feat(recipe): add our first recipe Summarize (#2028)
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* feat: add summary recipe

* test: add summary tests

* docs: move all recipes docs

* docs: add recipes and summarize doc

* docs: update openapi reference

* refactor: split method in two method (summary)

* feat: add initial summarize ui

* feat: add mode explanation

* fix: mypy

* feat: allow to configure async property in summarize

* refactor: move modes to enum and update mode explanations

* docs: fix url

* docs: remove list-llm pages

* docs: remove double header

* fix: summary description
This commit is contained in:
Javier Martinez 2024-07-31 16:53:27 +02:00 committed by GitHub
parent 40638a18a5
commit 8119842ae6
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GPG key ID: B5690EEEBB952194
13 changed files with 743 additions and 148 deletions

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@ -3,6 +3,7 @@ import base64
import logging
import time
from collections.abc import Iterable
from enum import Enum
from pathlib import Path
from typing import Any
@ -11,6 +12,7 @@ from fastapi import FastAPI
from gradio.themes.utils.colors import slate # type: ignore
from injector import inject, singleton
from llama_index.core.llms import ChatMessage, ChatResponse, MessageRole
from llama_index.core.types import TokenGen
from pydantic import BaseModel
from private_gpt.constants import PROJECT_ROOT_PATH
@ -19,6 +21,7 @@ from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.server.chat.chat_service import ChatService, CompletionGen
from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
from private_gpt.server.ingest.ingest_service import IngestService
from private_gpt.server.recipes.summarize.summarize_service import SummarizeService
from private_gpt.settings.settings import settings
from private_gpt.ui.images import logo_svg
@ -32,7 +35,20 @@ UI_TAB_TITLE = "My Private GPT"
SOURCES_SEPARATOR = "<hr>Sources: \n"
MODES = ["Query Files", "Search Files", "LLM Chat (no context from files)"]
class Modes(str, Enum):
RAG_MODE = "RAG"
SEARCH_MODE = "Search"
BASIC_CHAT_MODE = "Basic"
SUMMARIZE_MODE = "Summarize"
MODES: list[Modes] = [
Modes.RAG_MODE,
Modes.SEARCH_MODE,
Modes.BASIC_CHAT_MODE,
Modes.SUMMARIZE_MODE,
]
class Source(BaseModel):
@ -70,10 +86,12 @@ class PrivateGptUi:
ingest_service: IngestService,
chat_service: ChatService,
chunks_service: ChunksService,
summarizeService: SummarizeService,
) -> None:
self._ingest_service = ingest_service
self._chat_service = chat_service
self._chunks_service = chunks_service
self._summarize_service = summarizeService
# Cache the UI blocks
self._ui_block = None
@ -84,7 +102,9 @@ class PrivateGptUi:
self.mode = MODES[0]
self._system_prompt = self._get_default_system_prompt(self.mode)
def _chat(self, message: str, history: list[list[str]], mode: str, *_: Any) -> Any:
def _chat(
self, message: str, history: list[list[str]], mode: Modes, *_: Any
) -> Any:
def yield_deltas(completion_gen: CompletionGen) -> Iterable[str]:
full_response: str = ""
stream = completion_gen.response
@ -112,6 +132,12 @@ class PrivateGptUi:
full_response += sources_text
yield full_response
def yield_tokens(token_gen: TokenGen) -> Iterable[str]:
full_response: str = ""
for token in token_gen:
full_response += str(token)
yield full_response
def build_history() -> list[ChatMessage]:
history_messages: list[ChatMessage] = []
@ -143,8 +169,7 @@ class PrivateGptUi:
),
)
match mode:
case "Query Files":
case Modes.RAG_MODE:
# Use only the selected file for the query
context_filter = None
if self._selected_filename is not None:
@ -163,14 +188,14 @@ class PrivateGptUi:
context_filter=context_filter,
)
yield from yield_deltas(query_stream)
case "LLM Chat (no context from files)":
case Modes.BASIC_CHAT_MODE:
llm_stream = self._chat_service.stream_chat(
messages=all_messages,
use_context=False,
)
yield from yield_deltas(llm_stream)
case "Search Files":
case Modes.SEARCH_MODE:
response = self._chunks_service.retrieve_relevant(
text=message, limit=4, prev_next_chunks=0
)
@ -183,37 +208,76 @@ class PrivateGptUi:
f"{source.text}"
for index, source in enumerate(sources, start=1)
)
case Modes.SUMMARIZE_MODE:
# Summarize the given message, optionally using selected files
context_filter = None
if self._selected_filename:
docs_ids = []
for ingested_document in self._ingest_service.list_ingested():
if (
ingested_document.doc_metadata["file_name"]
== self._selected_filename
):
docs_ids.append(ingested_document.doc_id)
context_filter = ContextFilter(docs_ids=docs_ids)
summary_stream = self._summarize_service.stream_summarize(
use_context=True,
context_filter=context_filter,
instructions=message,
)
yield from yield_tokens(summary_stream)
# On initialization and on mode change, this function set the system prompt
# to the default prompt based on the mode (and user settings).
@staticmethod
def _get_default_system_prompt(mode: str) -> str:
def _get_default_system_prompt(mode: Modes) -> str:
p = ""
match mode:
# For query chat mode, obtain default system prompt from settings
case "Query Files":
case Modes.RAG_MODE:
p = settings().ui.default_query_system_prompt
# For chat mode, obtain default system prompt from settings
case "LLM Chat (no context from files)":
case Modes.BASIC_CHAT_MODE:
p = settings().ui.default_chat_system_prompt
# For summarization mode, obtain default system prompt from settings
case Modes.SUMMARIZE_MODE:
p = settings().ui.default_summarization_system_prompt
# For any other mode, clear the system prompt
case _:
p = ""
return p
@staticmethod
def _get_default_mode_explanation(mode: Modes) -> str:
match mode:
case Modes.RAG_MODE:
return "Get contextualized answers from selected files."
case Modes.SEARCH_MODE:
return "Find relevant chunks of text in selected files."
case Modes.BASIC_CHAT_MODE:
return "Chat with the LLM using its training data. Files are ignored."
case Modes.SUMMARIZE_MODE:
return "Generate a summary of the selected files. Prompt to customize the result."
case _:
return ""
def _set_system_prompt(self, system_prompt_input: str) -> None:
logger.info(f"Setting system prompt to: {system_prompt_input}")
self._system_prompt = system_prompt_input
def _set_current_mode(self, mode: str) -> Any:
def _set_explanatation_mode(self, explanation_mode: str) -> None:
self._explanation_mode = explanation_mode
def _set_current_mode(self, mode: Modes) -> Any:
self.mode = mode
self._set_system_prompt(self._get_default_system_prompt(mode))
# Update placeholder and allow interaction if default system prompt is set
if self._system_prompt:
return gr.update(placeholder=self._system_prompt, interactive=True)
# Update placeholder and disable interaction if no default system prompt is set
else:
return gr.update(placeholder=self._system_prompt, interactive=False)
self._set_explanatation_mode(self._get_default_mode_explanation(mode))
interactive = self._system_prompt is not None
return [
gr.update(placeholder=self._system_prompt, interactive=interactive),
gr.update(value=self._explanation_mode),
]
def _list_ingested_files(self) -> list[list[str]]:
files = set()
@ -326,10 +390,17 @@ class PrivateGptUi:
with gr.Row(equal_height=False):
with gr.Column(scale=3):
default_mode = MODES[0]
mode = gr.Radio(
MODES,
[mode.value for mode in MODES],
label="Mode",
value="Query Files",
value=default_mode,
)
explanation_mode = gr.Textbox(
placeholder=self._get_default_mode_explanation(default_mode),
show_label=False,
max_lines=3,
interactive=False,
)
upload_button = gr.components.UploadButton(
"Upload File(s)",
@ -413,9 +484,11 @@ class PrivateGptUi:
interactive=True,
render=False,
)
# When mode changes, set default system prompt
# When mode changes, set default system prompt, and other stuffs
mode.change(
self._set_current_mode, inputs=mode, outputs=system_prompt_input
self._set_current_mode,
inputs=mode,
outputs=[system_prompt_input, explanation_mode],
)
# On blur, set system prompt to use in queries
system_prompt_input.blur(