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Reduce size of context, hack for now

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Vasilije 2024-01-17 11:22:25 +01:00
parent eee6049e22
commit 444d4b3329
1 changed files with 6 additions and 6 deletions
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12
main.py
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@ -68,7 +68,7 @@ async def fetch_document_vectordb_namespace(session: AsyncSession, user_id: str,
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)
logging.info("Result is", result)
logging.info("Result is %s", str(result))
return result, namespace_id
@ -323,6 +323,7 @@ async def add_documents_to_graph_db(session: AsyncSession, user_id: str= None, d
class ResponseString(BaseModel):
response: str = Field(..., default_factory=list)
quotation: str = Field(..., default_factory=list)
#
@ -338,7 +339,7 @@ def generate_graph(input) -> ResponseString:
},
{ "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."""}
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,
)
@ -463,7 +464,7 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
for result in results['data']['Get'][namespace_id]:
# Assuming 'result' is a dictionary and has keys like 'source', 'text'
source = result['source']
source = result['source'].replace('-', ' ').replace('.pdf', '').replace('.data/', '')
text = result['text']
search_context += f"Document source: {source}, Document text: {text} \n"
@ -484,8 +485,7 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
return context
else:
generative_result = generate_graph(context)
translation_to_srb = translate_text(generative_result.response, "en", "sr")
return translation_to_srb
return generative_result
async def create_public_memory(user_id: str=None, labels:list=None, topic:str=None) -> Optional[int]:
@ -709,7 +709,7 @@ async def main():
# await attach_user_to_memory(user_id=user_id, labels=['sr'], topic="PublicMemory")
return_ = await user_context_enrichment(user_id=user_id, query="what should the size of a staircase in an apartment building be", session=session, memory_type="PublicMemory", generative_response=False)
return_ = await user_context_enrichment(user_id=user_id, query="what should the size of a staircase in an apartment building be", session=session, memory_type="PublicMemory", generative_response=True)
print(return_)
# aa = await relevance_feedback("I need to understand how to build a staircase in an apartment building", "PublicMemory")
# print(aa)