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Wrap up initial buffer flow

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This commit is contained in:
Vasilije 2023-08-24 12:04:53 +02:00
parent 2f85f6baff
commit b65d8016f3
1 changed files with 29 additions and 14 deletions
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43
level_2/level_2_pdf_vectorstore__dlt_contracts.py
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@ -510,6 +510,12 @@ class EpisodicBuffer:
model_name="gpt-4-0613", model_name="gpt-4-0613",
callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()], callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
) )
self.llm_base = OpenAI(
temperature=0.0,
max_tokens=1200,
openai_api_key=os.environ.get('OPENAI_API_KEY'),
model_name="gpt-4-0613"
)
# self.vector_db = VectorDB(user_id=user_id, memory_id= self.memory_id, st_memory_id = self.st_memory_id, index_name=index_name, db_type=db_type, namespace=self.namespace) # self.vector_db = VectorDB(user_id=user_id, memory_id= self.memory_id, st_memory_id = self.st_memory_id, index_name=index_name, db_type=db_type, namespace=self.namespace)
@ -652,6 +658,10 @@ class EpisodicBuffer:
list_of_operations = await self.available_operations() list_of_operations = await self.available_operations()
memory = Memory(user_id=self.user_id)
await memory.async_init()
await memory._delete_buffer_memory()
#we just filter the data here #we just filter the data here
prompt_filter = ChatPromptTemplate.from_template( prompt_filter = ChatPromptTemplate.from_template(
@ -691,9 +701,6 @@ class EpisodicBuffer:
#do i need to do it for the episodic + raw data, might make sense #do i need to do it for the episodic + raw data, might make sense
print("HERE WE ARE")
class Task(BaseModel): class Task(BaseModel):
"""Schema for an individual task.""" """Schema for an individual task."""
task_order: str = Field(..., description="The order at which the task needs to be performed") task_order: str = Field(..., description="The order at which the task needs to be performed")
@ -808,12 +815,15 @@ class EpisodicBuffer:
buffer_result = await self._fetch_memories(observation=str(output), namespace=self.namespace) buffer_result = await self._fetch_memories(observation=str(output), namespace=self.namespace)
print("HERE IS THE RESULT TASKS", str(buffer_result))
class EpisodicTask(BaseModel): class EpisodicTask(BaseModel):
"""Schema for an individual task.""" """Schema for an individual task."""
task_order: str = Field(..., description="The order at which the task needs to be performed") task_order: str = Field(..., description="The order at which the task needs to be performed")
task_name: str = Field(None, description="The task that needs to be performed") task_name: str = Field(None, description="The task that needs to be performed")
operation: str = Field(None, description="The operation to be performed") operation: str = Field(None, description="The operation to be performed")
operation_result: str = Field(None, description="The result of the operation")
class EpisodicList(BaseModel): class EpisodicList(BaseModel):
"""Schema for the record containing a list of tasks.""" """Schema for the record containing a list of tasks."""
@ -831,19 +841,24 @@ class EpisodicBuffer:
) )
_input = prompt.format_prompt(query=user_input, steps=str(tasks_list), buffer=buffer_result) _input = prompt.format_prompt(query=user_input, steps=str(tasks_list), buffer=buffer_result)
print("a few things to do like load episodic memory in a structured format")
return "a few things to do like load episodic memory in a structured format"
# output = self.llm(_input.to_string())
# #
# parser.parse(output) # print("a few things to do like load episodic memory in a structured format")
# memory = Memory(user_id=self.user_id)
# await memory.async_init()
# #
# lookup_value = await memory._add_episodic_memory(observation=str(output), params=params) # return "a few things to do like load episodic memory in a structured format"
# return lookup_value
output = self.llm_base(_input.to_string())
result_parsing = parser.parse(output)
print("here is the parsing result", result_parsing)
memory = Memory(user_id=self.user_id)
await memory.async_init()
#
lookup_value = await memory._add_episodic_memory(observation=str(output), params=params)
#now we clean up buffer memory
await memory._delete_buffer_memory()
return lookup_value
#load to buffer once is done #load to buffer once is done