diff --git a/level_2/api.py b/level_2/api.py index 7c539a18e..d2cc74e6c 100644 --- a/level_2/api.py +++ b/level_2/api.py @@ -114,7 +114,7 @@ async def upload_pdf_and_payload( logging.info(" PDF split into pages") Memory_ = Memory(index_name="my-agent", user_id='555' ) await Memory_.async_init() - Memory_._run_buffer(user_input="I want to get a schema for my data", content =pages) + Memory_._add_episodic_memory(user_input="I want to get a schema for my data", content =pages) # Run the buffer @@ -135,7 +135,82 @@ async def upload_pdf_and_payload( # Append the in-memory file to the files list # files.append(UploadFile(pdf_stream, filename="downloaded.pdf")) - # + + + +def memory_factory(memory_type): + class Payload(BaseModel): + payload: Dict[str, Any] + @app.post("/{memory_type}/add-memory", response_model=dict) + async def add_memory( + payload: Payload, + # files: List[UploadFile] = File(...), + ): + try: + + + decoded_payload = payload.payload + + Memory_ = Memory( user_id='555') + + await Memory_.async_init() + + memory_class = getattr(Memory_, f"_add_{memory_type}_memory", None) + output= memory_class(observation=decoded_payload['prompt']) + return JSONResponse(content={"response": output}, status_code=200) + + except Exception as e: + + return JSONResponse(content={"response": {"error": str(e)}}, status_code=503) + + @app.post("/{memory_type}/fetch-memory", response_model=dict) + async def add_memory( + payload: Payload, + # files: List[UploadFile] = File(...), + ): + try: + + decoded_payload = payload.payload + + Memory_ = Memory(user_id='555') + + await Memory_.async_init() + + memory_class = getattr(Memory_, f"_fetch_{memory_type}_memory", None) + output = memory_class(observation=decoded_payload['prompt']) + return JSONResponse(content={"response": output}, status_code=200) + + except Exception as e: + + return JSONResponse(content={"response": {"error": str(e)}}, status_code=503) + + @app.post("/{memory_type}/delete-memory", response_model=dict) + async def add_memory( + payload: Payload, + # files: List[UploadFile] = File(...), + ): + try: + + decoded_payload = payload.payload + + Memory_ = Memory(user_id='555') + + await Memory_.async_init() + + memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None) + output = memory_class(observation=decoded_payload['prompt']) + return JSONResponse(content={"response": output}, status_code=200) + + except Exception as e: + + return JSONResponse(content={"response": {"error": str(e)}}, status_code=503) + +memory_list = ["episodic", "buffer", "semantic"] +for memory_type in memory_list: + memory_factory(memory_type) + + +# # # Process each uploaded PDF file # results = [] # for file in files: diff --git a/level_2/level_2_pdf_vectorstore__dlt_contracts.py b/level_2/level_2_pdf_vectorstore__dlt_contracts.py index f69327407..1f8b1cdf9 100644 --- a/level_2/level_2_pdf_vectorstore__dlt_contracts.py +++ b/level_2/level_2_pdf_vectorstore__dlt_contracts.py @@ -10,7 +10,7 @@ import weaviate import os import json import asyncio -from typing import Any, Dict, List +from typing import Any, Dict, List, Coroutine from deep_translator import (GoogleTranslator) from langchain.chat_models import ChatOpenAI from langchain.schema import LLMResult, HumanMessage @@ -186,14 +186,12 @@ class VectorDB: return retriever.add_documents([ Document( metadata={ - "inserted_at": str(datetime.now()), "text": observation, "user_id": str(self.user_id), "memory_id": str(self.memory_id), "ltm_memory_id": str(self.ltm_memory_id), "st_memory_id": str(self.st_memory_id), "buffer_id": str(self.buffer_id), - "last_accessed_at": str(datetime.now()), # **source_metadata, }, @@ -205,7 +203,7 @@ class VectorDB: # index_name=self.index, embedding=OpenAIEmbeddings(), namespace=namespace # ) - async def fetch_memories(self, observation: str, params = None): + async def fetch_memories(self, observation: str, namespace:str, params = None): if self.db_type == "pinecone": # Fetch Pinecone memories here pass @@ -223,44 +221,71 @@ class VectorDB: Example: get_from_weaviate(query="some query", path=['year'], operator='Equal', valueText='2017*') """ - retriever = self.init_weaviate(self.namespace) + client = self.init_weaviate_client(self.namespace) print(self.namespace) print(str(datetime.now())) print(observation) + if namespace is None: + namespace= self.namespace - # Retrieve documents with filters applied - output = retriever.get_relevant_documents( - observation - # , - # score=True - # , - # where_filter=params - ) - print(output) - return output - - - # def - - def delete_memories(self, params: None): - auth_config = weaviate.auth.AuthApiKey(api_key=os.environ.get('WEAVIATE_API_KEY')) - client = weaviate.Client( - url=os.environ.get('WEAVIATE_API_KEY'), - auth_client_secret=auth_config, - - additional_headers={ - "X-OpenAI-Api-Key": os.environ.get('OPENAI_API_KEY') + params_user_id= { + "path": ["user_id"], + "operator": "Like", + "valueText": self.user_id } - ) - client.batch.delete_objects( - class_name=self.namespace, - # Same `where` filter as in the GraphQL API - where=params, - ) - def update_memories(self): - pass + if params: + 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() + return query_output + else: + 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() + return query_output + + async def delete_memories(self, params:dict = None): + client = self.init_weaviate_client(self.namespace) + if params: + where_filter = { + "path": ["id"], + "operator": "Equal", + "valueText": params.get('id', None) + } + return client.batch.delete_objects( + class_name=self.namespace, + # Same `where` filter as in the GraphQL API + where=where_filter, + ) + else: + #Delete all objects + + return client.batch.delete_objects( + class_name=self.namespace, + where={ + 'path': ['user_id'], + 'operator': 'Equal', + 'valueText': self.user_id + } + ) + + def update_memories(self, observation, namespace:str,params:dict = None): + client = self.init_weaviate_client(self.namespace) + client.data_object.update( + data_object={ + "text": observation, + "user_id": str(self.user_id), + "memory_id": str(self.memory_id), + "ltm_memory_id": str(self.ltm_memory_id), + "st_memory_id": str(self.st_memory_id), + "buffer_id": str(self.buffer_id), + + # **source_metadata, + + }, + class_name="Test", + uuid=params.get('id', None), + consistency_level=weaviate.data.replication.ConsistencyLevel.ALL, # default QUORUM + ) + return @@ -278,17 +303,64 @@ class SemanticMemory: - def _update_memories(self ,memory_id:str="None", semantic_memory: str="None") -> None: + async def _add_memories(self, semantic_memory: str="None") ->list[str]: """Update semantic memory for the user""" if self.db_type == "weaviate": - self.vector_db.add_memories( observation = semantic_memory) + out = await self.vector_db.add_memories( observation = semantic_memory) + return out elif self.db_type == "pinecone": pass - def _fetch_memories(self, observation: str,params) -> dict[str, str] | str: + async def _fetch_memories(self, observation: str,params:str=None) -> Coroutine[Any, Any, Any]: + """Fetch related characteristics, preferences or dislikes for a user.""" + # self.init_pinecone(index_name=self.index) + + if self.db_type == "weaviate": + + return await self.vector_db.fetch_memories(observation, params) + + elif self.db_type == "pinecone": + pass + + async def _delete_memories(self,params:str=None) -> Coroutine[Any, Any, Any]: + """Fetch related characteristics, preferences or dislikes for a user.""" + # self.init_pinecone(index_name=self.index) + + if self.db_type == "weaviate": + + return await self.vector_db.delete_memories( params=params) + + elif self.db_type == "pinecone": + pass + +class EpisodicMemory: + def __init__(self, user_id: str, memory_id:str, ltm_memory_id:str, index_name: str, db_type:str="weaviate", namespace:str="EPISODICMEMORY"): + # Add any semantic memory-related attributes or setup here + self.user_id=user_id + self.index_name = index_name + self.namespace = namespace + self.episodic_memory_id = str(uuid.uuid4()) + self.memory_id = memory_id + self.ltm_memory_id = ltm_memory_id + 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) + self.db_type = db_type + + + + async def _add_memories(self ,observation:str=None) -> list[str]: + """Update semantic memory for the user""" + + if self.db_type == "weaviate": + return await self.vector_db.add_memories( observation = observation) + + elif self.db_type == "pinecone": + pass + + + def _fetch_memories(self, observation: str,params:str=None) -> Coroutine[Any, Any, Any]: """Fetch related characteristics, preferences or dislikes for a user.""" # self.init_pinecone(index_name=self.index) @@ -298,7 +370,16 @@ class SemanticMemory: elif self.db_type == "pinecone": pass + async def _delete_memories(self, params:str=None) -> Coroutine[Any, Any, Any]: + """Fetch related characteristics, preferences or dislikes for a user.""" + # self.init_pinecone(index_name=self.index) + if self.db_type == "weaviate": + + return await self.vector_db.delete_memories( params=params) + + elif self.db_type == "pinecone": + pass class LongTermMemory: def __init__(self, user_id: str = "676", memory_id:str=None, index_name: str = None, namespace:str=None, db_type:str="weaviate"): @@ -310,6 +391,10 @@ class LongTermMemory: self.db_type = db_type # self.episodic_memory = EpisodicMemory() 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) + self.episodic_memory = EpisodicMemory(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) + class ShortTermMemory: def __init__(self, user_id: str = "676", memory_id:str=None, index_name: str = None, namespace:str=None, db_type:str="weaviate"): @@ -387,14 +472,49 @@ class EpisodicBuffer: query = await vector_db.add_memories(observation) return query + async def _delete_memories(self, params:str=None) -> Coroutine[Any, Any, Any]: + """Fetch related characteristics, preferences or dislikes for a user.""" + # self.init_pinecone(index_name=self.index) - def encoding(self, document: str, namespace: str = "EPISODICBUFFER") -> None: + if self.db_type == "weaviate": + + return await self.vector_db.delete_memories( params=params) + + elif self.db_type == "pinecone": + pass + + async def freshness(self, observation: str,namespace:str) -> str: + """Freshness - Score between 1 and 5 on how often was the information processed in episodic memory in the past""" + + memory = Memory(user_id=self.user_id) + await memory.async_init() + + # gg = await memory._run_buffer(user_input= "bla", content = "blablabla ") + # print(gg) + + ggur = await memory._fetch_episodic_memory(observation="bla bla bla") + print(ggur) + # @ai_classifier + # class MemoryRoute(Enum): + # """Represents classifer for semantic fetching of memories""" + # + # storage_of_documents_and_knowledge_to_memory = "SEMANTICMEMORY" + # raw_information_currently_processed_in_short_term_memory = "EPISODICBUFFER" + # raw_information_kept_in_short_term_memory = "SHORTTERMMEMORY" + # long_term_recollections_of_past_events_and_emotions = "EPISODICMEMORY" + # raw_information_to_store_as_events = "EVENTBUFFER" + # + # namespace= MemoryRoute(observation) + + return ggur + + async def encoding(self, document: str, namespace: str = "EPISODICBUFFER") -> None: """Encoding for the buffer, stores raw data in the buffer Note, this is not comp-sci encoding, but rather encoding in the sense of storing the content in the buffer""" vector_db = VectorDB(user_id=self.user_id, memory_id=self.memory_id, st_memory_id=self.st_memory_id, index_name=self.index_name, db_type=self.db_type, namespace=namespace) - query = vector_db.add_memories(document) + query = await vector_db.add_memories(document) return query async def main_buffer(self, user_input=None, content=None): @@ -406,13 +526,23 @@ class EpisodicBuffer: if content is not None: #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 - self.encoding(content) + output_translated = GoogleTranslator(source='auto', target='en').translate(text=content) + await self.encoding(output_translated) + freshness_score =await self.freshness(output_translated, namespace="EPISODICBUFFER") + print(freshness_score) + # shows how much the data is relevant for the user, provided by the user in a separate step, starts at 0 + user_relevance_score ="0" + # similarity score between the user input and the content already available in the buffer - prompt_filter = ChatPromptTemplate.from_template("Filter and remove uneccessary information that is not relevant in the user query {query}") - chain_filter = prompt_filter | self.llm - output = await chain_filter.ainvoke({"query": user_input}) - print(output) + + + + # prompt_filter = ChatPromptTemplate.from_template("Filter and remove uneccessary information that is not relevant in the user query {query}") + # chain_filter = prompt_filter | self.llm + # output = await chain_filter.ainvoke({"query": user_input}) + + # print(output) if content is None: # Sensory and Linguistic Processing @@ -424,7 +554,7 @@ class EpisodicBuffer: def top_down_processing(): """Top-down processing""" - + pass def bottom_up_processing(): """Bottom-up processing""" @@ -436,6 +566,9 @@ class EpisodicBuffer: pass + + + working_memory_activation = "bla" @@ -652,44 +785,74 @@ class Memory: # user_id=self.user_id, memory_id=self.memory_id, index_name=self.index_name, db_type=self.db_type # ) - def _update_semantic_memory(self, semantic_memory:str): - return self.long_term_memory.semantic_memory._update_memories( - memory_id=self.memory_id, + async def _add_semantic_memory(self, semantic_memory:str): + return await self.long_term_memory.semantic_memory._add_memories( semantic_memory=semantic_memory ) - def _fetch_semantic_memory(self, observation, params): - return self.long_term_memory.semantic_memory._fetch_memories( + async def _fetch_semantic_memory(self, observation, params): + return await self.long_term_memory.semantic_memory._fetch_memories( observation=observation, params=params + ) + async def _delete_semantic_memory(self, params:str=None): + return await self.long_term_memory.semantic_memory._delete_memories( + params=params ) - 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 diff --git a/level_2/poetry.lock b/level_2/poetry.lock index aa7a579b6..7b95c721a 100644 --- a/level_2/poetry.lock +++ b/level_2/poetry.lock @@ -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 = 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