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6c58cf4 dfa6a46 6c58cf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | import os
from typing import List, Literal
from typing_extensions import TypedDict
from pydantic import BaseModel, Field
from langchain.schema import Document
from langchain_core.output_parsers import StrOutputParser
from langgraph.graph import END, StateGraph, START
from project.pipeline.rag import RAGPipeline
from project.utils.model_loader import ModelLoader
from project.prompts.prompt_template import ROUTER_PROMPT, WEB_SEARCH_PROMPT
from project.logger.logging import get_logger
logger = get_logger(__name__)
class GradeDocuments(BaseModel):
binary_score: str = Field(description="Documents are relevant to the question, 'yes' or 'no'")
class GraphState(TypedDict):
question: str
generation: str
web_search: str
documents: List[str]
class AgentWorkflow:
def __init__(self, config_path: str = None):
self.config_path = config_path
self.model_loader = ModelLoader(config_path)
self.llm = self.model_loader.load_llm()
self.rag_pipeline = RAGPipeline(config_path)
self.web_search_tool = None
self._setup_web_search()
self.workflow = None
self.app = None
self._setup_graders()
logger.info("AgentWorkflow initialized")
def _setup_web_search(self):
tavily_key = os.getenv("TAVILY_API_KEY")
if tavily_key:
try:
from langchain_community.tools.tavily_search import TavilySearchResults
self.web_search_tool = TavilySearchResults(k=3)
logger.info("Web search tool initialized")
except Exception as e:
logger.warning(f"Could not initialize web search: {str(e)}")
self.web_search_tool = None
else:
logger.warning("TAVILY_API_KEY not found, web search disabled")
def _setup_graders(self):
grade_prompt = """You are a grader assessing relevance of a retrieved document to a user question.
If the document contains keywords or semantic meaning related to the question, grade it as relevant.
Give ONLY a binary score 'yes' or 'no' to indicate whether the document is relevant to the question.
Retrieved document: {document}
User question: {question}
Answer (yes or no):"""
self.grade_prompt_text = grade_prompt
self.retrieval_grader = self.llm | StrOutputParser()
rewrite_prompt = """You are a question re-writer that converts an input question to a better version optimized for web search.
Look at the input and try to reason about the underlying semantic intent/meaning.
Provide only the improved question without any explanation.
Initial question: {question}
Improved question:"""
self.rewrite_prompt_text = rewrite_prompt
self.question_rewriter = self.llm | StrOutputParser()
def setup(self, pdf_path: str = None, use_attention_paper: bool = True):
self.rag_pipeline.setup(pdf_path=pdf_path, use_attention_paper=use_attention_paper)
self._build_graph()
logger.info("Agent workflow setup complete")
def retrieve(self, state: GraphState):
logger.info("---RETRIEVE---")
question = state["question"]
documents = self.rag_pipeline.retriever.invoke(question)
return {"documents": documents, "question": question}
def grade_documents(self, state: GraphState):
logger.info("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
question = state["question"]
documents = state["documents"]
filtered_docs = []
web_search = "No"
for d in documents:
prompt_filled = self.grade_prompt_text.format(
document=d.page_content[:500],
question=question
)
score = self.retrieval_grader.invoke(prompt_filled)
grade = score.strip().lower()
if "yes" in grade:
logger.info("---GRADE: DOCUMENT RELEVANT---")
filtered_docs.append(d)
else:
logger.info("---GRADE: DOCUMENT NOT RELEVANT---")
web_search = "Yes"
return {"documents": filtered_docs, "question": question, "web_search": web_search}
def generate(self, state: GraphState):
logger.info("---GENERATE---")
question = state["question"]
documents = state["documents"]
generation = self.rag_pipeline.chain.invoke({"question": question})
return {"documents": documents, "question": question, "generation": generation}
def transform_query(self, state: GraphState):
logger.info("---TRANSFORM QUERY---")
question = state["question"]
documents = state["documents"]
prompt_filled = self.rewrite_prompt_text.format(question=question)
better_question = self.question_rewriter.invoke(prompt_filled)
return {"documents": documents, "question": better_question}
def web_search(self, state: GraphState):
logger.info("---WEB SEARCH---")
question = state["question"]
documents = state["documents"]
if self.web_search_tool is None:
logger.warning("Web search tool not available, skipping")
return {"documents": documents, "question": question}
try:
response = self.web_search_tool.invoke({"query": question})
if not response:
logger.warning("No results from web search")
return {"documents": documents, "question": question}
web_results = "\n".join([d["content"] for d in response if "content" in d])
web_doc = Document(page_content=web_results)
documents.append(web_doc)
except Exception as e:
logger.error(f"Web search failed: {str(e)}")
return {"documents": documents, "question": question}
def decide_to_generate(self, state: GraphState) -> Literal["transform_query", "generate"]:
logger.info("---ASSESS GRADED DOCUMENTS---")
documents = state.get("documents", [])
if len(documents) == 0:
logger.info("---DECISION: NO RELEVANT DOCUMENTS, TRANSFORM QUERY---")
return "transform_query"
else:
logger.info("---DECISION: RELEVANT DOCUMENTS FOUND, GENERATE---")
return "generate"
def _build_graph(self):
workflow = StateGraph(GraphState)
workflow.add_node("retrieve", self.retrieve)
workflow.add_node("grade_documents", self.grade_documents)
workflow.add_node("generate", self.generate)
workflow.add_node("transform_query", self.transform_query)
workflow.add_node("web_search", self.web_search)
workflow.add_edge(START, "retrieve")
workflow.add_edge("retrieve", "grade_documents")
workflow.add_conditional_edges(
"grade_documents",
self.decide_to_generate,
{
"transform_query": "transform_query",
"generate": "generate",
},
)
workflow.add_edge("transform_query", "web_search")
workflow.add_edge("web_search", "generate")
workflow.add_edge("generate", END)
self.app = workflow.compile()
logger.info("LangGraph workflow compiled")
def save_graph(self, output_path: str = "workflow.png"):
try:
from IPython.display import Image
graph_image = self.app.get_graph().draw_mermaid_png()
with open(output_path, "wb") as f:
f.write(graph_image)
logger.info(f"Workflow graph saved to {output_path}")
except Exception as e:
logger.error(f"Failed to save graph: {str(e)}")
def run(self, question: str) -> str:
if self.app is None:
raise ValueError("Workflow not setup. Call setup() first.")
inputs = {"question": question}
for output in self.app.stream(inputs):
for key, value in output.items():
logger.info(f"Node '{key}' completed")
final_generation = value.get("generation", "No answer generated")
return final_generation
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