separate source and content graph

open_notebook/domain/transformation.py

@@ -0,0 +1,19 @@
+from typing import ClassVar, List, Optional
+
+import yaml
+from pydantic import Field
+
+from open_notebook.domain.base import RecordModel
+
+
+class Transformation:
+    @classmethod
+    def get_all(cls):
+        with open("transformations.yaml", "r") as file:
+            transformations = yaml.safe_load(file)
+        return transformations
+
+
+class DefaultTransformations(RecordModel):
+    record_id: ClassVar[str] = "open_notebook:default_transformations"
+    source_insights: Optional[List[str]] = Field(default_factory=list)

open_notebook/graphs/content_processing/__init__.py

@@ -14,14 +14,14 @@ from open_notebook.graphs.content_processing.pdf import (
     SUPPORTED_FITZ_TYPES,
     extract_pdf,
 )
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 from open_notebook.graphs.content_processing.text import extract_txt
 from open_notebook.graphs.content_processing.url import extract_url, url_provider
 from open_notebook.graphs.content_processing.video import extract_best_audio_from_video
 from open_notebook.graphs.content_processing.youtube import extract_youtube_transcript
 
 
-def source_identification(state: SourceState):
+def source_identification(state: ContentState):
     """
     Identify the content source based on parameters
     """
@@ -37,7 +37,7 @@ def source_identification(state: SourceState):
     return {"source_type": doc_type}
 
 
-def file_type(state: SourceState):
+def file_type(state: ContentState):
     """
     Identify the file using python-magic
     """
@@ -45,10 +45,11 @@ def file_type(state: SourceState):
     file_path = state.get("file_path")
     if file_path is not None:
         return_dict["identified_type"] = magic.from_file(file_path, mime=True)
+        return_dict["title"] = os.path.basename(file_path)
     return return_dict
 
 
-def file_type_edge(data: SourceState):
+def file_type_edge(data: ContentState):
     assert data.get("identified_type"), "Type not identified"
     identified_type = data["identified_type"]
 
@@ -68,7 +69,7 @@ def file_type_edge(data: SourceState):
         )
 
 
-def delete_file(data: SourceState):
+def delete_file(data: ContentState):
     if data.get("delete_source"):
         logger.debug(f"Deleting file: {data.get('file_path')}")
         file_path = data.get("file_path")
@@ -82,7 +83,7 @@ def delete_file(data: SourceState):
         logger.debug("Not deleting file")
 
 
-workflow = StateGraph(SourceState)
+workflow = StateGraph(ContentState)
 workflow.add_node("source", source_identification)
 workflow.add_node("url_provider", url_provider)
 workflow.add_node("file_type", file_type)

open_notebook/graphs/content_processing/audio.py

@@ -5,7 +5,7 @@ from loguru import logger
 from pydub import AudioSegment
 
 from open_notebook.domain.models import model_manager
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 # todo: remove reference to model_manager
 # future: parallelize the transcription process
@@ -72,7 +72,7 @@ def split_audio(input_file, segment_length_minutes=15, output_prefix=None):
     return output_files
 
 
-def extract_audio(data: SourceState):
+def extract_audio(data: ContentState):
     SPEECH_TO_TEXT_MODEL = model_manager.speech_to_text
 
     input_audio_path = data.get("file_path")

open_notebook/graphs/content_processing/office.py

@@ -3,7 +3,7 @@ from loguru import logger
 from openpyxl import load_workbook
 from pptx import Presentation
 
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 SUPPORTED_OFFICE_TYPES = [
     "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
@@ -251,7 +251,7 @@ def get_xlsx_info(file_path):
         return None
 
 
-def extract_office_content(state: SourceState):
+def extract_office_content(state: ContentState):
     """Universal function to extract content from Office files"""
     assert state.get("file_path"), "No file path provided"
     assert (

open_notebook/graphs/content_processing/pdf.py

@@ -4,7 +4,7 @@ import unicodedata
 import fitz  # type: ignore
 from loguru import logger
 
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 # todo: find tables - https://pymupdf.readthedocs.io/en/latest/the-basics.html#extracting-tables-from-a-page
 # todo: what else can we do to make the text more readable?
@@ -127,7 +127,7 @@ def _extract_text_from_pdf(pdf_path):
         doc.close()
 
 
-def extract_pdf(state: SourceState):
+def extract_pdf(state: ContentState):
     """
     Parse the text file and print its content.
     """

open_notebook/graphs/content_processing/state.py

@@ -1,7 +1,7 @@
 from typing_extensions import TypedDict
 
 
-class SourceState(TypedDict):
+class ContentState(TypedDict):
     content: str
     file_path: str
     url: str

open_notebook/graphs/content_processing/text.py

@@ -1,9 +1,9 @@
 from loguru import logger
 
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 
-def extract_txt(state: SourceState):
+def extract_txt(state: ContentState):
     """
     Parse the text file and print its content.
     """

open_notebook/graphs/content_processing/url.py

@@ -5,14 +5,14 @@ import requests  # type: ignore
 from bs4 import BeautifulSoup, Comment
 from loguru import logger
 
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 # future: better extraction methods
 # https://github.com/buriy/python-readability
 # also try readability: from readability import Document
 
 
-def url_provider(state: SourceState):
+def url_provider(state: ContentState):
     """
     Identify the provider
     """
@@ -173,7 +173,7 @@ def extract_url_jina(url: str):
         return {"content": text}
 
 
-def extract_url(state: SourceState):
+def extract_url(state: ContentState):
     assert state.get("url"), "No URL provided"
     url = state["url"]
     try:

open_notebook/graphs/content_processing/video.py

@@ -4,7 +4,7 @@ import subprocess
 
 from loguru import logger
 
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 
 def extract_audio_from_video(input_file, output_file, stream_index):
@@ -102,7 +102,7 @@ def select_best_audio_stream(streams):
     return max(scored_streams, key=lambda x: x[0])[1]
 
 
-def extract_best_audio_from_video(data: SourceState):
+def extract_best_audio_from_video(data: ContentState):
     """
     Main function to extract the best audio stream from a video file
     """

open_notebook/graphs/content_processing/youtube.py

@@ -9,7 +9,7 @@ from youtube_transcript_api.formatters import TextFormatter  # type: ignore
 
 from open_notebook.config import CONFIG
 from open_notebook.exceptions import NoTranscriptFound
-from open_notebook.graphs.content_processing.state import SourceState
+from open_notebook.graphs.content_processing.state import ContentState
 
 ssl._create_default_https_context = ssl._create_unverified_context
 
@@ -129,7 +129,7 @@ def get_best_transcript(video_id, preferred_langs=["en", "es", "pt"]):
         return None
 
 
-def extract_youtube_transcript(state: SourceState):
+def extract_youtube_transcript(state: ContentState):
     """
     Parse the text file and print its content.
     """

open_notebook/graphs/source.py

@@ -0,0 +1,106 @@
+import operator
+from typing import List
+
+from langchain_core.runnables import (
+    RunnableConfig,
+)
+from langgraph.graph import END, START, StateGraph
+from langgraph.types import Send
+from loguru import logger
+from typing_extensions import Annotated, TypedDict
+
+from open_notebook.domain.notebook import Asset, Source
+from open_notebook.domain.transformation import Transformation
+from open_notebook.graphs.content_processing import ContentState
+from open_notebook.graphs.content_processing import graph as content_graph
+from open_notebook.graphs.multipattern import graph as transform_graph
+from open_notebook.utils import surreal_clean
+
+# todo: we can make this more efficient
+
+
+class SourceState(TypedDict):
+    content_state: ContentState
+    transformations: List[str]
+    notebook_id: str
+    source: Source
+    transformations: Annotated[list, operator.add]
+
+
+class TransformationState(TypedDict):
+    source: Source
+    transformation: dict
+
+
+def content_process(state: SourceState):
+    content_state = state["content_state"]
+    logger.debug("Content processing started for new content")
+    return {"content_state": content_graph.invoke(content_state)}
+
+
+def run_patterns(input_text, patterns):
+    output = transform_graph.invoke(dict(content_stack=[input_text], patterns=patterns))
+    return output["output"]
+
+
+def save_source(state: SourceState):
+    logger.debug("Saving source")
+    content_state = state["content_state"]
+    source = Source(
+        asset=Asset(
+            url=content_state.get("url"), file_path=content_state.get("file_path")
+        ),
+        full_text=surreal_clean(content_state["content"]),
+        title=content_state.get("title"),
+    )
+    source.save()
+
+    if state["notebook_id"]:
+        logger.debug(f"Adding source to notebook {state['notebook_id']}")
+        source.add_to_notebook(state["notebook_id"])
+    return {"source": source}
+
+
+def trigger_transformations(state: SourceState, config: RunnableConfig):
+    if len(state["transformations"]) == 0:
+        return []
+    transformations = Transformation.get_all()
+    to_apply = [
+        t
+        for t in transformations["source_insights"]
+        if t["name"] in state["transformations"]
+    ]
+    logger.debug(f"Applying transformations {to_apply}")
+    return [
+        Send(
+            "transform_content",
+            {
+                "source": state["source"],
+                "transformation": t,
+            },
+        )
+        for t in to_apply
+    ]
+
+
+def transform_content(state: TransformationState):
+    source = state["source"]
+    content = source.full_text
+    transformation = state["transformation"]
+    logger.debug(f"Applying transformation {transformation['name']}")
+    result = run_patterns(content, patterns=transformation["patterns"])
+    source.add_insight(transformation["name"], surreal_clean(result))
+    return {"transformations": [{"name": transformation["name"], "content": result}]}
+
+
+workflow = StateGraph(SourceState)
+workflow.add_node("content_process", content_process)
+workflow.add_node("save_source", save_source)
+workflow.add_node("transform_content", transform_content)
+workflow.add_edge(START, "content_process")
+workflow.add_edge("content_process", "save_source")
+workflow.add_conditional_edges(
+    "save_source", trigger_transformations, ["transform_content"]
+)
+workflow.add_edge("transform_content", END)
+source_graph = workflow.compile()

pages/stream_app/source.py

@@ -6,36 +6,15 @@ from humanize import naturaltime
 from loguru import logger
 
 from open_notebook.config import UPLOADS_FOLDER
-from open_notebook.domain.notebook import Asset, Source
+from open_notebook.domain.notebook import Source
+from open_notebook.domain.transformation import DefaultTransformations, Transformation
 from open_notebook.exceptions import UnsupportedTypeException
-from open_notebook.graphs.content_processing import graph
-from open_notebook.utils import surreal_clean
+from open_notebook.graphs.source import source_graph
 from pages.components import source_panel
-from pages.stream_app.utils import run_patterns
 
 from .consts import context_icons
 
 
-# moved it here to replace it with the pipeline on 0.1.0
-def generate_toc_and_title(source) -> "Source":
-    try:
-        patterns = ["patterns/default/toc"]
-        result = run_patterns(source.full_text, patterns=patterns)
-        source.add_insight("Table of Contents", surreal_clean(result))
-        if not source.title:
-            patterns = [
-                "Based on the Table of Contents below, please provide a Title for this content, with max 15 words"
-            ]
-            output = run_patterns(result, patterns=patterns)
-            source.title = surreal_clean(output)
-            source.save()
-        return source
-    except Exception as e:
-        logger.error(f"Error summarizing source {source.id}: {str(e)}")
-        logger.exception(e)
-        raise
-
-
 @st.dialog("Source", width="large")
 def source_panel_dialog(source_id):
     source_panel(source_id, modal=True)
@@ -48,6 +27,7 @@ def add_source(notebook_id):
     source_text = None
     source_type = st.radio("Type", ["Link", "Upload", "Text"])
     req = {}
+    transformations = Transformation.get_all()
     if source_type == "Link":
         source_link = st.text_input("Link")
         req["url"] = source_link
@@ -58,6 +38,14 @@ def add_source(notebook_id):
     else:
         source_text = st.text_area("Text")
         req["content"] = source_text
+
+    default_transformations = [t for t in DefaultTransformations().source_insights]
+    available_transformations = [t["name"] for t in transformations["source_insights"]]
+    apply_transformations = st.multiselect(
+        "Apply transformations",
+        options=available_transformations,
+        default=default_transformations,
+    )
     if st.button("Process", key="add_source"):
         logger.debug("Adding source")
         with st.status("Processing...", expanded=True):
@@ -82,17 +70,14 @@ def add_source(notebook_id):
                     with open(new_path, "wb") as f:
                         f.write(source_file.getbuffer())
 
-                result = graph.invoke(req)
-                st.write("Saving..")
-                source = Source(
-                    asset=Asset(url=req.get("url"), file_path=req.get("file_path")),
-                    full_text=surreal_clean(result["content"]),
-                    title=result.get("title"),
+                st.write("Processing content..")
+                source_graph.invoke(
+                    {
+                        "content_state": req,
+                        "notebook_id": notebook_id,
+                        "transformations": apply_transformations,
+                    }
                 )
-                source.save()
-                source.add_to_notebook(notebook_id)
-                st.write("Summarizing...")
-                generate_toc_and_title(source)
             except UnsupportedTypeException as e:
                 st.warning(
                     "This type of content is not supported yet. If you think it should be, let us know on the project Issues's page"