Renamed the extension classes of interfaces to drop the I prefix (#1190)

documentation/Tools.md

@@ -232,7 +232,7 @@ An ERI data source whose server does not answer the check of `DataSourceService`
 
 Every check of an ERI data source signs in to its server, and one message checks several times: when the tool is offered, before every search, and with every message of the classic process. `DataSourceService` therefore keeps a server's answer for one minute in an `ERISecurityCheckCache` and joins a check which is still running, so that a server which limits sign-ins does not start refusing. A server which did not answer is asked again after 15 seconds, and a changed configuration of the data source asks anew right away. Expired answers are dropped whenever a check starts. The minute holds for an answer which lets the data reach any provider as well, on purpose: when an operator opened a server by mistake and corrects it, every AI Studio follows within that minute, and until then, a chat with a cloud provider may still search the server. The cache lives only as long as the app runs, so no answer sticks for longer.
 
-The data sources are checked again before each search, since rounds may have passed since they were offered, and then searched in parallel. A data source which fails is reported as not searched rather than left out, so that the model does not take its silence for finding nothing. Every passage goes through the same filter and into the same shape as with the classic RAG process, `IRetrievalContextExtensions.PrepareForModelAsync`: all passages of a search in one filter request, within one `PromptInjectionGuardService.BeginAction` per search, so that the user hears about what was filtered once per search, as soon as it is done. A page of the search holds passages up to 100,000 characters, measured after the filter, which can make a passage longer. A data source returns its matches in pages of its own, as many as it is set to return, see `RetrievalPaging`; each of them is cut into one or more pages of the search, numbered on across the pages of the data source, see `SemanticSearchTool.SearchAsync`. Where everything fits, both are the same. `has_more` is exact: when a page ends with a page of the data source which might have more, the search looks whether a further one holds any passage. A passage longer than a whole page is shortened, its surrounding content first, and filtered again, see `IRetrievalContextExtensions.ShortenToFitAsync`; its label tells the model, and the user is shown the shorter passage. A search of several data sources shares the first page among them. The data sources take turns: first the best passage of each, then the second best of each, and a data source stops at its first passage which no longer fits. Otherwise, the data source listed first would take the whole budget, or a shorter passage would take the place of a longer one above it. What does not fit is counted in the result, with a search of that data source alone, from page 1, as the way out; that search shows the passages read already once more. An image an ERI server returns is not passed on, in neither process: written as base64 text, it is nothing a model can look at, and it would take more room than all other passages together. The passage tells the model that an image matched and where it is, so that it can point the user to it. What an ERI server says its content is, its category, is passed on as "Source category". The passages become sources through `IRetrievalContext.ToSources()`, as with the classic process, and only the data sources whose passages reached the model raise the requirements of the chat. The passages which reached the model are also kept with the answer, through `ToolExecutionResult.Passages` and `ContentText.AddSources`: the result of the call is not saved, but the user can still read what the answer rests on after the chat was reloaded. The title of a source names the pages of all its passages, see `SourcePassageExtensions.NamePassagesInTitles`.
+The data sources are checked again before each search, since rounds may have passed since they were offered, and then searched in parallel. A data source which fails is reported as not searched rather than left out, so that the model does not take its silence for finding nothing. Every passage goes through the same filter and into the same shape as with the classic RAG process, `RetrievalContextExtensions.PrepareForModelAsync`: all passages of a search in one filter request, within one `PromptInjectionGuardService.BeginAction` per search, so that the user hears about what was filtered once per search, as soon as it is done. A page of the search holds passages up to 100,000 characters, measured after the filter, which can make a passage longer. A data source returns its matches in pages of its own, as many as it is set to return, see `RetrievalPaging`; each of them is cut into one or more pages of the search, numbered on across the pages of the data source, see `SemanticSearchTool.SearchAsync`. Where everything fits, both are the same. `has_more` is exact: when a page ends with a page of the data source which might have more, the search looks whether a further one holds any passage. A passage longer than a whole page is shortened, its surrounding content first, and filtered again, see `RetrievalContextExtensions.ShortenToFitAsync`; its label tells the model, and the user is shown the shorter passage. A search of several data sources shares the first page among them. The data sources take turns: first the best passage of each, then the second best of each, and a data source stops at its first passage which no longer fits. Otherwise, the data source listed first would take the whole budget, or a shorter passage would take the place of a longer one above it. What does not fit is counted in the result, with a search of that data source alone, from page 1, as the way out; that search shows the passages read already once more. An image an ERI server returns is not passed on, in neither process: written as base64 text, it is nothing a model can look at, and it would take more room than all other passages together. The passage tells the model that an image matched and where it is, so that it can point the user to it. What an ERI server says its content is, its category, is passed on as "Source category". The passages become sources through `IRetrievalContext.ToSources()`, as with the classic process, and only the data sources whose passages reached the model raise the requirements of the chat. The passages which reached the model are also kept with the answer, through `ToolExecutionResult.Passages` and `ContentText.AddSources`: the result of the call is not saved, but the user can still read what the answer rests on after the chat was reloaded. The title of a source names the pages of all its passages, see `SourcePassageExtensions.NamePassagesInTitles`.
 
 The classic process sends its passages along with every message, in the system prompt, and a provider refuses a message which exceeds the context window of the model. Its passages therefore get a budget, `PassageBudget`: half of the context window when it is known, `ModelProfile.Context`, counted in tokens through `ConversationTokenCounter` with the tokenizer of the provider, or the built-in one; otherwise 100,000 characters, as many as one page of `semantic_search`. When the tokenizer does not answer, characters count against the same limit, which leaves the passages less room rather than more. The budget applies to what the validation agent left, measured after the filter, each passage with the longest header it can get. The data sources take turns as in a search of several data sources, but a passage which does not fit is skipped, and the next ones still get their chance: there is no further page which could bring it later. A passage longer than the whole budget is shortened and filtered again, as in `semantic_search`. The model reads the passages grouped by data source, followed by how many were left out, and the user gets a warning with every answer which had to do without some, naming the ways out: Semantic Search, a lower token limit, or fewer data sources. Only the passages the model received are listed with the answer.