Token safeguards, better deep /search

cogs/search_service_cog.py

@@ -119,9 +119,9 @@ class SearchService(discord.Cog, name="SearchService"):
             response, refined_text = await self.model.search(
                 ctx, query, user_api_key, search_scope, nodes, deep
             )
-        except ValueError:
+        except ValueError as e:
             await ctx.respond(
-                "The Google Search API returned an error. Check the console for more details.",
+                str(e),
                 ephemeral=True,
             )
             return

gpt3discord.py

@@ -31,7 +31,7 @@ from services.environment_service import EnvService
 from models.openai_model import Model
 
 
-__version__ = "10.7.1"
+__version__ = "10.7.3"
 
 
 PID_FILE = Path("bot.pid")

models/index_model.py

@@ -37,7 +37,7 @@ from gpt_index import (
     PromptHelper,
     IndexStructType,
     OpenAIEmbedding,
-    GithubRepositoryReader,
+    GithubRepositoryReader, MockEmbedding,
 )
 from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
 
@@ -46,6 +46,7 @@ from gpt_index.composability import ComposableGraph
 from services.environment_service import EnvService, app_root_path
 
 SHORT_TO_LONG_CACHE = {}
+MAX_DEEP_COMPOSE_PRICE = EnvService.get_max_deep_compose_price()
 
 
 def get_and_query(
@@ -218,7 +219,7 @@ class Index_handler:
         return index
 
     def index_github_repository(self, link, embed_model):
-        print("indexing github repo")
+
         # Extract the "owner" and the "repo" name from the github link.
         owner = link.split("/")[3]
         repo = link.split("/")[4]
@@ -274,7 +275,6 @@ class Index_handler:
         # Get the file path of this tempfile.NamedTemporaryFile
         # Save this temp file to an actual file that we can put into something else to read it
         documents = SimpleDirectoryReader(input_files=[f.name]).load_data()
-        print("Loaded the PDF document data")
 
         # Delete the temporary file
         return documents
@@ -310,6 +310,7 @@ class Index_handler:
         documents = BeautifulSoupWebReader(
             website_extractor=DEFAULT_WEBSITE_EXTRACTOR
         ).load_data(urls=[url])
+
         # index = GPTSimpleVectorIndex(documents, embed_model=embed_model, use_async=True)
         index = await self.loop.run_in_executor(
             None,
@@ -553,6 +554,25 @@ class Index_handler:
             )
             embedding_model = OpenAIEmbedding()
 
+            llm_predictor_mock = MockLLMPredictor(4096)
+            embedding_model_mock = MockEmbedding(1536)
+
+            # Run the mock call first
+            await self.loop.run_in_executor(
+                None,
+                partial(
+                    GPTTreeIndex,
+                    documents=documents,
+                    llm_predictor=llm_predictor_mock,
+                    embed_model=embedding_model_mock,
+                ),
+            )
+            total_usage_price = await self.usage_service.get_price(llm_predictor_mock.last_token_usage) + await self.usage_service.get_price(embedding_model_mock.last_token_usage, True)
+            print("The total composition price is: ", total_usage_price)
+            if total_usage_price > MAX_DEEP_COMPOSE_PRICE:
+                raise ValueError("Doing this deep search would be prohibitively expensive. Please try a narrower search scope.")
+
+
             tree_index = await self.loop.run_in_executor(
                 None,
                 partial(
@@ -574,6 +594,8 @@ class Index_handler:
                 name = (
                     f"composed_deep_index_{date.today().month}_{date.today().day}.json"
                 )
+            else:
+                name = name+"_deep"
 
             # Save the composed index
             tree_index.save_to_disk(f"indexes/{user_id}/{name}.json")
@@ -905,6 +927,7 @@ class ComposeModal(discord.ui.View):
                     delete_after=120,
                 )
                 # Compose the indexes
+                try:
                     await self.index_cog.compose_indexes(
                         self.user_id,
                         indexes,
@@ -913,6 +936,20 @@ class ComposeModal(discord.ui.View):
                         if not self.deep_select.values or self.deep_select.values[0] == "no"
                         else True,
                     )
+                except ValueError as e:
+                    await interaction.followup.send(
+                        str(e), ephemeral=True, delete_after=180
+                    )
+                    return False
+                except Exception as e:
+                    await interaction.followup.send(
+                    "An error occurred while composing the indexes: " + str(e),
+                    ephemeral=True,
+                    delete_after=180,)
+                    return False
+
+
+
                 await interaction.followup.send(
                     "Composed indexes", ephemeral=True, delete_after=180
                 )

models/search_model.py

@@ -19,7 +19,7 @@ from gpt_index import (
     PromptHelper,
     LLMPredictor,
     OpenAIEmbedding,
-    SimpleDirectoryReader,
+    SimpleDirectoryReader, GPTTreeIndex, MockLLMPredictor, MockEmbedding,
 )
 from gpt_index.indices.knowledge_graph import GPTKnowledgeGraphIndex
 from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
@@ -28,6 +28,7 @@ from langchain import OpenAI
 from services.environment_service import EnvService, app_root_path
 from services.usage_service import UsageService
 
+MAX_SEARCH_PRICE = EnvService.get_max_search_price()
 
 class Search:
     def __init__(self, gpt_model, usage_service):
@@ -130,13 +131,12 @@ class Search:
                     f.write(data)
                     f.close()
                 else:
-                    return "An error occurred while downloading the PDF."
+                    raise ValueError("Could not download PDF")
         # Get the file path of this tempfile.NamedTemporaryFile
         # Save this temp file to an actual file that we can put into something else to read it
         documents = SimpleDirectoryReader(input_files=[f.name]).load_data()
         for document in documents:
             document.extra_info = {"URL": url}
-        print("Loaded the PDF document data")
 
         # Delete the temporary file
         return documents
@@ -155,11 +155,7 @@ class Search:
                         [item["link"] for item in data["items"]],
                     )
                 else:
-                    print(
+                    raise ValueError("Error while retrieving links")
-                        "The Google Search API returned an error: "
-                        + str(response.status)
-                    )
-                    return ["An error occurred while searching.", None]
 
     async def try_edit(self, message, embed):
         try:
@@ -246,19 +242,17 @@ class Search:
             pdf = False
             try:
                 async with aiohttp.ClientSession() as session:
-                    async with session.get(link, timeout=2) as response:
+                    async with session.get(link, timeout=1) as response:
                         # Add another entry to links from all_links if the link is not already in it to compensate for the failed request
                         if response.status not in [200, 203, 202, 204]:
                             for link2 in all_links:
                                 if link2 not in links:
-                                    print("Found a replacement link")
                                     links.append(link2)
                                     break
                             continue
                         # Follow redirects
                         elif response.status in [301, 302, 303, 307, 308]:
                             try:
-                                print("Adding redirect")
                                 links.append(response.url)
                                 continue
                             except:
@@ -266,7 +260,6 @@ class Search:
                         else:
                             # Detect if the link is a PDF, if it is, we load it differently
                             if response.headers["Content-Type"] == "application/pdf":
-                                print("Found a PDF at the link " + link)
                                 pdf = True
 
             except:
@@ -275,7 +268,6 @@ class Search:
                     # Try to add a link from all_links, this is kind of messy.
                     for link2 in all_links:
                         if link2 not in links:
-                            print("Found a replacement link")
                             links.append(link2)
                             break
                 except:
@@ -307,9 +299,18 @@ class Search:
         )
 
         if not deep:
+            embed_model_mock = MockEmbedding(embed_dim=1536)
+            self.loop.run_in_executor(
+                None,
+                partial(GPTSimpleVectorIndex, documents, embed_model=embed_model_mock),
+            )
+            total_usage_price = await self.usage_service.get_price(embed_model_mock.last_token_usage, True)
+            if total_usage_price > 1.00:
+                raise ValueError("Doing this search would be prohibitively expensive. Please try a narrower search scope.")
+
             index = await self.loop.run_in_executor(
                 None,
-                partial(GPTSimpleVectorIndex, documents, embed_model=embedding_model),
+                partial(GPTSimpleVectorIndex, documents, embed_model=embedding_model, use_async=True),
             )
             # save the index to disk if not a redo
             if not redo:
@@ -320,22 +321,54 @@ class Search:
                     else ctx.author.id,
                     query,
                 )
+
+            await self.usage_service.update_usage(
+                embedding_model.last_token_usage, embeddings=True
+            )
         else:
-            print("Doing a deep search")
+            llm_predictor_deep = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
-            llm_predictor_deep = LLMPredictor(
+            # Try a mock call first
-                llm=OpenAI(model_name="text-davinci-002", temperature=0, max_tokens=-1)
+            llm_predictor_mock = MockLLMPredictor(4096)
+            embed_model_mock = MockEmbedding(embed_dim=1536)
+
+            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:
+                raise ValueError("Doing this deep search would be prohibitively expensive. Please try a narrower search scope.")
+
             index = await self.loop.run_in_executor(
                 None,
                 partial(
-                    GPTKnowledgeGraphIndex,
+                    GPTTreeIndex,
                     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,
+            #     ),
+            # )
             await self.usage_service.update_usage(
                 embedding_model.last_token_usage, embeddings=True
             )
@@ -348,10 +381,6 @@ class Search:
                 in_progress_message, self.build_search_indexed_embed(query_refined_text)
             )
 
-        await self.usage_service.update_usage(
-            embedding_model.last_token_usage, embeddings=True
-        )
-
         # Now we can search the index for a query:
         embedding_model.last_token_usage = 0
 
@@ -365,17 +394,31 @@ class Search:
                     llm_predictor=llm_predictor,
                     similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
                     text_qa_template=self.qaprompt,
+                    use_async=True,
+                    response_mode="tree_summarize",
                 ),
             )
         else:
+            # response = await self.loop.run_in_executor(
+            #     None,
+            #     partial(
+            #         index.query,
+            #         query,
+            #         include_text=True,
+            #         embed_model=embedding_model,
+            #         llm_predictor=llm_predictor_deep,
+            #         use_async=True,
+            #     ),
+            # )
             response = await self.loop.run_in_executor(
                 None,
                 partial(
                     index.query,
                     query,
-                    include_text=True,
+                    child_branch_factor=2,
+                    llm_predictor=llm_predictor,
                     embed_model=embedding_model,
-                    llm_predictor=llm_predictor_deep,
+                    use_async=True,
                 ),
             )
 

services/environment_service.py

@@ -379,3 +379,19 @@ class EnvService:
             return pinecone_region
         except Exception:
             return "us-west1-gcp"
+
+    @staticmethod
+    def get_max_search_price():
+        try:
+            search_price = float(os.getenv("MAX_SEARCH_PRICE"))
+            return search_price
+        except Exception:
+            return 1.00
+
+    @staticmethod
+    def get_max_deep_compose_price():
+        try:
+            deep_compose_price = float(os.getenv("MAX_DEEP_COMPOSE_PRICE"))
+            return deep_compose_price
+        except Exception:
+            return 3.00

services/usage_service.py

@@ -14,6 +14,16 @@ class UsageService:
                 f.close()
         self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
 
+    async def get_price(self, tokens_used, embeddings=False):
+        tokens_used = int(tokens_used)
+        if not embeddings:
+            price = (
+                tokens_used / 1000
+            ) * 0.02  # Just use the highest rate instead of model-based... I am overestimating on purpose.
+        else:
+            price = (tokens_used / 1000) * 0.0004
+        return price
+
     async def update_usage(self, tokens_used, embeddings=False):
         tokens_used = int(tokens_used)
         if not embeddings: