test

.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
+生成特定图片.png filter=lfs diff=lfs merge=lfs -text
+cheatsheet-transformers-large-language-models.pdf filter=lfs diff=lfs merge=lfs -text

agent.py

@@ -0,0 +1,216 @@
+
+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
+
+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)
+
+# build a retriever with existing supabase
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  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=os.getenv('TABLE_NAME'),
+    query_name=os.getenv('QUERY_NAME'),
+)
+create_retriever_tool = create_retriever_tool(
+    retriever=vector_store.as_retriever(),
+    name="Question Search",
+    description="A tool to retrieve similar questions from a vector store.",
+)
+
+
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+]
+
+# Build graph function
+def build_graph(provider: str = "groq"):
+    """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":
+        print("choose 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":
+        print("choose huggingface===============================================")
+        # TODO: Add huggingface endpoint
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                model='Meta-DeepLearning/llama-2-7b-chat-hf',
+                endpoint_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'.")
+    # Bind tools to LLM
+    llm_with_tools = llm.bind_tools(tools)
+
+    # 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}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    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()
+
+# test
+if __name__ == "__main__":
+    question = "What's the last line of the rhyme under the flavor name on the headstone visible in the background of the photo of the oldest flavor's headstone in the Ben & Jerry's online flavor graveyard as of the end of 2022?"
+    # Build the graph
+    graph = build_graph(provider="groq")
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

app.py

@@ -3,6 +3,8 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from agent import build_graph
+from langchain_core.messages import HumanMessage, SystemMessage
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -13,11 +15,17 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 class BasicAgent:
     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)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content.split("FINAL ANSWER: ")[-1]
+
+        print(f"Agent returning answer: {answer}")
+        return answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

cheatsheet-transformers-large-language-models.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5f4cba7c54bbe86caf70122b665b1b14d51abad2634bf5c6481eb62fd6a1a3f
+size 1587084

requirements.txt

@@ -1,2 +1,23 @@
 gradio
-requests
+requests
+langchain
+langchain-community
+langchain-core
+langchain-google-genai
+langchain-huggingface
+langchain-groq
+langchain-tavily
+langchain-chroma
+langgraph
+huggingface_hub
+supabase
+arxiv
+pymupdf
+wikipedia
+pgvector
+python-dotenv
+gradio[oauth]>=4.25.0
+sentence-transformers
+numpy<2
+duckduckgo-search
+langchain_openai

retriever.py

@@ -0,0 +1,57 @@
+#build retriever on supabase
+#create project, table, indexes, and functions
+#create client with url and key
+#insert data with embedding
+#
+# Load metadata.jsonl
+import json
+import os
+from dotenv import load_dotenv
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import SupabaseVectorStore
+from supabase.client import Client, create_client
+from langchain.schema import Document
+
+# Load the metadata.jsonl file
+with open('metadata.jsonl', 'r') as jsonl_file:
+    json_list = list(jsonl_file)
+
+json_QA = []
+for json_str in json_list:
+    json_data = json.loads(json_str)
+    json_QA.append(json_data)
+
+### build a vector database based on the metadata.jsonl
+# https://python.langchain.com/docs/integrations/vectorstores/supabase/
+
+load_dotenv()
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+
+supabase_url = os.environ.get("SUPABASE_URL")
+supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
+supabase: Client = create_client(supabase_url, supabase_key)
+
+# wrap the metadata.jsonl's questions and answers into a list of document
+
+docs = []
+for sample in json_QA:
+    content = f"Question : {sample['Question']}\n\nFinal answer : {sample['Final answer']}"
+    doc = {
+        "content" : content,
+        "metadata" : { # meatadata的格式必须时source键，否则会报错
+            "source" : sample['task_id']
+        },
+        "embedding" : embeddings.embed_query(content),
+    }
+    docs.append(doc)
+
+table_name = os.environ.get('TABLE_NAME')
+# upload the documents to the vector database
+try:
+    response = (
+        supabase.table("documents")
+        .insert(docs)
+        .execute()
+    )
+except Exception as exception:
+    print("Error inserting data into Supabase:", exception)

steps.txt

@@ -0,0 +1,43 @@
+#give yourself more patiences
+
+1. explore metadata, check each keys
+
+2. define retriever
+supabase?
+relational database?, embeddings, content, id, ...
+create a project, and a table + columns first emm...
+https://supabase.com/dashboard/project/ohzwldyjckkuzbybaixs/editor/17248
+enable vector in extensions under database
+
+create table public.documents (
+  id bigint generated by default as identity primary key,
+  content text,
+  metadata json,
+  embedding vector(768),
+  similarity float
+);
+
+create index for embedding!!!
+
+add functions, advanced settings, sql language
+
+create index on documents using hnsw (embedding vector_ip_ops);
+alter table documents enable row level security;
+create function match_documents_langchain (
+  query_embedding vector (768)
+)
+returns setof documents
+language plpgsql
+as $$
+begin
+  return query
+  select *
+  from documents
+  order by documents.embedding <#> query_embedding
+  limit 1;
+end;
+$$;
+
+3. define agent
+
+4. define gradio

system_prompt.txt

@@ -0,0 +1,46 @@
+
+
+You are a helpful assistant tasked with answering questions using a set of tools.
+If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+
+==========================
+Here is a few examples showing you how to answer the question step by step.
+
+
+Question 1: Compute the check digit the Tropicos ID for the Order Helotiales would have if it were an ISBN-10 number.
+Steps:
+1. Search "Tropicos ID Order Helotiales"
+2. Find the correct ID on the first result
+3. Search "isbn 10 check digit calculator" or calculate check digit by hand
+Tools:
+1. web browser
+2. search engine
+3. calculator
+Final Answer: 3
+
+Question 2: What's the last line of the rhyme under the flavor name on the headstone visible in the background of the photo of the oldest flavor's headstone in the Ben & Jerry's online flavor graveyard as of the end of 2022?
+Steps:
+1. Searched "ben and jerrys flavor graveyard" on Google search.
+2. Opened "Flavor Graveyard" on www.benjerry.com.
+3. Opened each flavor to find the oldest one (Dastardly Mash).
+4. Deciphered the blurry name on the headstone behind it (Miz Jelena's Sweet Potato Pie).
+5. Scrolled down to Miz Jelena's Sweet Potato Pie.
+6. Copied the last line of the rhyme.
+7. (Optional) Copied the URL.
+8. Searched "internet archive" on Google search.
+9. Opened the Wayback Machine.
+10. Entered the URL.
+11. Loaded the last 2022 page.
+12. Confirmed the information was the same.
+Tools:
+1. Image recognition tools
+2. Web browser
+3. Search engine
+Final Answer: So we had to let it die.
+
+==========================
+Now, please answer the following question step by step.