Rename agent to agent/agent.py

agent/agent.py

@@ -0,0 +1,87 @@
+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_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import SystemMessage, HumanMessage
+
+from langchain_core.tools import tool
+from langchain.tools.retriever import create_retriever_tool
+from supabase.client import Client, create_client
+
+from tools.basic_calculator import add, count_substring, divide, modulus, multiply, power, square_root, subtract
+from tools.code_interpreter import execute_code_multilang
+from tools.document_processing import save_and_read_file,download_file_from_url, extract_text_from_image, analyze_csv_file, analyze_excel_file
+from tools.image_processing import analyze_image, transform_image, draw_on_image, generate_simple_image, combine_images
+from tools.web_search import arxiv_search, similar_question_search, wiki_search, web_search
+
+
+load_dotenv() # load environment variables
+
+# load the system prompt from the file
+with open("prompts/system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+print(system_prompt)
+
+# System message
+sys_msg = SystemMessage(content=system_prompt)
+
+# build a retriever
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # set the model to generate embeddings;  dim=768
+supabase:Client = create_client(os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY"))
+vector_store = SupabaseVectorStore(client=supabase, embedding= embeddings, table_name="documents", query_name="match_documents_langchain")
+create_retriever_tool = create_retriever_tool(retriever=vector_store.as_retriever(), name="Question Retriever", description="A tool to retrieve similar questions from a vector store.")
+
+tools = [web_search, wiki_search, similar_question_search, arxiv_search, multiply, add, subtract, divide, modulus, power, square_root, count_substring, save_and_read_file, download_file_from_url, extract_text_from_image, analyze_csv_file, analyze_excel_file, execute_code_multilang, analyze_image, transform_image, draw_on_image, generate_simple_image, combine_images]
+
+
+# Build the agent graph
+def build_graph(provider: str = "huggingface-qwen"):
+    """Build the graph"""
+    # Load environment variables from .env file
+    if provider == "google": # Google Gemini
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    elif provider == "groq": # Groq https://console.groq.com/docs/models
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+    elif provider == "huggingface-qwen":
+        llm = ChatHuggingFace(llm=HuggingFaceEndpoint(repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"))
+    elif provider == "huggingface-llama":
+        llm = ChatHuggingFace(llm=HuggingFaceEndpoint(repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0", task="text-generation", max_new_tokens=1024, do_sample=False, repetition_penalty=1.03, temperature=0), verbose=True)
+    else:
+        raise ValueError("Invalid provider. Choose 'google', 'groq', 'huggingface-qwen' or 'huggingface-llama'.")
+        
+    llm_with_tools = llm.bind_tools(tools) # Bind tools to LLM
+
+    # Node
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        example_msg = HumanMessage(content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}")
+        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+
+    # create nodes - decision points
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools)) # equip the agents with the list of tools
+
+    # connect nodes - control flow
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges("assistant", tools_condition)
+    builder.add_edge("tools", "assistant")
+
+    # Compile graph
+    return builder.compile()