i

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+rag_docs/TOSSJ-11-3.pdf filter=lfs diff=lfs merge=lfs -text
+rag_docs/TOSSJ-12-1.pdf filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

@@ -0,0 +1,395 @@
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README.md

@@ -1,13 +1,152 @@
----
-title: Chatbot
-emoji: 📈
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
-license: cc-by-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<<<<<<< HEAD
+# Agentic RAG Sport Advisor Chatbot
+
+This project is a prototype of an **Agentic RAG (Retrieval-Augmented Generation) chatbot**. The application demonstrates core agentic principles by autonomously deciding which tools to use to answer user queries, integrating a RAG pipeline to access document knowledge sources, and utilizing a public knowledge base via Google Search.
+
+---
+
+## Core Concepts
+
+### Agentic Behavior
+The chatbot acts as an autonomous agent. Instead of simply generating a response, it first analyzes the user's intent and decides on the most appropriate action. This includes choosing from a set of specialized tools or a general conversation flow.
+
+### Retrieval-Augmented Generation (RAG)
+For questions requiring specific, factual information, the agent can retrieve relevant documents from a local vector database and use that information to formulate a grounded response.
+
+### Tool Use
+The system is equipped with several tools that allow it to perform specific, pre-defined tasks, such as looking up structured data or performing an external search.
+
+---
+
+## Architecture
+
+The application is built using the **LangGraph framework**, which provides a structured way to define the agent's behavior as a state machine. The graph's state is maintained by a `SportAdvicerState` object that stores the conversation history.
+
+**Key components of the LangGraph pipeline:**
+
+- **chatbot Node:**  
+  The entry point and main logic node. It receives a user's query, determines the user's intent (equipment, skills, document, sports_list, or general), and routes the request to the appropriate tool or invokes the LLM directly for general questions.
+
+- **tools Node:**  
+  Executes the specific tool chosen by the chatbot node.
+
+- **Conditional Edges:**  
+  Routes the flow from the chatbot node to either the tools node (if a specific tool is needed) or terminates the graph (`__end__`) after the response has been generated.
+
+---
+
+## System Components
+
+### RAG Pipeline (`rag_pipeline.py`)
+Responsible for setting up the document retrieval system:
+
+- Loads PDF documents from `rag_docs/` using `DirectoryLoader`.
+- Splits documents into manageable chunks via `RecursiveCharacterTextSplitter`.
+- Uses **ChromaDB** as the local vector store for document chunks and embeddings.
+- Generates vector representations with `GoogleGenerativeAIEmbeddings`.
+- Checks for an existing Chroma database to avoid re-processing documents on every run.
+
+### Chatbot Logic and Tools (`chatbot_nodes.py`)
+Defines the agent's behavior and tools:
+
+- **LLM:** Uses `gemini-2.5-flash-lite`, a lightweight free-to-use model from Google's Gemini family.
+
+**Intent and Parameter Extraction:**
+- `detect_intent(query)`: Classifies user queries into a specific category.
+- `extract_sport_name(query)`: Extracts the relevant sport name for tools.
+
+**Tools:**
+- `get_sports()`: Returns a list of sports from a local CSV.
+- `get_skills_by_sport(sport: str)`: Retrieves the top 3 highest-rated skills for a sport.
+- `get_document_answer(query: str)`: Core RAG tool using a retriever to find relevant document chunks and augment LLM prompts.
+- `get_equipment_by_sport(sport: str)`: Performs a Google Search to find sport equipment information.
+
+---
+
+## How to Run the Application
+
+### Prerequisites
+- Python 3.9+
+- pip
+- Google API Key
+
+### Setup
+1. Clone the repository and navigate to the project directory.
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+3. Place your PDF documents in the rag_docs/ directory.
+4. Create a .env file in the root directory and add your Google API key:
+
+```bash
+GOOGLE_API_KEY="your_api_key_here"
+```
+### Execution
+Run the main application:
+```bash
+python chatbot_nodes.py
+```
+The application will start a Gradio web interface, accessible via your browser to interact with the chatbot.
+
+---
+
+## Evaluation and Future Work
+
+### Bottlenecks and Performance
+- Multiple LLM calls for intent detection and sport name extraction may introduce latency.
+- RAG pipeline performance depends on document quality and chunk_size / chunk_overlap parameters.
+
+### Suggested Improvements
+- Advanced Tool Calling: Use a single LLM call to decide which tool to call with what parameters.
+- Performance Benchmarking: Test queries to measure end-to-end latency and response accuracy.
+- New Tools: Extend capabilities with weather updates, sports news, or game schedules.
+
+---
+
+## Example Human Questions to Tools Mapping
+This section provides examples of user questions that would trigger the various tools within the chatbot.
+
+- `get_sports()`: "Recommend me sports."
+
+- `get_skills_by_sport(sport: str)`: "What skills are needed for football?"
+
+- `get_document_answer(query: str)`: "How can I be successful in football based on documentations?"
+
+- `get_equipment_by_sport(sport: str)`: "What gears are needed for football?"
+
+---
+
+## References and Citations
+
+**RAG Input References:**
+- **Dataset license**: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
+- `rag_docs/TOSSJ-11-3.pdf`:
+  
+Lepschy H, Wäsche H, Woll A. How to be Successful in Football: A Systematic Review . Open Sports Sci J, 2018; 11: . http://dx.doi.org/10.2174/1875399X01811010003
+
+- `rag_docs/TOSSJ-12-1.pdf`:
+
+Mack M, Bryan M, Heyer G, Heinen T. Modeling Judges’ Scores in Artistic Gymnastics . Open Sports Sci J, 2019; 12: . http://dx.doi.org/10.2174/1875399X01912010001
+
+**Sport tool input csv file references:**
+- **Dataset license**: CC0: Public Domain
+- `sport_tool_docs/toughestsport.csv`:
+  
+[Ranking sports by skill requirement](https://www.kaggle.com/datasets/jainaru/ranking-sports-by-skill-requirement)
+=======
+---
+title: Chatbot
+emoji: 📈
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+license: cc-by-4.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+>>>>>>> ba7aceaa754482aade1de886dd9e017c9b5997d3

rag_docs/.gitattributes

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+*.pdf filter=lfs diff=lfs merge=lfs -text

rag_docs/TOSSJ-11-3.pdf

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+size 563702

rag_docs/TOSSJ-12-1.pdf

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requirements.txt

@@ -0,0 +1,15 @@
+python-dotenv
+langgraph
+langchain
+langchain_core
+langchain_community
+langchain_google_genai
+langchain_text_splitters
+chromadb
+google-genai
+pandas
+gradio
+typing-extensions
+protobuf
+unstructured
+unstructured[pdf]

sport_tool_docs/toughestsport.csv

@@ -0,0 +1,61 @@
+SPORT,Endurance,Strength,Power,Speed,Agility,Flexibility,Nerve,Durability,Hand-eye coordination,Analytical Aptitude,Total,Rank
+Boxing,8.63,8.13,8.63,6.38,6.25,4.38,8.88,8.5,7,5.63,72.375,1
+Ice Hockey,7.25,7.13,7.88,7.75,7.63,4.88,6,8.25,7.5,7.5,71.75,2
+Football,5.38,8.63,8.13,7.13,6.38,4.38,7.25,8.5,5.5,7.13,68.375,3
+Basketball,7.38,6.25,6.5,7.25,8.13,5.63,4.13,7.75,7.5,7.38,67.875,4
+Wrestling,6.63,8.38,7.13,5.13,6.38,7.5,5,6.75,4.25,6.38,63.5,5
+Martial Arts,5,5.88,7.75,6.38,6,7,6.63,5.88,6,6.88,63.375,6
+Tennis,7.25,5.13,7.13,6.75,7.75,5.63,3,5,8.38,6.75,62.75,7
+Gymnastics,5.38,6.13,6.63,5,6.38,10,7.5,6.88,4.5,4.13,62.5,8
+Baseball/Softball,4.63,5.75,7.63,6.5,6.75,4.75,5.13,5.63,9.25,6.25,62.25,9
+Soccer,7.75,4.5,5.13,7.25,8.25,4.75,3.63,6.25,6.5,7.5,61.5,10
+Skiing: Alpine,5.13,5.25,6,7.38,6.13,5.63,8.38,6,5.13,5.63,60.625,11
+Water Polo,7.88,6.63,6.88,5.38,6.38,5,4.25,6.38,6.25,5.63,60.625,11
+Rugby,6.75,7,6.38,5.88,6,4.13,6.5,7.88,4.38,5.63,60.5,13
+Lacrosse,6.63,5.13,5.75,7,6.63,4.75,4.38,6.13,7.13,6.88,60.375,14
+Rodeo: Steer Wrestling,4,7,7.88,3.88,4.88,5,7.88,6.88,5.13,4,56.5,15
+Track and Field: Pole Vault,3.38,6.88,7.25,6.13,5.38,7,6.63,4.25,5.25,3.75,55.875,16
+Field Hockey,6.75,4.5,5.38,6,5.75,4.63,3.75,5,6.63,6.5,54.875,17
+Speed Skating,7.63,7.25,7.38,8.88,4,4.25,4.5,4.63,2.88,3.5,54.875,17
+Figure Skating,6.38,5.25,6.63,5.13,6.88,8.25,4.88,4,3.13,4.25,54.75,19
+Cycling: Distance,9.63,6.38,6.25,5.13,3.75,2.63,5.88,6.88,3,4.88,54.375,20
+Volleyball,5.13,4.88,6.63,5,7,5.13,2.88,4.63,7.25,5.88,54.375,20
+Racquetball/Squash,6.13,3.75,5,5.5,7.25,5.88,2.38,2.88,8.38,6.5,53.625,22
+Surfing,4.63,5,4.13,4.25,6.63,5.5,8.25,5.5,4.38,4.88,53.125,23
+Fencing,4.63,3.75,4.25,5.13,6.13,5.63,4.88,4.25,7.25,6.88,52.75,24
+Skiing: Freestyle,4.13,5.13,4.88,5.13,6.63,6.88,6.63,5.13,4.13,3.88,52.5,25
+Team Handball,4.88,3.88,5.38,5.5,6,4.5,3,3.88,7.88,5.88,50.75,26
+Cycling: Sprints,4.25,6.13,7.88,7.5,4,2.88,4.75,4.5,3.63,4.5,50,27
+Bobsledding/Luge,3.5,5.5,6.5,6.75,4.13,3.25,7.75,3.5,4.13,4.25,49.25,28
+Ski Jumping,3.5,4.5,5.75,4.63,4,5,9,4.63,4.38,3.5,48.875,29
+Badminton,5.25,3.25,4,5.63,7.38,5.25,1.25,2.63,7.25,6.13,48,30
+Skiing: Nordic,9,5.75,4.38,5.13,4,4,2.75,5.5,3.63,3.88,48,30
+Auto Racing,5.88,3.5,2.63,1.63,2.75,1.75,9.88,4.38,8,7.5,47.875,32
+Track and Field: High Jump,3,6,7,6.13,5.63,6.63,3.5,3.5,3.5,2.88,47.75,33
+"Track and Field: Long, Triple jumps",4,5.63,7.13,6.75,5,5.75,2.75,3.25,4,3.13,47.375,34
+Diving,2.88,5.13,4.63,3,3.5,8.5,8.38,5,3,3,47,35
+Swimming (all strokes): Distance,9.25,5.25,4.63,5.5,3.63,5.5,2.63,4.63,2.88,3,46.875,36
+Skateboarding,4.13,3.75,3.75,4.13,6.13,5.13,6.5,5.25,4.88,3.13,46.75,37
+Track and Field: Sprints,3.5,5.13,7.25,9.88,4.63,5.13,2,4.13,2.63,2.38,46.625,38
+Rowing,8.13,7.75,7.13,4,2.5,4,1.75,4.38,2.88,3.63,46.125,39
+Rodeo: Calf Roping,3.13,5.38,5,4.25,5.63,3.88,4.88,3.75,6.38,3.75,46,40
+Track and Field: Distance,9.63,5.25,3.75,6,3.25,4.38,2,5.75,1.88,4.13,46,40
+Rodeo: Bull/Bareback/Bronc Riding,3.25,5.38,4,1.75,3.63,4.25,9.5,7.38,3.63,3.13,45.875,42
+Track and Field: Middle Distance,6,5.13,5.13,7.75,4,4.88,2,4.75,2.13,3.75,45.5,43
+Weight-Lifting,4.13,9.25,9.75,2.63,2.5,3.38,4,4.75,2.25,2.38,45,44
+Swimming (all strokes): Sprints,4.13,5.25,6.25,7.88,3.63,5.5,2.5,3.25,2.75,3,44.125,45
+Water Skiing,4.63,5,4.5,3,4.25,4.75,5.88,4.63,4.13,3.25,44,46
+Table Tennis,3.5,2.5,4.63,4.13,5.88,4.25,1.38,1.88,8.88,6,43,47
+Track and Field: Weights,3.25,7.88,9.13,3,3.13,3,2.25,3.63,4,2.88,42.125,48
+Canoe/Kayak,6.75,5.25,5.63,3.5,2.75,3.88,3.63,3.25,3.13,4.25,42,49
+Horse Racing,4,3.88,2.88,1.38,2.88,3.75,8,4.5,3.88,6.5,41.625,50
+Golf,3.25,3.88,6.13,1.63,1.75,4,2.5,2.38,6,6.38,37.875,51
+Cheerleading,3.63,3.63,3.38,2.25,4.13,7.5,3.63,3.38,2.5,2.25,36.25,52
+Roller Skating,4.75,3.38,4,5.13,4,3.5,2.63,3.38,2.88,2.63,36.25,52
+Equestrian,3.38,3.25,1.75,1.25,2.5,2.88,6,2.75,2.88,5.13,31.75,54
+Archery,2.88,4.5,3.13,1.13,1.63,2.63,2.75,2.13,6.63,3.25,30.625,55
+Curling,2.25,2.63,2.5,1.5,2.25,2.63,1.75,1.5,4.88,5.63,27.5,56
+Bowling,2.25,2.75,3.38,1,1.88,2.38,1.63,1.25,4.75,4.13,25.375,57
+Shooting,2.25,2.5,1.38,0.88,1.13,1.75,2.38,1.88,6.75,4,24.875,58
+Billiards,1,1,1.75,0.75,1,2.63,1.63,0.75,5.25,5.75,21.5,59
+Fishing,1.38,1.63,1.25,0.63,1.5,1.13,0.88,0.88,2.38,2.88,14.5,60

src/chatbot_nodes.py

@@ -0,0 +1,331 @@
+
+import os
+from pathlib import Path
+import traceback
+from typing import Annotated, List, Union
+from typing_extensions import TypedDict
+
+import pandas as pd
+import gradio as gr
+
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langgraph.graph import StateGraph
+from langgraph.prebuilt import ToolNode
+
+from rag_pipeline import load_or_create_vector_store
+
+from google import genai
+from google.genai import types
+from google.api_core import retry
+
+from dotenv import load_dotenv
+
+# ---------------------------
+# --- Setup Google API ---
+# ---------------------------
+# Modify this load_dotenv in the future
+load_dotenv(dotenv_path=os.path.join(os.path.dirname(os.path.dirname(__file__)), ".env"))
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+if not GOOGLE_API_KEY:
+    raise ValueError("GOOGLE_API_KEY not found in environment variables.")
+
+client = genai.Client(api_key=GOOGLE_API_KEY)
+
+# Retry policy
+is_retriable = lambda e: (isinstance(e, genai.errors.APIError) and e.code in {429, 503})
+if not hasattr(genai.models.Models.generate_content, '__wrapped__'):
+    genai.models.Models.generate_content = retry.Retry(predicate=is_retriable)(genai.models.Models.generate_content)
+
+# ---------------------------
+# --- Config ---
+# ---------------------------
+model_name = "gemini-2.5-flash-lite" #"gemini-2.0-flash-lite"#"gemini-2.0-flash"
+base_dir = Path(__file__).resolve().parent.parent
+doc2_path = str( base_dir / "sport_tool_docs/toughestsport.csv")
+search_kwargs_k = 5
+search_kwargs_fetch_k = 10
+
+# ---------------------------
+# --- RAG Setup ---
+# ---------------------------
+vector_store = load_or_create_vector_store()
+
+retriever = vector_store.as_retriever(
+    search_type="mmr",
+    search_kwargs={
+        "k": search_kwargs_k,
+        "fetch_k": search_kwargs_fetch_k
+        }
+)
+
+# ---------------------------
+# --- Sports Data Setup ---
+# ---------------------------
+sports_by_skills = pd.read_csv(doc2_path)
+sports_by_skills.columns = sports_by_skills.columns.str.lower()
+sports_by_skills['sport'] = sports_by_skills['sport'].str.lower()
+
+# ---------------------------
+# --- LangGraph Tools ---
+# ---------------------------
+@tool
+def get_sports() -> str:
+    """Return a structured list of sports from the dataset."""
+    
+    prompt = """Parse the provided sports into a structured list where each line has indentation and starts with a category, followed by a colon, 
+    and then a comma-separated list of sports within that category. 
+    If a sport has no obvious category, group it under "General"
+    
+    EXAMPLE:
+    Provide me sport options
+    
+    Answer: 
+    - Ball games: 
+        - Football, Baskettball
+    - Skiing: 
+        - Alpine, Nordic
+    - General: 
+        - Boxing, Water polo
+    
+    """
+    sports = sports_by_skills["sport"].tolist()
+    response = client.models.generate_content(model=model_name, contents=[prompt, sports])
+    return response.candidates[0].content.parts[0].text.strip()
+
+@tool
+def get_document_answer(query: str) -> str:
+    """Retrieve an answer from documents with a grounded paraphrase."""
+    
+    try:
+        results = retriever.invoke(query)
+        
+        if not results:
+            return "I could not find any relevant information in the documents."
+
+        # Combine the retrieved chunks for context
+        combined_text = "\n---\n".join([r.page_content for r in results])
+
+        prompt = f"""
+        Answer the question based on the following documents. 
+        If the information is not available, state that you cannot find the answer in the provided documents.
+        
+        Chunks:
+        {combined_text}
+        
+        Question: {query}
+        Answer:
+        """
+
+        # Call the LLM
+        response = client.models.generate_content(
+            model=model_name,  
+            contents=[prompt]
+        )
+
+        if response.candidates:
+            answer_text = response.candidates[0].content.parts[0].text.strip()
+            return answer_text
+        else:
+            return "I could not generate an answer."
+
+    except Exception as e:
+        return f"RAG error: {e}"
+
+@tool
+def get_skills_by_sport(sport: str) -> str:
+    """Get the sport name. Return: The top 3 highest skill rates."""
+    sport = sport.lower().strip()
+    skill_rates = sports_by_skills.loc[sports_by_skills['sport'] == sport]
+
+    if skill_rates.empty:
+        return f"No data found for sport '{sport}'. Please check the spelling or try another sport."
+
+    skills_only = skill_rates.drop(columns=['sport', 'total', 'rank'])
+    transposed = skills_only.T
+    col = transposed.columns[0]
+    top_3_skills = transposed.nlargest(3, col)
+    top_3_skill_names = "\n".join(f"{skill}" for skill, value in top_3_skills[col].items())
+    
+    return f"Top 3 skills for {sport.capitalize()}:\n{top_3_skill_names}"
+
+@tool
+def get_equipment_by_sport(sport: str) -> str:
+    """Get the equipment list for a sport using a google search grounded prompt."""
+    
+    sport = sport.lower()
+    prompt = """Parse a customer's sport equipment question to the list:
+    EXAMPLE: What are the necessary equipment for boxing?
+    Response: 
+    - Mandatory: 1 gloves, 3 socks 
+    - Recommended: 1 towel 
+    - Fun: resistance bands
+    """
+
+    config_with_search = types.GenerateContentConfig(
+        tools=[types.Tool(google_search=types.GoogleSearch())],
+        temperature=0.0,
+    )
+    
+    contents_text = "What are the necessary equipment for this " + sport + "?"
+    response = client.models.generate_content(
+        model=model_name,
+        contents=[prompt, contents_text],
+        config=config_with_search,
+    )
+    return response.candidates[0].content.parts[0].text if response.candidates else "No information found."
+
+# ---------------------------
+# --- Tool Node ---
+# ---------------------------
+tools_list = [get_sports, get_document_answer, get_skills_by_sport, get_equipment_by_sport]
+tool_node = ToolNode(tools_list)
+
+# ---------------------------
+# --- LangGraph LLM ---
+# ---------------------------
+llm = ChatGoogleGenerativeAI(model=model_name)
+llm_with_tools = llm.bind_tools(tools_list, return_direct=True) 
+
+# --- Graph State ---
+class SportAdvicerState(TypedDict):
+    messages: Annotated[List[Union[AIMessage, HumanMessage, ToolMessage]], list.__add__]
+
+def detect_intent(query: str) -> str:
+    """Classify user query into one of: equipment, skills, document, sports_list, general."""
+    classification_prompt = f"""
+    You are a classifier. 
+    Categorize the following user query into exactly ONE of these categories:
+    - equipment → if asking about gear, equipment, things needed for a sport
+    - skills → if asking about skills, abilities, rankings, requirements for a sport
+    - document → if asking about information that may be inside books, PDFs, or retrieved documents
+    - sports_list → if asking for a list of sports, categories of sports, or groupings of sports
+    - general → if it's a general sports question not fitting the above
+
+    Query: "{query}"
+
+    Answer with one word: equipment, skills, document, sports_list, or general.
+    """
+
+    response = client.models.generate_content(
+        model=model_name,
+        contents=[classification_prompt],
+        config=types.GenerateContentConfig(
+            temperature=0.0  # deterministic
+        )
+    )
+
+    if response.candidates:
+        return response.candidates[0].content.parts[0].text.strip().lower()
+    else:
+        return "general"
+
+def extract_sport_name(query: str) -> str:
+    """Extract the sport name from a user query."""
+    extraction_prompt = f"""
+    Extract the single sport name from the following query.
+    If multiple sports are mentioned, return the first one.
+    If no sport is mentioned, return an empty string.
+
+    Query: "{query}"
+
+    Extracted sport name:
+    """
+    response = client.models.generate_content(
+        model=model_name,
+        contents=[extraction_prompt],
+        config=types.GenerateContentConfig(
+            temperature=0.0
+        )
+    )
+    if response.candidates:
+        return response.candidates[0].content.parts[0].text.strip()
+    return ""
+
+def chatbot_node(state: SportAdvicerState) -> SportAdvicerState:
+    user_message = state["messages"][-1]
+    query = user_message.content
+    intent = detect_intent(query)
+    sport_name = extract_sport_name(query)
+
+    if intent == "equipment":
+        response_text = get_equipment_by_sport.invoke({"sport": sport_name})
+        return {"messages": [AIMessage(content=response_text)]}
+
+    elif intent == "skills":
+        response_text = get_skills_by_sport.invoke({"sport": sport_name})
+        return {"messages": [AIMessage(content=response_text)]}
+
+    elif intent == "document":
+        response_text = get_document_answer.invoke({"query": query})
+        return {"messages": [AIMessage(content=response_text)]}
+
+    elif intent == "sports_list":
+        response_text = get_sports.invoke({})
+        return {"messages": [AIMessage(content=response_text)]}
+
+    else:  # general
+        messages_with_instruction = [
+            HumanMessage(content="""You are a sports advisor chatbot.
+            You can answer general sports questions.
+            For equipment, skills, document, or sports list queries, tools are used automatically.""")
+        ] + state["messages"]
+
+        response = llm_with_tools.invoke(messages_with_instruction)
+        return {"messages": [response]}
+
+# Routing: always go to the single tool node if any tool call exists
+def should_route_to_tools(state: SportAdvicerState):
+    last_msg = state["messages"][-1]
+    if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
+        return "tools"
+    return "__end__"
+
+# ---------------------------
+# --- Graph Definition ---
+# ---------------------------
+graph_builder = StateGraph(SportAdvicerState)
+graph_builder.add_node("chatbot", chatbot_node)
+graph_builder.add_node("tools", tool_node)
+graph_builder.add_conditional_edges("chatbot", should_route_to_tools)
+graph_builder.add_edge("tools", "chatbot")
+graph_builder.set_entry_point("chatbot")
+graph_with_rag = graph_builder.compile()
+
+# ---------------------------
+# --- Gradio Interface ---
+# ---------------------------
+def chatbot_interface(message, history):
+    langchain_messages = []
+    for chat_entry in history:
+        if isinstance(chat_entry, list) and len(chat_entry) == 2:
+            if chat_entry[0]: langchain_messages.append(HumanMessage(content=chat_entry[0]))
+            if chat_entry[1]: langchain_messages.append(AIMessage(content=chat_entry[1]))
+        elif isinstance(chat_entry, dict):
+            if chat_entry["role"] == "user": langchain_messages.append(HumanMessage(content=chat_entry["content"]))
+            elif chat_entry["role"] == "assistant": langchain_messages.append(AIMessage(content=chat_entry["content"]))
+
+    langchain_messages.append(HumanMessage(content=message))
+    current_state = {"messages": langchain_messages}
+    
+    try:
+        response_state = graph_with_rag.invoke(current_state)
+        bot_response = response_state["messages"][-1].content
+        return bot_response
+    except Exception as e:
+        traceback.print_exc()
+        return f"Internal error: {e}"
+
+iface = gr.ChatInterface(
+    fn=chatbot_interface,
+    chatbot=gr.Chatbot(height=500, type="messages",
+                       value=[{"role": "assistant", "content": "Hello! I am your AI Sport Advisor. Ask me anything."}]),
+    title="Agentic RAG Sport Advisor Chatbot",
+    description="LangGraph chatbot integrated with RAG document retrieval and sports tools.",
+    type="messages",
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)

src/rag_pipeline.py

@@ -0,0 +1,38 @@
+from pathlib import Path
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_community.document_loaders import DirectoryLoader, UnstructuredPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+base_dir = Path(__file__).resolve().parent.parent
+chroma_db_path = "/chroma_db"
+docs_dir = str(base_dir / "rag_docs/")
+chunk_size_ = 1000
+chunk_overlap_ = 200
+
+def load_or_create_vector_store():
+    vector_store_path = Path(chroma_db_path)
+
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+    # Load existing store if present
+    if vector_store_path.exists() and any(vector_store_path.iterdir()):
+        print("Loading existing vector store...")
+        return Chroma(persist_directory=chroma_db_path, embedding_function=embeddings)
+
+    # Otherwise, create new vector store
+    print("Creating new vector store from documents...")
+    loader = DirectoryLoader(docs_dir, glob="**/*.pdf", loader_cls=UnstructuredPDFLoader)
+    documents = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size_,
+        chunk_overlap=chunk_overlap_,
+        length_function=len,
+        is_separator_regex = False
+    )
+    chunks = text_splitter.split_documents(documents)
+
+    vector_store = Chroma(persist_directory=chroma_db_path, embedding_function=embeddings)
+    vector_store.add_documents(chunks)
+    return vector_store