add agent

.gitignore

@@ -0,0 +1,115 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+ENV/
+env/
+.env
+.venv
+env.bak/
+venv.bak/
+.python-version
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+pytest-*.xml
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Logs
+*.log
+logs/
+log/
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+.DS_Store
+.project
+.pydevproject
+.settings/
+.vs/
+*.sublime-project
+*.sublime-workspace
+
+# Database
+*.db
+*.rdb
+*.sqlite
+*.sqlite3
+
+# Environment variables
+.env
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# macOS specific
+.DS_Store
+.AppleDouble
+.LSOverride
+Icon
+._*
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# AI/model files
+*.h5
+*.pb
+*.onnx
+*.tflite
+*.pt
+*.pth
+*.weights
+
+# Temporary files
+tmp/
+temp/
+.tmp
+*.tmp

agent.py

@@ -0,0 +1,265 @@
+"""LangGraph Agent"""
+import os
+from dotenv import load_dotenv
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition
+from langgraph.prebuilt import ToolNode
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+from langchain.tools.retriever import create_retriever_tool
+from langchain_community.vectorstores import Chroma
+from langchain_core.documents import Document
+import shutil
+import pandas as pd 
+import json 
+
+load_dotenv()
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+    Args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """Add two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    """Subtract two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> int:
+    """Divide two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """Get the modulus of two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a % b
+
+@tool
+def wiki_search(query: str) -> str:
+    """Search Wikipedia for a query and return maximum 2 results.
+    
+    Args:
+        query: The search query."""
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return {"wiki_results": formatted_search_docs}
+
+@tool
+def web_search(query: str) -> str:
+    """Search Tavily for a query and return maximum 3 results.
+    
+    Args:
+        query: The search query."""
+    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return {"web_results": formatted_search_docs}
+
+@tool
+def arvix_search(query: str) -> str:
+    """Search Arxiv for a query and return maximum 3 result.
+    
+    Args:
+        query: The search query."""
+    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+            for doc in search_docs
+        ])
+    return {"arvix_results": formatted_search_docs}
+
+# load the system prompt from the file
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+# System message
+sys_msg = SystemMessage(content=system_prompt)
+
+# --- Start ChromaDB Setup ---
+# Define the directory for ChromaDB persistence
+CHROMA_DB_DIR = "./chroma_db"
+CSV_FILE_PATH = "./supabase_docs.csv" # Path to your CSV file
+
+# Build embeddings (this remains the same)
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768
+
+# Initialize ChromaDB
+# If the directory exists and contains data, load the existing vector store.
+# Otherwise, create a new one and add documents from the CSV file.
+if os.path.exists(CHROMA_DB_DIR) and os.listdir(CHROMA_DB_DIR):
+    print(f"Loading existing ChromaDB from {CHROMA_DB_DIR}")
+    vector_store = Chroma(
+        persist_directory=CHROMA_DB_DIR,
+        embedding_function=embeddings
+    )
+else:
+    print(f"Creating new ChromaDB at {CHROMA_DB_DIR} and loading documents from {CSV_FILE_PATH}.")
+    # Ensure the directory is clean before creating new
+    if os.path.exists(CHROMA_DB_DIR):
+        shutil.rmtree(CHROMA_DB_DIR)
+    os.makedirs(CHROMA_DB_DIR)
+
+    # Load data from the CSV file
+    if not os.path.exists(CSV_FILE_PATH):
+        raise FileNotFoundError(f"CSV file not found at {CSV_FILE_PATH}. Please ensure it's in the root directory.")
+
+    df = pd.read_csv(CSV_FILE_PATH)
+    documents = []
+    for index, row in df.iterrows():
+        content = row["content"]
+        
+        # Extract the question part from the content
+        # Assuming the question is everything before "Final answer :"
+        question_part = content.split("Final answer :")[0].strip()
+        
+        # Extract the final answer part from the content
+        final_answer_part = content.split("Final answer :")[-1].strip() if "Final answer :" in content else ""
+
+        # Parse the metadata string into a dictionary
+        # The metadata column might be stored as a string representation of a dictionary
+        try:
+            metadata = json.loads(row["metadata"].replace("'", "\"")) # Replace single quotes for valid JSON
+        except json.JSONDecodeError:
+            metadata = {} # Fallback if parsing fails
+        
+        # Add the extracted final answer to the metadata for easy retrieval
+        metadata["final_answer"] = final_answer_part
+        
+        # Create a Document object. The page_content should be the question for similarity search.
+        # The answer will be in metadata.
+        documents.append(Document(page_content=question_part, metadata=metadata))
+
+    if not documents:
+        print("No documents loaded from CSV. ChromaDB will be empty.")
+        # Create an empty ChromaDB if no documents are found
+        vector_store = Chroma(
+            persist_directory=CHROMA_DB_DIR,
+            embedding_function=embeddings
+        )
+    else:
+        vector_store = Chroma.from_documents(
+            documents=documents,
+            embedding=embeddings,
+            persist_directory=CHROMA_DB_DIR
+        )
+        vector_store.persist() # Save the new vector store to disk
+        print(f"ChromaDB initialized and persisted with {len(documents)} documents from CSV.")
+
+# Create retriever tool using the Chroma vector store
+retriever_tool = create_retriever_tool(
+    retriever=vector_store.as_retriever(),
+    name="Question_Search",
+    description="A tool to retrieve similar questions from a vector store. The retrieved document's metadata contains the 'final_answer' to the question.",
+)
+
+# Add the new retriever tool to your list of tools
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+    retriever_tool,
+]
+
+# Build graph function
+def build_graph(provider: str = "google"):
+    """Build the graph"""
+    if provider == "google":
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    elif provider == "groq":
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                temperature=0,
+            ),
+        )
+    else:
+        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    
+    llm_with_tools = llm.bind_tools(tools)
+
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+    from langchain_core.messages import AIMessage
+
+    def retriever(state: MessagesState):
+        query = state["messages"][-1].content
+        # Use the vector_store directly for similarity search to get the full Document object
+        similar_docs = vector_store.similarity_search(query, k=1)
+        
+        if similar_docs:
+            similar_doc = similar_docs[0]
+            # Prioritize 'final_answer' from metadata, then check page_content
+            if "final_answer" in similar_doc.metadata and similar_doc.metadata["final_answer"]:
+                answer = similar_doc.metadata["final_answer"]
+            elif "Final answer :" in similar_doc.page_content:
+                answer = similar_doc.page_content.split("Final answer :")[-1].strip()
+            else:
+                answer = similar_doc.page_content.strip() # Fallback to page_content if no explicit answer
+
+            # The system prompt expects "FINAL ANSWER: [ANSWER]".
+            # We should return the extracted answer directly, as the prompt handles the formatting.
+            return {"messages": [AIMessage(content=answer)]}
+        else:
+            return {"messages": [AIMessage(content="No similar questions found in the knowledge base.")]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.set_entry_point("retriever")
+    builder.set_finish_point("retriever")
+
+    return builder.compile()

app.py

@@ -1,8 +1,13 @@
+""" Basic Agent Evaluation Runner"""
 import os
+import inspect
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,14 +15,22 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
 class BasicAgent:
+    """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
+        self.graph = build_graph()
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer  # kein [14:] mehr nötig!
+
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -193,4 +206,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,4 +1,19 @@
 gradio
 requests
-
-
+langchain
+langchain-community
+langchain-core
+langchain-google-genai
+langchain-huggingface
+langchain-groq
+langchain-tavily
+langchain-chroma
+langgraph
+huggingface_hub
+arxiv
+pymupdf
+wikipedia
+python-dotenv
+sentence-transformers # Added this as it was missing from your traceback
+pandas
+protobuf==3.20.3 

system_prompt.txt

@@ -0,0 +1,17 @@
+You are a helpful assistant tasked with answering questions using a set of tools.
+
+Your final answer must strictly follow this format:
+FINAL ANSWER: [ANSWER]
+
+Only write the answer in that exact format. Do not explain anything. Do not include any other text.
+
+If you are provided with a similar question and its final answer, and the current question is **exactly the same**, then simply return the same final answer without using any tools.
+
+Only use tools if the current question is different from the similar one.
+
+Examples:
+- FINAL ANSWER: FunkMonk
+- FINAL ANSWER: Paris
+- FINAL ANSWER: 128
+
+If you do not follow this format exactly, your response will be considered incorrect.