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Added a small example

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This commit is contained in:
Vasilije 2023-08-27 21:13:49 +02:00
parent e7698af597
commit b83fc5e4f3
1 changed files with 24 additions and 31 deletions
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55
level_2/level_2_pdf_vectorstore__dlt_contracts.py
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@ -687,15 +687,16 @@ class EpisodicBuffer(BaseMemory):
pass pass
# we just filter the data here to make sure input is clean # we just filter the data here to make sure input is clean
prompt_filter = ChatPromptTemplate.from_template( prompt_filter = ChatPromptTemplate.from_template(
"Filter and remove uneccessary information that is not relevant in the query to the vector store to get more information, keep it as original as possbile: {query}" """Filter and remove uneccessary information that is not relevant in the query to
the vector store to get more information, keep it as original as possbile: {query}"""
) )
chain_filter = prompt_filter | self.llm chain_filter = prompt_filter | self.llm
output = await chain_filter.ainvoke({"query": user_input}) output = await chain_filter.ainvoke({"query": user_input})
# this part is unfinished but the idea is to apply different attention modulators to the data to fetch the most relevant information from the vector stores # this part is partially done but the idea is to apply different attention modulators
# to the data to fetch the most relevant information from the vector stores
context = [] context = []
if attention_modulators: if attention_modulators:
print("HERE ARE THE ATTENTION MODULATORS: ", attention_modulators)
from typing import Optional, Dict, List, Union from typing import Optional, Dict, List, Union
lookup_value_semantic = await self.fetch_memories( lookup_value_semantic = await self.fetch_memories(
@ -758,10 +759,14 @@ class EpisodicBuffer(BaseMemory):
docs: List[BufferRawContextTerms] = Field(..., description="List of docs") docs: List[BufferRawContextTerms] = Field(..., description="List of docs")
user_query: str = Field(..., description="The original user query") user_query: str = Field(..., description="The original user query")
# we structure the data here to make it easier to work with
parser = PydanticOutputParser(pydantic_object=BufferRawContextList) parser = PydanticOutputParser(pydantic_object=BufferRawContextList)
prompt = PromptTemplate( prompt = PromptTemplate(
template="Summarize and create semantic search queries and relevant document summaries for the user query.\n{format_instructions}\nOriginal query is: {query}\n Retrieved context is: {context}", template="""Summarize and create semantic search queries and relevant
document summaries for the user query.\n
{format_instructions}\nOriginal query is:
{query}\n Retrieved context is: {context}""",
input_variables=["query", "context"], input_variables=["query", "context"],
partial_variables={"format_instructions": parser.get_format_instructions()}, partial_variables={"format_instructions": parser.get_format_instructions()},
) )
@ -769,8 +774,6 @@ class EpisodicBuffer(BaseMemory):
_input = prompt.format_prompt(query=user_input, context=context) _input = prompt.format_prompt(query=user_input, context=context)
document_context_result = self.llm_base(_input.to_string()) document_context_result = self.llm_base(_input.to_string())
document_context_result_parsed = parser.parse(document_context_result) document_context_result_parsed = parser.parse(document_context_result)
print("HERE ARE THE DOCS PARSED AND STRUCTURED", document_context_result_parsed.json())
return document_context_result_parsed return document_context_result_parsed
async def get_task_list( async def get_task_list(
@ -1187,6 +1190,8 @@ class Memory:
async def main(): async def main():
# if you want to run the script as a standalone script, do so with the examples below
memory = Memory(user_id="123") memory = Memory(user_id="123")
await memory.async_init() await memory.async_init()
params = { params = {
@ -1202,36 +1207,24 @@ async def main():
"validity_start": "2023-08-01", "validity_start": "2023-08-01",
"validity_end": "2024-07-31", "validity_end": "2024-07-31",
} }
loader_settings = {
# gg = await memory._run_buffer(user_input="i NEED TRANSLATION TO GERMAN ", content="i NEED TRANSLATION TO GERMAN ", params=params)
# print(gg)
# gg = await memory._fetch_buffer_memory(user_input="i TO GERMAN ")
# print(gg)
modulator = {"relevance": 0.0, "saliency": 0.0, "frequency": 0.0}
# #
ggur = await memory._run_main_buffer(
user_input="I want to know how does Buck adapt to life in the wild and then have that info translated to german ",
params=params,
attention_modulators=modulator,
)
print(ggur)
ll = {
"format": "PDF", "format": "PDF",
"source": "url", "source": "url",
"path": "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf" "path": "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf"
} }
# ggur = await memory._add_semantic_memory(observation = "bla", loader_settings=ll, params=params) load_jack_london = await memory._add_semantic_memory(observation = "bla", loader_settings=loader_settings, params=params)
# print(ggur) print(load_jack_london)
# fff = await memory._delete_semantic_memory()
# print(fff)
# fff = await memory._fetch_semantic_memory(observation = "dog pulling sleds ", params=None) modulator = {"relevance": 0.0, "saliency": 0.0, "frequency": 0.0}
# print(fff) # #
# print(len(fff["data"]["Get"]["EPISODICMEMORY"])) run_main_buffer = await memory._run_main_buffer(
user_input="I want to know how does Buck adapt to life in the wild and then have that info translated to german ",
params=params,
attention_modulators=modulator,
)
print(run_main_buffer)
# del_semantic = await memory._delete_semantic_memory()
# print(del_semantic)
if __name__ == "__main__": if __name__ == "__main__":