Vasilije
GitHub
commit
92a9e271f7
4 changed files with 467 additions and 202 deletions
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@ -114,7 +114,7 @@ async def upload_pdf_and_payload(
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logging.info(" PDF split into pages")
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Memory_ = Memory(index_name="my-agent", user_id='555' )
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await Memory_.async_init()
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Memory_._run_buffer(user_input="I want to get a schema for my data", content =pages)
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Memory_._add_episodic_memory(user_input="I want to get a schema for my data", content =pages)
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# Run the buffer
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@ -135,7 +135,82 @@ async def upload_pdf_and_payload(
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# Append the in-memory file to the files list
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# files.append(UploadFile(pdf_stream, filename="downloaded.pdf"))
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#
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def memory_factory(memory_type):
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class Payload(BaseModel):
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payload: Dict[str, Any]
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@app.post("/{memory_type}/add-memory", response_model=dict)
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async def add_memory(
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payload: Payload,
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# files: List[UploadFile] = File(...),
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):
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try:
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decoded_payload = payload.payload
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Memory_ = Memory( user_id='555')
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await Memory_.async_init()
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memory_class = getattr(Memory_, f"_add_{memory_type}_memory", None)
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output= memory_class(observation=decoded_payload['prompt'])
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return JSONResponse(content={"response": output}, status_code=200)
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except Exception as e:
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return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
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@app.post("/{memory_type}/fetch-memory", response_model=dict)
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async def add_memory(
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payload: Payload,
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# files: List[UploadFile] = File(...),
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):
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try:
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decoded_payload = payload.payload
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Memory_ = Memory(user_id='555')
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await Memory_.async_init()
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memory_class = getattr(Memory_, f"_fetch_{memory_type}_memory", None)
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output = memory_class(observation=decoded_payload['prompt'])
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return JSONResponse(content={"response": output}, status_code=200)
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except Exception as e:
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return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
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@app.post("/{memory_type}/delete-memory", response_model=dict)
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async def add_memory(
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payload: Payload,
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# files: List[UploadFile] = File(...),
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):
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try:
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decoded_payload = payload.payload
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Memory_ = Memory(user_id='555')
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await Memory_.async_init()
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memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
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output = memory_class(observation=decoded_payload['prompt'])
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return JSONResponse(content={"response": output}, status_code=200)
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except Exception as e:
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return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
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memory_list = ["episodic", "buffer", "semantic"]
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for memory_type in memory_list:
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memory_factory(memory_type)
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#
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# # Process each uploaded PDF file
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# results = []
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# for file in files:
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@ -10,7 +10,7 @@ import weaviate
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import os
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import json
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import asyncio
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from typing import Any, Dict, List
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from typing import Any, Dict, List, Coroutine
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from deep_translator import (GoogleTranslator)
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import LLMResult, HumanMessage
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@ -186,14 +186,12 @@ class VectorDB:
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return retriever.add_documents([
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Document(
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metadata={
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"inserted_at": str(datetime.now()),
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"text": observation,
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"user_id": str(self.user_id),
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"memory_id": str(self.memory_id),
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"ltm_memory_id": str(self.ltm_memory_id),
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"st_memory_id": str(self.st_memory_id),
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"buffer_id": str(self.buffer_id),
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"last_accessed_at": str(datetime.now()),
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# **source_metadata,
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},
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@ -205,7 +203,7 @@ class VectorDB:
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# index_name=self.index, embedding=OpenAIEmbeddings(), namespace=namespace
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# )
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async def fetch_memories(self, observation: str, params = None):
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async def fetch_memories(self, observation: str, namespace:str, params = None):
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if self.db_type == "pinecone":
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# Fetch Pinecone memories here
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pass
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@ -223,44 +221,71 @@ class VectorDB:
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Example:
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get_from_weaviate(query="some query", path=['year'], operator='Equal', valueText='2017*')
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"""
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retriever = self.init_weaviate(self.namespace)
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client = self.init_weaviate_client(self.namespace)
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print(self.namespace)
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print(str(datetime.now()))
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print(observation)
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if namespace is None:
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namespace= self.namespace
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# Retrieve documents with filters applied
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output = retriever.get_relevant_documents(
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observation
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# ,
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# score=True
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# ,
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# where_filter=params
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)
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print(output)
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return output
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# def
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def delete_memories(self, params: None):
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auth_config = weaviate.auth.AuthApiKey(api_key=os.environ.get('WEAVIATE_API_KEY'))
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client = weaviate.Client(
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url=os.environ.get('WEAVIATE_API_KEY'),
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auth_client_secret=auth_config,
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additional_headers={
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"X-OpenAI-Api-Key": os.environ.get('OPENAI_API_KEY')
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params_user_id= {
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"path": ["user_id"],
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"operator": "Like",
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"valueText": self.user_id
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}
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)
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client.batch.delete_objects(
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class_name=self.namespace,
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# Same `where` filter as in the GraphQL API
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where=params,
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)
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def update_memories(self):
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pass
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if params:
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query_output = client.query.get(namespace, ["text","user_id", "memory_id", "ltm_memory_id", "st_memory_id", "buffer_id"]).with_where(params).with_additional(['id','creationTimeUnix','lastUpdateTimeUnix']).with_where(params_user_id).do()
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return query_output
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else:
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query_output = client.query.get(namespace, ["text","user_id", "memory_id", "ltm_memory_id","st_memory_id", "buffer_id"]).with_additional(['id','creationTimeUnix','lastUpdateTimeUnix']).with_where(params_user_id).do()
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return query_output
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async def delete_memories(self, params:dict = None):
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client = self.init_weaviate_client(self.namespace)
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if params:
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where_filter = {
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"path": ["id"],
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"operator": "Equal",
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"valueText": params.get('id', None)
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}
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return client.batch.delete_objects(
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class_name=self.namespace,
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# Same `where` filter as in the GraphQL API
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where=where_filter,
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)
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else:
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#Delete all objects
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return client.batch.delete_objects(
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class_name=self.namespace,
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where={
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'path': ['user_id'],
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'operator': 'Equal',
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'valueText': self.user_id
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}
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)
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def update_memories(self, observation, namespace:str,params:dict = None):
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client = self.init_weaviate_client(self.namespace)
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client.data_object.update(
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data_object={
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"text": observation,
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"user_id": str(self.user_id),
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"memory_id": str(self.memory_id),
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"ltm_memory_id": str(self.ltm_memory_id),
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"st_memory_id": str(self.st_memory_id),
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"buffer_id": str(self.buffer_id),
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# **source_metadata,
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},
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class_name="Test",
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uuid=params.get('id', None),
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consistency_level=weaviate.data.replication.ConsistencyLevel.ALL, # default QUORUM
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)
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return
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@ -278,17 +303,64 @@ class SemanticMemory:
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def _update_memories(self ,memory_id:str="None", semantic_memory: str="None") -> None:
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async def _add_memories(self, semantic_memory: str="None") ->list[str]:
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"""Update semantic memory for the user"""
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if self.db_type == "weaviate":
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self.vector_db.add_memories( observation = semantic_memory)
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out = await self.vector_db.add_memories( observation = semantic_memory)
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return out
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elif self.db_type == "pinecone":
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pass
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def _fetch_memories(self, observation: str,params) -> dict[str, str] | str:
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async def _fetch_memories(self, observation: str,params:str=None) -> Coroutine[Any, Any, Any]:
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"""Fetch related characteristics, preferences or dislikes for a user."""
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# self.init_pinecone(index_name=self.index)
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if self.db_type == "weaviate":
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return await self.vector_db.fetch_memories(observation, params)
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elif self.db_type == "pinecone":
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pass
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async def _delete_memories(self,params:str=None) -> Coroutine[Any, Any, Any]:
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"""Fetch related characteristics, preferences or dislikes for a user."""
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# self.init_pinecone(index_name=self.index)
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if self.db_type == "weaviate":
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return await self.vector_db.delete_memories( params=params)
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elif self.db_type == "pinecone":
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pass
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class EpisodicMemory:
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def __init__(self, user_id: str, memory_id:str, ltm_memory_id:str, index_name: str, db_type:str="weaviate", namespace:str="EPISODICMEMORY"):
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# Add any semantic memory-related attributes or setup here
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self.user_id=user_id
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self.index_name = index_name
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self.namespace = namespace
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self.episodic_memory_id = str(uuid.uuid4())
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self.memory_id = memory_id
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self.ltm_memory_id = ltm_memory_id
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self.vector_db = VectorDB(user_id=user_id, memory_id= self.memory_id, ltm_memory_id = self.ltm_memory_id, index_name=index_name, db_type=db_type, namespace=self.namespace)
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self.db_type = db_type
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async def _add_memories(self ,observation:str=None) -> list[str]:
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"""Update semantic memory for the user"""
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if self.db_type == "weaviate":
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return await self.vector_db.add_memories( observation = observation)
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elif self.db_type == "pinecone":
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pass
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def _fetch_memories(self, observation: str,params:str=None) -> Coroutine[Any, Any, Any]:
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"""Fetch related characteristics, preferences or dislikes for a user."""
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# self.init_pinecone(index_name=self.index)
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@ -298,7 +370,16 @@ class SemanticMemory:
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elif self.db_type == "pinecone":
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pass
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async def _delete_memories(self, params:str=None) -> Coroutine[Any, Any, Any]:
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"""Fetch related characteristics, preferences or dislikes for a user."""
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# self.init_pinecone(index_name=self.index)
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if self.db_type == "weaviate":
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return await self.vector_db.delete_memories( params=params)
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elif self.db_type == "pinecone":
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pass
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class LongTermMemory:
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def __init__(self, user_id: str = "676", memory_id:str=None, index_name: str = None, namespace:str=None, db_type:str="weaviate"):
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@ -310,6 +391,10 @@ class LongTermMemory:
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self.db_type = db_type
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# self.episodic_memory = EpisodicMemory()
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self.semantic_memory = SemanticMemory(user_id = self.user_id, memory_id=self.memory_id, ltm_memory_id = self.ltm_memory_id, index_name=self.index_name, db_type=self.db_type)
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self.episodic_memory = EpisodicMemory(user_id=self.user_id, memory_id=self.memory_id,
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ltm_memory_id=self.ltm_memory_id, index_name=self.index_name,
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db_type=self.db_type)
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class ShortTermMemory:
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def __init__(self, user_id: str = "676", memory_id:str=None, index_name: str = None, namespace:str=None, db_type:str="weaviate"):
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@ -387,14 +472,49 @@ class EpisodicBuffer:
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query = await vector_db.add_memories(observation)
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return query
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async def _delete_memories(self, params:str=None) -> Coroutine[Any, Any, Any]:
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"""Fetch related characteristics, preferences or dislikes for a user."""
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# self.init_pinecone(index_name=self.index)
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def encoding(self, document: str, namespace: str = "EPISODICBUFFER") -> None:
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if self.db_type == "weaviate":
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return await self.vector_db.delete_memories( params=params)
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elif self.db_type == "pinecone":
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pass
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async def freshness(self, observation: str,namespace:str) -> str:
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"""Freshness - Score between 1 and 5 on how often was the information processed in episodic memory in the past"""
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memory = Memory(user_id=self.user_id)
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await memory.async_init()
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# gg = await memory._run_buffer(user_input= "bla", content = "blablabla ")
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# print(gg)
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ggur = await memory._fetch_episodic_memory(observation="bla bla bla")
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print(ggur)
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# @ai_classifier
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# class MemoryRoute(Enum):
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# """Represents classifer for semantic fetching of memories"""
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#
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# storage_of_documents_and_knowledge_to_memory = "SEMANTICMEMORY"
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# raw_information_currently_processed_in_short_term_memory = "EPISODICBUFFER"
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# raw_information_kept_in_short_term_memory = "SHORTTERMMEMORY"
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# long_term_recollections_of_past_events_and_emotions = "EPISODICMEMORY"
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# raw_information_to_store_as_events = "EVENTBUFFER"
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#
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# namespace= MemoryRoute(observation)
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return ggur
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async def encoding(self, document: str, namespace: str = "EPISODICBUFFER") -> None:
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"""Encoding for the buffer, stores raw data in the buffer
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Note, this is not comp-sci encoding, but rather encoding in the sense of storing the content in the buffer"""
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vector_db = VectorDB(user_id=self.user_id, memory_id=self.memory_id, st_memory_id=self.st_memory_id,
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index_name=self.index_name, db_type=self.db_type, namespace=namespace)
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query = vector_db.add_memories(document)
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query = await vector_db.add_memories(document)
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return query
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async def main_buffer(self, user_input=None, content=None):
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@ -406,13 +526,23 @@ class EpisodicBuffer:
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if content is not None:
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#We need to encode the content. Note, this is not comp-sci encoding, but rather encoding in the sense of storing the content in the buffer
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self.encoding(content)
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output_translated = GoogleTranslator(source='auto', target='en').translate(text=content)
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await self.encoding(output_translated)
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freshness_score =await self.freshness(output_translated, namespace="EPISODICBUFFER")
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print(freshness_score)
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# shows how much the data is relevant for the user, provided by the user in a separate step, starts at 0
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user_relevance_score ="0"
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# similarity score between the user input and the content already available in the buffer
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prompt_filter = ChatPromptTemplate.from_template("Filter and remove uneccessary information that is not relevant in the user query {query}")
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chain_filter = prompt_filter | self.llm
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output = await chain_filter.ainvoke({"query": user_input})
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print(output)
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# prompt_filter = ChatPromptTemplate.from_template("Filter and remove uneccessary information that is not relevant in the user query {query}")
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# chain_filter = prompt_filter | self.llm
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# output = await chain_filter.ainvoke({"query": user_input})
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# print(output)
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if content is None:
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# Sensory and Linguistic Processing
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@ -424,7 +554,7 @@ class EpisodicBuffer:
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def top_down_processing():
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"""Top-down processing"""
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pass
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def bottom_up_processing():
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"""Bottom-up processing"""
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@ -436,6 +566,9 @@ class EpisodicBuffer:
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pass
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working_memory_activation = "bla"
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@ -652,44 +785,74 @@ class Memory:
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# user_id=self.user_id, memory_id=self.memory_id, index_name=self.index_name, db_type=self.db_type
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# )
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def _update_semantic_memory(self, semantic_memory:str):
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return self.long_term_memory.semantic_memory._update_memories(
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memory_id=self.memory_id,
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async def _add_semantic_memory(self, semantic_memory:str):
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return await self.long_term_memory.semantic_memory._add_memories(
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semantic_memory=semantic_memory
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)
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def _fetch_semantic_memory(self, observation, params):
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return self.long_term_memory.semantic_memory._fetch_memories(
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async def _fetch_semantic_memory(self, observation, params):
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return await self.long_term_memory.semantic_memory._fetch_memories(
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observation=observation, params=params
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)
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async def _delete_semantic_memory(self, params:str=None):
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return await self.long_term_memory.semantic_memory._delete_memories(
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params=params
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)
|
||||
|
||||
async def _run_buffer(self, user_input:str):
|
||||
return await self.short_term_memory.episodic_buffer.main_buffer(user_input=user_input)
|
||||
async def _add_episodic_memory(self, observation:str):
|
||||
return await self.long_term_memory.episodic_memory._add_memories(
|
||||
observation=observation
|
||||
|
||||
)
|
||||
|
||||
async def _fetch_episodic_memory(self, observation, params:str=None):
|
||||
return await self.long_term_memory.episodic_memory._fetch_memories(
|
||||
observation=observation, params=params
|
||||
)
|
||||
|
||||
async def _delete_episodic_memory(self, params:str=None):
|
||||
return await self.long_term_memory.episodic_memory._delete_memories(
|
||||
params=params
|
||||
)
|
||||
|
||||
async def _run_buffer(self, user_input:str, content:str=None):
|
||||
return await self.short_term_memory.episodic_buffer.main_buffer(user_input=user_input, content=content)
|
||||
|
||||
|
||||
|
||||
async def _add_memories_buffer(self, user_input: str, namespace: str = None ):
|
||||
async def _add_buffer_memory(self, user_input: str, namespace: str = None ):
|
||||
return await self.short_term_memory.episodic_buffer._add_memories(observation=user_input, namespace=namespace)
|
||||
|
||||
async def _fetch_memories_buffer(self, user_input: str, namespace: str = None ):
|
||||
async def _fetch_buffer_memory(self, user_input: str, namespace: str = None ):
|
||||
return await self.short_term_memory.episodic_buffer._fetch_memories(observation=user_input, namespace=namespace)
|
||||
|
||||
async def _delete_buffer_memory(self, params:str=None):
|
||||
return await self.long_term_memory.episodic_buffer._delete_memories(
|
||||
params=params
|
||||
)
|
||||
|
||||
|
||||
|
||||
async def main():
|
||||
memory = Memory(user_id="123")
|
||||
await memory.async_init()
|
||||
|
||||
# gg = await memory._run_buffer(user_input="I want to get a my past data from 2017")
|
||||
# gg = await memory._run_buffer(user_input= "bla", content = "blablabla ")
|
||||
# print(gg)
|
||||
|
||||
ggur = await memory._add_memories_buffer(user_input = "bla bla bla", namespace="test")
|
||||
print(ggur)
|
||||
fff = await memory._fetch_memories_buffer(user_input = "bla bla bla", namespace="Test")
|
||||
print(fff)
|
||||
gg = await memory._delete_episodic_memory()
|
||||
print(gg)
|
||||
|
||||
# ggur = await memory._add_episodic_memory(observation = "bla bla bla")
|
||||
# print(ggur)
|
||||
# ggur = await memory._fetch_episodic_memory(observation = "bla bla bla")
|
||||
# print(ggur)
|
||||
# fff = await memory._fetch_memories_buffer(user_input = "bla bla bla", namespace="Test")
|
||||
# print(fff)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
|
|
|||
304
level_2/poetry.lock
generated
304
level_2/poetry.lock
generated
|
|
@ -261,17 +261,17 @@ numpy = {version = ">=1.19.0", markers = "python_version >= \"3.9\""}
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.28.26"
|
||||
version = "1.28.30"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
|
||||
{file = "boto3-1.28.26-py3-none-any.whl", hash = "sha256:63619ffa44bc7f799b525c86d73bdb7f7a70994942bbff78253585bf64084e6e"},
|
||||
{file = "boto3-1.28.26.tar.gz", hash = "sha256:a15841c7d04f87c63c9f2587b2b48198bec04d307d7b9950cbe4a021f845a5ba"},
|
||||
{file = "boto3-1.28.30-py3-none-any.whl", hash = "sha256:e095ede98d3680e65966ab71f273b7d86938f5d853773ef96f4cb646277c2a4b"},
|
||||
{file = "boto3-1.28.30.tar.gz", hash = "sha256:2b509a959966a572f15db5768a18066ce1f53022ac53fca9421c620219fa3998"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.31.26,<1.32.0"
|
||||
botocore = ">=1.31.30,<1.32.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.6.0,<0.7.0"
|
||||
|
||||
|
|
@ -280,13 +280,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
|||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.31.26"
|
||||
version = "1.31.30"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
|
||||
{file = "botocore-1.31.26-py3-none-any.whl", hash = "sha256:74d1c26144915312004a9f0232cdbe08946dfec9fc7dcd854456d2b73be9bfd9"},
|
||||
{file = "botocore-1.31.26.tar.gz", hash = "sha256:e68a50ba76425ede8693fdf1f95b8411e283bc7619c03d7eb666db9f1de48153"},
|
||||
{file = "botocore-1.31.30-py3-none-any.whl", hash = "sha256:269f20dcadd8dfd0c26d0e6fbceb84814ff6638ff3aafcc5324b9fb9949a7051"},
|
||||
{file = "botocore-1.31.30.tar.gz", hash = "sha256:3cf6a9d7621b897c9ff23cd02113826141b3dd3d7e90273b661efc4dc05f84e2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -492,13 +492,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.1.6"
|
||||
version = "8.1.7"
|
||||
description = "Composable command line interface toolkit"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "click-8.1.6-py3-none-any.whl", hash = "sha256:fa244bb30b3b5ee2cae3da8f55c9e5e0c0e86093306301fb418eb9dc40fbded5"},
|
||||
{file = "click-8.1.6.tar.gz", hash = "sha256:48ee849951919527a045bfe3bf7baa8a959c423134e1a5b98c05c20ba75a1cbd"},
|
||||
{file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
|
||||
{file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -642,6 +642,26 @@ files = [
|
|||
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "deep-translator"
|
||||
version = "1.11.4"
|
||||
description = "A flexible free and unlimited python tool to translate between different languages in a simple way using multiple translators"
|
||||
optional = false
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "deep_translator-1.11.4-py3-none-any.whl", hash = "sha256:d635df037e23fa35d12fd42dab72a0b55c9dd19e6292009ee7207e3f30b9e60a"},
|
||||
{file = "deep_translator-1.11.4.tar.gz", hash = "sha256:801260c69231138707ea88a0955e484db7d40e210c9e0ae0f77372ffda5f4bf5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
beautifulsoup4 = ">=4.9.1,<5.0.0"
|
||||
requests = ">=2.23.0,<3.0.0"
|
||||
|
||||
[package.extras]
|
||||
ai = ["openai (>=0.27.6,<0.28.0)"]
|
||||
docx = ["docx2txt (>=0.8,<0.9)"]
|
||||
pdf = ["pypdf (>=3.3.0,<4.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "deprecated"
|
||||
version = "1.2.14"
|
||||
|
|
@ -661,13 +681,13 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
|
|||
|
||||
[[package]]
|
||||
name = "dlt"
|
||||
version = "0.3.11"
|
||||
version = "0.3.12"
|
||||
description = "DLT is an open-source python-native scalable data loading framework that does not require any devops efforts to run."
|
||||
optional = false
|
||||
python-versions = ">=3.8,<4.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "dlt-0.3.11-py3-none-any.whl", hash = "sha256:fa3c9e2060faa12defbc6b4ed7b8641d8013f08d79787121584e2a9927cf1e0a"},
|
||||
{file = "dlt-0.3.11.tar.gz", hash = "sha256:c1f5e922d4936d0cffb38f367a9e7ee1d3adb46a236df07aad5e1920c5e770d6"},
|
||||
{file = "dlt-0.3.12-py3-none-any.whl", hash = "sha256:f9695a7fb98f5802e34f3f27d64e6ec1c0ccede48f26a1fdcd2db2c9280de9ac"},
|
||||
{file = "dlt-0.3.12.tar.gz", hash = "sha256:1fb4a947b2215627c1ee5725f2af80c6c7fcab60e7c3a57d49ab84b495444117"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -701,19 +721,21 @@ typing-extensions = ">=4.0.0"
|
|||
tzdata = ">=2022.1"
|
||||
|
||||
[package.extras]
|
||||
athena = ["boto3 (>=1.25)", "pyarrow (>=8.0.0)", "pyathena (>=2.9.6)", "s3fs (>=2022.4.0)"]
|
||||
bigquery = ["gcsfs (>=2022.4.0)", "google-cloud-bigquery (>=2.26.0)", "grpcio (>=1.50.0)", "pyarrow (>=8.0.0)"]
|
||||
cli = ["cron-descriptor (>=1.2.32)", "pipdeptree (>=2.9.0,<2.10)"]
|
||||
dbt = ["dbt-bigquery (>=1.2.0)", "dbt-core (>=1.2.0)", "dbt-duckdb (>=1.2.0)", "dbt-redshift (>=1.2.0)", "dbt-snowflake (>=1.2.0)"]
|
||||
dbt = ["dbt-athena-community (>=1.2.0)", "dbt-bigquery (>=1.2.0)", "dbt-core (>=1.2.0)", "dbt-duckdb (>=1.2.0)", "dbt-redshift (>=1.2.0)", "dbt-snowflake (>=1.2.0)"]
|
||||
duckdb = ["duckdb (>=0.6.1,<0.9.0)"]
|
||||
filesystem = ["boto3 (>=1.25)", "s3fs (>=2022.4.0)"]
|
||||
gcp = ["gcsfs (>=2022.4.0)", "google-cloud-bigquery (>=2.26.0)", "grpcio (>=1.50.0)"]
|
||||
gs = ["gcsfs (>=2022.4.0)"]
|
||||
motherduck = ["duckdb (>=0.6.1,<0.9.0)", "pyarrow (>=8.0.0)"]
|
||||
parquet = ["pyarrow (>=8.0.0)"]
|
||||
postgres = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
|
||||
pyarrow = ["pyarrow (>=8.0.0)"]
|
||||
redshift = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
|
||||
s3 = ["boto3 (>=1.25)", "s3fs (>=2022.4.0)"]
|
||||
snowflake = ["snowflake-connector-python[pandas] (>=2.9.0)"]
|
||||
weaviate = ["weaviate-client (>=3.22)"]
|
||||
|
||||
[[package]]
|
||||
name = "dnspython"
|
||||
|
|
@ -1212,13 +1234,13 @@ socks = ["socksio (==1.*)"]
|
|||
|
||||
[[package]]
|
||||
name = "humanize"
|
||||
version = "4.7.0"
|
||||
version = "4.8.0"
|
||||
description = "Python humanize utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "humanize-4.7.0-py3-none-any.whl", hash = "sha256:df7c429c2d27372b249d3f26eb53b07b166b661326e0325793e0a988082e3889"},
|
||||
{file = "humanize-4.7.0.tar.gz", hash = "sha256:7ca0e43e870981fa684acb5b062deb307218193bca1a01f2b2676479df849b3a"},
|
||||
{file = "humanize-4.8.0-py3-none-any.whl", hash = "sha256:8bc9e2bb9315e61ec06bf690151ae35aeb65651ab091266941edf97c90836404"},
|
||||
{file = "humanize-4.8.0.tar.gz", hash = "sha256:9783373bf1eec713a770ecaa7c2d7a7902c98398009dfa3d8a2df91eec9311e8"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -1393,13 +1415,13 @@ data = ["language-data (>=1.1,<2.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.0.22"
|
||||
version = "0.0.25"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langsmith-0.0.22-py3-none-any.whl", hash = "sha256:1bc94a2e5bfa355ca15d9e658c2c2d04c8cc45c61892a1be08a7c3b40f2fd3f4"},
|
||||
{file = "langsmith-0.0.22.tar.gz", hash = "sha256:5726c7841294db2a9e5863e20718878d16e28722bdaf3169a278ff3bda2f0be7"},
|
||||
{file = "langsmith-0.0.25-py3-none-any.whl", hash = "sha256:d595435ad21fa6077550d7c85472935d1e8241afa042c1e29287d2c95c3ed151"},
|
||||
{file = "langsmith-0.0.25.tar.gz", hash = "sha256:e728c398fc1adaa0ed8abeb21f6a92d7fb19fe3ab49d3911c22b03dfe25935d6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1869,67 +1891,71 @@ pydantic = ">=1.8.2"
|
|||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.9.4"
|
||||
version = "3.9.5"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "orjson-3.9.4-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:2e83ec1ee66d83b558a6d273d8a01b86563daa60bea9bc040e2c1cb8008de61f"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32a9e0f140c7d0d52f79553cabd1a471f6a4f187c59742239939f1139258a053"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fb429c56ea645e084e34976c2ea0efca7661ee961f61e51405f28bc5a9d1fb24"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c2fb7963c17ab347428412a0689f5c89ea480f5d5f7ba3e46c6c2f14f3159ee4"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:224ad19dcdc21bb220d893807f2563e219319a8891ead3c54243b51a4882d767"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4974cc2ebb53196081fef96743c02c8b073242b20a40b65d2aa2365ba8c949df"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b39747f8e57728b9d8c26bd1d28e9a31c028717617a5938a179244b9436c0b31"},
|
||||
{file = "orjson-3.9.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:0a31c2cab0ba86998205c2eba550c178a8b4ee7905cadeb402eed45392edb178"},
|
||||
{file = "orjson-3.9.4-cp310-none-win32.whl", hash = "sha256:04cd7f4a4f4cd2fe43d104eb70e7435c6fcbdde7aa0cde4230e444fbc66924d3"},
|
||||
{file = "orjson-3.9.4-cp310-none-win_amd64.whl", hash = "sha256:4fdb59cfa00e10c82e09d1c32a9ce08a38bd29496ba20a73cd7f498e3a0a5024"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:daeed2502ddf1f2b29ec8da2fe2ea82807a5c4acf869608ce6c476db8171d070"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73d9507a547202f0dd0672e529ce3ca45582d152369c684a9ce75677ce5ae089"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:144a3b8c7cbdd301e1b8cd7dd33e3cbfe7b011df2bebd45b84bacc8cb490302d"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ef7119ebc9b76d5e37c330596616c697d1957779c916aec30cefd28df808f796"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b75f0fc7a64a95027c6f0c70f17969299bdf2b6a85e342b29fc23be2788bad6f"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e4b20164809b21966b63e063f894927bc85391e60d0a96fa0bb552090f1319c"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0e7c3b7e29572ef2d845a59853475f40fdabec53b8b7d6effda4bb26119c07f5"},
|
||||
{file = "orjson-3.9.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d73c0fd54a52a1a1abfad69d4f1dfb7048cd0b3ef1828ddb4920ef2d3739d8fb"},
|
||||
{file = "orjson-3.9.4-cp311-none-win32.whl", hash = "sha256:e12492ce65cb10f385e70a88badc6046bc720fa7d468db27b7429d85d41beaeb"},
|
||||
{file = "orjson-3.9.4-cp311-none-win_amd64.whl", hash = "sha256:3b9f8bf43a5367d5522f80e7d533c98d880868cd0b640b9088c9237306eca6e8"},
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{file = "orjson-3.9.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17404333c40047888ac40bd8c4d49752a787e0a946e728a4e5723f111b6e55a5"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0eefb7cfdd9c2bc65f19f974a5d1dfecbac711dae91ed635820c6b12da7a3c11"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:68c78b2a3718892dc018adbc62e8bab6ef3c0d811816d21e6973dee0ca30c152"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:591ad7d9e4a9f9b104486ad5d88658c79ba29b66c5557ef9edf8ca877a3f8d11"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6cc2cbf302fbb2d0b2c3c142a663d028873232a434d89ce1b2604ebe5cc93ce8"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b26b5aa5e9ee1bad2795b925b3adb1b1b34122cb977f30d89e0a1b3f24d18450"},
|
||||
{file = "orjson-3.9.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ef84724f7d29dcfe3aafb1fc5fc7788dca63e8ae626bb9298022866146091a3e"},
|
||||
{file = "orjson-3.9.5-cp39-none-win32.whl", hash = "sha256:664cff27f85939059472afd39acff152fbac9a091b7137092cb651cf5f7747b5"},
|
||||
{file = "orjson-3.9.5-cp39-none-win_amd64.whl", hash = "sha256:91dda66755795ac6100e303e206b636568d42ac83c156547634256a2e68de694"},
|
||||
{file = "orjson-3.9.5.tar.gz", hash = "sha256:6daf5ee0b3cf530b9978cdbf71024f1c16ed4a67d05f6ec435c6e7fe7a52724c"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2270,18 +2296,18 @@ plugins = ["importlib-metadata"]
|
|||
|
||||
[[package]]
|
||||
name = "pypdf"
|
||||
version = "3.15.1"
|
||||
version = "3.15.2"
|
||||
description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "pypdf-3.15.1-py3-none-any.whl", hash = "sha256:99b337af7da8046d1e2e94354846e8c56753e1cdc817ac0fbe770c1e2281902b"},
|
||||
{file = "pypdf-3.15.1.tar.gz", hash = "sha256:d0dfaf4f10dfb06ac39e1d6a9cbffd63e77621d1e89c0ef08f346fd902df7b4b"},
|
||||
{file = "pypdf-3.15.2-py3-none-any.whl", hash = "sha256:f6e598292be34187287a609c72815c1502b3dc2c997b374ba0870ce79d2e975a"},
|
||||
{file = "pypdf-3.15.2.tar.gz", hash = "sha256:cdf7d75ebb8901f3352cf9488c5f662c6de9c52e432c429d15cada67ba372fce"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
crypto = ["PyCryptodome", "cryptography"]
|
||||
dev = ["black", "flit", "pip-tools", "pre-commit (<2.18.0)", "pytest-cov", "pytest-socket", "wheel"]
|
||||
dev = ["black", "flit", "pip-tools", "pre-commit (<2.18.0)", "pytest-cov", "pytest-socket", "pytest-timeout", "wheel"]
|
||||
docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
|
||||
full = ["Pillow (>=8.0.0)", "PyCryptodome", "cryptography"]
|
||||
image = ["Pillow (>=8.0.0)"]
|
||||
|
|
@ -2706,13 +2732,13 @@ pyasn1 = ">=0.1.3"
|
|||
|
||||
[[package]]
|
||||
name = "s3transfer"
|
||||
version = "0.6.1"
|
||||
version = "0.6.2"
|
||||
description = "An Amazon S3 Transfer Manager"
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
|
||||
{file = "s3transfer-0.6.1-py3-none-any.whl", hash = "sha256:3c0da2d074bf35d6870ef157158641178a4204a6e689e82546083e31e0311346"},
|
||||
{file = "s3transfer-0.6.1.tar.gz", hash = "sha256:640bb492711f4c0c0905e1f62b6aaeb771881935ad27884852411f8e9cacbca9"},
|
||||
{file = "s3transfer-0.6.2-py3-none-any.whl", hash = "sha256:b014be3a8a2aab98cfe1abc7229cc5a9a0cf05eb9c1f2b86b230fd8df3f78084"},
|
||||
{file = "s3transfer-0.6.2.tar.gz", hash = "sha256:cab66d3380cca3e70939ef2255d01cd8aece6a4907a9528740f668c4b0611861"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2751,18 +2777,18 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "68.0.0"
|
||||
version = "68.1.2"
|
||||
description = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"},
|
||||
{file = "setuptools-68.0.0.tar.gz", hash = "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"},
|
||||
{file = "setuptools-68.1.2-py3-none-any.whl", hash = "sha256:3d8083eed2d13afc9426f227b24fd1659489ec107c0e86cec2ffdde5c92e790b"},
|
||||
{file = "setuptools-68.1.2.tar.gz", hash = "sha256:3d4dfa6d95f1b101d695a6160a7626e15583af71a5f52176efa5d39a054d475d"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
|
||||
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5,<=7.1.2)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
|
||||
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
|
||||
testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3046,52 +3072,52 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "sqlalchemy"
|
||||
version = "2.0.19"
|
||||
version = "2.0.20"
|
||||
description = "Database Abstraction Library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:dd81466bdbc82b060c3c110b2937ab65ace41dfa7b18681fdfad2f37f27acdd7"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6fe7d61dc71119e21ddb0094ee994418c12f68c61b3d263ebaae50ea8399c4d4"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4e571af672e1bb710b3cc1a9794b55bce1eae5aed41a608c0401885e3491179"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3364b7066b3c7f4437dd345d47271f1251e0cfb0aba67e785343cdbdb0fff08c"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1be86ccea0c965a1e8cd6ccf6884b924c319fcc85765f16c69f1ae7148eba64b"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:1d35d49a972649b5080557c603110620a86aa11db350d7a7cb0f0a3f611948a0"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-win32.whl", hash = "sha256:27d554ef5d12501898d88d255c54eef8414576f34672e02fe96d75908993cf53"},
|
||||
{file = "SQLAlchemy-2.0.20-cp311-cp311-win_amd64.whl", hash = "sha256:411e7f140200c02c4b953b3dbd08351c9f9818d2bd591b56d0fa0716bd014f1e"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:3c6aceebbc47db04f2d779db03afeaa2c73ea3f8dcd3987eb9efdb987ffa09a3"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d3f175410a6db0ad96b10bfbb0a5530ecd4fcf1e2b5d83d968dd64791f810ed"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea8186be85da6587456c9ddc7bf480ebad1a0e6dcbad3967c4821233a4d4df57"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c3d99ba99007dab8233f635c32b5cd24fb1df8d64e17bc7df136cedbea427897"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:76fdfc0f6f5341987474ff48e7a66c3cd2b8a71ddda01fa82fedb180b961630a"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-win32.whl", hash = "sha256:d3793dcf5bc4d74ae1e9db15121250c2da476e1af8e45a1d9a52b1513a393459"},
|
||||
{file = "SQLAlchemy-2.0.20-cp37-cp37m-win_amd64.whl", hash = "sha256:79fde625a0a55220d3624e64101ed68a059c1c1f126c74f08a42097a72ff66a9"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:599ccd23a7146e126be1c7632d1d47847fa9f333104d03325c4e15440fc7d927"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1a58052b5a93425f656675673ef1f7e005a3b72e3f2c91b8acca1b27ccadf5f4"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79543f945be7a5ada9943d555cf9b1531cfea49241809dd1183701f94a748624"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63e73da7fb030ae0a46a9ffbeef7e892f5def4baf8064786d040d45c1d6d1dc5"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:3ce5e81b800a8afc870bb8e0a275d81957e16f8c4b62415a7b386f29a0cb9763"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:cb0d3e94c2a84215532d9bcf10229476ffd3b08f481c53754113b794afb62d14"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-win32.whl", hash = "sha256:8dd77fd6648b677d7742d2c3cc105a66e2681cc5e5fb247b88c7a7b78351cf74"},
|
||||
{file = "SQLAlchemy-2.0.20-cp38-cp38-win_amd64.whl", hash = "sha256:6f8a934f9dfdf762c844e5164046a9cea25fabbc9ec865c023fe7f300f11ca4a"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:26a3399eaf65e9ab2690c07bd5cf898b639e76903e0abad096cd609233ce5208"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4cde2e1096cbb3e62002efdb7050113aa5f01718035ba9f29f9d89c3758e7e4e"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1b09ba72e4e6d341bb5bdd3564f1cea6095d4c3632e45dc69375a1dbe4e26ec"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b74eeafaa11372627ce94e4dc88a6751b2b4d263015b3523e2b1e57291102f0"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:77d37c1b4e64c926fa3de23e8244b964aab92963d0f74d98cbc0783a9e04f501"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:eefebcc5c555803065128401a1e224a64607259b5eb907021bf9b175f315d2a6"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-win32.whl", hash = "sha256:3423dc2a3b94125094897118b52bdf4d37daf142cbcf26d48af284b763ab90e9"},
|
||||
{file = "SQLAlchemy-2.0.20-cp39-cp39-win_amd64.whl", hash = "sha256:5ed61e3463021763b853628aef8bc5d469fe12d95f82c74ef605049d810f3267"},
|
||||
{file = "SQLAlchemy-2.0.20-py3-none-any.whl", hash = "sha256:63a368231c53c93e2b67d0c5556a9836fdcd383f7e3026a39602aad775b14acf"},
|
||||
{file = "SQLAlchemy-2.0.20.tar.gz", hash = "sha256:ca8a5ff2aa7f3ade6c498aaafce25b1eaeabe4e42b73e25519183e4566a16fc6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3099,7 +3125,7 @@ greenlet = {version = "!=0.4.17", markers = "platform_machine == \"win32\" or pl
|
|||
typing-extensions = ">=4.2.0"
|
||||
|
||||
[package.extras]
|
||||
aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
|
||||
aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
|
||||
aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
|
||||
asyncio = ["greenlet (!=0.4.17)"]
|
||||
asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
|
||||
|
|
@ -3425,13 +3451,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
|
|||
|
||||
[[package]]
|
||||
name = "types-setuptools"
|
||||
version = "68.0.0.3"
|
||||
version = "68.1.0.0"
|
||||
description = "Typing stubs for setuptools"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "types-setuptools-68.0.0.3.tar.gz", hash = "sha256:d57ae6076100b5704b3cc869fdefc671e1baf4c2cd6643f84265dfc0b955bf05"},
|
||||
{file = "types_setuptools-68.0.0.3-py3-none-any.whl", hash = "sha256:fec09e5c18264c5c09351c00be01a34456fb7a88e457abe97401325f84ad9d36"},
|
||||
{file = "types-setuptools-68.1.0.0.tar.gz", hash = "sha256:2bc9b0c0818f77bdcec619970e452b320a423bb3ac074f5f8bc9300ac281c4ae"},
|
||||
{file = "types_setuptools-68.1.0.0-py3-none-any.whl", hash = "sha256:0c1618fb14850cb482adbec602bbb519c43f24942d66d66196bc7528320f33b1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3769,4 +3795,4 @@ multidict = ">=4.0"
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "cb545477c5347378b193e38e77c7fe3c0451eafbdeb4584a479ce192386b06d4"
|
||||
content-hash = "13258c777467d93ab73021225322da670e42513cedec6252a40aacf74822ea68"
|
||||
|
|
|
|||
|
|
@ -35,9 +35,10 @@ python-jose = "^3.3.0"
|
|||
pypdf = "^3.12.0"
|
||||
fastjsonschema = "^2.18.0"
|
||||
marvin = "^1.3.0"
|
||||
dlt = { version ="^0.3.8" , extras = ["duckdb"]}
|
||||
dlt = { version ="^0.3.8", extras = ["duckdb"]}
|
||||
weaviate-client = "^3.22.1"
|
||||
python-multipart = "^0.0.6"
|
||||
deep-translator = "^1.11.4"
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue