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ran black, fixed some linting issues

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This commit is contained in:
Vasilije 2024-02-15 18:01:12 +01:00
parent 1f3ac1ec97
commit b3f29d3f2d
36 changed files with 1778 additions and 966 deletions
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183
api.py
View file

@ -39,12 +39,13 @@ app = FastAPI(debug=True)
#
# auth = JWTBearer(jwks)
@app.get("/")
async def root():
"""
Root endpoint that returns a welcome message.
"""
return { "message": "Hello, World, I am alive!" }
return {"message": "Hello, World, I am alive!"}
@app.get("/health")
@ -70,24 +71,29 @@ async def add_memory(
async with session_scope(session=AsyncSessionLocal()) as session:
from main import load_documents_to_vectorstore
if 'settings' in decoded_payload and decoded_payload['settings'] is not None:
settings_for_loader = 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']
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)
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:
return JSONResponse(
content={"response": {"error": str(e)}}, status_code=503
)
return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
@app.post("/add-architecture-public-memory", response_model=dict)
@ -100,38 +106,41 @@ async def add_memory(
decoded_payload = payload.payload
async with session_scope(session=AsyncSessionLocal()) as session:
from main import load_documents_to_vectorstore
if 'content' in decoded_payload and decoded_payload['content'] is not None:
content = decoded_payload['content']
if "content" in decoded_payload and decoded_payload["content"] is not None:
content = decoded_payload["content"]
else:
content = None
user_id = 'system_user'
loader_settings = {
"format": "PDF",
"source": "DEVICE",
"path": [".data"]
}
user_id = "system_user"
loader_settings = {"format": "PDF", "source": "DEVICE", "path": [".data"]}
output = await load_documents_to_vectorstore(session, user_id=user_id, content=content,
loader_settings=loader_settings)
output = await load_documents_to_vectorstore(
session,
user_id=user_id,
content=content,
loader_settings=loader_settings,
)
return JSONResponse(content={"response": output}, status_code=200)
except Exception as e:
return JSONResponse(
content={"response": {"error": str(e)}}, status_code=503
)
return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
@app.post("/user-query-to-graph")
async def user_query_to_graph(payload: Payload):
try:
from main import user_query_to_graph_db
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 user_query_to_graph_db(session=session, user_id=decoded_payload['user_id'],
query_input=decoded_payload['query'])
result = await user_query_to_graph_db(
session=session,
user_id=decoded_payload["user_id"],
query_input=decoded_payload["query"],
)
return result
@ -144,17 +153,23 @@ async def document_to_graph_db(payload: Payload):
logging.info("Adding documents to graph db")
try:
decoded_payload = payload.payload
if 'settings' in decoded_payload and decoded_payload['settings'] is not None:
settings_for_loader = 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 'memory_type' in decoded_payload and decoded_payload['memory_type'] is not None:
memory_type = decoded_payload['memory_type']
if (
"memory_type" in decoded_payload
and decoded_payload["memory_type"] is not None
):
memory_type = decoded_payload["memory_type"]
else:
memory_type = None
async with session_scope(session=AsyncSessionLocal()) as session:
result = await add_documents_to_graph_db(session=session, user_id=decoded_payload['user_id'],
document_memory_types=memory_type)
result = await add_documents_to_graph_db(
session=session,
user_id=decoded_payload["user_id"],
document_memory_types=memory_type,
)
return result
except Exception as e:
@ -166,10 +181,13 @@ async def cognitive_context_enrichment(payload: Payload):
try:
decoded_payload = payload.payload
async with session_scope(session=AsyncSessionLocal()) as session:
result = await user_context_enrichment(session, user_id=decoded_payload['user_id'],
query=decoded_payload['query'],
generative_response=decoded_payload['generative_response'],
memory_type=decoded_payload['memory_type'])
result = await user_context_enrichment(
session,
user_id=decoded_payload["user_id"],
query=decoded_payload["query"],
generative_response=decoded_payload["generative_response"],
memory_type=decoded_payload["memory_type"],
)
return JSONResponse(content={"response": result}, status_code=200)
except Exception as e:
@ -182,8 +200,11 @@ async def classify_user_query(payload: Payload):
decoded_payload = payload.payload
async with session_scope(session=AsyncSessionLocal()) as session:
from main import relevance_feedback
result = await relevance_feedback(query=decoded_payload['query'],
input_type=decoded_payload['knowledge_type'])
result = await relevance_feedback(
query=decoded_payload["query"],
input_type=decoded_payload["knowledge_type"],
)
return JSONResponse(content={"response": result}, status_code=200)
except Exception as e:
@ -197,9 +218,14 @@ async def user_query_classfier(payload: Payload):
# Execute the query - replace this with the actual execution method
async with session_scope(session=AsyncSessionLocal()) as session:
from cognitive_architecture.classifiers.classifier import classify_user_query
from cognitive_architecture.classifiers.classifier import (
classify_user_query,
)
# Assuming you have a method in Neo4jGraphDB to execute the query
result = await classify_user_query(session, decoded_payload['user_id'], decoded_payload['query'])
result = await classify_user_query(
session, decoded_payload["user_id"], decoded_payload["query"]
)
return JSONResponse(content={"response": result}, status_code=200)
except Exception as e:
@ -211,41 +237,43 @@ async def drop_db(payload: Payload):
try:
decoded_payload = payload.payload
if decoded_payload['operation'] == 'drop':
if os.environ.get('AWS_ENV') == 'dev':
host = os.environ.get('POSTGRES_HOST')
username = os.environ.get('POSTGRES_USER')
password = os.environ.get('POSTGRES_PASSWORD')
database_name = os.environ.get('POSTGRES_DB')
if decoded_payload["operation"] == "drop":
if os.environ.get("AWS_ENV") == "dev":
host = os.environ.get("POSTGRES_HOST")
username = os.environ.get("POSTGRES_USER")
password = os.environ.get("POSTGRES_PASSWORD")
database_name = os.environ.get("POSTGRES_DB")
else:
pass
from cognitive_architecture.database.create_database import drop_database, create_admin_engine
from cognitive_architecture.database.create_database import (
drop_database,
create_admin_engine,
)
engine = create_admin_engine(username, password, host, database_name)
connection = engine.raw_connection()
drop_database(connection, database_name)
return JSONResponse(content={"response": "DB dropped"}, status_code=200)
else:
if os.environ.get('AWS_ENV') == 'dev':
host = os.environ.get('POSTGRES_HOST')
username = os.environ.get('POSTGRES_USER')
password = os.environ.get('POSTGRES_PASSWORD')
database_name = os.environ.get('POSTGRES_DB')
if os.environ.get("AWS_ENV") == "dev":
host = os.environ.get("POSTGRES_HOST")
username = os.environ.get("POSTGRES_USER")
password = os.environ.get("POSTGRES_PASSWORD")
database_name = os.environ.get("POSTGRES_DB")
else:
pass
from cognitive_architecture.database.create_database import create_database, create_admin_engine
from cognitive_architecture.database.create_database import (
create_database,
create_admin_engine,
)
engine = create_admin_engine(username, password, host, database_name)
connection = engine.raw_connection()
create_database(connection, database_name)
return JSONResponse(content={"response": " DB drop"}, status_code=200)
except Exception as e:
return HTTPException(status_code=500, detail=str(e))
@ -255,18 +283,18 @@ async def create_public_memory(payload: Payload):
try:
decoded_payload = payload.payload
if 'user_id' in decoded_payload and decoded_payload['user_id'] is not None:
user_id = decoded_payload['user_id']
if "user_id" in decoded_payload and decoded_payload["user_id"] is not None:
user_id = decoded_payload["user_id"]
else:
user_id = None
if 'labels' in decoded_payload and decoded_payload['labels'] is not None:
labels = decoded_payload['labels']
if "labels" in decoded_payload and decoded_payload["labels"] is not None:
labels = decoded_payload["labels"]
else:
labels = None
if 'topic' in decoded_payload and decoded_payload['topic'] is not None:
topic = decoded_payload['topic']
if "topic" in decoded_payload and decoded_payload["topic"] is not None:
topic = decoded_payload["topic"]
else:
topic = None
@ -286,21 +314,26 @@ async def attach_user_to_public_memory(payload: Payload):
try:
decoded_payload = payload.payload
if 'topic' in decoded_payload and decoded_payload['topic'] is not None:
topic = decoded_payload['topic']
if "topic" in decoded_payload and decoded_payload["topic"] is not None:
topic = decoded_payload["topic"]
else:
topic = None
if 'labels' in decoded_payload and decoded_payload['labels'] is not None:
labels = decoded_payload['labels']
if "labels" in decoded_payload and decoded_payload["labels"] is not None:
labels = decoded_payload["labels"]
else:
labels = ['sr']
labels = ["sr"]
# Execute the query - replace this with the actual execution method
async with session_scope(session=AsyncSessionLocal()) as session:
from main import attach_user_to_memory, create_public_memory
# Assuming you have a method in Neo4jGraphDB to execute the query
await create_public_memory(user_id=decoded_payload['user_id'], topic=topic, labels=labels)
result = await attach_user_to_memory(user_id=decoded_payload['user_id'], topic=topic, labels=labels)
await create_public_memory(
user_id=decoded_payload["user_id"], topic=topic, labels=labels
)
result = await attach_user_to_memory(
user_id=decoded_payload["user_id"], topic=topic, labels=labels
)
return JSONResponse(content={"response": result}, status_code=200)
except Exception as e:
@ -312,17 +345,21 @@ async def unlink_user_from_public_memory(payload: Payload):
try:
decoded_payload = payload.payload
if 'topic' in decoded_payload and decoded_payload['topic'] is not None:
topic = decoded_payload['topic']
if "topic" in decoded_payload and decoded_payload["topic"] is not None:
topic = decoded_payload["topic"]
else:
topic = None
# Execute the query - replace this with the actual execution method
async with session_scope(session=AsyncSessionLocal()) as session:
from main import unlink_user_from_memory
# Assuming you have a method in Neo4jGraphDB to execute the query
result = await unlink_user_from_memory(user_id=decoded_payload['user_id'], topic=topic,
labels=decoded_payload['labels'])
result = await unlink_user_from_memory(
user_id=decoded_payload["user_id"],
topic=topic,
labels=decoded_payload["labels"],
)
return JSONResponse(content={"response": result}, status_code=200)
except Exception as e:

130
cognitive_architecture/classifiers/classifier.py
View file

@ -3,12 +3,7 @@ import logging
from langchain.prompts import ChatPromptTemplate
import json
#TO DO, ADD ALL CLASSIFIERS HERE
# TO DO, ADD ALL CLASSIFIERS HERE
from langchain.chains import create_extraction_chain
@ -16,6 +11,7 @@ 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
@ -23,8 +19,7 @@ from langchain.document_loaders import TextLoader
from langchain.document_loaders import DirectoryLoader
async def classify_documents(query:str, document_id:str, content:str):
async def classify_documents(query: str, document_id: str, content: str):
document_context = content
logging.info("This is the document context", document_context)
@ -32,7 +27,8 @@ async def classify_documents(query:str, document_id:str, content:str):
prompt_classify = ChatPromptTemplate.from_template(
"""You are a summarizer and classifier. Determine what book this is and where does it belong in the output : {query}, Id: {d_id} Document context is: {context}"""
)
json_structure = [{
json_structure = [
{
"name": "summarizer",
"description": "Summarization and classification",
"parameters": {
@ -40,45 +36,46 @@ async def classify_documents(query:str, document_id:str, content:str):
"properties": {
"DocumentCategory": {
"type": "string",
"description": "The classification of documents in groups such as legal, medical, etc."
"description": "The classification of documents in groups such as legal, medical, etc.",
},
"Title": {
"type": "string",
"description": "The title of the document"
"description": "The title of the document",
},
"Summary": {
"type": "string",
"description": "The summary of the document"
"description": "The summary of the document",
},
"d_id": {"type": "string", "description": "The id of the document"},
},
"required": ["DocumentCategory", "Title", "Summary", "d_id"],
},
"d_id": {
"type": "string",
"description": "The id of the document"
}
}, "required": ["DocumentCategory", "Title", "Summary","d_id"] }
}]
chain_filter = prompt_classify | llm.bind(function_call={"name": "summarizer"}, functions=json_structure)
classifier_output = await chain_filter.ainvoke({"query": query, "d_id": document_id, "context": str(document_context)})
arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
]
chain_filter = prompt_classify | llm.bind(
function_call={"name": "summarizer"}, functions=json_structure
)
classifier_output = await chain_filter.ainvoke(
{"query": query, "d_id": document_id, "context": str(document_context)}
)
arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
print("This is the arguments string", arguments_str)
arguments_dict = json.loads(arguments_str)
return arguments_dict
# classify retrievals according to type of retrieval
def classify_retrieval():
pass
async def classify_user_input(query, input_type):
llm = ChatOpenAI(temperature=0, model=config.model)
prompt_classify = ChatPromptTemplate.from_template(
"""You are a classifier. Determine with a True or False if the following input: {query}, is relevant for the following memory category: {input_type}"""
)
json_structure = [{
json_structure = [
{
"name": "classifier",
"description": "Classification",
"parameters": {
@ -86,29 +83,36 @@ async def classify_user_input(query, input_type):
"properties": {
"InputClassification": {
"type": "boolean",
"description": "The classification of the input"
"description": "The classification of the input",
}
}, "required": ["InputClassification"] }
}]
chain_filter = prompt_classify | llm.bind(function_call={"name": "classifier"}, functions=json_structure)
classifier_output = await chain_filter.ainvoke({"query": query, "input_type": input_type})
arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
},
"required": ["InputClassification"],
},
}
]
chain_filter = prompt_classify | llm.bind(
function_call={"name": "classifier"}, functions=json_structure
)
classifier_output = await chain_filter.ainvoke(
{"query": query, "input_type": input_type}
)
arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
logging.info("This is the arguments string %s", arguments_str)
arguments_dict = json.loads(arguments_str)
logging.info("Relevant summary is %s", arguments_dict.get('DocumentSummary', None))
InputClassification = arguments_dict.get('InputClassification', None)
logging.info("Relevant summary is %s", arguments_dict.get("DocumentSummary", None))
InputClassification = arguments_dict.get("InputClassification", None)
logging.info("This is the classification %s", InputClassification)
return InputClassification
# classify documents according to type of document
async def classify_call(query, document_summaries):
llm = ChatOpenAI(temperature=0, model=config.model)
prompt_classify = ChatPromptTemplate.from_template(
"""You are a classifier. Determine what document are relevant for the given query: {query}, Document summaries and ids:{document_summaries}"""
)
json_structure = [{
json_structure = [
{
"name": "classifier",
"description": "Classification",
"parameters": {
@ -116,37 +120,38 @@ async def classify_call(query, document_summaries):
"properties": {
"DocumentSummary": {
"type": "string",
"description": "The summary of the document and the topic it deals with."
"description": "The summary of the document and the topic it deals with.",
},
"d_id": {"type": "string", "description": "The id of the document"},
},
"required": ["DocumentSummary"],
},
"d_id": {
"type": "string",
"description": "The id of the document"
}
}, "required": ["DocumentSummary"] }
}]
chain_filter = prompt_classify | llm.bind(function_call={"name": "classifier"}, functions=json_structure)
classifier_output = await chain_filter.ainvoke({"query": query, "document_summaries": document_summaries})
arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
]
chain_filter = prompt_classify | llm.bind(
function_call={"name": "classifier"}, functions=json_structure
)
classifier_output = await chain_filter.ainvoke(
{"query": query, "document_summaries": document_summaries}
)
arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
print("This is the arguments string", arguments_str)
arguments_dict = json.loads(arguments_str)
logging.info("Relevant summary is %s", arguments_dict.get('DocumentSummary', None))
classfier_id = arguments_dict.get('d_id', None)
logging.info("Relevant summary is %s", arguments_dict.get("DocumentSummary", None))
classfier_id = arguments_dict.get("d_id", None)
print("This is the classifier id ", classfier_id)
return classfier_id
async def classify_user_query(query, context, document_types):
llm = ChatOpenAI(temperature=0, model=config.model)
prompt_classify = ChatPromptTemplate.from_template(
"""You are a classifier. You store user memories, thoughts and feelings. Determine if you need to use them to answer this query : {query}"""
)
json_structure = [{
json_structure = [
{
"name": "classifier",
"description": "Classification",
"parameters": {
@ -154,18 +159,23 @@ async def classify_user_query(query, context, document_types):
"properties": {
"UserQueryClassifier": {
"type": "bool",
"description": "The classification of documents in groups such as legal, medical, etc."
"description": "The classification of documents in groups such as legal, medical, etc.",
}
}, "required": ["UserQueryClassiffier"] }
}]
chain_filter = prompt_classify | llm.bind(function_call={"name": "classifier"}, functions=json_structure)
classifier_output = await chain_filter.ainvoke({"query": query, "context": context, "document_types": document_types})
arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
},
"required": ["UserQueryClassiffier"],
},
}
]
chain_filter = prompt_classify | llm.bind(
function_call={"name": "classifier"}, functions=json_structure
)
classifier_output = await chain_filter.ainvoke(
{"query": query, "context": context, "document_types": document_types}
)
arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
print("This is the arguments string", arguments_str)
arguments_dict = json.loads(arguments_str)
classfier_value = arguments_dict.get('UserQueryClassifier', None)
classfier_value = arguments_dict.get("UserQueryClassifier", None)
print("This is the classifier value", classfier_value)

77
cognitive_architecture/config.py
View file

@ -10,52 +10,65 @@ from dotenv import load_dotenv
base_dir = Path(__file__).resolve().parent.parent
# Load the .env file from the base directory
dotenv_path = base_dir / '.env'
dotenv_path = base_dir / ".env"
load_dotenv(dotenv_path=dotenv_path)
@dataclass
class Config:
# Paths and Directories
memgpt_dir: str = field(default_factory=lambda: os.getenv('COG_ARCH_DIR', 'cognitive_achitecture'))
config_path: str = field(default_factory=lambda: os.path.join(os.getenv('COG_ARCH_DIR', 'cognitive_achitecture'), 'config'))
memgpt_dir: str = field(
default_factory=lambda: os.getenv("COG_ARCH_DIR", "cognitive_achitecture")
)
config_path: str = field(
default_factory=lambda: os.path.join(
os.getenv("COG_ARCH_DIR", "cognitive_achitecture"), "config"
)
)
vectordb:str = 'lancedb'
vectordb: str = "lancedb"
# Model parameters
model: str = 'gpt-4-1106-preview'
model_endpoint: str = 'openai'
openai_key: Optional[str] = os.getenv('OPENAI_API_KEY')
model: str = "gpt-4-1106-preview"
model_endpoint: str = "openai"
openai_key: Optional[str] = os.getenv("OPENAI_API_KEY")
openai_temperature: float = float(os.getenv("OPENAI_TEMPERATURE", 0.0))
# Embedding parameters
embedding_model: str = 'openai'
embedding_model: str = "openai"
embedding_dim: int = 1536
embedding_chunk_size: int = 300
# Database parameters
if os.getenv('ENV') == 'prod' or os.getenv('ENV') == 'dev' or os.getenv('AWS_ENV') == 'dev' or os.getenv('AWS_ENV') == 'prd':
graph_database_url: str = os.getenv('GRAPH_DB_URL_PROD')
graph_database_username: str = os.getenv('GRAPH_DB_USER')
graph_database_password: str = os.getenv('GRAPH_DB_PW')
if (
os.getenv("ENV") == "prod"
or os.getenv("ENV") == "dev"
or os.getenv("AWS_ENV") == "dev"
or os.getenv("AWS_ENV") == "prd"
):
graph_database_url: str = os.getenv("GRAPH_DB_URL_PROD")
graph_database_username: str = os.getenv("GRAPH_DB_USER")
graph_database_password: str = os.getenv("GRAPH_DB_PW")
else:
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')
postgres_user: str = os.getenv('POSTGRES_USER')
postgres_password: str = os.getenv('POSTGRES_PASSWORD')
postgres_db: str = os.getenv('POSTGRES_DB')
if os.getenv('ENV') == 'prod' or os.getenv('ENV') == 'dev' or os.getenv('AWS_ENV') == 'dev' or os.getenv('AWS_ENV') == 'prd':
postgres_host: str = os.getenv('POSTGRES_PROD_HOST')
elif os.getenv('ENV') == 'docker':
postgres_host: str = os.getenv('POSTGRES_HOST_DOCKER')
elif os.getenv('ENV') == 'local':
postgres_host: str = os.getenv('POSTGRES_HOST_LOCAL')
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")
postgres_user: str = os.getenv("POSTGRES_USER")
postgres_password: str = os.getenv("POSTGRES_PASSWORD")
postgres_db: str = os.getenv("POSTGRES_DB")
if (
os.getenv("ENV") == "prod"
or os.getenv("ENV") == "dev"
or os.getenv("AWS_ENV") == "dev"
or os.getenv("AWS_ENV") == "prd"
):
postgres_host: str = os.getenv("POSTGRES_PROD_HOST")
elif os.getenv("ENV") == "docker":
postgres_host: str = os.getenv("POSTGRES_HOST_DOCKER")
elif os.getenv("ENV") == "local":
postgres_host: str = os.getenv("POSTGRES_HOST_LOCAL")
# Client ID
anon_clientid: Optional[str] = field(default_factory=lambda: uuid.uuid4().hex)
@ -84,12 +97,12 @@ class Config:
# Save the current settings to the config file
for attr, value in self.__dict__.items():
section, option = attr.split('_', 1)
section, option = attr.split("_", 1)
if not config.has_section(section):
config.add_section(section)
config.set(section, option, str(value))
with open(self.config_path, 'w') as configfile:
with open(self.config_path, "w") as configfile:
config.write(configfile)
def to_dict(self) -> Dict[str, Any]:

20
cognitive_architecture/database/create_database.py
View file

@ -22,12 +22,17 @@ from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError
from contextlib import contextmanager
from dotenv import load_dotenv
from relationaldb.database import Base # Assuming all models are imported within this module
from relationaldb.database import DatabaseConfig # Assuming DatabaseConfig is defined as before
from relationaldb.database import (
Base,
) # Assuming all models are imported within this module
from relationaldb.database import (
DatabaseConfig,
) # Assuming DatabaseConfig is defined as before
load_dotenv()
logger = logging.getLogger(__name__)
class DatabaseManager:
def __init__(self, config: DatabaseConfig):
self.config = config
@ -36,7 +41,7 @@ class DatabaseManager:
@contextmanager
def get_connection(self):
if self.db_type in ['sqlite', 'duckdb']:
if self.db_type in ["sqlite", "duckdb"]:
# For SQLite and DuckDB, the engine itself manages connections
yield self.engine
else:
@ -47,7 +52,7 @@ class DatabaseManager:
connection.close()
def database_exists(self, db_name):
if self.db_type in ['sqlite', 'duckdb']:
if self.db_type in ["sqlite", "duckdb"]:
# For SQLite and DuckDB, check if the database file exists
return os.path.exists(db_name)
else:
@ -57,14 +62,14 @@ class DatabaseManager:
return result is not None
def create_database(self, db_name):
if self.db_type not in ['sqlite', 'duckdb']:
if self.db_type not in ["sqlite", "duckdb"]:
# For databases like PostgreSQL, create the database explicitly
with self.get_connection() as connection:
connection.execution_options(isolation_level="AUTOCOMMIT")
connection.execute(f"CREATE DATABASE {db_name}")
def drop_database(self, db_name):
if self.db_type in ['sqlite', 'duckdb']:
if self.db_type in ["sqlite", "duckdb"]:
# For SQLite and DuckDB, simply remove the database file
os.remove(db_name)
else:
@ -75,9 +80,10 @@ class DatabaseManager:
def create_tables(self):
Base.metadata.create_all(bind=self.engine)
if __name__ == "__main__":
# Example usage with SQLite
config = DatabaseConfig(db_type='sqlite', db_name='mydatabase.db')
config = DatabaseConfig(db_type="sqlite", db_name="mydatabase.db")
# For DuckDB, you would set db_type to 'duckdb' and provide the database file name
# config = DatabaseConfig(db_type='duckdb', db_name='mydatabase.duckdb')

456
cognitive_architecture/database/graphdb/graph.py
View file

@ -1,4 +1,3 @@
import logging
import os
@ -26,13 +25,19 @@ from abc import ABC, abstractmethod
from pydantic import BaseModel, Field
from typing import List, Dict, Optional
from ...utils import format_dict, append_uuid_to_variable_names, create_edge_variable_mapping, \
create_node_variable_mapping, get_unsumarized_vector_db_namespace
from ...utils import (
format_dict,
append_uuid_to_variable_names,
create_edge_variable_mapping,
create_node_variable_mapping,
get_unsumarized_vector_db_namespace,
)
from ...llm.queries import generate_summary, generate_graph
import logging
from neo4j import AsyncGraphDatabase, Neo4jError
from contextlib import asynccontextmanager
from typing import Any, Dict, Optional, List
DEFAULT_PRESET = "promethai_chat"
preset_options = [DEFAULT_PRESET]
PROMETHAI_DIR = os.path.join(os.path.expanduser("~"), ".")
@ -41,7 +46,13 @@ load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
from ...config import Config
from ...shared.data_models import Node, Edge, KnowledgeGraph, GraphQLQuery, MemorySummary
from ...shared.data_models import (
Node,
Edge,
KnowledgeGraph,
GraphQLQuery,
MemorySummary,
)
config = Config()
config.load()
@ -53,8 +64,8 @@ OPENAI_API_KEY = config.openai_key
aclient = instructor.patch(OpenAI())
class AbstractGraphDB(ABC):
class AbstractGraphDB(ABC):
@abstractmethod
def query(self, query: str, params=None):
pass
@ -73,8 +84,12 @@ class AbstractGraphDB(ABC):
class Neo4jGraphDB(AbstractGraphDB):
def __init__(self, url: str, username: str, password: str, driver: Optional[Any] = None):
self.driver = driver or AsyncGraphDatabase.driver(url, auth=(username, password))
def __init__(
self, url: str, username: str, password: str, driver: Optional[Any] = None
):
self.driver = driver or AsyncGraphDatabase.driver(
url, auth=(username, password)
)
async def close(self) -> None:
await self.driver.close()
@ -84,7 +99,9 @@ class Neo4jGraphDB(AbstractGraphDB):
async with self.driver.session() as session:
yield session
async def query(self, query: str, params: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]:
async def query(
self, query: str, params: Optional[Dict[str, Any]] = None
) -> List[Dict[str, Any]]:
try:
async with self.get_session() as session:
result = await session.run(query, parameters=params)
@ -93,30 +110,28 @@ class Neo4jGraphDB(AbstractGraphDB):
logging.error(f"Neo4j query error: {e.message}")
raise
# class Neo4jGraphDB(AbstractGraphDB):
# def __init__(self, url, username, password):
# # self.graph = Neo4jGraph(url=url, username=username, password=password)
# from neo4j import GraphDatabase
# self.driver = GraphDatabase.driver(url, auth=(username, password))
# self.openai_key = config.openai_key
#
#
#
# def close(self):
# # Method to close the Neo4j driver instance
# self.driver.close()
#
# def query(self, query, params=None):
# try:
# with self.driver.session() as session:
# result = session.run(query, params).data()
# return result
# except Exception as e:
# logging.error(f"An error occurred while executing the query: {e}")
# raise e
#
# class Neo4jGraphDB(AbstractGraphDB):
# def __init__(self, url, username, password):
# # self.graph = Neo4jGraph(url=url, username=username, password=password)
# from neo4j import GraphDatabase
# self.driver = GraphDatabase.driver(url, auth=(username, password))
# self.openai_key = config.openai_key
#
#
#
# def close(self):
# # Method to close the Neo4j driver instance
# self.driver.close()
#
# def query(self, query, params=None):
# try:
# with self.driver.session() as session:
# result = session.run(query, params).data()
# return result
# except Exception as e:
# logging.error(f"An error occurred while executing the query: {e}")
# raise e
#
def create_base_cognitive_architecture(self, user_id: str):
# Create the user and memory components if they don't exist
@ -131,16 +146,22 @@ class Neo4jGraphDB(AbstractGraphDB):
"""
return user_memory_cypher
async def retrieve_memory(self, user_id: str, memory_type: str, timestamp: float = None, summarized: bool = None):
if memory_type == 'SemanticMemory':
relationship = 'SEMANTIC_MEMORY'
memory_rel = 'HAS_KNOWLEDGE'
elif memory_type == 'EpisodicMemory':
relationship = 'EPISODIC_MEMORY'
memory_rel = 'HAS_EVENT'
elif memory_type == 'Buffer':
relationship = 'BUFFER'
memory_rel = 'CURRENTLY_HOLDING'
async def retrieve_memory(
self,
user_id: str,
memory_type: str,
timestamp: float = None,
summarized: bool = None,
):
if memory_type == "SemanticMemory":
relationship = "SEMANTIC_MEMORY"
memory_rel = "HAS_KNOWLEDGE"
elif memory_type == "EpisodicMemory":
relationship = "EPISODIC_MEMORY"
memory_rel = "HAS_EVENT"
elif memory_type == "Buffer":
relationship = "BUFFER"
memory_rel = "CURRENTLY_HOLDING"
if timestamp is not None and summarized is not None:
query = f"""
MATCH (user:User {{userId: '{user_id}' }})-[:HAS_{relationship}]->(memory:{memory_type})
@ -172,79 +193,100 @@ class Neo4jGraphDB(AbstractGraphDB):
output = self.query(query, params={"user_id": user_id})
print("Here is the output", output)
reduced_graph = await generate_summary(input = output)
reduced_graph = await generate_summary(input=output)
return reduced_graph
def cypher_statement_correcting(self, input: str) ->str:
def cypher_statement_correcting(self, input: str) -> str:
return aclient.chat.completions.create(
model=config.model,
messages=[
{
"role": "user",
"content": f"""Check the cypher query for syntax issues, and fix any if found and return it as is: {input}. """,
},
{"role": "system", "content": """You are a top-tier algorithm
designed for checking cypher queries for neo4j graph databases. You have to return input provided to you as is"""}
{
"role": "system",
"content": """You are a top-tier algorithm
designed for checking cypher queries for neo4j graph databases. You have to return input provided to you as is""",
},
],
response_model=GraphQLQuery,
)
def generate_create_statements_for_nodes_with_uuid(self, nodes, unique_mapping, base_node_mapping):
def generate_create_statements_for_nodes_with_uuid(
self, nodes, unique_mapping, base_node_mapping
):
create_statements = []
for node in nodes:
original_variable_name = base_node_mapping[node['id']]
original_variable_name = base_node_mapping[node["id"]]
unique_variable_name = unique_mapping[original_variable_name]
node_label = node['category'].capitalize()
properties = {k: v for k, v in node.items() if k not in ['id', 'category']}
node_label = node["category"].capitalize()
properties = {k: v for k, v in node.items() if k not in ["id", "category"]}
try:
properties = format_dict(properties)
except:
pass
create_statements.append(f"CREATE ({unique_variable_name}:{node_label} {properties})")
create_statements.append(
f"CREATE ({unique_variable_name}:{node_label} {properties})"
)
return create_statements
# Update the function to generate Cypher CREATE statements for edges with unique variable names
def generate_create_statements_for_edges_with_uuid(self, user_id, edges, unique_mapping, base_node_mapping):
def generate_create_statements_for_edges_with_uuid(
self, user_id, edges, unique_mapping, base_node_mapping
):
create_statements = []
with_statement = f"WITH {', '.join(unique_mapping.values())}, user , semantic, episodic, buffer"
create_statements.append(with_statement)
for edge in edges:
# print("HERE IS THE EDGE", edge)
source_variable = unique_mapping[base_node_mapping[edge['source']]]
target_variable = unique_mapping[base_node_mapping[edge['target']]]
relationship = edge['description'].replace(" ", "_").upper()
create_statements.append(f"CREATE ({source_variable})-[:{relationship}]->({target_variable})")
source_variable = unique_mapping[base_node_mapping[edge["source"]]]
target_variable = unique_mapping[base_node_mapping[edge["target"]]]
relationship = edge["description"].replace(" ", "_").upper()
create_statements.append(
f"CREATE ({source_variable})-[:{relationship}]->({target_variable})"
)
return create_statements
def generate_memory_type_relationships_with_uuid_and_time_context(self, user_id, nodes, unique_mapping, base_node_mapping):
def generate_memory_type_relationships_with_uuid_and_time_context(
self, user_id, nodes, unique_mapping, base_node_mapping
):
create_statements = []
with_statement = f"WITH {', '.join(unique_mapping.values())}, user, semantic, episodic, buffer"
create_statements.append(with_statement)
# Loop through each node and create relationships based on memory_type
for node in nodes:
original_variable_name = base_node_mapping[node['id']]
original_variable_name = base_node_mapping[node["id"]]
unique_variable_name = unique_mapping[original_variable_name]
if node['memory_type'] == 'semantic':
create_statements.append(f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_variable_name})")
elif node['memory_type'] == 'episodic':
create_statements.append(f"CREATE (episodic)-[:HAS_EVENT]->({unique_variable_name})")
if node['category'] == 'time':
create_statements.append(f"CREATE (buffer)-[:HAS_TIME_CONTEXT]->({unique_variable_name})")
if node["memory_type"] == "semantic":
create_statements.append(
f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_variable_name})"
)
elif node["memory_type"] == "episodic":
create_statements.append(
f"CREATE (episodic)-[:HAS_EVENT]->({unique_variable_name})"
)
if node["category"] == "time":
create_statements.append(
f"CREATE (buffer)-[:HAS_TIME_CONTEXT]->({unique_variable_name})"
)
# Assuming buffer holds all actions and times
# if node['category'] in ['action', 'time']:
create_statements.append(f"CREATE (buffer)-[:CURRENTLY_HOLDING]->({unique_variable_name})")
create_statements.append(
f"CREATE (buffer)-[:CURRENTLY_HOLDING]->({unique_variable_name})"
)
return create_statements
async def generate_cypher_query_for_user_prompt_decomposition(self, user_id:str, query:str):
async def generate_cypher_query_for_user_prompt_decomposition(
self, user_id: str, query: str
):
graph: KnowledgeGraph = generate_graph(query)
import time
for node in graph.nodes:
node.created_at = time.time()
node.summarized = False
@ -254,19 +296,41 @@ class Neo4jGraphDB(AbstractGraphDB):
edge.summarized = False
graph_dic = graph.dict()
node_variable_mapping = create_node_variable_mapping(graph_dic['nodes'])
edge_variable_mapping = create_edge_variable_mapping(graph_dic['edges'])
node_variable_mapping = create_node_variable_mapping(graph_dic["nodes"])
edge_variable_mapping = create_edge_variable_mapping(graph_dic["edges"])
# Create unique variable names for each node
unique_node_variable_mapping = append_uuid_to_variable_names(node_variable_mapping)
unique_edge_variable_mapping = append_uuid_to_variable_names(edge_variable_mapping)
create_nodes_statements = self.generate_create_statements_for_nodes_with_uuid(graph_dic['nodes'], unique_node_variable_mapping, node_variable_mapping)
create_edges_statements =self.generate_create_statements_for_edges_with_uuid(user_id, graph_dic['edges'], unique_node_variable_mapping, node_variable_mapping)
unique_node_variable_mapping = append_uuid_to_variable_names(
node_variable_mapping
)
unique_edge_variable_mapping = append_uuid_to_variable_names(
edge_variable_mapping
)
create_nodes_statements = self.generate_create_statements_for_nodes_with_uuid(
graph_dic["nodes"], unique_node_variable_mapping, node_variable_mapping
)
create_edges_statements = self.generate_create_statements_for_edges_with_uuid(
user_id,
graph_dic["edges"],
unique_node_variable_mapping,
node_variable_mapping,
)
memory_type_statements_with_uuid_and_time_context = self.generate_memory_type_relationships_with_uuid_and_time_context(user_id,
graph_dic['nodes'], unique_node_variable_mapping, node_variable_mapping)
memory_type_statements_with_uuid_and_time_context = (
self.generate_memory_type_relationships_with_uuid_and_time_context(
user_id,
graph_dic["nodes"],
unique_node_variable_mapping,
node_variable_mapping,
)
)
# # Combine all statements
cypher_statements = [self.create_base_cognitive_architecture(user_id)] + create_nodes_statements + create_edges_statements + memory_type_statements_with_uuid_and_time_context
cypher_statements = (
[self.create_base_cognitive_architecture(user_id)]
+ create_nodes_statements
+ create_edges_statements
+ memory_type_statements_with_uuid_and_time_context
)
cypher_statements_joined = "\n".join(cypher_statements)
logging.info("User Cypher Query raw: %s", cypher_statements_joined)
# corrected_cypher_statements = self.cypher_statement_correcting(input = cypher_statements_joined)
@ -274,15 +338,15 @@ class Neo4jGraphDB(AbstractGraphDB):
# return corrected_cypher_statements.query
return cypher_statements_joined
def update_user_query_for_user_prompt_decomposition(self, user_id, user_query):
pass
def delete_all_user_memories(self, user_id):
try:
# Check if the user exists
user_exists = self.query(f"MATCH (user:User {{userId: '{user_id}'}}) RETURN user")
user_exists = self.query(
f"MATCH (user:User {{userId: '{user_id}'}}) RETURN user"
)
if not user_exists:
return f"No user found with ID: {user_id}"
@ -304,12 +368,14 @@ class Neo4jGraphDB(AbstractGraphDB):
def delete_specific_memory_type(self, user_id, memory_type):
try:
# Check if the user exists
user_exists = self.query(f"MATCH (user:User {{userId: '{user_id}'}}) RETURN user")
user_exists = self.query(
f"MATCH (user:User {{userId: '{user_id}'}}) RETURN user"
)
if not user_exists:
return f"No user found with ID: {user_id}"
# Validate memory type
if memory_type not in ['SemanticMemory', 'EpisodicMemory', 'Buffer']:
if memory_type not in ["SemanticMemory", "EpisodicMemory", "Buffer"]:
return "Invalid memory type. Choose from 'SemanticMemory', 'EpisodicMemory', or 'Buffer'."
# Delete specific memory type nodes and relationships for the given user
@ -322,7 +388,9 @@ class Neo4jGraphDB(AbstractGraphDB):
except Exception as e:
return f"An error occurred: {str(e)}"
def retrieve_semantic_memory(self, user_id: str, timestamp: float = None, summarized: bool = None):
def retrieve_semantic_memory(
self, user_id: str, timestamp: float = None, summarized: bool = None
):
if timestamp is not None and summarized is not None:
query = f"""
MATCH (user:User {{userId: '{user_id}' }})-[:HAS_SEMANTIC_MEMORY]->(semantic:SemanticMemory)
@ -352,7 +420,9 @@ class Neo4jGraphDB(AbstractGraphDB):
"""
return self.query(query, params={"user_id": user_id})
def retrieve_episodic_memory(self, user_id: str, timestamp: float = None, summarized: bool = None):
def retrieve_episodic_memory(
self, user_id: str, timestamp: float = None, summarized: bool = None
):
if timestamp is not None and summarized is not None:
query = f"""
MATCH (user:User {{userId: '{user_id}' }})-[:HAS_EPISODIC_MEMORY]->(episodic:EpisodicMemory)
@ -382,8 +452,9 @@ class Neo4jGraphDB(AbstractGraphDB):
"""
return self.query(query, params={"user_id": user_id})
def retrieve_buffer_memory(self, user_id: str, timestamp: float = None, summarized: bool = None):
def retrieve_buffer_memory(
self, user_id: str, timestamp: float = None, summarized: bool = None
):
if timestamp is not None and summarized is not None:
query = f"""
MATCH (user:User {{userId: '{user_id}' }})-[:HAS_BUFFER]->(buffer:Buffer)
@ -413,8 +484,6 @@ class Neo4jGraphDB(AbstractGraphDB):
"""
return self.query(query, params={"user_id": user_id})
def retrieve_public_memory(self, user_id: str):
query = """
MATCH (user:User {userId: $user_id})-[:HAS_PUBLIC_MEMORY]->(public:PublicMemory)
@ -422,23 +491,33 @@ class Neo4jGraphDB(AbstractGraphDB):
RETURN document
"""
return self.query(query, params={"user_id": user_id})
def generate_graph_semantic_memory_document_summary(self, document_summary : str, unique_graphdb_mapping_values: dict, document_namespace: str):
""" This function takes a document and generates a document summary in Semantic Memory"""
def generate_graph_semantic_memory_document_summary(
self,
document_summary: str,
unique_graphdb_mapping_values: dict,
document_namespace: str,
):
"""This function takes a document and generates a document summary in Semantic Memory"""
create_statements = []
with_statement = f"WITH {', '.join(unique_graphdb_mapping_values.values())}, user, semantic, episodic, buffer"
create_statements.append(with_statement)
# Loop through each node and create relationships based on memory_type
create_statements.append(f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_graphdb_mapping_values})")
create_statements.append(
f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_graphdb_mapping_values})"
)
return create_statements
def generate_document_summary(self, document_summary : str, unique_graphdb_mapping_values: dict, document_namespace: str):
""" This function takes a document and generates a document summary in Semantic Memory"""
def generate_document_summary(
self,
document_summary: str,
unique_graphdb_mapping_values: dict,
document_namespace: str,
):
"""This function takes a document and generates a document summary in Semantic Memory"""
# fetch namespace from postgres db
# fetch 1st and last page from vector store
@ -450,12 +529,15 @@ class Neo4jGraphDB(AbstractGraphDB):
# Loop through each node and create relationships based on memory_type
create_statements.append(f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_graphdb_mapping_values})")
create_statements.append(
f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_graphdb_mapping_values})"
)
return create_statements
async def get_memory_linked_document_summaries(self, user_id: str, memory_type: str = "PublicMemory"):
async def get_memory_linked_document_summaries(
self, user_id: str, memory_type: str = "PublicMemory"
):
"""
Retrieve a list of summaries for all documents associated with a given memory type for a user.
@ -474,23 +556,30 @@ class Neo4jGraphDB(AbstractGraphDB):
elif memory_type == "SemanticMemory":
relationship = "HAS_SEMANTIC_MEMORY"
try:
query = f'''
query = f"""
MATCH (user:User {{userId: '{user_id}'}})-[:{relationship}]->(memory:{memory_type})-[:HAS_DOCUMENT]->(document:Document)
RETURN document.d_id AS d_id, document.summary AS summary
'''
"""
logging.info(f"Generated Cypher query: {query}")
result = self.query(query)
logging.info(f"Result: {result}")
return [{"d_id": record.get("d_id", None), "summary": record.get("summary", "No summary available")} for
record in result]
return [
{
"d_id": record.get("d_id", None),
"summary": record.get("summary", "No summary available"),
}
for record in result
]
except Exception as e:
logging.error(f"An error occurred while retrieving document summary: {str(e)}")
logging.error(
f"An error occurred while retrieving document summary: {str(e)}"
)
return None
async def get_memory_linked_document_ids(self, user_id: str, summary_id: str, memory_type: str = "PublicMemory"):
async def get_memory_linked_document_ids(
self, user_id: str, summary_id: str, memory_type: str = "PublicMemory"
):
"""
Retrieve a list of document IDs for a specific category associated with a given memory type for a user.
@ -511,11 +600,11 @@ class Neo4jGraphDB(AbstractGraphDB):
elif memory_type == "SemanticMemory":
relationship = "HAS_SEMANTIC_MEMORY"
try:
query = f'''
query = f"""
MATCH (user:User {{userId: '{user_id}'}})-[:{relationship}]->(memory:{memory_type})-[:HAS_DOCUMENT]->(document:Document)
WHERE document.d_id = '{summary_id}'
RETURN document.d_id AS d_id
'''
"""
logging.info(f"Generated Cypher query: {query}")
result = self.query(query)
return [record["d_id"] for record in result]
@ -523,9 +612,13 @@ class Neo4jGraphDB(AbstractGraphDB):
logging.error(f"An error occurred while retrieving document IDs: {str(e)}")
return None
def create_document_node_cypher(self, document_summary: dict, user_id: str,
memory_type: str = "PublicMemory",public_memory_id:str=None) -> str:
def create_document_node_cypher(
self,
document_summary: dict,
user_id: str,
memory_type: str = "PublicMemory",
public_memory_id: str = None,
) -> str:
"""
Generate a Cypher query to create a Document node. If the memory type is 'Semantic',
link it to a SemanticMemory node for a user. If the memory type is 'PublicMemory',
@ -546,37 +639,46 @@ class Neo4jGraphDB(AbstractGraphDB):
# Validate the input parameters
if not isinstance(document_summary, dict):
raise ValueError("The document_summary must be a dictionary.")
if not all(key in document_summary for key in ['DocumentCategory', 'Title', 'Summary', 'd_id']):
raise ValueError("The document_summary dictionary is missing required keys.")
if not all(
key in document_summary
for key in ["DocumentCategory", "Title", "Summary", "d_id"]
):
raise ValueError(
"The document_summary dictionary is missing required keys."
)
if not isinstance(user_id, str) or not user_id:
raise ValueError("The user_id must be a non-empty string.")
if memory_type not in ["SemanticMemory", "PublicMemory"]:
raise ValueError("The memory_type must be either 'Semantic' or 'PublicMemory'.")
raise ValueError(
"The memory_type must be either 'Semantic' or 'PublicMemory'."
)
# Escape single quotes in the document summary data
title = document_summary['Title'].replace("'", "\\'")
summary = document_summary['Summary'].replace("'", "\\'")
document_category = document_summary['DocumentCategory'].replace("'", "\\'")
d_id = document_summary['d_id'].replace("'", "\\'")
title = document_summary["Title"].replace("'", "\\'")
summary = document_summary["Summary"].replace("'", "\\'")
document_category = document_summary["DocumentCategory"].replace("'", "\\'")
d_id = document_summary["d_id"].replace("'", "\\'")
memory_node_type = "SemanticMemory" if memory_type == "SemanticMemory" else "PublicMemory"
memory_node_type = (
"SemanticMemory" if memory_type == "SemanticMemory" else "PublicMemory"
)
user_memory_link = ''
user_memory_link = ""
if memory_type == "SemanticMemory":
user_memory_link = f'''
user_memory_link = f"""
// Ensure the User node exists
MERGE (user:User {{ userId: '{user_id}' }})
MERGE (memory:SemanticMemory {{ userId: '{user_id}' }})
MERGE (user)-[:HAS_SEMANTIC_MEMORY]->(memory)
'''
"""
elif memory_type == "PublicMemory":
logging.info(f"Public memory id: {public_memory_id}")
user_memory_link = f'''
user_memory_link = f"""
// Merge with the existing PublicMemory node or create a new one if it does not exist
MATCH (memory:PublicMemory {{ memoryId: {public_memory_id} }})
'''
"""
cypher_query = f'''
cypher_query = f"""
{user_memory_link}
// Create the Document node with its properties
@ -590,13 +692,15 @@ class Neo4jGraphDB(AbstractGraphDB):
// Link the Document node to the {memory_node_type} node
MERGE (memory)-[:HAS_DOCUMENT]->(document)
'''
"""
logging.info(f"Generated Cypher query: {cypher_query}")
return cypher_query
def update_document_node_with_db_ids(self, vectordb_namespace: str, document_id: str, user_id: str = None):
def update_document_node_with_db_ids(
self, vectordb_namespace: str, document_id: str, user_id: str = None
):
"""
Update the namespace of a Document node in the database. The document can be linked
either to a SemanticMemory node (if a user ID is provided) or to a PublicMemory node.
@ -612,23 +716,24 @@ class Neo4jGraphDB(AbstractGraphDB):
if user_id:
# Update for a document linked to a SemanticMemory node
cypher_query = f'''
cypher_query = f"""
MATCH (user:User {{userId: '{user_id}' }})-[:HAS_SEMANTIC_MEMORY]->(:SemanticMemory)-[:HAS_DOCUMENT]->(document:Document {{d_id: '{document_id}'}})
SET document.vectordbNamespace = '{vectordb_namespace}'
RETURN document
'''
"""
else:
# Update for a document linked to a PublicMemory node
cypher_query = f'''
cypher_query = f"""
MATCH (:PublicMemory)-[:HAS_DOCUMENT]->(document:Document {{d_id: '{document_id}'}})
SET document.vectordbNamespace = '{vectordb_namespace}'
RETURN document
'''
"""
return cypher_query
def run_merge_query(self, user_id: str, memory_type: str,
similarity_threshold: float) -> str:
def run_merge_query(
self, user_id: str, memory_type: str, similarity_threshold: float
) -> str:
"""
Constructs a Cypher query to merge nodes in a Neo4j database based on a similarity threshold.
@ -645,29 +750,28 @@ class Neo4jGraphDB(AbstractGraphDB):
Returns:
str: A Cypher query string that can be executed in a Neo4j session.
"""
if memory_type == 'SemanticMemory':
relationship_base = 'HAS_SEMANTIC_MEMORY'
relationship_type = 'HAS_KNOWLEDGE'
memory_label = 'semantic'
elif memory_type == 'EpisodicMemory':
relationship_base = 'HAS_EPISODIC_MEMORY'
if memory_type == "SemanticMemory":
relationship_base = "HAS_SEMANTIC_MEMORY"
relationship_type = "HAS_KNOWLEDGE"
memory_label = "semantic"
elif memory_type == "EpisodicMemory":
relationship_base = "HAS_EPISODIC_MEMORY"
# relationship_type = 'EPISODIC_MEMORY'
relationship_type = 'HAS_EVENT'
memory_label='episodic'
elif memory_type == 'Buffer':
relationship_base = 'HAS_BUFFER_MEMORY'
relationship_type = 'CURRENTLY_HOLDING'
memory_label= 'buffer'
relationship_type = "HAS_EVENT"
memory_label = "episodic"
elif memory_type == "Buffer":
relationship_base = "HAS_BUFFER_MEMORY"
relationship_type = "CURRENTLY_HOLDING"
memory_label = "buffer"
query= f"""MATCH (u:User {{userId: '{user_id}'}})-[:{relationship_base}]->(sm:{memory_type})
query = f"""MATCH (u:User {{userId: '{user_id}'}})-[:{relationship_base}]->(sm:{memory_type})
MATCH (sm)-[:{relationship_type}]->(n)
RETURN labels(n) AS NodeType, collect(n) AS Nodes
"""
node_results = self.query(query)
node_types = [record['NodeType'] for record in node_results]
node_types = [record["NodeType"] for record in node_results]
for node in node_types:
query = f"""
@ -703,16 +807,18 @@ class Neo4jGraphDB(AbstractGraphDB):
- Exception: If an error occurs during the database query execution.
"""
try:
query = f'''
query = f"""
MATCH (user:User {{userId: '{user_id}'}})-[:HAS_SEMANTIC_MEMORY]->(semantic:SemanticMemory)-[:HAS_DOCUMENT]->(document:Document)
WHERE document.documentCategory = '{category}'
RETURN document.vectordbNamespace AS namespace
'''
"""
result = self.query(query)
namespaces = [record["namespace"] for record in result]
return namespaces
except Exception as e:
logging.error(f"An error occurred while retrieving namespaces by document category: {str(e)}")
logging.error(
f"An error occurred while retrieving namespaces by document category: {str(e)}"
)
return None
async def create_memory_node(self, labels, topic=None):
@ -734,7 +840,7 @@ class Neo4jGraphDB(AbstractGraphDB):
topic = "PublicMemory"
# Prepare labels as a string
label_list = ', '.join(f"'{label}'" for label in labels)
label_list = ", ".join(f"'{label}'" for label in labels)
# Cypher query to find or create the memory node with the given description and labels
memory_cypher = f"""
@ -746,17 +852,24 @@ class Neo4jGraphDB(AbstractGraphDB):
try:
result = self.query(memory_cypher)
# Assuming the result is a list of records, where each record contains 'memoryId'
memory_id = result[0]['memoryId'] if result else None
memory_id = result[0]["memoryId"] if result else None
self.close()
return memory_id
except Neo4jError as e:
logging.error(f"Error creating or finding memory node: {e}")
raise
def link_user_to_public(self, user_id: str, public_property_value: str, public_property_name: str = 'name',
relationship_type: str = 'HAS_PUBLIC'):
def link_user_to_public(
self,
user_id: str,
public_property_value: str,
public_property_name: str = "name",
relationship_type: str = "HAS_PUBLIC",
):
if not user_id or not public_property_value:
raise ValueError("Valid User ID and Public property value are required for linking.")
raise ValueError(
"Valid User ID and Public property value are required for linking."
)
try:
link_cypher = f"""
@ -784,7 +897,9 @@ class Neo4jGraphDB(AbstractGraphDB):
logging.error(f"Error deleting {topic} memory node: {e}")
raise
def unlink_memory_from_user(self, memory_id: int, user_id: str, topic: str='PublicMemory') -> None:
def unlink_memory_from_user(
self, memory_id: int, user_id: str, topic: str = "PublicMemory"
) -> None:
"""
Unlink a memory node from a user node.
@ -801,9 +916,13 @@ class Neo4jGraphDB(AbstractGraphDB):
raise ValueError("Valid User ID and Memory ID are required for unlinking.")
if topic not in ["SemanticMemory", "PublicMemory"]:
raise ValueError("The memory_type must be either 'SemanticMemory' or 'PublicMemory'.")
raise ValueError(
"The memory_type must be either 'SemanticMemory' or 'PublicMemory'."
)
relationship_type = "HAS_SEMANTIC_MEMORY" if topic == "SemanticMemory" else "HAS_PUBLIC_MEMORY"
relationship_type = (
"HAS_SEMANTIC_MEMORY" if topic == "SemanticMemory" else "HAS_PUBLIC_MEMORY"
)
try:
unlink_cypher = f"""
@ -815,7 +934,6 @@ class Neo4jGraphDB(AbstractGraphDB):
logging.error(f"Error unlinking {topic} from user: {e}")
raise
def link_public_memory_to_user(self, memory_id, user_id):
# Link an existing Public Memory node to a User node
link_cypher = f"""
@ -825,7 +943,7 @@ class Neo4jGraphDB(AbstractGraphDB):
"""
self.query(link_cypher)
def retrieve_node_id_for_memory_type(self, topic: str = 'SemanticMemory'):
def retrieve_node_id_for_memory_type(self, topic: str = "SemanticMemory"):
link_cypher = f""" MATCH(publicMemory: {topic})
RETURN
id(publicMemory)
@ -835,18 +953,14 @@ class Neo4jGraphDB(AbstractGraphDB):
return node_ids
from .networkx_graph import NetworkXGraphDB
class GraphDBFactory:
def create_graph_db(self, db_type, **kwargs):
if db_type == 'neo4j':
if db_type == "neo4j":
return Neo4jGraphDB(**kwargs)
elif db_type == 'networkx':
elif db_type == "networkx":
return NetworkXGraphDB(**kwargs)
else:
raise ValueError(f"Unsupported database type: {db_type}")

80
cognitive_architecture/database/graphdb/networkx_graph.py
View file

@ -4,7 +4,7 @@ import networkx as nx
class NetworkXGraphDB:
def __init__(self, filename='networkx_graph.pkl'):
def __init__(self, filename="networkx_graph.pkl"):
self.filename = filename
try:
self.graph = self.load_graph() # Attempt to load an existing graph
@ -12,32 +12,36 @@ class NetworkXGraphDB:
self.graph = nx.Graph() # Create a new graph if loading failed
def save_graph(self):
""" Save the graph to a file using pickle """
with open(self.filename, 'wb') as f:
"""Save the graph to a file using pickle"""
with open(self.filename, "wb") as f:
pickle.dump(self.graph, f)
def load_graph(self):
""" Load the graph from a file using pickle """
with open(self.filename, 'rb') as f:
"""Load the graph from a file using pickle"""
with open(self.filename, "rb") as f:
return pickle.load(f)
def create_base_cognitive_architecture(self, user_id: str):
# Add nodes for user and memory types if they don't exist
self.graph.add_node(user_id, type='User')
self.graph.add_node(f"{user_id}_semantic", type='SemanticMemory')
self.graph.add_node(f"{user_id}_episodic", type='EpisodicMemory')
self.graph.add_node(f"{user_id}_buffer", type='Buffer')
self.graph.add_node(user_id, type="User")
self.graph.add_node(f"{user_id}_semantic", type="SemanticMemory")
self.graph.add_node(f"{user_id}_episodic", type="EpisodicMemory")
self.graph.add_node(f"{user_id}_buffer", type="Buffer")
# Add edges to connect user to memory types
self.graph.add_edge(user_id, f"{user_id}_semantic", relation='HAS_SEMANTIC_MEMORY')
self.graph.add_edge(user_id, f"{user_id}_episodic", relation='HAS_EPISODIC_MEMORY')
self.graph.add_edge(user_id, f"{user_id}_buffer", relation='HAS_BUFFER')
self.graph.add_edge(
user_id, f"{user_id}_semantic", relation="HAS_SEMANTIC_MEMORY"
)
self.graph.add_edge(
user_id, f"{user_id}_episodic", relation="HAS_EPISODIC_MEMORY"
)
self.graph.add_edge(user_id, f"{user_id}_buffer", relation="HAS_BUFFER")
self.save_graph() # Save the graph after modifying it
def delete_all_user_memories(self, user_id: str):
# Remove nodes and edges related to the user's memories
for memory_type in ['semantic', 'episodic', 'buffer']:
for memory_type in ["semantic", "episodic", "buffer"]:
memory_node = f"{user_id}_{memory_type}"
self.graph.remove_node(memory_node)
@ -60,31 +64,59 @@ class NetworkXGraphDB:
def retrieve_buffer_memory(self, user_id: str):
return [n for n in self.graph.neighbors(f"{user_id}_buffer")]
def generate_graph_semantic_memory_document_summary(self, document_summary, unique_graphdb_mapping_values, document_namespace, user_id):
def generate_graph_semantic_memory_document_summary(
self,
document_summary,
unique_graphdb_mapping_values,
document_namespace,
user_id,
):
for node, attributes in unique_graphdb_mapping_values.items():
self.graph.add_node(node, **attributes)
self.graph.add_edge(f"{user_id}_semantic", node, relation='HAS_KNOWLEDGE')
self.graph.add_edge(f"{user_id}_semantic", node, relation="HAS_KNOWLEDGE")
self.save_graph()
def generate_document_summary(self, document_summary, unique_graphdb_mapping_values, document_namespace, user_id):
self.generate_graph_semantic_memory_document_summary(document_summary, unique_graphdb_mapping_values, document_namespace, user_id)
def generate_document_summary(
self,
document_summary,
unique_graphdb_mapping_values,
document_namespace,
user_id,
):
self.generate_graph_semantic_memory_document_summary(
document_summary, unique_graphdb_mapping_values, document_namespace, user_id
)
async def get_document_categories(self, user_id):
return [self.graph.nodes[n]['category'] for n in self.graph.neighbors(f"{user_id}_semantic") if 'category' in self.graph.nodes[n]]
return [
self.graph.nodes[n]["category"]
for n in self.graph.neighbors(f"{user_id}_semantic")
if "category" in self.graph.nodes[n]
]
async def get_document_ids(self, user_id, category):
return [n for n in self.graph.neighbors(f"{user_id}_semantic") if self.graph.nodes[n].get('category') == category]
return [
n
for n in self.graph.neighbors(f"{user_id}_semantic")
if self.graph.nodes[n].get("category") == category
]
def create_document_node(self, document_summary, user_id):
d_id = document_summary['d_id']
d_id = document_summary["d_id"]
self.graph.add_node(d_id, **document_summary)
self.graph.add_edge(f"{user_id}_semantic", d_id, relation='HAS_DOCUMENT')
self.graph.add_edge(f"{user_id}_semantic", d_id, relation="HAS_DOCUMENT")
self.save_graph()
def update_document_node_with_namespace(self, user_id, vectordb_namespace, document_id):
def update_document_node_with_namespace(
self, user_id, vectordb_namespace, document_id
):
if self.graph.has_node(document_id):
self.graph.nodes[document_id]['vectordbNamespace'] = vectordb_namespace
self.graph.nodes[document_id]["vectordbNamespace"] = vectordb_namespace
self.save_graph()
def get_namespaces_by_document_category(self, user_id, category):
return [self.graph.nodes[n].get('vectordbNamespace') for n in self.graph.neighbors(f"{user_id}_semantic") if self.graph.nodes[n].get('category') == category]
return [
self.graph.nodes[n].get("vectordbNamespace")
for n in self.graph.neighbors(f"{user_id}_semantic")
if self.graph.nodes[n].get("category") == category
]

62
cognitive_architecture/database/relationaldb/database.py
View file

@ -31,36 +31,47 @@ import os
class DatabaseConfig:
def __init__(self, db_type=None, db_name=None, host=None, user=None, password=None, port=None, config_file=None):
def __init__(
self,
db_type=None,
db_name=None,
host=None,
user=None,
password=None,
port=None,
config_file=None,
):
if config_file:
self.load_from_file(config_file)
else:
# Load default values from environment variables or use provided values
self.db_type = db_type or os.getenv('DB_TYPE', 'sqlite')
self.db_name = db_name or os.getenv('DB_NAME', 'database.db')
self.host = host or os.getenv('DB_HOST', 'localhost')
self.user = user or os.getenv('DB_USER', 'user')
self.password = password or os.getenv('DB_PASSWORD', 'password')
self.port = port or os.getenv('DB_PORT', '5432')
self.db_type = db_type or os.getenv("DB_TYPE", "sqlite")
self.db_name = db_name or os.getenv("DB_NAME", "database.db")
self.host = host or os.getenv("DB_HOST", "localhost")
self.user = user or os.getenv("DB_USER", "user")
self.password = password or os.getenv("DB_PASSWORD", "password")
self.port = port or os.getenv("DB_PORT", "5432")
def load_from_file(self, file_path):
with open(file_path, 'r') as file:
with open(file_path, "r") as file:
config = json.load(file)
self.db_type = config.get('db_type', 'sqlite')
self.db_name = config.get('db_name', 'database.db')
self.host = config.get('host', 'localhost')
self.user = config.get('user', 'user')
self.password = config.get('password', 'password')
self.port = config.get('port', '5432')
self.db_type = config.get("db_type", "sqlite")
self.db_name = config.get("db_name", "database.db")
self.host = config.get("host", "localhost")
self.user = config.get("user", "user")
self.password = config.get("password", "password")
self.port = config.get("port", "5432")
def get_sqlalchemy_database_url(self):
if self.db_type == 'sqlite':
if self.db_type == "sqlite":
db_path = Path(self.db_name).absolute() # Ensure the path is absolute
return f"sqlite+aiosqlite:///{db_path}" # SQLite uses file path
elif self.db_type == 'duckdb':
db_path = Path(self.db_name).absolute() # Ensure the path is absolute for DuckDB as well
elif self.db_type == "duckdb":
db_path = Path(
self.db_name
).absolute() # Ensure the path is absolute for DuckDB as well
return f"duckdb+aiosqlite:///{db_path}"
elif self.db_type == 'postgresql':
elif self.db_type == "postgresql":
# Ensure optional parameters are handled gracefully
port_str = f":{self.port}" if self.port else ""
password_str = f":{self.password}" if self.password else ""
@ -68,10 +79,18 @@ class DatabaseConfig:
else:
raise ValueError(f"Unsupported DB_TYPE: {self.db_type}")
# Example usage with a configuration file:
# config = DatabaseConfig(config_file='path/to/config.json')
# Or set them programmatically:
config = DatabaseConfig(db_type='postgresql', db_name='mydatabase', user='myuser', password='mypassword', host='myhost', port='5432')
config = DatabaseConfig(
db_type="postgresql",
db_name="mydatabase",
user="myuser",
password="mypassword",
host="myhost",
port="5432",
)
SQLALCHEMY_DATABASE_URL = config.get_sqlalchemy_database_url()
@ -79,7 +98,7 @@ SQLALCHEMY_DATABASE_URL = config.get_sqlalchemy_database_url()
engine = create_async_engine(
SQLALCHEMY_DATABASE_URL,
pool_recycle=3600,
echo=True # Enable logging for tutorial purposes
echo=True, # Enable logging for tutorial purposes
)
# Use AsyncSession for the session
AsyncSessionLocal = sessionmaker(
@ -90,6 +109,7 @@ AsyncSessionLocal = sessionmaker(
Base = declarative_base()
# Use asynccontextmanager to define an async context manager
@asynccontextmanager
async def get_db():
@ -99,6 +119,7 @@ async def get_db():
finally:
await db.close()
#
# if os.environ.get('AWS_ENV') == 'prd' or os.environ.get('AWS_ENV') == 'dev':
# host = os.environ.get('POSTGRES_HOST')
@ -127,4 +148,3 @@ async def get_db():
#
# # Use the asyncpg driver for async operation
# SQLALCHEMY_DATABASE_URL = f"postgresql+asyncpg://{username}:{password}@{host}:5432/{database_name}"

5
cognitive_architecture/database/relationaldb/database_crud.py
View file

@ -1,4 +1,3 @@
from contextlib import asynccontextmanager
import logging
from .models.sessions import Session
@ -9,9 +8,9 @@ from .models.metadatas import MetaDatas
from .models.docs import DocsModel
logger = logging.getLogger(__name__)
@asynccontextmanager
async def session_scope(session):
"""Provide a transactional scope around a series of operations."""
@ -44,6 +43,8 @@ def update_entity_graph_summary(session, model, entity_id, new_value):
return "Successfully updated entity"
else:
return "Entity not found"
async def update_entity(session, model, entity_id, new_value):
async with session_scope(session) as s:
# Retrieve the entity from the database

8
cognitive_architecture/database/relationaldb/models/docs.py
View file

@ -1,20 +1,20 @@
from datetime import datetime
from sqlalchemy import Column, String, DateTime, ForeignKey, Boolean
from sqlalchemy.orm import relationship
import os
import sys
from ..database import Base
class DocsModel(Base):
__tablename__ = 'docs'
__tablename__ = "docs"
id = Column(String, primary_key=True)
operation_id = Column(String, ForeignKey('operations.id'), index=True)
operation_id = Column(String, ForeignKey("operations.id"), index=True)
doc_name = Column(String, nullable=True)
graph_summary = Column(Boolean, nullable=True)
memory_category = Column(String, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, onupdate=datetime.utcnow)
operations = relationship("Operation", back_populates="docs")

14
cognitive_architecture/database/relationaldb/models/memory.py
View file

@ -5,22 +5,26 @@ from sqlalchemy.orm import relationship
import os
import sys
from ..database import Base
class MemoryModel(Base):
__tablename__ = 'memories'
__tablename__ = "memories"
id = Column(String, primary_key=True)
user_id = Column(String, ForeignKey('users.id'), index=True)
operation_id = Column(String, ForeignKey('operations.id'), index=True)
user_id = Column(String, ForeignKey("users.id"), index=True)
operation_id = Column(String, ForeignKey("operations.id"), index=True)
memory_name = Column(String, nullable=True)
memory_category = Column(String, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, onupdate=datetime.utcnow)
methods_list = Column(String , nullable=True)
methods_list = Column(String, nullable=True)
attributes_list = Column(String, nullable=True)
user = relationship("User", back_populates="memories")
operation = relationship("Operation", back_populates="memories")
metadatas = relationship("MetaDatas", back_populates="memory", cascade="all, delete-orphan")
metadatas = relationship(
"MetaDatas", back_populates="memory", cascade="all, delete-orphan"
)
def __repr__(self):
return f"<Memory(id={self.id}, user_id={self.user_id}, created_at={self.created_at}, updated_at={self.updated_at})>"

8
cognitive_architecture/database/relationaldb/models/metadatas.py
View file

@ -5,14 +5,16 @@ from sqlalchemy.orm import relationship
import os
import sys
from ..database import Base
class MetaDatas(Base):
__tablename__ = 'metadatas'
__tablename__ = "metadatas"
id = Column(String, primary_key=True)
user_id = Column(String, ForeignKey('users.id'), index=True)
user_id = Column(String, ForeignKey("users.id"), index=True)
version = Column(String, nullable=False)
contract_metadata = Column(String, nullable=False)
memory_id = Column(String, ForeignKey('memories.id'), index=True)
memory_id = Column(String, ForeignKey("memories.id"), index=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, onupdate=datetime.utcnow)

5
cognitive_architecture/database/relationaldb/models/operation.py
View file

@ -6,11 +6,12 @@ import os
import sys
from ..database import Base
class Operation(Base):
__tablename__ = 'operations'
__tablename__ = "operations"
id = Column(String, primary_key=True)
user_id = Column(String, ForeignKey('users.id'), index=True) # Link to User
user_id = Column(String, ForeignKey("users.id"), index=True) # Link to User
operation_type = Column(String, nullable=True)
operation_status = Column(String, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)

4
cognitive_architecture/database/relationaldb/models/sessions.py
View file

@ -9,10 +9,10 @@ from ..database import Base
class Session(Base):
__tablename__ = 'sessions'
__tablename__ = "sessions"
id = Column(String, primary_key=True)
user_id = Column(String, ForeignKey('users.id'), index=True)
user_id = Column(String, ForeignKey("users.id"), index=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, onupdate=datetime.utcnow)

14
cognitive_architecture/database/relationaldb/models/user.py
View file

@ -14,7 +14,7 @@ from ..database import Base
class User(Base):
__tablename__ = 'users'
__tablename__ = "users"
id = Column(String, primary_key=True, index=True)
session_id = Column(String, nullable=True, unique=True)
@ -22,9 +22,15 @@ class User(Base):
updated_at = Column(DateTime, onupdate=datetime.utcnow)
# Relationships
memories = relationship("MemoryModel", back_populates="user", cascade="all, delete-orphan")
operations = relationship("Operation", back_populates="user", cascade="all, delete-orphan")
sessions = relationship("Session", back_populates="user", cascade="all, delete-orphan")
memories = relationship(
"MemoryModel", back_populates="user", cascade="all, delete-orphan"
)
operations = relationship(
"Operation", back_populates="user", cascade="all, delete-orphan"
)
sessions = relationship(
"Session", back_populates="user", cascade="all, delete-orphan"
)
metadatas = relationship("MetaDatas", back_populates="user")
def __repr__(self):

76
cognitive_architecture/database/vectordb/basevectordb.py
View file

@ -1,11 +1,12 @@
import logging
from io import BytesIO
import os, sys
# Add the parent directory to sys.path
# sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import sqlalchemy as sa
print(os.getcwd())
logging.basicConfig(level=logging.INFO)
# import marvin
@ -22,6 +23,7 @@ from cognitive_architecture.database.relationaldb.models.operation import Operat
from cognitive_architecture.database.relationaldb.models.docs import DocsModel
from sqlalchemy.orm import sessionmaker
from cognitive_architecture.database.relationaldb.database import engine
load_dotenv()
from typing import Optional
import time
@ -31,7 +33,11 @@ tracemalloc.start()
from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from cognitive_architecture.database.vectordb.vectordb import PineconeVectorDB, WeaviateVectorDB, LanceDB
from cognitive_architecture.database.vectordb.vectordb import (
PineconeVectorDB,
WeaviateVectorDB,
LanceDB,
)
from langchain.schema import Document
import uuid
import weaviate
@ -43,6 +49,7 @@ from vector_db_type import VectorDBType
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
# marvin.settings.openai.api_key = os.environ.get("OPENAI_API_KEY")
class VectorDBFactory:
def __init__(self):
self.db_map = {
@ -63,15 +70,12 @@ class VectorDBFactory:
):
if db_type in self.db_map:
return self.db_map[db_type](
user_id,
index_name,
memory_id,
namespace,
embeddings
user_id, index_name, memory_id, namespace, embeddings
)
raise ValueError(f"Unsupported database type: {db_type}")
class BaseMemory:
def __init__(
self,
@ -95,21 +99,18 @@ class BaseMemory:
self.memory_id,
db_type=self.db_type,
namespace=self.namespace,
embeddings=self.embeddings
embeddings=self.embeddings,
)
def init_client(self, embeddings, namespace: str):
return self.vector_db.init_client(embeddings, namespace)
def create_field(self, field_type, **kwargs):
field_mapping = {
"Str": fields.Str,
"Int": fields.Int,
"Float": fields.Float,
"Bool": fields.Bool,
}
return field_mapping[field_type](**kwargs)
@ -121,7 +122,6 @@ class BaseMemory:
dynamic_schema_instance = Schema.from_dict(dynamic_fields)()
return dynamic_schema_instance
async def get_version_from_db(self, user_id, memory_id):
# Logic to retrieve the version from the database.
@ -137,11 +137,11 @@ class BaseMemory:
)
if result:
version_in_db, created_at = result
logging.info(f"version_in_db: {version_in_db}")
from ast import literal_eval
version_in_db= literal_eval(version_in_db)
version_in_db = literal_eval(version_in_db)
version_in_db = version_in_db.get("version")
return [version_in_db, created_at]
else:
@ -157,20 +157,33 @@ class BaseMemory:
# If there is no metadata, insert it.
if version_from_db is None:
session.add(MetaDatas(id = str(uuid.uuid4()), user_id=self.user_id, version = str(int(time.time())) ,memory_id=self.memory_id, contract_metadata=params))
session.add(
MetaDatas(
id=str(uuid.uuid4()),
user_id=self.user_id,
version=str(int(time.time())),
memory_id=self.memory_id,
contract_metadata=params,
)
)
session.commit()
return params
# If params version is higher, update the metadata.
elif version_in_params > version_from_db[0]:
session.add(MetaDatas(id = str(uuid.uuid4()), user_id=self.user_id, memory_id=self.memory_id, contract_metadata=params))
session.add(
MetaDatas(
id=str(uuid.uuid4()),
user_id=self.user_id,
memory_id=self.memory_id,
contract_metadata=params,
)
)
session.commit()
return params
else:
return params
async def add_memories(
self,
observation: Optional[str] = None,
@ -179,11 +192,14 @@ class BaseMemory:
namespace: Optional[str] = None,
custom_fields: Optional[str] = None,
embeddings: Optional[str] = None,
):
return await self.vector_db.add_memories(
observation=observation, loader_settings=loader_settings,
params=params, namespace=namespace, metadata_schema_class = None, embeddings=embeddings
observation=observation,
loader_settings=loader_settings,
params=params,
namespace=namespace,
metadata_schema_class=None,
embeddings=embeddings,
)
# Add other db_type conditions if necessary
@ -200,17 +216,15 @@ class BaseMemory:
logging.info(observation)
return await self.vector_db.fetch_memories(
observation=observation, search_type= search_type, params=params,
observation=observation,
search_type=search_type,
params=params,
namespace=namespace,
n_of_observations=n_of_observations
n_of_observations=n_of_observations,
)
async def delete_memories(self, namespace:str, params: Optional[str] = None):
return await self.vector_db.delete_memories(namespace,params)
async def count_memories(self, namespace:str, params: Optional[str] = None):
return await self.vector_db.count_memories(namespace,params)
async def delete_memories(self, namespace: str, params: Optional[str] = None):
return await self.vector_db.delete_memories(namespace, params)
async def count_memories(self, namespace: str, params: Optional[str] = None):
return await self.vector_db.count_memories(namespace, params)

12
cognitive_architecture/database/vectordb/chunkers/chunk_strategy.py
View file

@ -1,8 +1,10 @@
from enum import Enum
class ChunkStrategy(Enum):
EXACT = 'exact'
PARAGRAPH = 'paragraph'
SENTENCE = 'sentence'
VANILLA = 'vanilla'
SUMMARY = 'summary'
"""Chunking strategies for the vector database."""
EXACT = "exact"
PARAGRAPH = "paragraph"
SENTENCE = "sentence"
VANILLA = "vanilla"
SUMMARY = "summary"

87
cognitive_architecture/database/vectordb/chunkers/chunkers.py
View file

@ -1,13 +1,17 @@
from cognitive_architecture.database.vectordb.chunkers.chunk_strategy import ChunkStrategy
import re
def chunk_data(chunk_strategy=None, source_data=None, chunk_size=None, chunk_overlap=None):
"""Module for chunking text data based on various strategies."""
import re
import logging
from cognitive_architecture.database.vectordb.chunkers.chunk_strategy import ChunkStrategy
from langchain.text_splitter import RecursiveCharacterTextSplitter
def chunk_data(chunk_strategy=None, source_data=None, chunk_size=None, chunk_overlap=None):
"""Chunk the given source data into smaller parts based on the specified strategy."""
if chunk_strategy == ChunkStrategy.VANILLA:
chunked_data = vanilla_chunker(source_data, chunk_size, chunk_overlap)
elif chunk_strategy == ChunkStrategy.PARAGRAPH:
chunked_data = chunk_data_by_paragraph(source_data,chunk_size, chunk_overlap)
chunked_data = chunk_data_by_paragraph(source_data, chunk_size, chunk_overlap)
elif chunk_strategy == ChunkStrategy.SENTENCE:
chunked_data = chunk_by_sentence(source_data, chunk_size, chunk_overlap)
elif chunk_strategy == ChunkStrategy.EXACT:
@ -21,68 +25,41 @@ def chunk_data(chunk_strategy=None, source_data=None, chunk_size=None, chunk_ove
def vanilla_chunker(source_data, chunk_size=100, chunk_overlap=20):
# adapt this for different chunking strategies
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=len
)
# try:
# pages = text_splitter.create_documents([source_data])
# except:
# try:
"""Chunk the given source data into smaller parts using a vanilla strategy."""
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size
, chunk_overlap=chunk_overlap
, length_function=len)
pages = text_splitter.create_documents([source_data])
# except:
# pages = text_splitter.create_documents(source_data.content)
# pages = source_data.load_and_split()
return pages
def summary_chunker(source_data, chunk_size=400, chunk_overlap=20):
"""
Chunk the given source data into smaller parts, returning the first five and last five chunks.
Parameters:
- source_data (str): The source data to be chunked.
- chunk_size (int): The size of each chunk.
- chunk_overlap (int): The overlap between consecutive chunks.
Returns:
- List: A list containing the first five and last five chunks of the chunked source data.
"""
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=len
)
"""Chunk the given source data into smaller parts, focusing on summarizing content."""
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size
, chunk_overlap=chunk_overlap
, length_function=len)
try:
pages = text_splitter.create_documents([source_data])
except:
except Exception as e:
pages = text_splitter.create_documents(source_data.content)
logging.error(f"An error occurred: %s {str(e)}")
# Return the first 5 and last 5 chunks
if len(pages) > 10:
return pages[:5] + pages[-5:]
else:
return pages # Return all chunks if there are 10 or fewer
return pages
def chunk_data_exact(data_chunks, chunk_size, chunk_overlap):
"""Chunk the data into exact sizes as specified, without considering content."""
data = "".join(data_chunks)
chunks = []
for i in range(0, len(data), chunk_size - chunk_overlap):
chunks.append(data[i:i + chunk_size])
chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size - chunk_overlap)]
return chunks
def chunk_by_sentence(data_chunks, chunk_size, overlap):
# Split by periods, question marks, exclamation marks, and ellipses
"""Chunk the data by sentences, ensuring each chunk does not exceed the specified size."""
data = "".join(data_chunks)
# The regular expression is used to find series of charaters that end with one the following chaacters (. ! ? ...)
sentence_endings = r'(?<=[.!?…]) +'
sentence_endings = r"(?<=[.!?…]) +"
sentences = re.split(sentence_endings, data)
sentence_chunks = []
@ -96,6 +73,7 @@ def chunk_by_sentence(data_chunks, chunk_size, overlap):
def chunk_data_by_paragraph(data_chunks, chunk_size, overlap, bound=0.75):
"""Chunk the data by paragraphs, with consideration for chunk size and overlap."""
data = "".join(data_chunks)
total_length = len(data)
chunks = []
@ -103,20 +81,13 @@ def chunk_data_by_paragraph(data_chunks, chunk_size, overlap, bound=0.75):
start_idx = 0
while start_idx < total_length:
# Set the end index to the minimum of start_idx + default_chunk_size or total_length
end_idx = min(start_idx + chunk_size, total_length)
next_paragraph_index = data.find("\n\n", start_idx + check_bound, end_idx)
# Find the next paragraph index within the current chunk and bound
next_paragraph_index = data.find('\n\n', start_idx + check_bound, end_idx)
# If a next paragraph index is found within the current chunk
if next_paragraph_index != -1:
# Update end_idx to include the paragraph delimiter
end_idx = next_paragraph_index + 2
chunks.append(data[start_idx:end_idx + overlap])
# Update start_idx to be the current end_idx
start_idx = end_idx
return chunks

46
cognitive_architecture/database/vectordb/cognee_manager.py
View file

@ -7,7 +7,9 @@ from .response import Response
class CogneeManager:
def __init__(self, embeddings: Embeddings = None,
def __init__(
self,
embeddings: Embeddings = None,
vector_db: VectorDB = None,
vector_db_key: str = None,
embedding_api_key: str = None,
@ -17,7 +19,8 @@ class CogneeManager:
document_id: str = None,
chunk_validation_url: str = None,
internal_api_key: str = "test123",
base_url="http://localhost:8000"):
base_url="http://localhost:8000",
):
self.embeddings = embeddings if embeddings else Embeddings()
self.vector_db = vector_db if vector_db else VectorDB()
self.webhook_url = webhook_url
@ -32,12 +35,12 @@ class CogneeManager:
def serialize(self):
data = {
'EmbeddingsMetadata': json.dumps(self.embeddings.serialize()),
'VectorDBMetadata': json.dumps(self.vector_db.serialize()),
'WebhookURL': self.webhook_url,
'LinesPerBatch': self.lines_per_batch,
'DocumentID': self.document_id,
'ChunkValidationURL': self.chunk_validation_url,
"EmbeddingsMetadata": json.dumps(self.embeddings.serialize()),
"VectorDBMetadata": json.dumps(self.vector_db.serialize()),
"WebhookURL": self.webhook_url,
"LinesPerBatch": self.lines_per_batch,
"DocumentID": self.document_id,
"ChunkValidationURL": self.chunk_validation_url,
}
return {k: v for k, v in data.items() if v is not None}
@ -49,11 +52,22 @@ class CogneeManager:
data = self.serialize()
headers = self.generate_headers()
multipart_form_data = [('file', (os.path.basename(filepath), open(filepath, 'rb'), 'application/octet-stream'))
for filepath in file_paths]
multipart_form_data = [
(
"file",
(
os.path.basename(filepath),
open(filepath, "rb"),
"application/octet-stream",
),
)
for filepath in file_paths
]
print(f"embedding {len(file_paths)} documents at {url}")
response = requests.post(url, files=multipart_form_data, headers=headers, stream=True, data=data)
response = requests.post(
url, files=multipart_form_data, headers=headers, stream=True, data=data
)
if response.status_code == 500:
print(response.text)
@ -75,9 +89,7 @@ class CogneeManager:
"Authorization": self.internal_api_key,
}
data = {
'JobIDs': job_ids
}
data = {"JobIDs": job_ids}
print(f"retrieving job statuses for {len(job_ids)} jobs at {url}")
response = requests.post(url, headers=headers, json=data)
@ -101,9 +113,7 @@ class CogneeManager:
data = self.serialize()
headers = self.generate_headers()
files = {
'SourceData': open(filepath, 'rb')
}
files = {"SourceData": open(filepath, "rb")}
print(f"embedding document at file path {filepath} at {url}")
response = requests.post(url, headers=headers, data=data, files=files)
@ -146,6 +156,6 @@ class CogneeManager:
"Authorization": self.internal_api_key,
"X-EmbeddingAPI-Key": self.embeddings_api_key,
"X-VectorDB-Key": self.vector_db_key,
"X-Webhook-Key": self.webhook_key
"X-Webhook-Key": self.webhook_key,
}
return {k: v for k, v in headers.items() if v is not None}

21
cognitive_architecture/database/vectordb/embeddings/embeddings.py
View file

@ -3,12 +3,15 @@ from ..chunkers.chunk_strategy import ChunkStrategy
class Embeddings:
def __init__(self, embeddings_type: EmbeddingsType = EmbeddingsType.OPEN_AI,
def __init__(
self,
embeddings_type: EmbeddingsType = EmbeddingsType.OPEN_AI,
chunk_size: int = 256,
chunk_overlap: int = 128,
chunk_strategy: ChunkStrategy = ChunkStrategy.EXACT,
docker_image: str = None,
hugging_face_model_name: str = None):
hugging_face_model_name: str = None,
):
self.embeddings_type = embeddings_type
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
@ -18,12 +21,14 @@ class Embeddings:
def serialize(self):
data = {
'embeddings_type': self.embeddings_type.name if self.embeddings_type else None,
'chunk_size': self.chunk_size,
'chunk_overlap': self.chunk_overlap,
'chunk_strategy': self.chunk_strategy.name if self.chunk_strategy else None,
'docker_image': self.docker_image,
'hugging_face_model_name': self.hugging_face_model_name
"embeddings_type": self.embeddings_type.name
if self.embeddings_type
else None,
"chunk_size": self.chunk_size,
"chunk_overlap": self.chunk_overlap,
"chunk_strategy": self.chunk_strategy.name if self.chunk_strategy else None,
"docker_image": self.docker_image,
"hugging_face_model_name": self.hugging_face_model_name,
}
return {k: v for k, v in data.items() if v is not None}

11
cognitive_architecture/database/vectordb/embeddings/embeddings_type.py
View file

@ -1,8 +1,9 @@
from enum import Enum
class EmbeddingsType(Enum):
OPEN_AI = 'open_ai'
COHERE = 'cohere'
SELF_HOSTED = 'self_hosted'
HUGGING_FACE = 'hugging_face'
IMAGE = 'image'
OPEN_AI = "open_ai"
COHERE = "cohere"
SELF_HOSTED = "self_hosted"
HUGGING_FACE = "hugging_face"
IMAGE = "image"

66
cognitive_architecture/database/vectordb/loaders/loaders.py
View file

@ -4,7 +4,10 @@ import os
import sys
from cognitive_architecture.database.vectordb.chunkers.chunkers import chunk_data
from cognitive_architecture.shared.language_processing import translate_text, detect_language
from cognitive_architecture.shared.language_processing import (
translate_text,
detect_language,
)
from langchain.document_loaders import UnstructuredURLLoader
from langchain.document_loaders import DirectoryLoader
@ -17,26 +20,34 @@ import requests
async def fetch_pdf_content(file_url):
response = requests.get(file_url)
pdf_stream = BytesIO(response.content)
with fitz.open(stream=pdf_stream, filetype='pdf') as doc:
with fitz.open(stream=pdf_stream, filetype="pdf") as doc:
return "".join(page.get_text() for page in doc)
async def fetch_text_content(file_url):
loader = UnstructuredURLLoader(urls=file_url)
return loader.load()
async def process_content(content, metadata, loader_strategy, chunk_size, chunk_overlap):
pages = chunk_data(chunk_strategy=loader_strategy, source_data=content, chunk_size=chunk_size,
chunk_overlap=chunk_overlap)
async def process_content(
content, metadata, loader_strategy, chunk_size, chunk_overlap
):
pages = chunk_data(
chunk_strategy=loader_strategy,
source_data=content,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
if metadata is None:
metadata = {"metadata": "None"}
chunk_count= 0
chunk_count = 0
for chunk in pages:
chunk_count+=1
chunk_count += 1
chunk.metadata = metadata
chunk.metadata["chunk_count"]=chunk_count
chunk.metadata["chunk_count"] = chunk_count
if detect_language(pages) != "en":
logging.info("Translating Page")
for page in pages:
@ -45,6 +56,7 @@ async def process_content(content, metadata, loader_strategy, chunk_size, chunk
return pages
async def _document_loader(observation: str, loader_settings: dict):
document_format = loader_settings.get("format", "text")
loader_strategy = loader_settings.get("strategy", "VANILLA")
@ -65,7 +77,13 @@ async def _document_loader(observation: str, loader_settings: dict):
else:
raise ValueError(f"Unsupported document format: {document_format}")
pages = await process_content(content, metadata=None, loader_strategy=loader_strategy, chunk_size= chunk_size, chunk_overlap= chunk_overlap)
pages = await process_content(
content,
metadata=None,
loader_strategy=loader_strategy,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
chunked_doc.append(pages)
elif loader_settings.get("source") == "DEVICE":
@ -76,17 +94,28 @@ async def _document_loader(observation: str, loader_settings: dict):
documents = loader.load()
for document in documents:
# print ("Document: ", document.page_content)
pages = await process_content(content= str(document.page_content), metadata=document.metadata, loader_strategy= loader_strategy, chunk_size = chunk_size, chunk_overlap = chunk_overlap)
pages = await process_content(
content=str(document.page_content),
metadata=document.metadata,
loader_strategy=loader_strategy,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
chunked_doc.append(pages)
else:
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader(loader_settings.get("single_document_path"))
documents= loader.load()
documents = loader.load()
for document in documents:
pages = await process_content(content=str(document.page_content), metadata=document.metadata,
loader_strategy=loader_strategy, chunk_size=chunk_size,
chunk_overlap=chunk_overlap)
pages = await process_content(
content=str(document.page_content),
metadata=document.metadata,
loader_strategy=loader_strategy,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
chunked_doc.append(pages)
else:
raise ValueError(f"Unsupported source type: {loader_settings.get('source')}")
@ -94,7 +123,6 @@ async def _document_loader(observation: str, loader_settings: dict):
return chunked_doc
# async def _document_loader( observation: str, loader_settings: dict):
#
# document_format = loader_settings.get("format", "text")
@ -196,11 +224,3 @@ async def _document_loader(observation: str, loader_settings: dict):
# else:
# raise ValueError(f"Error: ")
# return chunked_doc

44
cognitive_architecture/database/vectordb/response.py
View file

@ -2,9 +2,19 @@ from .job import Job
class Response:
def __init__(self, error=None, message=None, successful_uploads=None, failed_uploads=None,
empty_files_count=None, duplicate_files_count=None, job_id=None,
jobs=None, job_status=None, status_code=None):
def __init__(
self,
error=None,
message=None,
successful_uploads=None,
failed_uploads=None,
empty_files_count=None,
duplicate_files_count=None,
job_id=None,
jobs=None,
job_status=None,
status_code=None,
):
self.error = error
self.message = message
self.successful_uploads = successful_uploads
@ -18,33 +28,37 @@ class Response:
@classmethod
def from_json(cls, json_dict, status_code):
successful_uploads = cls._convert_successful_uploads_to_jobs(json_dict.get('successful_uploads', None))
jobs = cls._convert_to_jobs(json_dict.get('Jobs', None))
successful_uploads = cls._convert_successful_uploads_to_jobs(
json_dict.get("successful_uploads", None)
)
jobs = cls._convert_to_jobs(json_dict.get("Jobs", None))
return cls(
error=json_dict.get('error'),
message=json_dict.get('message'),
error=json_dict.get("error"),
message=json_dict.get("message"),
successful_uploads=successful_uploads,
failed_uploads=json_dict.get('failed_uploads'),
empty_files_count=json_dict.get('empty_files_count'),
duplicate_files_count=json_dict.get('duplicate_files_count'),
job_id=json_dict.get('JobID'),
failed_uploads=json_dict.get("failed_uploads"),
empty_files_count=json_dict.get("empty_files_count"),
duplicate_files_count=json_dict.get("duplicate_files_count"),
job_id=json_dict.get("JobID"),
jobs=jobs,
job_status=json_dict.get('JobStatus'),
status_code=status_code
job_status=json_dict.get("JobStatus"),
status_code=status_code,
)
@classmethod
def _convert_successful_uploads_to_jobs(cls, successful_uploads):
if not successful_uploads:
return None
return [Job(filename=key, job_id=val) for key, val in successful_uploads.items()]
return [
Job(filename=key, job_id=val) for key, val in successful_uploads.items()
]
@classmethod
def _convert_to_jobs(cls, jobs):
if not jobs:
return None
return [Job(job_id=job['JobID'], job_status=job['JobStatus']) for job in jobs]
return [Job(job_id=job["JobID"], job_status=job["JobStatus"]) for job in jobs]
def __str__(self):
attributes = []

23
cognitive_architecture/database/vectordb/vector_db_type.py
View file

@ -1,14 +1,15 @@
from enum import Enum
class VectorDBType(Enum):
PINECONE = 'pinecone'
WEAVIATE = 'weaviate'
MILVUS = 'milvus'
QDRANT = 'qdrant'
DEEPLAKE = 'deeplake'
VESPA = 'vespa'
PGVECTOR = 'pgvector'
REDIS = 'redis'
LANCEDB = 'lancedb'
MONGODB = 'mongodb'
FAISS = 'faiss'
PINECONE = "pinecone"
WEAVIATE = "weaviate"
MILVUS = "milvus"
QDRANT = "qdrant"
DEEPLAKE = "deeplake"
VESPA = "vespa"
PGVECTOR = "pgvector"
REDIS = "redis"
LANCEDB = "lancedb"
MONGODB = "mongodb"
FAISS = "faiss"

244
cognitive_architecture/database/vectordb/vectordb.py
View file

@ -1,10 +1,10 @@
# Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
import logging
from langchain.text_splitter import RecursiveCharacterTextSplitter
from marshmallow import Schema, fields
from cognitive_architecture.database.vectordb.loaders.loaders import _document_loader
# Add the parent directory to sys.path
@ -12,6 +12,7 @@ logging.basicConfig(level=logging.INFO)
from langchain.retrievers import WeaviateHybridSearchRetriever, ParentDocumentRetriever
from weaviate.gql.get import HybridFusion
import tracemalloc
tracemalloc.start()
import os
from langchain.embeddings.openai import OpenAIEmbeddings
@ -28,6 +29,8 @@ config.load()
LTM_MEMORY_ID_DEFAULT = "00000"
ST_MEMORY_ID_DEFAULT = "0000"
BUFFER_ID_DEFAULT = "0000"
class VectorDB:
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
@ -37,7 +40,7 @@ class VectorDB:
index_name: str,
memory_id: str,
namespace: str = None,
embeddings = None,
embeddings=None,
):
self.user_id = user_id
self.index_name = index_name
@ -45,6 +48,7 @@ class VectorDB:
self.memory_id = memory_id
self.embeddings = embeddings
class PineconeVectorDB(VectorDB):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@ -54,13 +58,21 @@ class PineconeVectorDB(VectorDB):
# Pinecone initialization logic
pass
import langchain.embeddings
class WeaviateVectorDB(VectorDB):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.init_weaviate(embeddings= self.embeddings, namespace = self.namespace)
self.init_weaviate(embeddings=self.embeddings, namespace=self.namespace)
def init_weaviate(self, embeddings=OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY", "")), namespace=None,retriever_type="",):
def init_weaviate(
self,
embeddings=OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY", "")),
namespace=None,
retriever_type="",
):
# Weaviate initialization logic
auth_config = weaviate.auth.AuthApiKey(
api_key=os.environ.get("WEAVIATE_API_KEY")
@ -91,7 +103,7 @@ class WeaviateVectorDB(VectorDB):
create_schema_if_missing=True,
)
return retriever
else :
else:
return client
# child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# store = InMemoryStore()
@ -100,6 +112,7 @@ class WeaviateVectorDB(VectorDB):
# docstore=store,
# child_splitter=child_splitter,
# )
from marshmallow import Schema, fields
def create_document_structure(observation, params, metadata_schema_class=None):
@ -111,10 +124,7 @@ class WeaviateVectorDB(VectorDB):
:param metadata_schema_class: Custom metadata schema class (optional).
:return: A list containing the validated document data.
"""
document_data = {
"metadata": params,
"page_content": observation
}
document_data = {"metadata": params, "page_content": observation}
def get_document_schema():
class DynamicDocumentSchema(Schema):
@ -128,30 +138,42 @@ class WeaviateVectorDB(VectorDB):
loaded_document = CurrentDocumentSchema().load(document_data)
return [loaded_document]
def _stuct(self, observation, params, metadata_schema_class =None):
def _stuct(self, observation, params, metadata_schema_class=None):
"""Utility function to create the document structure with optional custom fields."""
# Construct document data
document_data = {
"metadata": params,
"page_content": observation
}
document_data = {"metadata": params, "page_content": observation}
def get_document_schema():
class DynamicDocumentSchema(Schema):
metadata = fields.Nested(metadata_schema_class, required=True)
page_content = fields.Str(required=True)
return DynamicDocumentSchema
# Validate and deserialize # Default to "1.0" if not provided
CurrentDocumentSchema = get_document_schema()
loaded_document = CurrentDocumentSchema().load(document_data)
return [loaded_document]
async def add_memories(self, observation, loader_settings=None, params=None, namespace=None, metadata_schema_class=None, embeddings = 'hybrid'):
async def add_memories(
self,
observation,
loader_settings=None,
params=None,
namespace=None,
metadata_schema_class=None,
embeddings="hybrid",
):
# Update Weaviate memories here
if namespace is None:
namespace = self.namespace
params['user_id'] = self.user_id
params["user_id"] = self.user_id
logging.info("User id is %s", self.user_id)
retriever = self.init_weaviate(embeddings=OpenAIEmbeddings(),namespace = namespace, retriever_type="single_document_context")
retriever = self.init_weaviate(
embeddings=OpenAIEmbeddings(),
namespace=namespace,
retriever_type="single_document_context",
)
if loader_settings:
# Assuming _document_loader returns a list of documents
documents = await _document_loader(observation, loader_settings)
@ -160,27 +182,49 @@ class WeaviateVectorDB(VectorDB):
for doc_list in documents:
for doc in doc_list:
chunk_count += 1
params['chunk_count'] = doc.metadata.get("chunk_count", "None")
logging.info("Loading document with provided loader settings %s", str(doc))
params['source'] = doc.metadata.get("source", "None")
params["chunk_count"] = doc.metadata.get("chunk_count", "None")
logging.info(
"Loading document with provided loader settings %s", str(doc)
)
params["source"] = doc.metadata.get("source", "None")
logging.info("Params are %s", str(params))
retriever.add_documents([
Document(metadata=params, page_content=doc.page_content)])
retriever.add_documents(
[Document(metadata=params, page_content=doc.page_content)]
)
else:
chunk_count = 0
from cognitive_architecture.database.vectordb.chunkers.chunkers import chunk_data
documents = [chunk_data(chunk_strategy="VANILLA", source_data=observation, chunk_size=300,
chunk_overlap=20)]
from cognitive_architecture.database.vectordb.chunkers.chunkers import (
chunk_data,
)
documents = [
chunk_data(
chunk_strategy="VANILLA",
source_data=observation,
chunk_size=300,
chunk_overlap=20,
)
]
for doc in documents[0]:
chunk_count += 1
params['chunk_order'] = chunk_count
params['source'] = "User loaded"
logging.info("Loading document with default loader settings %s", str(doc))
params["chunk_order"] = chunk_count
params["source"] = "User loaded"
logging.info(
"Loading document with default loader settings %s", str(doc)
)
logging.info("Params are %s", str(params))
retriever.add_documents([
Document(metadata=params, page_content=doc.page_content)])
retriever.add_documents(
[Document(metadata=params, page_content=doc.page_content)]
)
async def fetch_memories(self, observation: str, namespace: str = None, search_type: str = 'hybrid',params=None, **kwargs):
async def fetch_memories(
self,
observation: str,
namespace: str = None,
search_type: str = "hybrid",
params=None,
**kwargs,
):
"""
Fetch documents from weaviate.
@ -196,12 +240,9 @@ class WeaviateVectorDB(VectorDB):
Example:
fetch_memories(query="some query", search_type='text', additional_param='value')
"""
client = self.init_weaviate(namespace =self.namespace)
client = self.init_weaviate(namespace=self.namespace)
if search_type is None:
search_type = 'hybrid'
search_type = "hybrid"
if not namespace:
namespace = self.namespace
@ -222,37 +263,41 @@ class WeaviateVectorDB(VectorDB):
for prop in class_obj["properties"]
]
base_query = client.query.get(
base_query = (
client.query.get(
namespace, list(list_objects_of_class(namespace, client.schema.get()))
).with_additional(
["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", 'distance']
).with_where(params_user_id).with_limit(10)
)
.with_additional(
["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", "distance"]
)
.with_where(params_user_id)
.with_limit(10)
)
n_of_observations = kwargs.get('n_of_observations', 2)
n_of_observations = kwargs.get("n_of_observations", 2)
# try:
if search_type == 'text':
if search_type == "text":
query_output = (
base_query
.with_near_text({"concepts": [observation]})
base_query.with_near_text({"concepts": [observation]})
.with_autocut(n_of_observations)
.do()
)
elif search_type == 'hybrid':
elif search_type == "hybrid":
query_output = (
base_query
.with_hybrid(query=observation, fusion_type=HybridFusion.RELATIVE_SCORE)
base_query.with_hybrid(
query=observation, fusion_type=HybridFusion.RELATIVE_SCORE
)
.with_autocut(n_of_observations)
.do()
)
elif search_type == 'bm25':
elif search_type == "bm25":
query_output = (
base_query
.with_bm25(query=observation)
base_query.with_bm25(query=observation)
.with_autocut(n_of_observations)
.do()
)
elif search_type == 'summary':
elif search_type == "summary":
filter_object = {
"operator": "And",
"operands": [
@ -266,20 +311,32 @@ class WeaviateVectorDB(VectorDB):
"operator": "LessThan",
"valueNumber": 30,
},
]
],
}
base_query = client.query.get(
namespace, list(list_objects_of_class(namespace, client.schema.get()))
).with_additional(
["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", 'distance']
).with_where(filter_object).with_limit(30)
base_query = (
client.query.get(
namespace,
list(list_objects_of_class(namespace, client.schema.get())),
)
.with_additional(
[
"id",
"creationTimeUnix",
"lastUpdateTimeUnix",
"score",
"distance",
]
)
.with_where(filter_object)
.with_limit(30)
)
query_output = (
base_query
# .with_hybrid(query=observation, fusion_type=HybridFusion.RELATIVE_SCORE)
.do()
)
elif search_type == 'summary_filter_by_object_name':
elif search_type == "summary_filter_by_object_name":
filter_object = {
"operator": "And",
"operands": [
@ -293,17 +350,27 @@ class WeaviateVectorDB(VectorDB):
"operator": "Equal",
"valueText": params,
},
]
],
}
base_query = client.query.get(
namespace, list(list_objects_of_class(namespace, client.schema.get()))
).with_additional(
["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", 'distance']
).with_where(filter_object).with_limit(30).with_hybrid(query=observation, fusion_type=HybridFusion.RELATIVE_SCORE)
query_output = (
base_query
.do()
base_query = (
client.query.get(
namespace,
list(list_objects_of_class(namespace, client.schema.get())),
)
.with_additional(
[
"id",
"creationTimeUnix",
"lastUpdateTimeUnix",
"score",
"distance",
]
)
.with_where(filter_object)
.with_limit(30)
.with_hybrid(query=observation, fusion_type=HybridFusion.RELATIVE_SCORE)
)
query_output = base_query.do()
# from weaviate.classes import Filter
# client = weaviate.connect_to_wcs(
# cluster_url=config.weaviate_url,
@ -311,20 +378,18 @@ class WeaviateVectorDB(VectorDB):
# )
return query_output
elif search_type == 'generate':
generate_prompt = kwargs.get('generate_prompt', "")
elif search_type == "generate":
generate_prompt = kwargs.get("generate_prompt", "")
query_output = (
base_query
.with_generate(single_prompt=observation)
base_query.with_generate(single_prompt=observation)
.with_near_text({"concepts": [observation]})
.with_autocut(n_of_observations)
.do()
)
elif search_type == 'generate_grouped':
generate_prompt = kwargs.get('generate_prompt', "")
elif search_type == "generate_grouped":
generate_prompt = kwargs.get("generate_prompt", "")
query_output = (
base_query
.with_generate(grouped_task=observation)
base_query.with_generate(grouped_task=observation)
.with_near_text({"concepts": [observation]})
.with_autocut(n_of_observations)
.do()
@ -338,12 +403,10 @@ class WeaviateVectorDB(VectorDB):
return query_output
async def delete_memories(self, namespace:str, params: dict = None):
async def delete_memories(self, namespace: str, params: dict = None):
if namespace is None:
namespace = self.namespace
client = self.init_weaviate(namespace = self.namespace)
client = self.init_weaviate(namespace=self.namespace)
if params:
where_filter = {
"path": ["id"],
@ -366,7 +429,6 @@ class WeaviateVectorDB(VectorDB):
},
)
async def count_memories(self, namespace: str = None, params: dict = None) -> int:
"""
Count memories in a Weaviate database.
@ -380,7 +442,7 @@ class WeaviateVectorDB(VectorDB):
if namespace is None:
namespace = self.namespace
client = self.init_weaviate(namespace =namespace)
client = self.init_weaviate(namespace=namespace)
try:
object_count = client.query.aggregate(namespace).with_meta_count().do()
@ -391,7 +453,7 @@ class WeaviateVectorDB(VectorDB):
return 0
def update_memories(self, observation, namespace: str, params: dict = None):
client = self.init_weaviate(namespace = self.namespace)
client = self.init_weaviate(namespace=self.namespace)
client.data_object.update(
data_object={
@ -416,12 +478,15 @@ class WeaviateVectorDB(VectorDB):
)
return
import os
import lancedb
from pydantic import BaseModel
from typing import List, Optional
import pandas as pd
import pyarrow as pa
class LanceDB(VectorDB):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@ -434,21 +499,28 @@ class LanceDB(VectorDB):
db = lancedb.connect(uri, api_key=os.getenv("LANCEDB_API_KEY"))
return db
def create_table(self, name: str, schema: Optional[pa.Schema] = None, data: Optional[pd.DataFrame] = None):
def create_table(
self,
name: str,
schema: Optional[pa.Schema] = None,
data: Optional[pd.DataFrame] = None,
):
# Create a table in LanceDB. If schema is not provided, it will be inferred from the data.
if data is not None and schema is None:
schema = pa.Schema.from_pandas(data)
table = self.db.create_table(name, schema=schema)
if data is not None:
table.add(data.to_dict('records'))
table.add(data.to_dict("records"))
return table
def add_memories(self, table_name: str, data: pd.DataFrame):
# Add data to an existing table in LanceDB
table = self.db.open_table(table_name)
table.add(data.to_dict('records'))
table.add(data.to_dict("records"))
def fetch_memories(self, table_name: str, query_vector: List[float], top_k: int = 10):
def fetch_memories(
self, table_name: str, query_vector: List[float], top_k: int = 10
):
# Perform a vector search in the specified table
table = self.db.open_table(table_name)
results = table.search(query_vector).limit(top_k).to_pandas()

6
cognitive_architecture/fetch_secret.py
View file

@ -16,15 +16,16 @@ sys.path.insert(0, parent_dir)
environment = os.getenv("AWS_ENV", "dev")
def fetch_secret(secret_name, region_name, env_file_path):
print("Initializing session")
session = boto3.session.Session()
print("Session initialized")
client = session.client(service_name="secretsmanager", region_name = region_name)
client = session.client(service_name="secretsmanager", region_name=region_name)
print("Client initialized")
try:
response = client.get_secret_value(SecretId = secret_name)
response = client.get_secret_value(SecretId=secret_name)
except Exception as e:
print(f"Error retrieving secret: {e}")
return None
@ -46,6 +47,7 @@ def fetch_secret(secret_name, region_name, env_file_path):
else:
print(f"The .env file was not found at: {env_file_path}.")
ENV_FILE_PATH = os.path.abspath("../.env")
if os.path.exists(ENV_FILE_PATH):

25
cognitive_architecture/llm/queries.py
View file

@ -6,6 +6,7 @@ from ..shared.data_models import Node, Edge, KnowledgeGraph, GraphQLQuery, Memor
from ..config import Config
import instructor
from openai import OpenAI
config = Config()
config.load()
@ -23,7 +24,7 @@ import logging
# Function to read query prompts from files
def read_query_prompt(filename):
try:
with open(filename, 'r') as file:
with open(filename, "r") as file:
return file.read()
except FileNotFoundError:
logging.info(f"Error: File not found. Attempted to read: {filename}")
@ -37,7 +38,9 @@ def read_query_prompt(filename):
def generate_graph(input) -> KnowledgeGraph:
model = "gpt-4-1106-preview"
user_prompt = f"Use the given format to extract information from the following input: {input}."
system_prompt = read_query_prompt('cognitive_architecture/llm/prompts/generate_graph_prompt.txt')
system_prompt = read_query_prompt(
"cognitive_architecture/llm/prompts/generate_graph_prompt.txt"
)
out = aclient.chat.completions.create(
model=model,
@ -56,7 +59,6 @@ def generate_graph(input) -> KnowledgeGraph:
return out
async def generate_summary(input) -> MemorySummary:
out = aclient.chat.completions.create(
model=config.model,
@ -64,30 +66,33 @@ async def generate_summary(input) -> MemorySummary:
{
"role": "user",
"content": f"""Use the given format summarize and reduce the following input: {input}. """,
},
{ "role":"system", "content": """You are a top-tier algorithm
{
"role": "system",
"content": """You are a top-tier algorithm
designed for summarizing existing knowledge graphs in structured formats based on a knowledge graph.
## 1. Strict Compliance
Adhere to the rules strictly. Non-compliance will result in termination.
## 2. Don't forget your main goal is to reduce the number of nodes in the knowledge graph while preserving the information contained in it."""}
## 2. Don't forget your main goal is to reduce the number of nodes in the knowledge graph while preserving the information contained in it.""",
},
],
response_model=MemorySummary,
)
return out
def user_query_to_edges_and_nodes( input: str) ->KnowledgeGraph:
system_prompt = read_query_prompt('cognitive_architecture/llm/prompts/generate_graph_prompt.txt')
def user_query_to_edges_and_nodes(input: str) -> KnowledgeGraph:
system_prompt = read_query_prompt(
"cognitive_architecture/llm/prompts/generate_graph_prompt.txt"
)
return aclient.chat.completions.create(
model=config.model,
messages=[
{
"role": "user",
"content": f"""Use the given format to extract information from the following input: {input}. """,
},
{"role": "system", "content":system_prompt}
{"role": "system", "content": system_prompt},
],
response_model=KnowledgeGraph,
)

12
cognitive_architecture/openai_tools.py
View file

@ -4,7 +4,6 @@ import os
import time
HOST = os.getenv("OPENAI_API_BASE")
HOST_TYPE = os.getenv("BACKEND_TYPE") # default None == ChatCompletion
@ -41,7 +40,9 @@ def retry_with_exponential_backoff(
# Check if max retries has been reached
if num_retries > max_retries:
raise Exception(f"Maximum number of retries ({max_retries}) exceeded.")
raise Exception(
f"Maximum number of retries ({max_retries}) exceeded."
)
# Increment the delay
delay *= exponential_base * (1 + jitter * random.random())
@ -90,7 +91,9 @@ def aretry_with_exponential_backoff(
# Check if max retries has been reached
if num_retries > max_retries:
raise Exception(f"Maximum number of retries ({max_retries}) exceeded.")
raise Exception(
f"Maximum number of retries ({max_retries}) exceeded."
)
# Increment the delay
delay *= exponential_base * (1 + jitter * random.random())
@ -135,6 +138,3 @@ def get_embedding_with_backoff(text, model="text-embedding-ada-002"):
response = create_embedding_with_backoff(input=[text], model=model)
embedding = response["data"][0]["embedding"]
return embedding

2
cognitive_architecture/presets.py
View file

@ -1,9 +1,7 @@
DEFAULT_PRESET = "cognitive_architecture_chat"
preset_options = [DEFAULT_PRESET]
def use_preset():
"""Placeholder for different present options"""

13
cognitive_architecture/shared/data_models.py
View file

@ -1,33 +1,36 @@
from typing import Optional, List
from pydantic import BaseModel, Field
class Node(BaseModel):
id: int
description: str
category: str
color: str ="blue"
color: str = "blue"
memory_type: str
created_at: Optional[float] = None
summarized: Optional[bool] = None
class Edge(BaseModel):
source: int
target: int
description: str
color: str= "blue"
color: str = "blue"
created_at: Optional[float] = None
summarized: Optional[bool] = None
class KnowledgeGraph(BaseModel):
nodes: List[Node] = Field(..., default_factory=list)
edges: List[Edge] = Field(..., default_factory=list)
class GraphQLQuery(BaseModel):
query: str
class MemorySummary(BaseModel):
nodes: List[Node] = Field(..., default_factory=list)
edges: List[Edge] = Field(..., default_factory=list)

34
cognitive_architecture/shared/language_processing.py
View file

@ -3,13 +3,15 @@ from botocore.exceptions import BotoCoreError, ClientError
from langdetect import detect, LangDetectException
import iso639
from dotenv import load_dotenv
load_dotenv()
import logging
# Basic configuration of the logging system
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
def detect_language(text):
@ -34,8 +36,8 @@ def detect_language(text):
logging.info(f"Detected ISO 639-1 code: {detected_lang_iso639_1}")
# Special case: map 'hr' (Croatian) to 'sr' (Serbian ISO 639-2)
if detected_lang_iso639_1 == 'hr':
return 'sr'
if detected_lang_iso639_1 == "hr":
return "sr"
return detected_lang_iso639_1
except LangDetectException as e:
@ -46,8 +48,12 @@ def detect_language(text):
return -1
def translate_text(text, source_language:str='sr', target_language:str='en', region_name='eu-west-1'):
def translate_text(
text,
source_language: str = "sr",
target_language: str = "en",
region_name="eu-west-1",
):
"""
Translate text from source language to target language using AWS Translate.
@ -68,9 +74,15 @@ def translate_text(text, source_language:str='sr', target_language:str='en', reg
return "Both source and target language codes are required."
try:
translate = boto3.client(service_name='translate', region_name=region_name, use_ssl=True)
result = translate.translate_text(Text=text, SourceLanguageCode=source_language, TargetLanguageCode=target_language)
return result.get('TranslatedText', 'No translation found.')
translate = boto3.client(
service_name="translate", region_name=region_name, use_ssl=True
)
result = translate.translate_text(
Text=text,
SourceLanguageCode=source_language,
TargetLanguageCode=target_language,
)
return result.get("TranslatedText", "No translation found.")
except BotoCoreError as e:
logging.info(f"BotoCoreError occurred: {e}")
@ -81,8 +93,8 @@ def translate_text(text, source_language:str='sr', target_language:str='en', reg
return "Error with AWS client or network issue."
source_language = 'sr'
target_language = 'en'
source_language = "sr"
target_language = "en"
text_to_translate = "Ja volim da pecam i idem na reku da šetam pored nje ponekad"
translated_text = translate_text(text_to_translate, source_language, target_language)

74
cognitive_architecture/utils.py
View file

@ -22,12 +22,15 @@ class Node:
self.description = description
self.color = color
class Edge:
def __init__(self, source, target, label, color):
self.source = source
self.target = target
self.label = label
self.color = color
# def visualize_knowledge_graph(kg: KnowledgeGraph):
# dot = Digraph(comment="Knowledge Graph")
#
@ -82,6 +85,7 @@ def get_document_names(doc_input):
# doc_input is not valid
return []
def format_dict(d):
# Initialize an empty list to store formatted items
formatted_items = []
@ -89,7 +93,9 @@ def format_dict(d):
# Iterate through all key-value pairs
for key, value in d.items():
# Format key-value pairs with a colon and space, and adding quotes for string values
formatted_item = f"{key}: '{value}'" if isinstance(value, str) else f"{key}: {value}"
formatted_item = (
f"{key}: '{value}'" if isinstance(value, str) else f"{key}: {value}"
)
formatted_items.append(formatted_item)
# Join all formatted items with a comma and a space
@ -114,7 +120,7 @@ def create_node_variable_mapping(nodes):
mapping = {}
for node in nodes:
variable_name = f"{node['category']}{node['id']}".lower()
mapping[node['id']] = variable_name
mapping[node["id"]] = variable_name
return mapping
@ -123,18 +129,23 @@ def create_edge_variable_mapping(edges):
for edge in edges:
# Construct a unique identifier for the edge
variable_name = f"edge{edge['source']}to{edge['target']}".lower()
mapping[(edge['source'], edge['target'])] = variable_name
mapping[(edge["source"], edge["target"])] = variable_name
return mapping
def generate_letter_uuid(length=8):
"""Generate a random string of uppercase letters with the specified length."""
letters = string.ascii_uppercase # A-Z
return "".join(random.choice(letters) for _ in range(length))
from cognitive_architecture.database.relationaldb.models.operation import Operation
from cognitive_architecture.database.relationaldb.database_crud import session_scope, add_entity, update_entity, fetch_job_id
from cognitive_architecture.database.relationaldb.database_crud import (
session_scope,
add_entity,
update_entity,
fetch_job_id,
)
from cognitive_architecture.database.relationaldb.models.metadatas import MetaDatas
from cognitive_architecture.database.relationaldb.models.docs import DocsModel
from cognitive_architecture.database.relationaldb.models.memory import MemoryModel
@ -142,42 +153,56 @@ from cognitive_architecture.database.relationaldb.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())
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)}")
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())
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)}")
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())
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)}")
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.
@ -207,13 +232,13 @@ async def get_unsumarized_vector_db_namespace(session: AsyncSession, user_id: st
.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
contains_eager(Operation.memories), # Informs ORM of the join for memories
)
.where(
(Operation.user_id == user_id) & # Filter by user_id
or_(
(Operation.user_id == user_id)
& or_( # Filter by user_id
DocsModel.graph_summary == False, # Condition 1: graph_summary is False
DocsModel.graph_summary == None # Condition 3: graph_summary is None
DocsModel.graph_summary == None, # Condition 3: graph_summary is None
) # Filter by user_id
)
.order_by(Operation.created_at.desc()) # Order by creation date
@ -223,7 +248,11 @@ async def get_unsumarized_vector_db_namespace(session: AsyncSession, user_id: st
# Extract memory names and document names and IDs
# memory_names = [memory.memory_name for op in operations for memory in op.memories]
memory_details = [(memory.memory_name, memory.memory_category) for op in operations for memory in op.memories]
memory_details = [
(memory.memory_name, memory.memory_category)
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_details, docs
@ -232,6 +261,8 @@ async def get_unsumarized_vector_db_namespace(session: AsyncSession, user_id: st
# # 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.
@ -254,8 +285,12 @@ async def get_memory_name_by_doc_id(session: AsyncSession, docs_id: str):
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
.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
)
@ -269,7 +304,6 @@ async def get_memory_name_by_doc_id(session: AsyncSession, docs_id: str):
return None
#
# async def main():
# user_id = "user"

83
cognitive_architecture/vectorstore_manager.py
View file

@ -9,8 +9,6 @@ import os
print(os.getcwd())
from cognitive_architecture.database.relationaldb.models.user import User
from cognitive_architecture.database.relationaldb.models.memory import MemoryModel
@ -27,7 +25,6 @@ import uuid
load_dotenv()
from cognitive_architecture.database.vectordb.basevectordb import BaseMemory
from cognitive_architecture.config import Config
@ -36,8 +33,6 @@ config = Config()
config.load()
class DynamicBaseMemory(BaseMemory):
def __init__(
self,
@ -145,8 +140,8 @@ class Memory:
db_type: str = None,
namespace: str = None,
memory_id: str = None,
memory_class = None,
job_id:str = None
memory_class=None,
job_id: str = None,
) -> None:
self.load_environment_variables()
self.memory_id = memory_id
@ -157,20 +152,25 @@ class Memory:
self.namespace = namespace
self.memory_instances = []
self.memory_class = memory_class
self.job_id=job_id
self.job_id = job_id
# self.memory_class = DynamicBaseMemory(
# "Memory", user_id, str(self.memory_id), index_name, db_type, namespace
# )
def load_environment_variables(self) -> None:
load_dotenv()
self.OPENAI_TEMPERATURE = config.openai_temperature
self.OPENAI_API_KEY = config.openai_key
@classmethod
async def create_memory(cls, user_id: str, session, job_id:str=None, memory_label:str=None, **kwargs):
async def create_memory(
cls,
user_id: str,
session,
job_id: str = None,
memory_label: str = None,
**kwargs,
):
"""
Class method that acts as a factory method for creating Memory instances.
It performs necessary DB checks or updates before instance creation.
@ -180,9 +180,14 @@ class Memory:
if existing_user:
# Handle existing user scenario...
memory_id = await cls.check_existing_memory(user_id,memory_label, session)
memory_id = await cls.check_existing_memory(user_id, memory_label, session)
if memory_id is None:
memory_id = await cls.handle_new_memory(user_id = user_id, session= session,job_id=job_id, memory_name= memory_label)
memory_id = await cls.handle_new_memory(
user_id=user_id,
session=session,
job_id=job_id,
memory_name=memory_label,
)
logging.info(
f"Existing user {user_id} found in the DB. Memory ID: {memory_id}"
)
@ -190,16 +195,33 @@ class Memory:
# Handle new user scenario...
await cls.handle_new_user(user_id, session)
memory_id = await cls.handle_new_memory(user_id =user_id, session=session, job_id=job_id, memory_name= memory_label)
memory_id = await cls.handle_new_memory(
user_id=user_id,
session=session,
job_id=job_id,
memory_name=memory_label,
)
logging.info(
f"New user {user_id} created in the DB. Memory ID: {memory_id}"
)
memory_class = DynamicBaseMemory(
memory_label, user_id, str(memory_id), index_name=memory_label , db_type=config.vectordb, **kwargs
memory_label,
user_id,
str(memory_id),
index_name=memory_label,
db_type=config.vectordb,
**kwargs,
)
return cls(user_id=user_id, session=session, memory_id=memory_id, job_id =job_id, memory_class=memory_class, **kwargs)
return cls(
user_id=user_id,
session=session,
memory_id=memory_id,
job_id=job_id,
memory_class=memory_class,
**kwargs,
)
async def list_memory_classes(self):
"""
@ -215,19 +237,20 @@ class Memory:
return result.scalar_one_or_none()
@staticmethod
async def check_existing_memory(user_id: str, memory_label:str, session):
async def check_existing_memory(user_id: str, memory_label: str, session):
"""Check if a user memory exists in the DB and return it. Filters by user and label"""
try:
result = await session.execute(
select(MemoryModel.id).where(MemoryModel.user_id == user_id)
select(MemoryModel.id)
.where(MemoryModel.user_id == user_id)
.filter_by(memory_name=memory_label)
.order_by(MemoryModel.created_at)
)
return result.scalar_one_or_none()
except Exception as e:
logging.error(f"An error occurred: {str(e)}")
return None
@staticmethod
async def handle_new_user(user_id: str, session):
"""
@ -251,7 +274,13 @@ class Memory:
return f"Error creating user: {str(e)}"
@staticmethod
async def handle_new_memory(user_id: str, session, job_id: str = None, memory_name: str = None, memory_category:str='PUBLIC'):
async def handle_new_memory(
user_id: str,
session,
job_id: str = None,
memory_name: str = None,
memory_category: str = "PUBLIC",
):
"""
Handle new memory creation associated with a user.
@ -296,7 +325,6 @@ class Memory:
except Exception as e:
return f"Error creating memory: {str(e)}"
async def add_memory_instance(self, memory_class_name: str):
"""Add a new memory instance to the memory_instances list."""
instance = DynamicBaseMemory(
@ -446,7 +474,9 @@ async def main():
from database.relationaldb.database import AsyncSessionLocal
async with session_scope(AsyncSessionLocal()) as session:
memory = await Memory.create_memory("677", session, "SEMANTICMEMORY", namespace="SEMANTICMEMORY")
memory = await Memory.create_memory(
"677", session, "SEMANTICMEMORY", namespace="SEMANTICMEMORY"
)
ff = memory.memory_instances
logging.info("ssss %s", ff)
@ -462,8 +492,13 @@ async def main():
await memory.add_dynamic_memory_class("semanticmemory", "SEMANTICMEMORY")
await memory.add_method_to_class(memory.semanticmemory_class, "add_memories")
await memory.add_method_to_class(memory.semanticmemory_class, "fetch_memories")
sss = await memory.dynamic_method_call(memory.semanticmemory_class, 'add_memories',
observation='some_observation', params=params, loader_settings=loader_settings)
sss = await memory.dynamic_method_call(
memory.semanticmemory_class,
"add_memories",
observation="some_observation",
params=params,
loader_settings=loader_settings,
)
# susu = await memory.dynamic_method_call(
# memory.semanticmemory_class,

423
main.py
View file

@ -7,7 +7,10 @@ from cognitive_architecture.database.relationaldb.models.memory import MemoryMod
from cognitive_architecture.classifiers.classifier import classify_documents
import os
from dotenv import load_dotenv
from cognitive_architecture.database.relationaldb.database_crud import session_scope, update_entity_graph_summary
from cognitive_architecture.database.relationaldb.database_crud import (
session_scope,
update_entity_graph_summary,
)
from cognitive_architecture.database.relationaldb.database import AsyncSessionLocal
from cognitive_architecture.utils import generate_letter_uuid
import instructor
@ -17,12 +20,18 @@ from cognitive_architecture.database.relationaldb.database_crud import fetch_job
import uuid
from cognitive_architecture.database.relationaldb.models.sessions import Session
from cognitive_architecture.database.relationaldb.models.operation import Operation
from cognitive_architecture.database.relationaldb.database_crud import session_scope, add_entity, update_entity, fetch_job_id
from cognitive_architecture.database.relationaldb.database_crud import (
session_scope,
add_entity,
update_entity,
fetch_job_id,
)
from cognitive_architecture.database.relationaldb.models.metadatas import MetaDatas
from cognitive_architecture.database.relationaldb.models.docs import DocsModel
from cognitive_architecture.database.relationaldb.models.memory import MemoryModel
from cognitive_architecture.database.relationaldb.models.user import User
from cognitive_architecture.classifiers.classifier import classify_call
aclient = instructor.patch(OpenAI())
DEFAULT_PRESET = "promethai_chat"
preset_options = [DEFAULT_PRESET]
@ -30,6 +39,7 @@ PROMETHAI_DIR = os.path.join(os.path.expanduser("~"), ".")
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
from cognitive_architecture.config import Config
config = Config()
config.load()
from cognitive_architecture.utils import get_document_names
@ -37,14 +47,28 @@ 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
from cognitive_architecture.shared.language_processing import translate_text, detect_language
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,
)
from cognitive_architecture.shared.language_processing import (
translate_text,
detect_language,
)
from cognitive_architecture.classifiers.classifier import classify_user_input
async def fetch_document_vectordb_namespace(session: AsyncSession, user_id: str, namespace_id:str, doc_id:str=None):
logging.info("user id is", user_id)
memory = await Memory.create_memory(user_id, session, namespace=namespace_id, memory_label=namespace_id)
async def fetch_document_vectordb_namespace(
session: AsyncSession, user_id: str, namespace_id: str, doc_id: str = None
):
logging.info("user id is", user_id)
memory = await Memory.create_memory(
user_id, session, namespace=namespace_id, memory_label=namespace_id
)
# Managing memory attributes
existing_user = await Memory.check_existing_user(user_id, session)
@ -66,15 +90,26 @@ async def fetch_document_vectordb_namespace(session: AsyncSession, user_id: str,
print(f"No attribute named in memory.")
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_filter_by_object_name", params=doc_id)
result = await memory.dynamic_method_call(
dynamic_memory_class,
"fetch_memories",
observation="placeholder",
search_type="summary_filter_by_object_name",
params=doc_id,
)
logging.info("Result is %s", str(result))
return result, namespace_id
async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, content:str=None, job_id:str=None, loader_settings:dict=None, memory_type:str="PRIVATE"):
async def load_documents_to_vectorstore(
session: AsyncSession,
user_id: str,
content: str = None,
job_id: str = None,
loader_settings: dict = None,
memory_type: str = "PRIVATE",
):
namespace_id = str(generate_letter_uuid()) + "_" + "SEMANTICMEMORY"
namespace_class = namespace_id + "_class"
@ -96,12 +131,21 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, con
operation_type="DATA_LOAD",
),
)
memory = await Memory.create_memory(user_id, session, namespace=namespace_id, job_id=job_id, memory_label=namespace_id)
memory = await Memory.create_memory(
user_id,
session,
namespace=namespace_id,
job_id=job_id,
memory_label=namespace_id,
)
if content is not None:
document_names = [content[:30]]
if loader_settings is not None:
document_source = loader_settings.get("document_names") if isinstance(loader_settings.get("document_names"),
list) else loader_settings.get("path", "None")
document_source = (
loader_settings.get("document_names")
if isinstance(loader_settings.get("document_names"), list)
else loader_settings.get("path", "None")
)
logging.info("Document source is %s", document_source)
# try:
document_names = get_document_names(document_source[0])
@ -109,12 +153,21 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, con
# except:
# document_names = document_source
for doc in document_names:
from cognitive_architecture.shared.language_processing import translate_text, detect_language
#translates doc titles to english
from cognitive_architecture.shared.language_processing import (
translate_text,
detect_language,
)
# translates doc titles to english
if loader_settings is not None:
logging.info("Detecting language of document %s", doc)
loader_settings["single_document_path"]= loader_settings.get("path", "None")[0] +"/"+doc
logging.info("Document path is %s", loader_settings.get("single_document_path", "None"))
loader_settings["single_document_path"] = (
loader_settings.get("path", "None")[0] + "/" + doc
)
logging.info(
"Document path is %s",
loader_settings.get("single_document_path", "None"),
)
memory_category = loader_settings.get("memory_category", "PUBLIC")
if loader_settings is None:
memory_category = "CUSTOM"
@ -122,7 +175,7 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, con
doc_ = doc.strip(".pdf").replace("-", " ")
doc_ = translate_text(doc_, "sr", "en")
else:
doc_=doc
doc_ = doc
doc_id = str(uuid.uuid4())
logging.info("Document name is %s", doc_)
@ -131,17 +184,15 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, con
DocsModel(
id=doc_id,
operation_id=job_id,
graph_summary= False,
memory_category= memory_category,
doc_name=doc_
)
graph_summary=False,
memory_category=memory_category,
doc_name=doc_,
),
)
# Managing memory attributes
existing_user = await Memory.check_existing_user(user_id, session)
await memory.manage_memory_attributes(existing_user)
params = {
"doc_id":doc_id
}
params = {"doc_id": doc_id}
print("Namespace id is %s", namespace_id)
await memory.add_dynamic_memory_class(namespace_id.lower(), namespace_id)
@ -157,13 +208,18 @@ async def load_documents_to_vectorstore(session: AsyncSession, user_id: str, con
print(f"No attribute named in memory.")
print("Available memory classes:", await memory.list_memory_classes())
result = await memory.dynamic_method_call(dynamic_memory_class, 'add_memories',
observation=content, params=params, loader_settings=loader_settings)
result = await memory.dynamic_method_call(
dynamic_memory_class,
"add_memories",
observation=content,
params=params,
loader_settings=loader_settings,
)
await update_entity(session, Operation, job_id, "SUCCESS")
return 1
async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_input: str):
async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_input: str):
try:
new_user = User(id=user_id)
await add_entity(session, new_user)
@ -189,19 +245,32 @@ async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_inpu
else:
translated_query = query_input
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username, password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
cypher_query = await neo4j_graph_db.generate_cypher_query_for_user_prompt_decomposition(user_id, translated_query)
cypher_query = (
await neo4j_graph_db.generate_cypher_query_for_user_prompt_decomposition(
user_id, translated_query
)
)
result = neo4j_graph_db.query(cypher_query)
neo4j_graph_db.run_merge_query(user_id=user_id, memory_type="SemanticMemory", similarity_threshold=0.8)
neo4j_graph_db.run_merge_query(user_id=user_id, memory_type="EpisodicMemory", similarity_threshold=0.8)
neo4j_graph_db.run_merge_query(
user_id=user_id, memory_type="SemanticMemory", similarity_threshold=0.8
)
neo4j_graph_db.run_merge_query(
user_id=user_id, memory_type="EpisodicMemory", similarity_threshold=0.8
)
neo4j_graph_db.close()
await update_entity(session, Operation, job_id, "SUCCESS")
return result
# async def add_documents_to_graph_db(session: AsyncSession, user_id: Optional[str] = None,
# document_memory_types: Optional[List[str]] = None):
# """ Add documents to a graph database, handling multiple memory types """
@ -256,89 +325,132 @@ async def user_query_to_graph_db(session: AsyncSession, user_id: str, query_inpu
# return e
async def add_documents_to_graph_db(session: AsyncSession, user_id: str= None, document_memory_types:list=None):
async def add_documents_to_graph_db(
session: AsyncSession, user_id: str = None, document_memory_types: list = None
):
""""""
if document_memory_types is None:
document_memory_types = ['PUBLIC']
document_memory_types = ["PUBLIC"]
logging.info("Document memory types are", document_memory_types)
try:
# await update_document_vectordb_namespace(postgres_session, user_id)
memory_details, docs = await get_unsumarized_vector_db_namespace(session, user_id)
memory_details, docs = await get_unsumarized_vector_db_namespace(
session, user_id
)
logging.info("Docs are", docs)
memory_details= [detail for detail in memory_details if detail[1] in document_memory_types]
memory_details = [
detail for detail in memory_details if detail[1] in document_memory_types
]
logging.info("Memory details", memory_details)
for doc in docs:
logging.info("Memory names are", memory_details)
doc_name, doc_id = doc
logging.info("Doc id is", doc_id)
try:
classification_content = await fetch_document_vectordb_namespace(session, user_id, memory_details[0][0], doc_id)
retrieval_chunks = [item['text'] for item in
classification_content[0]['data']['Get'][memory_details[0][0]]]
classification_content = await fetch_document_vectordb_namespace(
session, user_id, memory_details[0][0], doc_id
)
retrieval_chunks = [
item["text"]
for item in classification_content[0]["data"]["Get"][
memory_details[0][0]
]
]
logging.info("Classification content is", classification_content)
except:
classification_content = ""
retrieval_chunks = ""
# retrieval_chunks = [item['text'] for item in classification_content[0]['data']['Get'][memory_details[0]]]
# Concatenating the extracted text values
concatenated_retrievals = ' '.join(retrieval_chunks)
concatenated_retrievals = " ".join(retrieval_chunks)
print(concatenated_retrievals)
logging.info("Retrieval chunks are", retrieval_chunks)
classification = await classify_documents(doc_name, document_id =doc_id, content=concatenated_retrievals)
classification = await classify_documents(
doc_name, document_id=doc_id, content=concatenated_retrievals
)
logging.info("Classification is %s", str(classification))
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
if document_memory_types == ['PUBLIC']:
await create_public_memory(user_id=user_id, labels=['sr'], topic="PublicMemory")
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(topic="PublicMemory")
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
if document_memory_types == ["PUBLIC"]:
await create_public_memory(
user_id=user_id, labels=["sr"], topic="PublicMemory"
)
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(
topic="PublicMemory"
)
neo4j_graph_db.close()
print(ids)
else:
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(topic="SemanticMemory")
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(
topic="SemanticMemory"
)
neo4j_graph_db.close()
print(ids)
for id in ids:
print(id.get('memoryId'))
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
if document_memory_types == ['PUBLIC']:
rs = neo4j_graph_db.create_document_node_cypher(classification, user_id, public_memory_id=id.get('memoryId'))
print(id.get("memoryId"))
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
if document_memory_types == ["PUBLIC"]:
rs = neo4j_graph_db.create_document_node_cypher(
classification, user_id, public_memory_id=id.get("memoryId")
)
neo4j_graph_db.close()
else:
rs = neo4j_graph_db.create_document_node_cypher(classification, user_id, memory_type='SemanticMemory')
rs = neo4j_graph_db.create_document_node_cypher(
classification, user_id, memory_type="SemanticMemory"
)
neo4j_graph_db.close()
logging.info("Cypher query is %s", str(rs))
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
neo4j_graph_db.query(rs)
neo4j_graph_db.close()
logging.info("WE GOT HERE")
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
if memory_details[0][1] == "PUBLIC":
neo4j_graph_db.update_document_node_with_db_ids( vectordb_namespace=memory_details[0][0],
document_id=doc_id)
neo4j_graph_db.update_document_node_with_db_ids(
vectordb_namespace=memory_details[0][0], document_id=doc_id
)
neo4j_graph_db.close()
else:
neo4j_graph_db.update_document_node_with_db_ids( vectordb_namespace=memory_details[0][0],
document_id=doc_id, user_id=user_id)
neo4j_graph_db.update_document_node_with_db_ids(
vectordb_namespace=memory_details[0][0],
document_id=doc_id,
user_id=user_id,
)
neo4j_graph_db.close()
# await update_entity_graph_summary(session, DocsModel, doc_id, True)
except Exception as e:
return e
class ResponseString(BaseModel):
response: str = Field(default=None) # Defaulting to None or you can use a default string like ""
response: str = Field(
default=None
) # Defaulting to None or you can use a default string like ""
quotation: str = Field(default=None) # Same here
#
def generate_graph(input) -> ResponseString:
out = aclient.chat.completions.create(
model="gpt-4-1106-preview",
@ -346,16 +458,26 @@ def generate_graph(input) -> ResponseString:
{
"role": "user",
"content": f"""Use the given context to answer query and use help of associated context: {input}. """,
},
{ "role":"system", "content": """You are a top-tier algorithm
{
"role": "system",
"content": """You are a top-tier algorithm
designed for using context summaries based on cognitive psychology to answer user queries, and provide a simple response.
Do not mention anything explicit about cognitive architecture, but use the context to answer the query. If you are using a document, reference document metadata field"""}
Do not mention anything explicit about cognitive architecture, but use the context to answer the query. If you are using a document, reference document metadata field""",
},
],
response_model=ResponseString,
)
return out
async def user_context_enrichment(session, user_id:str, query:str, generative_response:bool=False, memory_type:str=None)->str:
async def user_context_enrichment(
session,
user_id: str,
query: str,
generative_response: bool = False,
memory_type: str = None,
) -> str:
"""
Asynchronously enriches the user context by integrating various memory systems and document classifications.
@ -387,31 +509,38 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
enriched_context = await user_context_enrichment(session, "user123", "How does cognitive architecture work?")
```
"""
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
# await user_query_to_graph_db(session, user_id, query)
semantic_mem = neo4j_graph_db.retrieve_semantic_memory(user_id=user_id)
neo4j_graph_db.close()
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
episodic_mem = neo4j_graph_db.retrieve_episodic_memory(user_id=user_id)
neo4j_graph_db.close()
# public_mem = neo4j_graph_db.retrieve_public_memory(user_id=user_id)
if detect_language(query) != "en":
query = translate_text(query, "sr", "en")
logging.info("Translated query is %s", str(query))
if memory_type=='PublicMemory':
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
summaries = await neo4j_graph_db.get_memory_linked_document_summaries(user_id=user_id, memory_type=memory_type)
if memory_type == "PublicMemory":
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
summaries = await neo4j_graph_db.get_memory_linked_document_summaries(
user_id=user_id, memory_type=memory_type
)
neo4j_graph_db.close()
logging.info("Summaries are is %s", summaries)
# logging.info("Context from graphdb is %s", context)
@ -424,7 +553,9 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
relevant_summary_id = None
for _ in range(max_attempts):
relevant_summary_id = await classify_call( query= query, document_summaries=str(summaries))
relevant_summary_id = await classify_call(
query=query, document_summaries=str(summaries)
)
logging.info("Relevant summary id is %s", relevant_summary_id)
@ -432,22 +563,32 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
break
# logging.info("Relevant categories after the classifier are %s", relevant_categories)
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
postgres_id = await neo4j_graph_db.get_memory_linked_document_ids(user_id, summary_id = relevant_summary_id, memory_type=memory_type)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
postgres_id = await neo4j_graph_db.get_memory_linked_document_ids(
user_id, summary_id=relevant_summary_id, memory_type=memory_type
)
neo4j_graph_db.close()
# postgres_id = neo4j_graph_db.query(get_doc_ids)
logging.info("Postgres ids are %s", postgres_id)
namespace_id = await get_memory_name_by_doc_id(session, postgres_id[0])
logging.info("Namespace ids are %s", namespace_id)
params= {"doc_id":postgres_id[0]}
params = {"doc_id": postgres_id[0]}
namespace_id = namespace_id[0]
namespace_class = namespace_id + "_class"
if memory_type =='PublicMemory':
user_id = 'system_user'
if memory_type == "PublicMemory":
user_id = "system_user"
memory = await Memory.create_memory(user_id, session, namespace=namespace_id, job_id="23232",
memory_label=namespace_id)
memory = await Memory.create_memory(
user_id,
session,
namespace=namespace_id,
job_id="23232",
memory_label=namespace_id,
)
existing_user = await Memory.check_existing_user(user_id, session)
print("here is the existing user", existing_user)
@ -468,17 +609,26 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
print(f"No attribute named in memory.")
print("Available memory classes:", await memory.list_memory_classes())
results = await memory.dynamic_method_call(dynamic_memory_class, 'fetch_memories',
observation=query, params=postgres_id[0], search_type="summary_filter_by_object_name")
results = await memory.dynamic_method_call(
dynamic_memory_class,
"fetch_memories",
observation=query,
params=postgres_id[0],
search_type="summary_filter_by_object_name",
)
logging.info("Result is %s", str(results))
search_context = ""
for result in results['data']['Get'][namespace_id]:
for result in results["data"]["Get"][namespace_id]:
# Assuming 'result' is a dictionary and has keys like 'source', 'text'
source = result['source'].replace('-', ' ').replace('.pdf', '').replace('.data/', '')
text = result['text']
source = (
result["source"]
.replace("-", " ")
.replace(".pdf", "")
.replace(".data/", "")
)
text = result["text"]
search_context += f"Document source: {source}, Document text: {text} \n"
else:
@ -502,7 +652,9 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
return generative_result.model_dump_json()
async def create_public_memory(user_id: str=None, labels:list=None, topic:str=None) -> Optional[int]:
async def create_public_memory(
user_id: str = None, labels: list = None, topic: str = None
) -> Optional[int]:
"""
Create a public memory node associated with a user in a Neo4j graph database.
If Public Memory exists, it will return the id of the memory.
@ -521,16 +673,17 @@ async def create_public_memory(user_id: str=None, labels:list=None, topic:str=No
"""
# Validate input parameters
if not labels:
labels = ['sr'] # Labels for the memory node
labels = ["sr"] # Labels for the memory node
if not topic:
topic = "PublicMemory"
try:
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url,
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password)
password=config.graph_database_password,
)
# Assuming the topic for public memory is predefined, e.g., "PublicMemory"
# Create the memory node
@ -541,7 +694,10 @@ async def create_public_memory(user_id: str=None, labels:list=None, topic:str=No
logging.error(f"Error creating public memory node: {e}")
return None
async def attach_user_to_memory(user_id: str=None, labels:list=None, topic:str=None) -> Optional[int]:
async def attach_user_to_memory(
user_id: str = None, labels: list = None, topic: str = None
) -> Optional[int]:
"""
Link user to public memory
@ -560,33 +716,41 @@ async def attach_user_to_memory(user_id: str=None, labels:list=None, topic:str=N
if not user_id:
raise ValueError("User ID is required.")
if not labels:
labels = ['sr'] # Labels for the memory node
labels = ["sr"] # Labels for the memory node
if not topic:
topic = "PublicMemory"
try:
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url,
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password)
password=config.graph_database_password,
)
# Assuming the topic for public memory is predefined, e.g., "PublicMemory"
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(topic=topic)
neo4j_graph_db.close()
for id in ids:
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url,
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password)
linked_memory = neo4j_graph_db.link_public_memory_to_user(memory_id=id.get('memoryId'), user_id=user_id)
password=config.graph_database_password,
)
linked_memory = neo4j_graph_db.link_public_memory_to_user(
memory_id=id.get("memoryId"), user_id=user_id
)
neo4j_graph_db.close()
return 1
except Neo4jError as e:
logging.error(f"Error creating public memory node: {e}")
return None
async def unlink_user_from_memory(user_id: str=None, labels:list=None, topic:str=None) -> Optional[int]:
async def unlink_user_from_memory(
user_id: str = None, labels: list = None, topic: str = None
) -> Optional[int]:
"""
Unlink user from memory
@ -604,34 +768,39 @@ async def unlink_user_from_memory(user_id: str=None, labels:list=None, topic:str
if not user_id:
raise ValueError("User ID is required.")
if not labels:
labels = ['sr'] # Labels for the memory node
labels = ["sr"] # Labels for the memory node
if not topic:
topic = "PublicMemory"
try:
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url,
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password)
password=config.graph_database_password,
)
# Assuming the topic for public memory is predefined, e.g., "PublicMemory"
ids = neo4j_graph_db.retrieve_node_id_for_memory_type(topic=topic)
neo4j_graph_db.close()
for id in ids:
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url,
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password)
linked_memory = neo4j_graph_db.unlink_memory_from_user(memory_id=id.get('memoryId'), user_id=user_id)
password=config.graph_database_password,
)
linked_memory = neo4j_graph_db.unlink_memory_from_user(
memory_id=id.get("memoryId"), user_id=user_id
)
neo4j_graph_db.close()
return 1
except Neo4jError as e:
logging.error(f"Error creating public memory node: {e}")
return None
async def relevance_feedback(query: str, input_type: str):
async def relevance_feedback(query: str, input_type: str):
max_attempts = 6
result = None
for attempt in range(1, max_attempts + 1):
@ -641,7 +810,6 @@ async def relevance_feedback(query: str, input_type: str):
return result
async def main():
user_id = "user_test_1_1"
@ -649,8 +817,6 @@ async def main():
# await update_entity(session, DocsModel, "8cd9a022-5a7a-4af5-815a-f988415536ae", True)
# output = await get_unsumarized_vector_db_namespace(session, user_id)
class GraphQLQuery(BaseModel):
query: str
@ -713,7 +879,7 @@ async def main():
# print(out)
# load_doc_to_graph = await add_documents_to_graph_db(session, user_id)
# print(load_doc_to_graph)
user_id = 'test_user'
user_id = "test_user"
# loader_settings = {
# "format": "PDF",
# "source": "DEVICE",
@ -723,10 +889,15 @@ async def main():
# await create_public_memory(user_id=user_id, labels=['sr'], topic="PublicMemory")
# await add_documents_to_graph_db(session, user_id)
#
neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
password=config.graph_database_password)
neo4j_graph_db = Neo4jGraphDB(
url=config.graph_database_url,
username=config.graph_database_username,
password=config.graph_database_password,
)
out = neo4j_graph_db.run_merge_query(user_id = user_id, memory_type="SemanticMemory", similarity_threshold=0.5)
out = neo4j_graph_db.run_merge_query(
user_id=user_id, memory_type="SemanticMemory", similarity_threshold=0.5
)
bb = neo4j_graph_db.query(out)
print(bb)
@ -798,6 +969,4 @@ async def main():
if __name__ == "__main__":
import asyncio
asyncio.run(main())

201
poetry.lock generated
View file

@ -167,6 +167,20 @@ doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-
test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (<0.22)"]
[[package]]
name = "astroid"
version = "3.0.3"
description = "An abstract syntax tree for Python with inference support."
optional = false
python-versions = ">=3.8.0"
files = [
{file = "astroid-3.0.3-py3-none-any.whl", hash = "sha256:92fcf218b89f449cdf9f7b39a269f8d5d617b27be68434912e11e79203963a17"},
{file = "astroid-3.0.3.tar.gz", hash = "sha256:4148645659b08b70d72460ed1921158027a9e53ae8b7234149b1400eddacbb93"},
]
[package.dependencies]
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
[[package]]
name = "astunparse"
version = "1.6.3"
@ -520,6 +534,17 @@ files = [
[package.dependencies]
pycparser = "*"
[[package]]
name = "cfgv"
version = "3.4.0"
description = "Validate configuration and produce human readable error messages."
optional = false
python-versions = ">=3.8"
files = [
{file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
{file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
]
[[package]]
name = "chardet"
version = "5.2.0"
@ -1027,6 +1052,32 @@ files = [
[package.dependencies]
packaging = "*"
[[package]]
name = "dill"
version = "0.3.8"
description = "serialize all of Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
{file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
]
[package.extras]
graph = ["objgraph (>=1.7.2)"]
profile = ["gprof2dot (>=2022.7.29)"]
[[package]]
name = "distlib"
version = "0.3.8"
description = "Distribution utilities"
optional = false
python-versions = "*"
files = [
{file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
{file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
]
[[package]]
name = "distro"
version = "1.9.0"
@ -1962,6 +2013,20 @@ files = [
[package.extras]
tests = ["freezegun", "pytest", "pytest-cov"]
[[package]]
name = "identify"
version = "2.5.34"
description = "File identification library for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "identify-2.5.34-py2.py3-none-any.whl", hash = "sha256:a4316013779e433d08b96e5eabb7f641e6c7942e4ab5d4c509ebd2e7a8994aed"},
{file = "identify-2.5.34.tar.gz", hash = "sha256:ee17bc9d499899bc9eaec1ac7bf2dc9eedd480db9d88b96d123d3b64a9d34f5d"},
]
[package.extras]
license = ["ukkonen"]
[[package]]
name = "idna"
version = "3.6"
@ -2037,6 +2102,20 @@ files = [
{file = "iso639-0.1.4.tar.gz", hash = "sha256:88b70cf6c64ee9c2c2972292818c8beb32db9ea6f4de1f8471a9b081a3d92e98"},
]
[[package]]
name = "isort"
version = "5.13.2"
description = "A Python utility / library to sort Python imports."
optional = false
python-versions = ">=3.8.0"
files = [
{file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
{file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
]
[package.extras]
colors = ["colorama (>=0.4.6)"]
[[package]]
name = "itsdangerous"
version = "2.1.2"
@ -2760,6 +2839,17 @@ pillow = ">=8"
pyparsing = ">=2.3.1"
python-dateutil = ">=2.7"
[[package]]
name = "mccabe"
version = "0.7.0"
description = "McCabe checker, plugin for flake8"
optional = false
python-versions = ">=3.6"
files = [
{file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"},
{file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
]
[[package]]
name = "mdurl"
version = "0.1.2"
@ -2996,6 +3086,20 @@ plot = ["matplotlib"]
tgrep = ["pyparsing"]
twitter = ["twython"]
[[package]]
name = "nodeenv"
version = "1.8.0"
description = "Node.js virtual environment builder"
optional = false
python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
files = [
{file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"},
{file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"},
]
[package.dependencies]
setuptools = "*"
[[package]]
name = "numpy"
version = "1.26.2"
@ -3317,8 +3421,8 @@ files = [
[package.dependencies]
numpy = [
{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
]
python-dateutil = ">=2.8.2"
pytz = ">=2020.1"
@ -3618,6 +3722,21 @@ urllib3 = ">=1.21.1"
[package.extras]
grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv2 (==0.1.0)", "grpcio (>=1.44.0)", "lz4 (>=3.1.3)", "protobuf (>=3.19.5,<3.20.0)"]
[[package]]
name = "platformdirs"
version = "4.2.0"
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
optional = false
python-versions = ">=3.8"
files = [
{file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
{file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
]
[package.extras]
docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
[[package]]
name = "plotly"
version = "5.18.0"
@ -3663,6 +3782,24 @@ docs = ["sphinx (>=1.7.1)"]
redis = ["redis"]
tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)", "types-redis"]
[[package]]
name = "pre-commit"
version = "3.6.1"
description = "A framework for managing and maintaining multi-language pre-commit hooks."
optional = false
python-versions = ">=3.9"
files = [
{file = "pre_commit-3.6.1-py2.py3-none-any.whl", hash = "sha256:9fe989afcf095d2c4796ce7c553cf28d4d4a9b9346de3cda079bcf40748454a4"},
{file = "pre_commit-3.6.1.tar.gz", hash = "sha256:c90961d8aa706f75d60935aba09469a6b0bcb8345f127c3fbee4bdc5f114cf4b"},
]
[package.dependencies]
cfgv = ">=2.0.0"
identify = ">=1.0.0"
nodeenv = ">=0.11.1"
pyyaml = ">=5.1"
virtualenv = ">=20.10.0"
[[package]]
name = "preshed"
version = "3.0.9"
@ -4076,6 +4213,35 @@ benchmarks = ["pytest-benchmark"]
tests = ["datasets", "duckdb", "ml_dtypes", "pandas", "pillow", "polars[pandas,pyarrow]", "pytest", "tensorflow", "tqdm"]
torch = ["torch"]
[[package]]
name = "pylint"
version = "3.0.3"
description = "python code static checker"
optional = false
python-versions = ">=3.8.0"
files = [
{file = "pylint-3.0.3-py3-none-any.whl", hash = "sha256:7a1585285aefc5165db81083c3e06363a27448f6b467b3b0f30dbd0ac1f73810"},
{file = "pylint-3.0.3.tar.gz", hash = "sha256:58c2398b0301e049609a8429789ec6edf3aabe9b6c5fec916acd18639c16de8b"},
]
[package.dependencies]
astroid = ">=3.0.1,<=3.1.0-dev0"
colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
dill = [
{version = ">=0.2", markers = "python_version < \"3.11\""},
{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
{version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
]
isort = ">=4.2.5,<5.13.0 || >5.13.0,<6"
mccabe = ">=0.6,<0.8"
platformdirs = ">=2.2.0"
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
tomlkit = ">=0.10.1"
[package.extras]
spelling = ["pyenchant (>=3.2,<4.0)"]
testutils = ["gitpython (>3)"]
[[package]]
name = "pymupdf"
version = "1.23.8"
@ -5790,6 +5956,17 @@ dev = ["tokenizers[testing]"]
docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
[[package]]
name = "tomli"
version = "2.0.1"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
files = [
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
]
[[package]]
name = "tomlkit"
version = "0.12.3"
@ -6213,6 +6390,26 @@ testing = ["pytest (>=7.4.0)"]
tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"]
tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"]
[[package]]
name = "virtualenv"
version = "20.25.0"
description = "Virtual Python Environment builder"
optional = false
python-versions = ">=3.7"
files = [
{file = "virtualenv-20.25.0-py3-none-any.whl", hash = "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3"},
{file = "virtualenv-20.25.0.tar.gz", hash = "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"},
]
[package.dependencies]
distlib = ">=0.3.7,<1"
filelock = ">=3.12.2,<4"
platformdirs = ">=3.9.1,<5"
[package.extras]
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
[[package]]
name = "wasabi"
version = "1.1.2"
@ -6513,4 +6710,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "671f878d3fc3b864ac68ef553f3f48ac247bfee0ae60540f260fea7fda727e86"
content-hash = "d484dd5ab17563c78699c17296b56155a967f10c432f715a96efbd07e15b34e1"

1
pyproject.toml
View file

@ -62,6 +62,7 @@ iso639 = "^0.1.4"
debugpy = "^1.8.0"
lancedb = "^0.5.5"
pyarrow = "^15.0.0"
pylint = "^3.0.3"
[build-system]
requires = ["poetry-core"]