unused graphs
This commit is contained in:
LUIS NOVO
parent
4f4abf7098
commit
edf839cd1b
5 changed files with 11 additions and 268 deletions
|
|
@ -1,47 +0,0 @@
|
|||
from langchain_core.runnables import (
|
||||
RunnableConfig,
|
||||
)
|
||||
from langgraph.graph import END, START, StateGraph
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from open_notebook.config import load_default_models
|
||||
from open_notebook.domain.notebook import Note, Notebook, Source
|
||||
from open_notebook.graphs.utils import run_pattern
|
||||
|
||||
DEFAULT_MODELS, EMBEDDING_MODEL, SPEECH_TO_TEXT_MODEL = load_default_models()
|
||||
|
||||
|
||||
class DocQueryState(TypedDict):
|
||||
doc_id: str
|
||||
doc_content: str
|
||||
question: str
|
||||
answer: str
|
||||
notebook: Notebook
|
||||
|
||||
|
||||
def call_model(state: dict, config: RunnableConfig) -> dict:
|
||||
model_id = config.get("configurable", {}).get(
|
||||
"model_id", DEFAULT_MODELS.default_transformation_model
|
||||
)
|
||||
return {"answer": run_pattern("doc_query", model_id, state)}
|
||||
|
||||
|
||||
# todo: there is probably a better way to do this and avoid repetition
|
||||
def get_content(state: DocQueryState) -> dict:
|
||||
doc_id = state["doc_id"]
|
||||
if "note:" in doc_id:
|
||||
doc: Note = Note.get(id=doc_id)
|
||||
elif "source:" in doc_id:
|
||||
doc: Source = Source.get(id=doc_id)
|
||||
doc_content = doc.get_context("long") if doc else None
|
||||
return {"doc_content": doc_content}
|
||||
|
||||
|
||||
agent_state = StateGraph(DocQueryState)
|
||||
agent_state.add_node("get_content", get_content)
|
||||
agent_state.add_node("agent", call_model)
|
||||
agent_state.add_edge(START, "get_content")
|
||||
agent_state.add_edge("get_content", "agent")
|
||||
agent_state.add_edge("agent", END)
|
||||
|
||||
graph = agent_state.compile()
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
from langchain_core.runnables import (
|
||||
RunnableConfig,
|
||||
)
|
||||
from langgraph.graph import END, START, StateGraph
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from open_notebook.config import load_default_models
|
||||
from open_notebook.graphs.utils import run_pattern
|
||||
|
||||
DEFAULT_MODELS, EMBEDDING_MODEL, SPEECH_TO_TEXT_MODEL = load_default_models()
|
||||
|
||||
|
||||
class PatternState(TypedDict):
|
||||
input_text: str
|
||||
pattern: str
|
||||
output: str
|
||||
|
||||
|
||||
def call_model(state: dict, config: RunnableConfig) -> dict:
|
||||
model_id = config.get("configurable", {}).get(
|
||||
"model_id", DEFAULT_MODELS.default_transformation_model
|
||||
)
|
||||
return {
|
||||
"output": run_pattern(
|
||||
pattern_name=state["pattern"],
|
||||
model_id=model_id,
|
||||
state=state,
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
agent_state = StateGraph(PatternState)
|
||||
agent_state.add_node("agent", call_model)
|
||||
agent_state.add_edge(START, "agent")
|
||||
agent_state.add_edge("agent", END)
|
||||
graph = agent_state.compile()
|
||||
|
|
@ -1,81 +0,0 @@
|
|||
import os
|
||||
from typing import List, Literal
|
||||
|
||||
from langchain_core.runnables import (
|
||||
RunnableConfig,
|
||||
)
|
||||
from langgraph.graph import END, START, StateGraph
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from open_notebook.config import load_default_models
|
||||
from open_notebook.graphs.utils import run_pattern
|
||||
from open_notebook.utils import split_text
|
||||
|
||||
DEFAULT_MODELS, EMBEDDING_MODEL, SPEECH_TO_TEXT_MODEL = load_default_models()
|
||||
|
||||
|
||||
class TocState(TypedDict):
|
||||
chunks: List[str]
|
||||
content: str
|
||||
toc: str
|
||||
|
||||
|
||||
def build_chunks(state: TocState) -> dict:
|
||||
"""
|
||||
Split the input text into chunks.
|
||||
"""
|
||||
return {
|
||||
"chunks": split_text(
|
||||
state["content"],
|
||||
chunk=int(os.environ.get("SUMMARY_CHUNK_SIZE", 200000)),
|
||||
overlap=int(os.environ.get("SUMMARY_CHUNK_OVERLAP", 1000)),
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
def setup_next_chunk(state: TocState) -> dict:
|
||||
"""
|
||||
Move the next item in the chunk to the processing area
|
||||
"""
|
||||
state["content"] = state["chunks"].pop(0)
|
||||
return {"chunks": state["chunks"], "content": state["content"]}
|
||||
|
||||
|
||||
def chunk_condition(state: TocState) -> Literal["get_chunk", END]: # type: ignore
|
||||
"""
|
||||
Checks whether there are more chunks to process.
|
||||
"""
|
||||
if len(state["chunks"]) > 0:
|
||||
return "get_chunk"
|
||||
return END
|
||||
|
||||
|
||||
def call_model(state: TocState, config: RunnableConfig) -> dict:
|
||||
model_id = config.get("configurable", {}).get(
|
||||
"model_id", DEFAULT_MODELS.default_transformation_model
|
||||
)
|
||||
return {
|
||||
"toc": run_pattern(
|
||||
pattern_name="recursive_toc",
|
||||
model_id=model_id,
|
||||
state=state,
|
||||
).content
|
||||
}
|
||||
|
||||
|
||||
agent_state = StateGraph(TocState)
|
||||
agent_state.add_node("setup_chunk", build_chunks)
|
||||
agent_state.add_edge(START, "setup_chunk")
|
||||
agent_state.add_conditional_edges(
|
||||
"setup_chunk",
|
||||
chunk_condition,
|
||||
)
|
||||
agent_state.add_node("get_chunk", setup_next_chunk)
|
||||
agent_state.add_node("agent", call_model)
|
||||
agent_state.add_edge("get_chunk", "agent")
|
||||
agent_state.add_conditional_edges(
|
||||
"agent",
|
||||
chunk_condition,
|
||||
)
|
||||
|
||||
graph = agent_state.compile()
|
||||
|
|
@ -1,93 +0,0 @@
|
|||
import os
|
||||
from typing import List, Literal
|
||||
|
||||
from langchain_core.output_parsers import PydanticOutputParser
|
||||
from langchain_core.runnables import (
|
||||
RunnableConfig,
|
||||
)
|
||||
from langgraph.graph import END, START, StateGraph
|
||||
from pydantic import BaseModel
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from open_notebook.config import load_default_models
|
||||
from open_notebook.graphs.utils import run_pattern
|
||||
from open_notebook.utils import split_text
|
||||
|
||||
DEFAULT_MODELS, EMBEDDING_MODEL, SPEECH_TO_TEXT_MODEL = load_default_models()
|
||||
|
||||
|
||||
class SummaryResponse(BaseModel):
|
||||
"""This is schema of your response. Please provide a JSON object with the enclosed keys"""
|
||||
|
||||
summary: str
|
||||
topics: List[str]
|
||||
title: str
|
||||
|
||||
|
||||
class SummaryState(TypedDict):
|
||||
chunks: List[str]
|
||||
content: str
|
||||
output: SummaryResponse
|
||||
|
||||
|
||||
def build_chunks(state: SummaryState) -> dict:
|
||||
"""
|
||||
Split the input text into chunks.
|
||||
"""
|
||||
return {
|
||||
"chunks": split_text(
|
||||
state["content"],
|
||||
chunk=int(os.environ.get("SUMMARY_CHUNK_SIZE", 200000)),
|
||||
overlap=int(os.environ.get("SUMMARY_CHUNK_OVERLAP", 1000)),
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
def setup_next_chunk(state: SummaryState) -> dict:
|
||||
"""
|
||||
Move the next item in the chunk to the processing area
|
||||
"""
|
||||
state["content"] = state["chunks"].pop(0)
|
||||
return {"chunks": state["chunks"], "content": state["content"]}
|
||||
|
||||
|
||||
def chunk_condition(state: SummaryState) -> Literal["get_chunk", END]: # type: ignore
|
||||
"""
|
||||
Checks whether there are more chunks to process.
|
||||
"""
|
||||
if len(state["chunks"]) > 0:
|
||||
return "get_chunk"
|
||||
return END
|
||||
|
||||
|
||||
def call_model(state: dict, config: RunnableConfig) -> dict:
|
||||
model_id = config.get("configurable", {}).get(
|
||||
"model_id", DEFAULT_MODELS.default_transformation_model
|
||||
)
|
||||
parser = PydanticOutputParser(pydantic_object=SummaryResponse)
|
||||
return {
|
||||
"output": run_pattern(
|
||||
pattern_name="summarize",
|
||||
model_id=model_id,
|
||||
state=state,
|
||||
parser=parser,
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
agent_state = StateGraph(SummaryState)
|
||||
agent_state.add_node("setup_chunk", build_chunks)
|
||||
agent_state.add_edge(START, "setup_chunk")
|
||||
agent_state.add_conditional_edges(
|
||||
"setup_chunk",
|
||||
chunk_condition,
|
||||
)
|
||||
agent_state.add_node("get_chunk", setup_next_chunk)
|
||||
agent_state.add_node("agent", call_model)
|
||||
agent_state.add_edge("get_chunk", "agent")
|
||||
agent_state.add_conditional_edges(
|
||||
"agent",
|
||||
chunk_condition,
|
||||
)
|
||||
|
||||
graph = agent_state.compile()
|
||||
|
|
@ -12,15 +12,15 @@ def get_current_timestamp() -> str:
|
|||
return datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
|
||||
|
||||
@tool
|
||||
def doc_query(doc_id: str, question: str):
|
||||
"""
|
||||
name: doc_query
|
||||
Use this tool if you need to investigate into a particular document.
|
||||
Another LLM will read the document and answer the question that you might have.
|
||||
Use this when the user question cannot be answered with the content you have in context.
|
||||
"""
|
||||
from open_notebook.graphs.doc_query import graph
|
||||
# @tool
|
||||
# def doc_query(doc_id: str, question: str):
|
||||
# """
|
||||
# name: doc_query
|
||||
# Use this tool if you need to investigate into a particular document.
|
||||
# Another LLM will read the document and answer the question that you might have.
|
||||
# Use this when the user question cannot be answered with the content you have in context.
|
||||
# """
|
||||
# from temp.doc_query import graph
|
||||
|
||||
result = graph.invoke({"doc_id": doc_id, "question": question})
|
||||
return result["answer"]
|
||||
# result = graph.invoke({"doc_id": doc_id, "question": question})
|
||||
# return result["answer"]
|
||||
|
|
|
|||
Loading…
Reference in a new issue