code init

.gitignore

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+*.log
+/logs
+**__pycache__**

.vscode/launch.json

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+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Python Debugger: Current File",
+      "type": "debugpy",
+      "request": "launch",
+      "program": "${file}",
+      "console": "integratedTerminal"
+    }
+  ]
+}

README.md

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+
+```bash
+conda env create -f env/agent2.yaml
+# conda create -n agent2 python
+
+conda activate agent2
+
+pip install -r env/requirements.txt
+
+python app.py
+
+streamlit run streamlit_app.py
+```

app.py

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+# Displaying final output format
+# from IPython.display import display, Markdown, Latex
+# LangChain Dependencies
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
+from langchain_community.chat_models import ChatOllama
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+from langgraph.graph import END, StateGraph
+# For State Graph 
+from typing_extensions import TypedDict
+import os
+
+# Defining LLM
+local_llm = 'llama3.2'
+llama3 = ChatOllama(model=local_llm, temperature=0)
+llama3_json = ChatOllama(model=local_llm, format='json', temperature=0)
+
+# Web Search Tool
+
+wrapper = DuckDuckGoSearchAPIWrapper(max_results=25)
+web_search_tool = DuckDuckGoSearchRun(api_wrapper=wrapper)
+
+# Generation Prompt
+
+generate_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|> 
+    
+    You are an AI assistant for Research Question Tasks, that synthesizes web search results. 
+    Strictly use the following pieces of web search context to answer the question. If you don't know the answer, just say that you don't know. 
+    keep the answer concise, but provide all of the details you can in the form of a research report. 
+    Only make direct references to material if provided in the context.
+    
+    <|eot_id|>
+    
+    <|start_header_id|>user<|end_header_id|>
+    
+    Question: {question} 
+    Web Search Context: {context} 
+    Answer: 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>""",
+    input_variables=["question", "context"],
+)
+
+# Chain
+generate_chain = generate_prompt | llama3 | StrOutputParser()
+
+# Test Run
+# question = "who is Yan Lecun?"
+# context = ""
+# generation = generate_chain.invoke({"context": context, "question": question})
+# print(generation)
+
+# Router
+
+router_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|>
+    
+    You are an expert at routing a user question to either the generation stage or web search. 
+    Use the web search for questions that require more context for a better answer, or recent events.
+    Otherwise, you can skip and go straight to the generation phase to respond.
+    You do not need to be stringent with the keywords in the question related to these topics.
+    Give a binary choice 'web_search' or 'generate' based on the question. 
+    Return the JSON with a single key 'choice' with no premable or explanation. 
+    
+    Question to route: {question} 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>
+    
+    """,
+    input_variables=["question"],
+)
+
+# Chain
+question_router = router_prompt | llama3_json | JsonOutputParser()
+
+# Test Run
+# question = "What's up?"
+# print(question_router.invoke({"question": question}))
+
+# Query Transformation
+
+query_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|> 
+    
+    You are an expert at crafting web search queries for research questions.
+    More often than not, a user will ask a basic question that they wish to learn more about, however it might not be in the best format. 
+    Reword their query to be the most effective web search string possible.
+    Return the JSON with a single key 'query' with no premable or explanation. 
+    
+    Question to transform: {question} 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>
+    
+    """,
+    input_variables=["question"],
+)
+
+# Chain
+query_chain = query_prompt | llama3_json | JsonOutputParser()
+
+# Test Run
+# question = "What's happened recently with Gaza?"
+# print(query_chain.invoke({"question": question}))
+
+
+# Graph State
+class GraphState(TypedDict):
+    """
+    Represents the state of our graph.
+
+    Attributes:
+        question: question
+        generation: LLM generation
+        search_query: revised question for web search
+        context: web_search result
+    """
+    question : str
+    generation : str
+    search_query : str
+    context : str
+
+# Node - Generate
+
+def generate(state):
+    """
+    Generate answer
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): New key added to state, generation, that contains LLM generation
+    """
+    
+    print("Step: Generating Final Response")
+    question = state["question"]
+    context = state["context"]
+    print(context)
+    # TODO:: 根据context特定的内容生产答案
+    # Answer Generation
+    generation = generate_chain.invoke({"context": context, "question": question})
+    return {"generation": generation}
+
+# Node - Query Transformation
+
+def transform_query(state):
+    """
+    Transform user question to web search
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): Appended search query
+    """
+    
+    print("Step: Optimizing Query for Web Search")
+    question = state['question']
+    gen_query = query_chain.invoke({"question": question})
+    search_query = gen_query["query"]
+    return {"search_query": search_query}
+
+
+# Node - Web Search
+
+def web_search(state):
+    """
+    Web search based on the question
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): Appended web results to context
+    """
+
+    # search_query = state['search_query']
+    # print(f'Step: Searching the Web for: "{search_query}"')
+    
+    # # Web search tool call
+    # search_result = web_search_tool.invoke(search_query)
+    print("Step: Web Search")
+    search_result = "Web Search Results"
+    return {"context": search_result}
+
+
+# Conditional Edge, Routing
+
+def route_question(state):
+    """
+    route question to web search or generation.
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        str: Next node to call
+    """
+
+    print("Step: Routing Query")
+    question = state['question']
+    output = question_router.invoke({"question": question})
+    if output['choice'] == "web_search":
+        print("Step: Routing Query to Web Search")
+        return "websearch"
+    elif output['choice'] == 'generate':
+        print("Step: Routing Query to Generation")
+        return "generate"
+
+# Build the nodes
+workflow = StateGraph(GraphState)
+workflow.add_node("websearch", web_search)
+workflow.add_node("transform_query", transform_query)
+workflow.add_node("generate", generate)
+
+# Build the edges
+workflow.set_conditional_entry_point(
+    route_question,
+    {
+        "websearch": "transform_query",
+        "generate": "generate",
+    },
+)
+workflow.add_edge("transform_query", "websearch")
+workflow.add_edge("websearch", "generate")
+workflow.add_edge("generate", END)
+
+# Compile the workflow
+local_agent = workflow.compile()
+
+def run_agent(query):
+    output = local_agent.invoke({"question": query})
+    print("=======")
+    print(output["generation"])
+    # display(Markdown(output["generation"]))
+
+run_agent("What is Latest news About Open AI?")

env/agent2.yaml

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+name: agent2
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+    - python=3.10
+    - pip:
+      - streamlit==1.40.2

env/requirements.txt

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+langchain==0.2.12
+langgraph==0.2.2
+langchain-ollama==0.1.1
+langsmith== 0.1.98
+langchain_community==0.2.11
+duckduckgo-search==6.2.13

streamlit_app.py

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+# Displaying final output format
+from IPython.display import display, Markdown, Latex
+# LangChain Dependencies
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
+from langchain_community.chat_models import ChatOllama
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+from langgraph.graph import END, StateGraph
+# For State Graph 
+from typing_extensions import TypedDict
+import streamlit as st
+import os
+# Defining LLM
+def configure_llm():
+    st.sidebar.header("Configure LLM")
+    
+    # Model Selection
+    model_options = ["llama3.2"]
+    selected_model = st.sidebar.selectbox("Choose the LLM Model", options=model_options, index=0)
+    
+    # Temperature Setting
+    temperature = st.sidebar.slider("Set the Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.1)
+   
+    # Create LLM Instances based on user selection
+    llama_model = ChatOllama(model=selected_model, temperature=temperature)
+    llama_model_json = ChatOllama(model=selected_model, format='json', temperature=temperature)
+    
+    return llama_model, llama_model_json
+
+# Streamlit Application Interface
+st.title("Personal Research Assistant powered By Llama3.2")
+llama3, llama3_json=configure_llm()
+wrapper = DuckDuckGoSearchAPIWrapper(max_results=25)
+web_search_tool = DuckDuckGoSearchRun(api_wrapper=wrapper)
+generate_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|> 
+    
+    You are an AI assistant for Research Question Tasks, that synthesizes web search results. 
+    Strictly use the following pieces of web search context to answer the question. If you don't know the answer, just say that you don't know. 
+    keep the answer concise, but provide all of the details you can in the form of a research report. 
+    Only make direct references to material if provided in the context.
+    
+    <|eot_id|>
+    
+    <|start_header_id|>user<|end_header_id|>
+    
+    Question: {question} 
+    Web Search Context: {context} 
+    Answer: 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>""",
+    input_variables=["question", "context"],
+)
+
+# Chain
+generate_chain = generate_prompt | llama3 | StrOutputParser()
+router_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|>
+    
+    You are an expert at routing a user question to either the generation stage or web search. 
+    Use the web search for questions that require more context for a better answer, or recent events.
+    Otherwise, you can skip and go straight to the generation phase to respond.
+    You do not need to be stringent with the keywords in the question related to these topics.
+    Give a binary choice 'web_search' or 'generate' based on the question. 
+    Return the JSON with a single key 'choice' with no premable or explanation. 
+    
+    Question to route: {question} 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>
+    
+    """,
+    input_variables=["question"],
+)
+
+# Chain
+question_router = router_prompt | llama3_json | JsonOutputParser()
+
+query_prompt = PromptTemplate(
+    template="""
+    
+    <|begin_of_text|>
+    
+    <|start_header_id|>system<|end_header_id|> 
+    
+    You are an expert at crafting web search queries for research questions.
+    More often than not, a user will ask a basic question that they wish to learn more about, however it might not be in the best format. 
+    Reword their query to be the most effective web search string possible.
+    Return the JSON with a single key 'query' with no premable or explanation. 
+    
+    Question to transform: {question} 
+    
+    <|eot_id|>
+    
+    <|start_header_id|>assistant<|end_header_id|>
+    
+    """,
+    input_variables=["question"],
+)
+
+# Chain
+query_chain = query_prompt | llama3_json | JsonOutputParser()
+# Graph State
+class GraphState(TypedDict):
+    """
+    Represents the state of our graph.
+
+    Attributes:
+        question: question
+        generation: LLM generation
+        search_query: revised question for web search
+        context: web_search result
+    """
+    question : str
+    generation : str
+    search_query : str
+    context : str
+
+# Node - Generate
+
+def generate(state):
+    """
+    Generate answer
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): New key added to state, generation, that contains LLM generation
+    """
+    
+    print("Step: Generating Final Response")
+    question = state["question"]
+    context = state["context"]
+
+    # Answer Generation
+    generation = generate_chain.invoke({"context": context, "question": question})
+    return {"generation": generation}
+
+# Node - Query Transformation
+
+def transform_query(state):
+    """
+    Transform user question to web search
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): Appended search query
+    """
+    
+    print("Step: Optimizing Query for Web Search")
+    question = state['question']
+    gen_query = query_chain.invoke({"question": question})
+    search_query = gen_query["query"]
+    return {"search_query": search_query}
+
+
+# Node - Web Search
+
+def web_search(state):
+    """
+    Web search based on the question
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): Appended web results to context
+    """
+
+    search_query = state['search_query']
+    print(f'Step: Searching the Web for: "{search_query}"')
+    
+    # Web search tool call
+    search_result = web_search_tool.invoke(search_query)
+    return {"context": search_result}
+
+
+# Conditional Edge, Routing
+
+def route_question(state):
+    """
+    route question to web search or generation.
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        str: Next node to call
+    """
+
+    print("Step: Routing Query")
+    question = state['question']
+    output = question_router.invoke({"question": question})
+    if output['choice'] == "web_search":
+        print("Step: Routing Query to Web Search")
+        return "websearch"
+    elif output['choice'] == 'generate':
+        print("Step: Routing Query to Generation")
+        return "generate"
+# Build the nodes
+workflow = StateGraph(GraphState)
+workflow.add_node("websearch", web_search)
+workflow.add_node("transform_query", transform_query)
+workflow.add_node("generate", generate)
+
+# Build the edges
+workflow.set_conditional_entry_point(
+    route_question,
+    {
+        "websearch": "transform_query",
+        "generate": "generate",
+    },
+)
+workflow.add_edge("transform_query", "websearch")
+workflow.add_edge("websearch", "generate")
+workflow.add_edge("generate", END)
+
+# Compile the workflow
+local_agent = workflow.compile()
+def run_agent(query):
+    output = local_agent.invoke({"question": query})
+    print("=======")
+
+    return output["generation"]
+user_query = st.text_input("Enter your research question:", "")
+
+if st.button("Run Query"):
+    if user_query:
+        st.write(run_agent(user_query))