + # "Str": fields.Str, + # "Int": fields.Int, + # "Float": fields.Float, + # "Bool": fields.Bool, + # } + # return field_mapping[field_type](**kwargs) + # + # # Dynamic Schema Creation + # params['user_id'] = self.user_id + # + # + # schema_instance = self.create_dynamic_schema(params) # Always creating Str field, adjust as needed + # + # logging.info(f"params : {params}") + # + # # Schema Validation + # schema_instance = schema_instance + # print("Schema fields: ", [field for field in schema_instance._declared_fields]) + # loaded_params = schema_instance.load(params) return await self.vector_db.add_memories( observation=observation, loader_settings=loader_settings, - params=loaded_params, namespace=namespace, metadata_schema_class = schema_instance, embeddings=embeddings + params=params, namespace=namespace, metadata_schema_class = None, embeddings=embeddings ) # Add other db_type conditions if necessary diff --git a/level_4/cognitive_architecture/database/vectordb/vectordb.py b/level_4/cognitive_architecture/database/vectordb/vectordb.py index bccbfdc1d..54708346b 100644 --- a/level_4/cognitive_architecture/database/vectordb/vectordb.py +++ b/level_4/cognitive_architecture/database/vectordb/vectordb.py @@ -20,7 +20,10 @@ from langchain.schema import Document import weaviate load_dotenv() +from ...config import Config +config = Config() +config.load() LTM_MEMORY_ID_DEFAULT = "00000" ST_MEMORY_ID_DEFAULT = "0000" @@ -153,18 +156,26 @@ class WeaviateVectorDB(VectorDB): # Assuming _document_loader returns a list of documents documents = await _document_loader(observation, loader_settings) logging.info("here are the docs %s", str(documents)) + chunk_count = 0 for doc in documents[0]: - document_to_load = self._stuct(doc.page_content, params, metadata_schema_class) + chunk_count += 1 + params['chunk_order'] = chunk_count + # document_to_load = self._stuct(doc.page_content, params, metadata_schema_class) - logging.info("Loading document with provided loader settings %s", str(document_to_load)) + # logging.info("Loading document with provided loader settings %s", str(document_to_load)) retriever.add_documents([ - Document(metadata=document_to_load[0]['metadata'], page_content=document_to_load[0]['page_content'])]) + Document(metadata=params, page_content=doc.page_content)]) else: - document_to_load = self._stuct(observation, params, metadata_schema_class) + chunk_count = 0 + documents = await _document_loader(observation, loader_settings) + for doc in documents[0]: + chunk_count += 1 + params['chunk_order'] = chunk_count + # document_to_load = self._stuct(observation, params, metadata_schema_class) - logging.info("Loading document with defautl loader settings %s", str(document_to_load)) - retriever.add_documents([ - Document(metadata=document_to_load[0]['metadata'], page_content=document_to_load[0]['page_content'])]) + # logging.info("Loading document with defautl loader settings %s", str(document_to_load)) + retriever.add_documents([ + Document(metadata=params, page_content=doc)]) async def fetch_memories(self, observation: str, namespace: str = None, search_type: str = 'hybrid', **kwargs): """ @@ -185,7 +196,22 @@ class WeaviateVectorDB(VectorDB): client = self.init_weaviate(namespace =self.namespace) if search_type is None: search_type = 'hybrid' - logging.info("The search type is 2 %", search_type) + logging.info("The search type is s%", search_type) + + if search_type == 'summary': + from weaviate.classes import Filter + client = weaviate.connect_to_wcs( + cluster_url=config.weaviate_url, + auth_credentials=weaviate.AuthApiKey(config.weaviate_api_key) + ) + + summary_collection = client.collections.get(self.namespace) + response = summary_collection.query.fetch_objects( + filters=Filter("user_id").equal(self.user_id) & + Filter("chunk_order").less_than(25), + limit=15 + ) + return response if not namespace: namespace = self.namespace @@ -280,7 +306,6 @@ class WeaviateVectorDB(VectorDB): ) else: # Delete all objects - print("HERE IS THE USER ID", self.user_id) return client.batch.delete_objects( class_name=namespace, where={ diff --git a/level_4/cognitive_architecture/utils.py b/level_4/cognitive_architecture/utils.py index 7d97149b2..b2dc63536 100644 --- a/level_4/cognitive_architecture/utils.py +++ b/level_4/cognitive_architecture/utils.py @@ -4,6 +4,8 @@ import string import uuid from graphviz import Digraph +from sqlalchemy.orm import contains_eager + # from graph_database.graph import KnowledgeGraph @@ -125,4 +127,137 @@ def generate_letter_uuid(length=8): letters = string.ascii_uppercase # A-Z return "".join(random.choice(letters) for _ in range(length)) +from cognitive_architecture.database.postgres.models.operation import Operation +from cognitive_architecture.database.postgres.database_crud import session_scope, add_entity, update_entity, fetch_job_id +from cognitive_architecture.database.postgres.models.metadatas import MetaDatas +from cognitive_architecture.database.postgres.models.docs import DocsModel +from cognitive_architecture.database.postgres.models.memory import MemoryModel +from cognitive_architecture.database.postgres.models.user import User +from sqlalchemy.ext.asyncio import AsyncSession +from sqlalchemy.future import select +import logging +async def get_vectordb_namespace(session: AsyncSession, user_id: str): + try: + result = await session.execute( + select(MemoryModel.memory_name).where(MemoryModel.user_id == user_id).order_by(MemoryModel.created_at.desc()) + ) + namespace = [row[0] for row in result.fetchall()] + return namespace + except Exception as e: + logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") + return None + +async def get_vectordb_document_name(session: AsyncSession, user_id: str): + try: + result = await session.execute( + select(DocsModel.doc_name).where(DocsModel.user_id == user_id).order_by(DocsModel.created_at.desc()) + ) + doc_names = [row[0] for row in result.fetchall()] + return doc_names + except Exception as e: + logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") + return None + + +async def get_model_id_name(session: AsyncSession, id: str): + try: + result = await session.execute( + select(MemoryModel.memory_name).where(MemoryModel.id == id).order_by(MemoryModel.created_at.desc()) + ) + doc_names = [row[0] for row in result.fetchall()] + return doc_names + except Exception as e: + logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") + return None + + + +async def get_unsumarized_vector_db_namespace(session: AsyncSession, user_id: str): + """ + Asynchronously retrieves the latest memory names and document details for a given user. + + This function executes a database query to fetch memory names and document details + associated with operations performed by a specific user. It leverages explicit joins + with the 'docs' and 'memories' tables and applies eager loading to optimize performance. + + Parameters: + - session (AsyncSession): The database session for executing the query. + - user_id (str): The unique identifier of the user. + + Returns: + - Tuple[List[str], List[Tuple[str, str]]]: A tuple containing a list of memory names and + a list of tuples with document names and their corresponding IDs. + Returns None if an exception occurs. + + Raises: + - Exception: Propagates any exceptions that occur during query execution. + + Example Usage: + """ + try: + result = await session.execute( + select(Operation) + .join(Operation.docs) # Explicit join with docs table + .join(Operation.memories) # Explicit join with memories table + .options( + contains_eager(Operation.docs), # Informs ORM of the join for docs + contains_eager(Operation.memories) # Informs ORM of the join for memories + ) + .where( + (Operation.user_id == user_id) & # Filter by user_id + (Operation.docs.graph_summary == False) # Filter by user_id + ) + .order_by(Operation.created_at.desc()) # Order by creation date + ) + + operations = result.unique().scalars().all() + + # Extract memory names and document names and IDs + memory_names = [memory.memory_name for op in operations for memory in op.memories] + docs = [(doc.doc_name, doc.id) for op in operations for doc in op.docs] + + return memory_names, docs + + except Exception as e: + # Handle the exception as needed + print(f"An error occurred: {e}") + return None +async def get_memory_name_by_doc_id(session: AsyncSession, docs_id: str): + """ + Asynchronously retrieves memory names associated with a specific document ID. + + This function executes a database query to fetch memory names linked to a document + through operations. The query is filtered based on a given document ID and retrieves + only the memory names without loading the entire Operation entity. + + Parameters: + - session (AsyncSession): The database session for executing the query. + - docs_id (str): The unique identifier of the document. + + Returns: + - List[str]: A list of memory names associated with the given document ID. + Returns None if an exception occurs. + + Raises: + - Exception: Propagates any exceptions that occur during query execution. + """ + try: + result = await session.execute( + select(MemoryModel.memory_name) + .join(Operation, Operation.id == MemoryModel.operation_id) # Join with Operation + .join(DocsModel, DocsModel.operation_id == Operation.id) # Join with DocsModel + .where(DocsModel.id == docs_id) # Filtering based on the passed document ID + .distinct() # To avoid duplicate memory names + ) + + memory_names = [row[0] for row in result.fetchall()] + return memory_names + + except Exception as e: + # Handle the exception as needed + print(f"An error occurred: {e}") + return None + + + diff --git a/level_4/docker-compose.yml b/level_4/docker-compose.yml index d8fb92483..8e10b99eb 100644 --- a/level_4/docker-compose.yml +++ b/level_4/docker-compose.yml @@ -29,6 +29,7 @@ services: - 8000:8000 - 443:443 - 80:80 + - 50051:50051 depends_on: - postgres - neo4j diff --git a/level_4/main.py b/level_4/main.py index 8ed14a18b..655613bb1 100644 --- a/level_4/main.py +++ b/level_4/main.py @@ -35,132 +35,37 @@ from sqlalchemy.orm import selectinload, joinedload, contains_eager import logging from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.future import select +from cognitive_architecture.utils import get_document_names, generate_letter_uuid, get_memory_name_by_doc_id, get_unsumarized_vector_db_namespace, get_vectordb_namespace, get_vectordb_document_name -async def get_vectordb_namespace(session: AsyncSession, user_id: str): - try: - result = await session.execute( - select(MemoryModel.memory_name).where(MemoryModel.user_id == user_id).order_by(MemoryModel.created_at.desc()) - ) - namespace = [row[0] for row in result.fetchall()] - return namespace - except Exception as e: - logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") - return None - -async def get_vectordb_document_name(session: AsyncSession, user_id: str): - try: - result = await session.execute( - select(DocsModel.doc_name).where(DocsModel.user_id == user_id).order_by(DocsModel.created_at.desc()) - ) - doc_names = [row[0] for row in result.fetchall()] - return doc_names - except Exception as e: - logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") - return None +async def fetch_document_vectordb_namespace(session: AsyncSession, user_id: str, namespace_id:str): + memory = await Memory.create_memory(user_id, session, namespace=namespace_id, memory_label=namespace_id) -async def get_model_id_name(session: AsyncSession, id: str): - try: - result = await session.execute( - select(MemoryModel.memory_name).where(MemoryModel.id == id).order_by(MemoryModel.created_at.desc()) - ) - doc_names = [row[0] for row in result.fetchall()] - return doc_names - except Exception as e: - logging.error(f"An error occurred while retrieving the Vectordb_namespace: {str(e)}") - return None + # Managing memory attributes + existing_user = await Memory.check_existing_user(user_id, session) + print("here is the existing user", existing_user) + await memory.manage_memory_attributes(existing_user) + print("Namespace id is %s", namespace_id) + await memory.add_dynamic_memory_class(namespace_id.lower(), namespace_id) + dynamic_memory_class = getattr(memory, namespace_id.lower(), None) + methods_to_add = ["add_memories", "fetch_memories", "delete_memories"] -async def get_vectordb_data(session: AsyncSession, user_id: str): - """ - Asynchronously retrieves the latest memory names and document details for a given user. + if dynamic_memory_class is not None: + for method_name in methods_to_add: + await memory.add_method_to_class(dynamic_memory_class, method_name) + print(f"Memory method {method_name} has been added") + else: + print(f"No attribute named in memory.") - This function executes a database query to fetch memory names and document details - associated with operations performed by a specific user. It leverages explicit joins - with the 'docs' and 'memories' tables and applies eager loading to optimize performance. + print("Available memory classes:", await memory.list_memory_classes()) + result = await memory.dynamic_method_call(dynamic_memory_class, 'fetch_memories', + observation="placeholder", search_type="summary") - Parameters: - - session (AsyncSession): The database session for executing the query. - - user_id (str): The unique identifier of the user. + return result, namespace_id - Returns: - - Tuple[List[str], List[Tuple[str, str]]]: A tuple containing a list of memory names and - a list of tuples with document names and their corresponding IDs. - Returns None if an exception occurs. - - Raises: - - Exception: Propagates any exceptions that occur during query execution. - - Example Usage: - """ - try: - result = await session.execute( - select(Operation) - .join(Operation.docs) # Explicit join with docs table - .join(Operation.memories) # Explicit join with memories table - .options( - contains_eager(Operation.docs), # Informs ORM of the join for docs - contains_eager(Operation.memories) # Informs ORM of the join for memories - ) - .where( - (Operation.user_id == user_id) # Filter by user_id - # Optionally, you can add more filters here - ) - .order_by(Operation.created_at.desc()) # Order by creation date - ) - - operations = result.unique().scalars().all() - - # Extract memory names and document names and IDs - memory_names = [memory.memory_name for op in operations for memory in op.memories] - docs = [(doc.doc_name, doc.id) for op in operations for doc in op.docs] - - return memory_names, docs - - except Exception as e: - # Handle the exception as needed - print(f"An error occurred: {e}") - return None -async def get_memory_name_by_doc_id(session: AsyncSession, docs_id: str): - """ - Asynchronously retrieves memory names associated with a specific document ID. - - This function executes a database query to fetch memory names linked to a document - through operations. The query is filtered based on a given document ID and retrieves - only the memory names without loading the entire Operation entity. - - Parameters: - - session (AsyncSession): The database session for executing the query. - - docs_id (str): The unique identifier of the document. - - Returns: - - List[str]: A list of memory names associated with the given document ID. - Returns None if an exception occurs. - - Raises: - - Exception: Propagates any exceptions that occur during query execution. - """ - try: - result = await session.execute( - select(MemoryModel.memory_name) - .join(Operation, Operation.id == MemoryModel.operation_id) # Join with Operation - .join(DocsModel, DocsModel.operation_id == Operation.id) # Join with DocsModel - .where(DocsModel.id == docs_id) # Filtering based on the passed document ID - .distinct() # To avoid duplicate memory names - ) - - memory_names = [row[0] for row in result.fetchall()] - return memory_names - - except Exception as e: - # Handle the exception as needed - print(f"An error occurred: {e}") - return None - - - -async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, job_id:str=None, loader_settings:dict=None): +async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, content:str=None, job_id:str=None, loader_settings:dict=None): namespace_id = str(generate_letter_uuid()) + "_" + "SEMANTICMEMORY" namespace_class = namespace_id + "_class" @@ -184,7 +89,11 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, job ), ) memory = await Memory.create_memory(user_id, session, namespace=namespace_id, job_id=job_id, memory_label=namespace_id) - document_names = get_document_names(loader_settings.get("path", "None")) + + if content is not None: + document_names = [content[:30]] + if loader_settings is not None: + document_names = get_document_names(loader_settings.get("path", "None")) for doc in document_names: await add_entity( session, @@ -227,10 +136,10 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, job print("Available memory classes:", await memory.list_memory_classes()) result = await memory.dynamic_method_call(dynamic_memory_class, 'add_memories', - observation='some_observation', params=params, loader_settings=loader_settings) + observation=content, params=params, loader_settings=loader_settings) await update_entity(session, Operation, job_id, "SUCCESS") - return result + return result, namespace_id async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_input: str): @@ -264,16 +173,15 @@ async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_inpu -async def add_documents_to_graph_db(postgres_session: AsyncSession, user_id: str, loader_settings:dict=None, stupid_local_testing_flag=False): #clean this up Vasilije, don't be sloppy +async def add_documents_to_graph_db(session: AsyncSession, user_id: str= None, loader_settings:dict=None, stupid_local_testing_flag=False): #clean this up Vasilije, don't be sloppy """""" try: # await update_document_vectordb_namespace(postgres_session, user_id) - memory_names, docs = await get_vectordb_data(postgres_session, user_id) + memory_names, docs = await get_unsumarized_vector_db_namespace(session, user_id) logging.info("Memory names are", memory_names) logging.info("Docs are", docs) for doc, memory_name in zip(docs, memory_names): doc_name, doc_id = doc - logging.info("hereee %s", doc_name) if stupid_local_testing_flag: classification = [{ "DocumentCategory": "Literature", @@ -316,7 +224,12 @@ async def add_documents_to_graph_db(postgres_session: AsyncSession, user_id: str # select doc from the store neo4j_graph_db.update_document_node_with_namespace(user_id, vectordb_namespace=memory_name, document_id=doc_id) else: - classification = await classify_documents(doc_name, document_id =doc_id, loader_settings=loader_settings) + try: + classification_content = fetch_document_vectordb_namespace(session, user_id, memory_name) + except: + classification_content = "None" + + classification = await classify_documents(doc_name, document_id =doc_id, content=classification_content) logging.info("Classification is", str(classification)) neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username, @@ -327,6 +240,7 @@ async def add_documents_to_graph_db(postgres_session: AsyncSession, user_id: str # select doc from the store neo4j_graph_db.update_document_node_with_namespace(user_id, vectordb_namespace=memory_name, document_id=doc_id) + await update_entity(session, DocsModel, doc_id, True) except: pass diff --git a/level_4/poetry.lock b/level_4/poetry.lock index 4d001b4de..ab48d70bd 100644 --- a/level_4/poetry.lock +++ b/level_4/poetry.lock @@ -1,88 +1,99 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and 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