@ -25,6 +25,7 @@ from gpt_index import (
MockEmbedding ,
)
from gpt_index . indices . knowledge_graph import GPTKnowledgeGraphIndex
from gpt_index . langchain_helpers . chatgpt import ChatGPTLLMPredictor
from gpt_index . prompts . prompt_type import PromptType
from gpt_index . readers . web import DEFAULT_WEBSITE_EXTRACTOR
from langchain import OpenAI
@ -49,7 +50,7 @@ class Search:
" \n --------------------- \n "
" Never say ' <|endofstatement|> ' \n "
" Given the context information and not prior knowledge, "
" answer the question, say that you were unable to answer the question if there is not sufficient context to formulate a decisive answer. The search query was: {query_str} \n "
" answer the question, say that you were unable to answer the question if there is not sufficient context to formulate a decisive answer. If the prior knowledge/context was sufficient, simply repeat it. The search query was: {query_str} \n "
)
self . openai_key = os . getenv ( " OPENAI_TOKEN " )
self . EMBED_CUTOFF = 2000
@ -215,7 +216,7 @@ class Search:
try :
llm_predictor_presearch = OpenAI (
max_tokens = 50 ,
temperature = 0. 25 ,
temperature = 0. 4 ,
presence_penalty = 0.65 ,
model_name = " text-davinci-003 " ,
)
@ -314,9 +315,7 @@ class Search:
embedding_model = OpenAIEmbedding ( )
llm_predictor = LLMPredictor (
llm = OpenAI ( model_name = " text-davinci-003 " , max_tokens = - 1 )
)
llm_predictor = ChatGPTLLMPredictor ( )
if not deep :
embed_model_mock = MockEmbedding ( embed_dim = 1536 )
@ -325,7 +324,7 @@ class Search:
partial ( GPTSimpleVectorIndex , documents , embed_model = embed_model_mock ) ,
)
total_usage_price = await self . usage_service . get_price (
embed_model_mock . last_token_usage , True
embed_model_mock . last_token_usage , embeddings = True
)
if total_usage_price > 1.00 :
raise ValueError (
@ -356,63 +355,60 @@ class Search:
)
price + = total_usage_price
else :
llm_predictor_deep = LLMPredictor ( llm = OpenAI ( model_name = " text-davinci-003 " ) )
# Try a mock call first
llm_predictor_mock = MockLLMPredictor ( 4096 )
embed_model_mock = MockEmbedding ( embed_dim = 1536 )
llm_predictor_deep = ChatGPTLLMPredictor ( )
await self . loop . run_in_executor (
None ,
partial (
GPTTreeIndex ,
documents ,
embed_model = embed_model_mock ,
llm_predictor = llm_predictor_mock ,
) ,
)
total_usage_price = await self . usage_service . get_price (
llm_predictor_mock . last_token_usage
) + await self . usage_service . get_price (
embed_model_mock . last_token_usage , True
)
if total_usage_price > MAX_SEARCH_PRICE :
await self . try_delete ( in_progress_message )
raise ValueError (
" Doing this deep search would be prohibitively expensive. Please try a narrower search scope. This deep search indexing would have cost $ {:.2f} . " . format (
total_usage_price
)
)
# # Try a mock call first
# llm_predictor_mock = MockLLMPredictor(4096)
# embed_model_mock = MockEmbedding(embed_dim=1536)
# await self.loop.run_in_executor(
# None,
# partial(
# GPTKnowledgeGraphIndex,
# documents,
# chunk_size_limit=512,
# max_triplets_per_chunk=2,
# embed_model=embed_model_mock,
# llm_predictor=llm_predictor_mock,
# ),
# )
# total_usage_price = await self.usage_service.get_price(
# llm_predictor_mock.last_token_usage, chatgpt=True,
# ) + await self.usage_service.get_price(
# embed_model_mock.last_token_usage, embeddings=True
# )
# print(f"Total usage price: {total_usage_price}")
# if total_usage_price > MAX_SEARCH_PRICE:
# await self.try_delete(in_progress_message)
# raise ValueError(
# "Doing this deep search would be prohibitively expensive. Please try a narrower search scope. This deep search indexing would have cost ${:.2f}.".format(
# total_usage_price
# )
# )
# TODO Add back the mock when fixed!
index = await self . loop . run_in_executor (
None ,
partial (
GPTTreeIndex ,
GPT KnowledgeGraph Index,
documents ,
chunk_size_limit = 512 ,
max_triplets_per_chunk = 2 ,
embed_model = embedding_model ,
llm_predictor = llm_predictor_deep ,
use_async = True ,
) ,
)
# llm_predictor_deep = LLMPredictor(
# llm=OpenAI(model_name="text-davinci-002", temperature=0, max_tokens=-1)
# )
# index = await self.loop.run_in_executor(
# None,
# partial(
# GPTKnowledgeGraphIndex,
# documents,
# chunk_size_limit=512,
# max_triplets_per_chunk=2,
# embed_model=embedding_model,
# llm_predictor=llm_predictor_deep,
# ),
# )
total_usage_price = await self . usage_service . get_price (
llm_predictor_deep . last_token_usage , chatgpt = True ,
) + await self . usage_service . get_price (
embedding_model . last_token_usage , embeddings = True )
await self . usage_service . update_usage (
embedding_model . last_token_usage , embeddings = True
)
await self . usage_service . update_usage (
llm_predictor_deep . last_token_usage , embeddings= False
llm_predictor_deep . last_token_usage , chatgpt = True ,
)
price + = total_usage_price
@ -455,14 +451,17 @@ class Search:
partial (
index . query ,
query ,
child_branch_factor= 2 ,
embedding_mode= ' hybrid ' ,
llm_predictor = llm_predictor ,
include_text = True ,
embed_model = embedding_model ,
use_async = True ,
similarity_top_k = nodes or DEFAULT_SEARCH_NODES ,
response_mode = response_mode ,
) ,
)
await self . usage_service . update_usage ( llm_predictor . last_token_usage
await self . usage_service . update_usage ( llm_predictor . last_token_usage , chatgpt = True )
await self . usage_service . update_usage (
embedding_model . last_token_usage , embeddings = True
)
Kaveen Kumarasinghe
1 year ago