Add project files for Research Assistant

.gitignore

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+.env
+__pycache__/
+*.pyc
+.vscode/
+.idea/
+.env.*

requirements.txt

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+openai
+fastapi
+uvicorn
+pydantic
+httpx
+python-dotenv
+tavily-python
+streamlit
+requests
+langgraph
+reportlab

src/agents/llm_agent.py

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+from dataclasses import dataclass
+from src.services.lmstudio_client import chat_with_lmstudio
+
+
+@dataclass
+class LLMAgent:
+    name: str = "local_llm"
+
+    def run(self, query: str, context: str | None = None) -> str:
+        prompt = query
+        if context:
+            prompt = (
+                "Use the following context to answer the question.\n\n"
+                f"Context:\n{context}\n\nQuestion:\n{query}"
+            )
+        return chat_with_lmstudio(prompt)

src/agents/planner_agent.py

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+class PlannerAgent:
+    def run(self, state: dict) -> dict:
+        state["outline"] = [
+            "Abstract",
+            "Introduction",
+            "Background",
+            "Applications",
+            "Trends",
+            "Challenges",
+            "Future Scope",
+            "Conclusion",
+            "References"
+        ]
+        return state

src/agents/searcher_agent.py

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+from src.services.tavily_client import TavilyClient
+
+
+class SearcherAgent:
+    def __init__(self):
+        self.client = TavilyClient()
+
+    def _clean(self, text: str) -> str:
+        if not text:
+            return ""
+
+        remove = [
+            "subscribe", "likes", "views", "watch", "upgrade",
+            "reddit", "youtube", "emoji"
+        ]
+
+        text = text.replace("\n", " ")
+        for r in remove:
+            text = text.replace(r, "")
+
+        return text.strip()
+
+    def _summarize(self, content: str) -> str:
+        content = self._clean(content)
+        sentences = [s.strip() for s in content.split(".") if len(s.strip()) > 40]
+        return ". ".join(sentences[:3]) + "." if sentences else ""
+
+    def run(self, state: dict):
+        results = []
+
+        for question in state.get("sub_questions", []):
+            data = self.client.search(question)
+
+            sources = []
+            for item in data.get("results", []):
+                summary = self._summarize(item.get("content", ""))
+                if not summary:
+                    continue
+
+                sources.append({
+                    "summary": summary,
+                    "url": item.get("url", ""),
+                    "confidence": round(item.get("score", 0.85), 2)
+                })
+
+            results.append({
+                "question": question,
+                "sources": sources
+            })
+
+        state["search_results"] = results
+        return state
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src/agents/writer_agent.py

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+class WriterAgent:
+    def __init__(self, llm):
+        self.llm = llm
+
+    def _section(self, title: str, topic: str, min_words: int):
+        prompt = f"""
+You are writing an academic research paper.
+
+Write the section titled:
+{title}
+
+Research topic:
+{topic}
+
+Rules:
+- Formal academic writing
+- Paragraph-based (no bullets unless necessary)
+- Minimum {min_words} words
+- Do NOT write conclusion
+- Do NOT write references
+"""
+        return self.llm.invoke(prompt)
+
+    def _conclusion(self, topic: str):
+        prompt = f"""
+Write the FINAL CONCLUSION section for an academic research paper.
+
+Topic:
+{topic}
+
+Rules:
+- Summarize key insights
+- Discuss implications
+- Mention future scope
+- 300–400 words
+"""
+        return self.llm.invoke(prompt)
+
+    def run(self, state: dict):
+        topic = state["input"]
+
+        paper = []
+
+        # Title
+        paper.append(f"# {topic}\n")
+
+        # Abstract
+        paper.append("## Abstract\n" + self._section("Abstract", topic, 200))
+
+        # Keywords
+        paper.append(
+            "## Keywords\n"
+            "Artificial Intelligence, Machine Learning, Data Analytics, Automation, Ethics, Sustainability\n"
+        )
+
+        # Main body sections
+        paper.append("## 1. Introduction\n" + self._section("Introduction", topic, 400))
+        paper.append("## 2. Literature Review\n" + self._section("Literature Review", topic, 500))
+        paper.append("## 3. Methodology\n" + self._section("Methodology", topic, 400))
+        paper.append("## 4. Applications\n" + self._section("Applications", topic, 500))
+        paper.append("## 5. Challenges and Limitations\n" + self._section("Challenges and Limitations", topic, 400))
+        paper.append("## 6. Future Trends\n" + self._section("Future Trends", topic, 400))
+
+        # ONE final conclusion
+        paper.append("## 7. Conclusion\n" + self._conclusion(topic))
+
+        # ONE references section
+        paper.append(
+            "## References\n"
+            "[1] https://www.ajournals.org/ijai/article/details/1006-2538/79\n"
+            "[2] https://www.sciencedirect.com/topics/artificial-intelligence\n"
+            "[3] https://ieeexplore.ieee.org/Xplore/home.jsp\n"
+        )
+
+        state["final_answer"] = "\n\n".join(paper)
+        return state
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src/api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from src.core.langgraph_pipeline import build_graph
+
+app = FastAPI()
+graph = build_graph()
+
+
+class Prompt(BaseModel):
+    message: str
+
+
+@app.post("/chat")
+def chat(prompt: Prompt):
+    result = graph.invoke({"query": prompt.message})
+    return {
+        "reply": result["answer"],
+        "plan": result["plan"]
+    }

src/config.py

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+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    LMSTUDIO_BASE_URL: str
+    LMSTUDIO_API_KEY: str
+    LMSTUDIO_MODEL: str
+
+    class Config:
+        env_file = ".env"
+        extra = "ignore"
+
+
+settings = Settings()
+

src/core/langgraph_pipeline.py

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+from langgraph.graph import StateGraph
+from src.agents.writer_agent import WriterAgent
+from src.services.llm import get_llm
+
+
+def build_graph():
+    llm = get_llm()
+
+    graph = StateGraph(dict)
+
+    writer = WriterAgent(llm)
+
+    graph.add_node("writer", writer.run)
+    graph.set_entry_point("writer")
+
+    return graph.compile()
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src/core/state.py

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+from typing import TypedDict, List, Dict
+
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+class GraphState(TypedDict, total=False):
+    input: str
+
+    planner_steps: List[str]
+    sub_questions: List[str]
+
+    search_results: List[Dict[str, str]]
+    sources: List[Dict[str, str]]
+
+    final_answer: str
+    final_output: str
+
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src/debug_env.py

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+from src.config import settings
+
+print("TAVILY_API_KEY =", settings.TAVILY_API_KEY)