Spaces:
Sleeping
Sleeping
Started building out the new graph
Browse files- .gitignore +1 -0
- langgraph.json +1 -1
- pstuts_rag/pstuts_rag/nodes.py +82 -40
- pstuts_rag/pstuts_rag/prompts.py +53 -0
- pyproject.toml +5 -4
- uv.lock +850 -13
.gitignore
CHANGED
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@@ -7,3 +7,4 @@ __pycache__/
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.vscode/qdrant_local_db/
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.embeddings_cache/
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notebooks/*/
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.vscode/qdrant_local_db/
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.embeddings_cache/
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notebooks/*/
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*.pckl
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langgraph.json
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@@ -1,7 +1,7 @@
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{
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"dockerfile_lines": [],
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"graphs": {
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-
"enhanced_video_archive": "./pstuts_rag/pstuts_rag/
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},
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"python_version": "3.11",
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"env": "./.env",
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{
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"dockerfile_lines": [],
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"graphs": {
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"enhanced_video_archive": "./pstuts_rag/pstuts_rag/nodes.py:graph"
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},
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"python_version": "3.11",
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"env": "./.env",
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pstuts_rag/pstuts_rag/nodes.py
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# nodes.py
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from
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from langgraph.graph import StateGraph, MessagesState, START, END
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from enum import Enum
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from typing import Any, Callable, Dict
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):
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def
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state: PsTutsTeamState,
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):
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pass
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def search_help(state:
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pass
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def search_rag(state:
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pass
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def join(state:
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pass
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def write_answer(state:
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pass
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"""State management for tutorial team workflow orchestration."""
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team_members: list[str]
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next: str
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video_references: set[Any]
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url_references: set[Any]
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## MISSING: CONDITIONAL NODES
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-
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graph_builder
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graph_builder.add_node(research)
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graph_builder.add_node(search_help)
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graph_builder.add_node(search_rag)
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graph_builder.add_node(join)
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graph_builder.add_node(write_answer)
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graph_builder.
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)
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graph_builder.add_edge(research.__name__, search_help.__name__)
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graph_builder.add_edge(research.__name__, search_rag.__name__)
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graph_builder.add_edge(search_help.__name__,
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graph_builder.add_edge(search_rag.__name__,
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graph_builder.add_conditional_edges(
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-
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route_is_complete,
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{
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)
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graph_builder.add_edge(write_answer.__name__, END)
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graph = graph_builder.compile()
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# nodes.py
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from langchain_openai import ChatOpenAI
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from langgraph.graph import StateGraph, MessagesState, START, END
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from langgraph.types import Command
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from langchain_core.runnables import RunnableConfig
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from langchain_core.messages import AnyMessage, HumanMessage
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from langgraph.checkpoint.memory import InMemorySaver
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from pstuts_rag.prompts import NODE_PROMPTS
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from pydantic import BaseModel, Field
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from pstuts_rag.utils import ChatAPISelector
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from pstuts_rag.configuration import Configuration
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from enum import Enum
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from typing import Any, Callable, Dict, Literal
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class TutorialState(MessagesState):
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"""State management for tutorial team workflow orchestration."""
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# next: str
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query: str
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video_references: set[Any]
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url_references: set[Any]
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def research(state: TutorialState, config: RunnableConfig):
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# retrieve the LLM
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# configurable = Configuration.from_runnable_config(config)
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# cls = ChatAPISelector.get(configurable.llm_api, ChatOpenAI)
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# llm = cls(model=configurable.llm_tool_model)
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# # format the prompt
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# prompt = NODE_PROMPTS["research"]
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# history = [
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# msg.content
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# for msg in state["messages"]
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# if getattr(msg, "role", "") == "ai"
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# ]
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# prompt = prompt.format(history=history)
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pass
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def search_help(state: TutorialState, config: RunnableConfig):
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pass
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def search_rag(state: TutorialState, config: RunnableConfig):
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pass
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def join(state: TutorialState, config: RunnableConfig):
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pass
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def write_answer(state: TutorialState, config: RunnableConfig):
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pass
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## MISSING: CONDITIONAL NODES
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class YesNoDecision(BaseModel):
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decision: Literal["yes", "no"] = Field(description="Yes or no decision.")
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def route_is_relevant(
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state: TutorialState, config: RunnableConfig
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) -> Command[Literal["research", "write_answer"]]:
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# retrieve the LLM
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configurable = Configuration.from_runnable_config(config)
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cls = ChatAPISelector.get(configurable.llm_api, ChatOpenAI)
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llm = cls(model=configurable.llm_tool_model).with_structured_output(
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YesNoDecision
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)
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# format the prompt
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prompt = NODE_PROMPTS["relevance"].format(query=state["query"])
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relevance = llm.invoke([HumanMessage(content=prompt)])
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where = "research" if relevance.decision == "yes" else "write_answer"
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answer = f"Query is {'not' if relevance.decision == 'no' else ''} relevant to Photoshop."
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return Command(
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update={"messages": {"role": "ai", "content": answer}},
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goto=where,
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)
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def route_is_complete(
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state: TutorialState, config: RunnableConfig
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) -> Literal["yes", "no"]:
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if True:
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return "yes"
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else:
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return "no"
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graph_builder = StateGraph(TutorialState)
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# graph_builder.add_node(route_is_relevant)
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# graph_builder.add_node(route_is_complete, defer=True)
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graph_builder.add_node(research)
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graph_builder.add_node(search_help)
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graph_builder.add_node(search_rag)
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graph_builder.add_node(join)
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graph_builder.add_node(write_answer)
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# graph_builder.add_conditional_edges(
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# START,
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# route_is_relevant,
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# {"yes": research.__name__, "no": write_answer.__name__},
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# )
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graph_builder.add_node(route_is_relevant)
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graph_builder.add_edge(START, route_is_relevant.__name__)
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graph_builder.add_edge(research.__name__, search_help.__name__)
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graph_builder.add_edge(research.__name__, search_rag.__name__)
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graph_builder.add_edge(search_help.__name__, join.__name__)
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graph_builder.add_edge(search_rag.__name__, join.__name__)
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graph_builder.add_conditional_edges(
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join.__name__,
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route_is_complete,
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{"no": research.__name__, "yes": write_answer.__name__},
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)
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graph_builder.add_edge(write_answer.__name__, END)
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graph = graph_builder.compile()
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pstuts_rag/pstuts_rag/prompts.py
CHANGED
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And, once there's no further research needed:
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FINISH
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"""
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And, once there's no further research needed:
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FINISH
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"""
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NODE_PROMPTS = {}
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+
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NODE_PROMPTS[
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"research"
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] = """
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<QUERY>
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{query}
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</QUERY>
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+
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<HISTORY>
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{history}
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</HISTORY>
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+
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<TASK>
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Your job is to generate good succinct search phrases based on
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1. user's query (QUERY)
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2. research history (HISTORY)
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The primary topic is Adobe Photoshop use cases. Search phrases
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will be used to look up tutorial videos and online help.
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It is important to ground yourself in provided context (QUERY + HISTORY). Do not deviate
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or invent terms adjacent to the prompt provided.
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Write a short 1-10 word search that can be used to look up the most relevant
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topic for QUERY that is **not yet** analyzed in HISTORY.
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</TASK>
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+
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+
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Generate the search phrase here:
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"""
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NODE_PROMPTS[
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"relevance"
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] = """
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<QUERY>
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{query}
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</QUERY>
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<TASK>
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You are a gatekeeper for the system.
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Determine of the given QUERY is within the scope
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of Adobe Photoshop general topic area.
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If it is relevant, respond with "yes",
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otherwise respond with "no".
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Your response should be a single word: yes if the QUERY
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is relevant to Adobe Photoshop, otherwise no.
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</TASK>
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+
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Relevant?
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"""
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pyproject.toml
CHANGED
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"langgraph>=0.2.55",
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"qdrant-client>=1.8.0",
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# API integration
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-
"chainlit
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"requests>=2.31.0",
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"python-dotenv>=0.9.9",
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# Utilities
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"transformers[torch]>=4.48.3",
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"pyarrow>=19.0.0",
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"datasets>=3.6.0",
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-
"ragas
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"bidict>=0.23.1",
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"wandb>=0.19.11",
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"langchain-huggingface>=0.2.0",
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@@ -43,11 +43,12 @@ dependencies = [
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"scipy>=1.10.0",
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"langsmith>=0.0.50",
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"google>=3.0.0",
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-
"numpy
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"tavily-python>=0.7.2",
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"logging>=0.4.9.6",
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"langchain-ollama>=0.3.2",
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"simsimd>=6.2.1",
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]
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authors = [{ name = "Marko Budisic", email = "mbudisic@gmail.com" }]
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license = "MIT"
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web = [
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"fastapi>=0.115.3,<0.116",
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-
"uvicorn>=0.25.0,<0.
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"python-multipart>=0.0.18,<0.0.19",
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"websockets==14.2",
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"httpx==0.27.0",
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"langgraph>=0.2.55",
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| 16 |
"qdrant-client>=1.8.0",
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| 17 |
# API integration
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| 18 |
+
"chainlit>=2.0.4",
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| 19 |
"requests>=2.31.0",
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| 20 |
"python-dotenv>=0.9.9",
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| 21 |
# Utilities
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| 30 |
"transformers[torch]>=4.48.3",
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| 31 |
"pyarrow>=19.0.0",
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| 32 |
"datasets>=3.6.0",
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| 33 |
+
"ragas>=0.2.15",
|
| 34 |
"bidict>=0.23.1",
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| 35 |
"wandb>=0.19.11",
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| 36 |
"langchain-huggingface>=0.2.0",
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| 43 |
"scipy>=1.10.0",
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| 44 |
"langsmith>=0.0.50",
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| 45 |
"google>=3.0.0",
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| 46 |
+
"numpy>=2.2.2",
|
| 47 |
"tavily-python>=0.7.2",
|
| 48 |
"logging>=0.4.9.6",
|
| 49 |
"langchain-ollama>=0.3.2",
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| 50 |
"simsimd>=6.2.1",
|
| 51 |
+
"langgraph-cli[inmem]>=0.1.55",
|
| 52 |
]
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| 53 |
authors = [{ name = "Marko Budisic", email = "mbudisic@gmail.com" }]
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| 54 |
license = "MIT"
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| 75 |
web = [
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| 76 |
"fastapi>=0.115.3,<0.116",
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| 77 |
+
"uvicorn>=0.25.0,<0.27.0",
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"python-multipart>=0.0.18,<0.0.19",
|
| 79 |
"websockets==14.2",
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| 80 |
"httpx==0.27.0",
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uv.lock
CHANGED
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@@ -134,6 +134,24 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
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]
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name = "anyio"
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version = "4.9.0"
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{ url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
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name = "beautifulsoup4"
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version = "4.13.4"
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version = "2025.4.26"
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name = "chainlit"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "pydantic" },
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{ url = "https://files.pythonhosted.org/packages/a2/58/1f37bf81e3c689cc74ffa42102fa8915b59085f54a6e4a80bc6265c0f6bf/click-8.2.0-py3-none-any.whl", hash = "sha256:6b303f0b2aa85f1cb4e5303078fadcbcd4e476f114fab9b5007005711839325c", size = 102156 },
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name = "colorama"
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version = "0.4.6"
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{ url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180 },
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name = "dataclasses-json"
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version = "0.6.7"
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{ url = "https://files.pythonhosted.org/packages/83/5c/0627be4c9976d56b1217cb5187b7504e7fd7d3503f8bfd312a04077bd4f7/flake8-7.2.0-py2.py3-none-any.whl", hash = "sha256:93b92ba5bdb60754a6da14fa3b93a9361fd00a59632ada61fd7b130436c40343", size = 57786 },
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name = "fqdn"
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version = "1.5.1"
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{ url = "https://files.pythonhosted.org/packages/79/9d/0fb148dc4d6fa4a7dd1d8378168d9b4cd8d4560a6fbf6f0121c5fc34eb68/importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e", size = 26971 },
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name = "iniconfig"
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version = "2.1.0"
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name = "jsonschema-specifications"
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version = "2025.4.1"
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@@ -1774,6 +1908,36 @@ wheels = [
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| 1774 |
{ url = "https://files.pythonhosted.org/packages/35/53/0a20edd9f41eb3707722444ec1b43752b792bbe904d1c8cc3ba27f8eb2c8/langgraph-0.4.3-py3-none-any.whl", hash = "sha256:dec926e034f4d440b92a3c52139cb6e9763bc1791e79a6ea53a233309cec864f", size = 151191 },
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name = "langgraph-checkpoint"
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version = "2.0.25"
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| 1787 |
{ url = "https://files.pythonhosted.org/packages/12/52/bceb5b5348c7a60ef0625ab0a0a0a9ff5d78f0e12aed8cc55c49d5e8a8c9/langgraph_checkpoint-2.0.25-py3-none-any.whl", hash = "sha256:23416a0f5bc9dd712ac10918fc13e8c9c4530c419d2985a441df71a38fc81602", size = 42312 },
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| 1790 |
[[package]]
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| 1791 |
name = "langgraph-prebuilt"
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| 1792 |
version = "0.1.8"
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@@ -1800,6 +1983,23 @@ wheels = [
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| 1800 |
{ url = "https://files.pythonhosted.org/packages/36/72/9e092665502f8f52f2708065ed14fbbba3f95d1a1b65d62049b0c5fcdf00/langgraph_prebuilt-0.1.8-py3-none-any.whl", hash = "sha256:ae97b828ae00be2cefec503423aa782e1bff165e9b94592e224da132f2526968", size = 25903 },
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| 1803 |
[[package]]
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| 1804 |
name = "langgraph-sdk"
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| 1805 |
version = "0.1.66"
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@@ -1842,15 +2042,16 @@ wheels = [
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| 1842 |
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| 1843 |
[[package]]
|
| 1844 |
name = "literalai"
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| 1845 |
-
version = "0.1.
|
| 1846 |
source = { registry = "https://pypi.org/simple" }
|
| 1847 |
dependencies = [
|
| 1848 |
{ name = "chevron" },
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| 1849 |
{ name = "httpx" },
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| 1850 |
{ name = "packaging" },
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| 1851 |
{ name = "pydantic" },
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| 1852 |
]
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| 1853 |
-
sdist = { url = "https://files.pythonhosted.org/packages/
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[[package]]
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name = "logging"
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@@ -1998,6 +2199,26 @@ wheels = [
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| 1998 |
{ url = "https://files.pythonhosted.org/packages/27/1a/1f68f9ba0c207934b35b86a8ca3aad8395a3d6dd7921c0686e23853ff5a9/mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e", size = 7350 },
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| 1999 |
]
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| 2001 |
[[package]]
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| 2002 |
name = "mistune"
|
| 2003 |
version = "3.1.3"
|
|
@@ -2007,6 +2228,15 @@ wheels = [
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|
| 2007 |
{ url = "https://files.pythonhosted.org/packages/01/4d/23c4e4f09da849e127e9f123241946c23c1e30f45a88366879e064211815/mistune-3.1.3-py3-none-any.whl", hash = "sha256:1a32314113cff28aa6432e99e522677c8587fd83e3d51c29b82a52409c842bd9", size = 53410 },
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| 2008 |
]
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| 2010 |
[[package]]
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| 2011 |
name = "mpmath"
|
| 2012 |
version = "1.3.0"
|
|
@@ -2552,6 +2782,489 @@ wheels = [
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|
| 2552 |
{ url = "https://files.pythonhosted.org/packages/61/dd/a2b35078170941990e7a5194b9600fa75868958a9a2196a752da0e7b97a0/opentelemetry_instrumentation-0.52b1-py3-none-any.whl", hash = "sha256:8c0059c4379d77bbd8015c8d8476020efe873c123047ec069bb335e4b8717477", size = 31036 },
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| 2553 |
]
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| 2555 |
[[package]]
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| 2556 |
name = "opentelemetry-proto"
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| 2591 |
{ url = "https://files.pythonhosted.org/packages/98/be/d4ba300cfc1d4980886efbc9b48ee75242b9fcf940d9c4ccdc9ef413a7cf/opentelemetry_semantic_conventions-0.52b1-py3-none-any.whl", hash = "sha256:72b42db327e29ca8bb1b91e8082514ddf3bbf33f32ec088feb09526ade4bc77e", size = 183409 },
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| 2594 |
[[package]]
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| 2595 |
name = "orjson"
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| 2866 |
[[package]]
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| 2867 |
name = "prometheus-client"
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| 2994 |
{ name = "langchain-openai" },
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{ name = "langchain-qdrant" },
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| 2996 |
{ name = "langgraph" },
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{ name = "langsmith" },
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{ name = "logging" },
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{ name = "nest-asyncio" },
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@@ -3040,7 +3789,7 @@ requires-dist = [
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| 3040 |
{ name = "aiofiles", specifier = ">=23.1.0" },
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{ name = "langchain-ollama", specifier = ">=0.3.2" },
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{ name = "langchain-openai" },
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{ name = "langchain-qdrant", specifier = ">=0.2.0" },
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{ name = "langsmith", specifier = ">=0.0.50" },
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{ name = "logging", specifier = ">=0.4.9.6" },
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{ name = "nest-asyncio", specifier = ">=1.5.6" },
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| 3069 |
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| 3070 |
{ name = "pandas", specifier = ">=2.0.0" },
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{ name = "pylint-venv", marker = "extra == 'dev'", specifier = ">=3.0.4" },
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| 3074 |
{ name = "python-dotenv", specifier = ">=0.9.9" },
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{ name = "python-multipart", marker = "extra == 'web'", specifier = ">=0.0.18,<0.0.19" },
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{ name = "qdrant-client", specifier = ">=1.8.0" },
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| 4106 |
[[package]]
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| 4132 |
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| 4379 |
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| 4429 |
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