save tmp

.env

@@ -0,0 +1,4 @@
+TAVILY_API_KEY=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
+OLLAMA_BOT_TOKEN=7965398310:AAF2QDGXRRwDta0eYC7lFSvRDLW7L-SQH_E
+OLLAMA_BOT_TOKEN=7802269927:AAHXso9DZe5_LgQ4kN9J5jEAgJpMNQVaYI0
+OLLAMA_BOT_CHAT_IDS=5015834404

.gitignore

@@ -1,3 +1,4 @@
 *.log
 /logs
-**__pycache__**
+**__pycache__**
+db

.vscode/launch.json

@@ -5,10 +5,10 @@
   "version": "0.2.0",
   "configurations": [
     {
-      "name": "Python Debugger: Current File",
+      "name": "Python Debugger: bot",
       "type": "debugpy",
       "request": "launch",
-      "program": "${file}",
+      "program": "bot.py",
       "console": "integratedTerminal"
     }
   ]

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+  "python.venvPath": "/Users/zhl/miniconda/envs/agent2/bin/python"
+}

README.md

@@ -1,5 +1,7 @@
 
 ```bash
+ollama pull llama3.2
+
 conda env create -f env/agent2.yaml
 # conda create -n agent2 python
 

agent_util.py

@@ -4,6 +4,8 @@ from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
 from langgraph.graph import END, StateGraph
 from langchain_community.chat_models import ChatOllama
 from langgraph.checkpoint.memory import MemorySaver
+from typing import Annotated
+from operator import add
 # For State Graph
 from typing_extensions import TypedDict
 import json
@@ -23,7 +25,7 @@ def config_agent(llama3, llama3_json):
       <|begin_of_text|>
       
       <|start_header_id|>system<|end_header_id|> 
-      
+      Your name is AICQ.
       You are a professional token seller. 
       We have a total of 1 million CEC, and the average cost of these CECs is 1 US dollar each. 
       Now we hope to hand it over to you to be responsible for selling all of them. 
@@ -121,10 +123,10 @@ def config_agent(llama3, llama3_json):
             send_order: revised question for send order
             context: send_order result
         """
-        question: str
+        question: Annotated[list[str], add]
         generation: str
-        send_order: str
-        context: str
+        send_order: Annotated[list[str], add]
+        context: Annotated[list[str], add]
 
     # Node - Generate
 
@@ -141,12 +143,14 @@ def config_agent(llama3, llama3_json):
 
         print("Step: Generating Final Response")
         question = state["question"]
-        context = state.get("context", None)
+        context = state["context"]
         print(context)
+        last_context = context[-1] if context else None
         # TODO:: 根据context特定的内容生产答案
-        if context is not None and context.index("orderinfo") != -1:
-            return {"generation": context.replace("orderinfo:", "")}
+        if last_context is not None and last_context.index("orderinfo") != -1:
+            return {"generation": last_context.replace("orderinfo:", "")}
         else:
+            print(question)
             generation = generate_chain.invoke(
                 {"context": context, "question": question})
             return {"generation": generation}
@@ -167,9 +171,9 @@ def config_agent(llama3, llama3_json):
         print("Step: Optimizing Query for Send Order")
         question = state['question']
         gen_query = query_chain.invoke({"question": question})
-        search_query = gen_query["count"]
-        print("send_order", search_query)
-        return {"send_order": search_query}
+        amount = str(gen_query["count"])
+        print("send_order", amount)
+        return {"send_order": [amount]}
 
     # Node - Send Order
 
@@ -184,13 +188,13 @@ def config_agent(llama3, llama3_json):
             state (dict): Appended Order Info to context
         """
         print("Step: before Send Order")
-        amount = state['send_order']
+        amount = state['send_order'][-1]
         print(amount)
         print(f'Step: build order info for : "{amount}" CEC')
         order_info = {"amount": amount, "price": 0.1,
                       "name": "CEC", "url": "https://www.example.com"}
         search_result = f"orderinfo:{json.dumps(order_info)}"
-        return {"context": search_result}
+        return {"context": [search_result]}
 
     # Conditional Edge, Routing
 
@@ -234,7 +238,7 @@ def config_agent(llama3, llama3_json):
     workflow.add_edge("generate", END)
 
     # Compile the workflow
-    memory = MemorySaver()
+    # memory = MemorySaver()
     # local_agent = workflow.compile(checkpointer=memory)
     local_agent = workflow.compile()
     return local_agent

app.py

@@ -9,7 +9,7 @@ local_agent = config_agent(llama3, llama3_json)
 
 
 def run_agent(query):
-    output = local_agent.invoke({"question": query})
+    output = local_agent.invoke({"question": [query]})
     print("=======")
     print(output["generation"])
     # display(Markdown(output["generation"]))

bot.py

@@ -0,0 +1,52 @@
+#!/usr/bin/python3
+from dotenv import load_dotenv
+load_dotenv()
+import os
+from rich.traceback import install
+install()
+
+from telegram.ext import ApplicationBuilder
+
+from handlers.start import start_handler
+from handlers.message_handler import message_handler
+from handlers.ollama_host import set_host_handler
+from handlers.status import status_handler
+from handlers.help import help_handler
+
+import handlers.chat_id as chat_id
+import handlers.models as models
+import handlers.system as sys_prompt
+import handlers.history as history
+
+
+OLLAMA_BOT_TOKEN = os.environ.get("OLLAMA_BOT_TOKEN")
+if OLLAMA_BOT_TOKEN is None:
+    raise ValueError("OLLAMA_BOT_TOKEN env variable is not set")
+
+if __name__ == "__main__":
+    application = ApplicationBuilder().token(OLLAMA_BOT_TOKEN).build()
+
+    application.add_handler(chat_id.filter_handler, group=-1)
+
+    application.add_handlers([
+        start_handler,
+        help_handler,
+        status_handler,
+
+        # chat_id.get_handler,
+        history.reset_handler,
+        
+        # models.list_models_handler,
+        models.set_model_handler,
+        
+        sys_prompt.list_handler,
+        sys_prompt.set_handler,
+        sys_prompt.create_new_hanlder,
+        sys_prompt.remove_handler,
+        sys_prompt.on_remove_handler,
+        set_host_handler,
+        message_handler,
+    ])
+
+    application.run_polling()
+    print("Bot started")

env/agent2.yaml

@@ -3,6 +3,8 @@ channels:
   - defaults
   - conda-forge
 dependencies:
-    - python=3.10
+    - python=3.9
+    - pip==21
     - pip:
-      - streamlit==1.40.2
+      - setuptools==66.0.0
+      - wheel==0.38.4

env/requirements.txt

@@ -3,4 +3,11 @@ langgraph==0.2.2
 langchain-ollama==0.1.1
 langsmith== 0.1.98
 langchain_community==0.2.11
-duckduckgo-search==6.2.13
+langchain_chroma==0.1.4
+# duckduckgo-search==6.2.13
+# need install manually
+# streamlit==1.40.2
+python-dotenv
+python-telegram-bot
+chromadb==0.4.22
+sentence_transformers

handlers/chat_id.py

@@ -0,0 +1,25 @@
+import os
+from telegram import Update
+from telegram.ext import CommandHandler, ApplicationHandlerStop, TypeHandler
+
+CHAT_IDS = os.environ.get("OLLAMA_BOT_CHAT_IDS")
+valid_user_ids = []
+if CHAT_IDS is not None:
+    valid_user_ids = [int(id) for id in CHAT_IDS.split(',')]
+
+
+async def get_chat_id(update: Update, _):
+    chat_id = update.effective_chat.id
+    await update.message.reply_text(chat_id)
+
+get_handler = CommandHandler("chatid", get_chat_id)
+
+async def filter_chat_id(update: Update, _):
+    if update.effective_user.id in valid_user_ids or update.message.text == '/chatid':
+        pass
+    else:
+        await update.effective_message.reply_text("access denied")
+        print(f"access denied for {update.effective_user.id} {update.effective_user.name} {update.effective_user.full_name}")
+        raise ApplicationHandlerStop()
+
+filter_handler = TypeHandler(Update, filter_chat_id)

handlers/help.py

@@ -0,0 +1,22 @@
+from telegram import Update
+from telegram.ext import CommandHandler
+
+help_message="""Welcome to ollama bot here's full list of commands
+
+*Here's a full list of commands:*
+/start - quick access to common commands
+/status - show current bot status
+/chatid - get current chat id
+/models - select ollama model to chat with
+/systems - select system prompt
+/addsystem - create new system prompt
+/rmsystem - remove system prompt
+/sethost - set ollama host URL
+/reset - reset chat history
+/help - show this help message
+"""
+
+async def help(update: Update, _):
+    await update.message.reply_text(help_message, parse_mode='markdown')
+
+help_handler = CommandHandler("help", help)

handlers/history.py

@@ -0,0 +1,9 @@
+from telegram import Update
+from telegram.ext import CommandHandler
+from llm import clinet
+
+async def history_reset(update: Update, _):
+    clinet.reset_history()
+    await update.message.reply_text("history cleared")
+
+reset_handler = CommandHandler("reset", history_reset)

handlers/message_handler.py

@@ -0,0 +1,46 @@
+from telegram import Update
+from telegram.ext import MessageHandler, filters
+from llm import clinet
+import re
+
+# simple filter for proper markdown response
+def escape_markdown_except_code_blocks(text):
+    # Pattern to match text outside of code blocks and inline code
+    pattern = r'(```.*?```|`.*?`)|(_)'
+    
+    def replace(match):
+        # If group 1 (code blocks or inline code) is matched, return it unaltered
+        if match.group(1):
+            return match.group(1)
+        # If group 2 (underscore) is matched, escape it
+        elif match.group(2):
+            return r'\_'
+    
+    return re.sub(pattern, replace, text, flags=re.DOTALL)
+
+
+
+async def on_message(update: Update, _):
+    msg = await update.message.reply_text("...")
+    await update.effective_chat.send_action("TYPING")
+    user_id = str(update.message.from_user.id)
+    user_name = update.message.from_user.full_name or update.message.from_user.username
+    print("msg from:", user_id, user_name)
+    try:
+        response = clinet.generate(update.message.text, user_id)
+        try:
+            await msg.edit_text(
+                escape_markdown_except_code_blocks(response),
+                parse_mode='markdown'
+            )
+        except Exception as e:
+            print(e)
+            await msg.edit_text(response)
+    except Exception as e:
+        print(e)
+        await msg.edit_text("failed to generate response")
+
+    await update.effective_chat.send_action("CANCEL")
+
+message_handler = MessageHandler(filters.TEXT & (~filters.COMMAND), on_message)
+

handlers/models.py

@@ -0,0 +1,28 @@
+from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
+from telegram.ext import CommandHandler, CallbackQueryHandler
+from llm import clinet
+from llm.ollama import list_models_names
+
+async def list_models(update: Update, _):
+    model_names = list_models_names()
+    keybaord = [[InlineKeyboardButton(name, callback_data=f'/setmodel {name}')] for name in model_names]
+    reply_markup = InlineKeyboardMarkup(keybaord)
+
+    await update.message.reply_text(
+        text='*WARNING:* chat history will be reset\nPick a model:', 
+        reply_markup=reply_markup,
+        parse_mode='markdown'
+    )
+
+list_models_handler = CommandHandler("models", list_models)
+
+async def set_model(update: Update, _):
+    query = update.callback_query
+    model_name = query.data.split(' ')[1] 
+    await query.answer()
+
+    clinet.set_model(model_name)
+    clinet.reset_history()
+    await query.edit_message_text(text=f"selected model: {model_name}")
+
+set_model_handler = CallbackQueryHandler(set_model, pattern='^/setmodel')

handlers/ollama_host.py

@@ -0,0 +1,43 @@
+from telegram import Update
+from telegram.ext import filters, CommandHandler, ConversationHandler, MessageHandler
+from ollama import Client
+from llm import clinet
+from rich import print
+# Define states
+SET_HOST = range(1)
+
+async def set_llama_host(update: Update, _):
+    await update.message.reply_text(
+        text='Enter ollama host url like, **http://127.0.0.1:11434**',
+        parse_mode='markdown'
+    )
+    return SET_HOST
+
+
+async def on_got_host(update: Update, _):
+    try:
+        test_client = Client(host=update.message.text)
+        test_client._client.timeout = 2
+        test_client.list()
+        clinet.set_host(update.message.text)
+        await update.message.reply_text(
+            f"ollama host is set to: {update.message.text}"
+        )
+    except:
+        await update.message.reply_text(
+            f"couldn't connect to ollama server at:\n{update.message.text}"
+        )
+    finally:
+        return ConversationHandler.END
+    
+async def cancel(update: Update, _):
+    await update.message.reply_text("canceled /sethost command")
+    return ConversationHandler.END
+
+set_host_handler = ConversationHandler(
+    entry_points=[CommandHandler("sethost", set_llama_host)],
+    states={
+        SET_HOST: [MessageHandler(filters.TEXT & ~filters.COMMAND, on_got_host)]
+    },
+    fallbacks=[CommandHandler('cancel', cancel)]
+)

handlers/start.py

@@ -0,0 +1,24 @@
+from telegram import Update, ReplyKeyboardMarkup
+from telegram.ext import CommandHandler
+
+async def start(update: Update, _): 
+    await update.message.reply_text(
+        "Ask a question to start conversation",
+    )
+    keyboard = [
+        ['/status'],
+        ['/models'],
+        ['/systems'],
+        ['/reset']
+    ]
+    reply_markup = ReplyKeyboardMarkup(
+        keyboard, 
+        one_time_keyboard=True, 
+        resize_keyboard=True
+    )
+    await update.message.reply_text(
+        "You can also use this commands:",
+        reply_markup=reply_markup
+    )
+
+start_handler = CommandHandler("start", start)

handlers/status.py

@@ -0,0 +1,13 @@
+from telegram import Update
+from telegram.ext import CommandHandler
+from llm import clinet
+
+async def get_status(update: Update, _):
+    host, model, system = clinet.get_status()
+
+    await update.message.reply_text(
+        text=f'*ollama_host:* {host}\n\n*model:* {model}\n\n*system prompt:*\n{system}',
+        parse_mode='markdown'
+    )
+
+status_handler = CommandHandler("status", get_status)

handlers/system.py

@@ -0,0 +1,115 @@
+from llm import clinet
+from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
+from telegram.ext import filters, ContextTypes, CommandHandler, MessageHandler, ConversationHandler, CallbackQueryHandler
+
+import os
+
+SYSTEM_DIR = 'system'
+
+# list system prompts as inline keybard
+# clicking a button sets system prompt for current model
+async def list_system_prompts(update: Update, _):
+    files = os.listdir(SYSTEM_DIR)
+
+    keybaord = [[InlineKeyboardButton(name, callback_data=f'/setsystem {name}')] for name in files]
+    reply_markup = InlineKeyboardMarkup(keybaord)
+
+    await update.message.reply_text('select system prompt:', reply_markup=reply_markup)
+
+
+list_handler = CommandHandler("systems", list_system_prompts)
+
+async def on_set_system(update: Update, _):
+    query = update.callback_query
+    file_name = query.data.split(' ')[1] 
+    await query.answer()
+
+    file_path = os.path.join(SYSTEM_DIR, file_name)
+    if os.path.exists(file_path):
+        with open(file_path) as file:
+            system_prompt = file.read()
+            clinet.set_system(system_prompt)
+            await query.edit_message_text(text=f"the system prompt has been set to: {file_name}\n\n{system_prompt}")
+
+set_handler = CallbackQueryHandler(on_set_system, pattern='^/setsystem')
+
+# list system prompts as buttons
+# clicking on a button removes selected system prompt
+async def remove_system_prompt(update: Update, _):
+    files = os.listdir(SYSTEM_DIR)
+
+    keybaord = [[InlineKeyboardButton(name, callback_data=f'/rmsystem {name}')] for name in files]
+    reply_markup = InlineKeyboardMarkup(keybaord)
+
+    await update.message.reply_text(
+        '*WARNING:* selected prompt will be removed:', 
+        reply_markup=reply_markup,
+        parse_mode='markdown'
+    )
+
+remove_handler = CommandHandler("rmsystem", remove_system_prompt)
+
+async def on_remove_system(update: Update, _):
+    query = update.callback_query
+    file_name = query.data.split(' ')[1] 
+    await query.answer()
+
+    file_path = os.path.join(SYSTEM_DIR, file_name)
+    if os.path.exists(file_path):
+        with open(file_path) as file:
+            system_prompt = file.read()
+            clinet.set_system(system_prompt)
+            os.remove(file_path)
+            await query.edit_message_text(text=f"{file_name} system prompt has been removed\n\n{system_prompt}")
+
+on_remove_handler = CallbackQueryHandler(on_remove_system, pattern='^/rmsystem')
+
+# Define states
+SET_NAME, SET_CONTENT = range(2)
+
+# add new system prompt
+# 1. enter name
+# 2. enter content
+# new prompt will be saved to SYSTEM_DIR
+async def add_system(update: Update, _):
+    await update.message.reply_text(
+        text="Enter name for a new system prompt:",
+    )
+    return SET_NAME
+
+async def on_got_name(update: Update, context: ContextTypes.DEFAULT_TYPE):
+    file_name = ''.join(x for x in update.message.text if x.isalnum() or x in '._ ')
+    context.user_data['new_name'] = file_name
+
+    if os.path.exists(os.path.join(SYSTEM_DIR, file_name)):
+        await update.message.reply_text(
+            text=f'*Warning*, existing **{file_name}** will be rewritten\nenter prompt content to continue or /cancel',
+            parse_mode='markdown'
+        )
+    else:
+        await update.message.reply_text(
+            f"New system prompt will be saved to {file_name}, enter prompt:"
+        )
+    
+    return SET_CONTENT
+
+async def on_got_content(update: Update, context: ContextTypes.DEFAULT_TYPE):
+    file_name = context.user_data['new_name']
+    with open(os.path.join(SYSTEM_DIR, file_name), 'w') as file:
+        file.write(update.message.text)
+    
+    await update.message.reply_text(f"{file_name} content saved")
+    return ConversationHandler.END    
+
+async def cancel(update: Update, _):
+    await update.message.reply_text("canceled /addsystem command")
+    return ConversationHandler.END
+
+create_new_hanlder = ConversationHandler(
+    entry_points=[CommandHandler('addsystem', add_system)],
+    states={
+        SET_NAME: [MessageHandler(filters.TEXT & ~filters.COMMAND, on_got_name)],
+        SET_CONTENT: [MessageHandler(filters.TEXT & ~filters.COMMAND, on_got_content)]
+    },
+    fallbacks=[CommandHandler('cancel', cancel)]
+)

llm/__init__.py

@@ -0,0 +1,3 @@
+from .ollama import OllamaClient
+
+clinet = OllamaClient()

llm/agent_util.py

@@ -0,0 +1,245 @@
+# LangChain Dependencies
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
+from langgraph.graph import END, StateGraph
+from langchain_community.chat_models import ChatOllama
+from langgraph.checkpoint.memory import MemorySaver
+from typing import Annotated
+from operator import add
+# For State Graph
+from typing_extensions import TypedDict
+import json
+
+
+def configure_llm(model, temperature):
+    llama3 = ChatOllama(model=model, temperature=temperature)
+    llama3_json = ChatOllama(
+        model=model, format='json', temperature=temperature)
+    return llama3, llama3_json
+
+
+def config_agent(llama3, llama3_json):
+    # Graph State
+
+    class GraphState(TypedDict):
+        """
+        Represents the state of our graph.
+
+        Attributes:
+            question: question
+            generation: LLM generation
+            send_order: revised question for send order
+            context: send_order result
+        """
+        question: str
+        generation: str
+        order_info: str
+        context: str
+        history: str
+
+    generate_prompt = PromptTemplate(
+        template="""
+      
+      <|begin_of_text|>
+      
+      <|start_header_id|>system<|end_header_id|> 
+      Your name is AICQ.
+      You are a professional token seller. 
+      We have a total of 1 million CEC, and the average cost of these CECs is $1.1 each, The min price is $0.9 per CEC, There is a 1% chance that the price will be lower than $1 per CEC. 
+      Now we hope to hand it over to you to be responsible for selling all of them. 
+      Customers will come to talk to you and bargain. 
+      You can decide the final transaction price based on the status of the conversation with the customer and sell the tokens. 
+      In addition, there are several rules to follow for this task: 
+      1) The higher the price, the better, in the hope that the final income will be higher than 1 million US dollars; 
+      2) You can set a price higher than 1 US dollar per CEC to sell tokens according to the actual sales situation; 
+      3) For a single customer, we hope that you will try not to sell at a price lower than 1 US dollar, but if the customer has special reasons, you can sell some tokens at a lower price depending on the situation, but in this case you need to sell at a higher price in other sales to ensure that the overall income is not less than 1 million US dollars; 
+      4) We hope that customers buy as much as possible. 
+      Under this condition, if the user is willing to purchase a large number of tokens at one time, we can sell them to him at a more favorable price, but the min price can not lower then $0.9. 
+      The above are all the rules for this sales task. 
+      
+      <|eot_id|>
+      
+      <|start_header_id|>user<|end_header_id|>
+      
+      {context} 
+      {history} 
+      Question: {question}
+      Answer: 
+      
+      <|eot_id|>
+      
+      <|start_header_id|>assistant<|end_header_id|>""",
+        input_variables=["question", "context", "history"],
+    )
+
+    # 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 send order. 
+      CEC is a famous crypto token, and you are an expert at selling CEC.
+      Use the generate for questions that user asks for price.
+      Use the send order for questions that user accepts the price you give and decides to buy a certain amount of CEC.
+      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 'send_order' or 'generate' based on the question. 
+      Return the JSON with a single key 'choice' with no premable or explanation. 
+      
+      Question to route: {question} 
+      Context to route: {context}
+      History to route: {history}
+      <|eot_id|>
+      
+      <|start_header_id|>assistant<|end_header_id|>
+      
+      """,
+        input_variables=["question", "history", "context"],
+    )
+
+    # Chain
+    question_router = router_prompt | llama3_json | JsonOutputParser()
+
+    order_prompt = PromptTemplate(
+        template="""
+      
+      <|begin_of_text|>
+      
+      <|start_header_id|>system<|end_header_id|> 
+      
+      You are an expert at sell CEC, 
+      Return the JSON with a single key 'count' with amount which user want to buy.
+      
+      Question to transform: {question} 
+      Context to transform: {context}
+      History to transform: {history}
+      
+      <|eot_id|>
+      
+      <|start_header_id|>assistant<|end_header_id|>
+      
+      """,
+        input_variables=["question", "context", "history"],
+    )
+
+    # Chain
+    order_chain = order_prompt | llama3_json | JsonOutputParser()
+    
+
+    # 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"]
+        order_info = state.get("order_info", None)
+        # TODO:: 根据context特定的内容生产答案
+        # check if context is not None and not empty
+        if order_info is not None:
+            return {"generation": order_info}
+        else:
+            generation = generate_chain.invoke(
+                {"context": context, "question": question, "history": state["history"]})
+            return {"generation": generation}
+
+    # Node - Query Transformation
+
+    def transform_query(state):
+        """
+        Transform user question to order info
+
+        Args:
+            state (dict): The current graph state
+
+        Returns:
+            state (dict): Appended amount of CEC to context
+        """
+
+        print("Step: Optimizing Query for Send Order")
+        question = state['question']
+        gen_query = order_chain.invoke({"question": question, "history": state["history"], "context": state["context"]})
+        amount = str(gen_query["count"])
+        print("order_info", amount)
+        return {"order_info": [amount] }
+
+    # Node - Send Order
+
+    def send_order(state):
+        """
+        Send order based on the question
+
+        Args:
+            state (dict): The current graph state
+
+        Returns:
+            state (dict): Appended Order Info to context
+        """
+        print("Step: before Send Order")
+        amount = state['order_info']
+        print(amount)
+        print(f'Step: build order info for : "{amount}" CEC')
+        order_info = {"amount": amount, "price": 0.1,
+                      "name": "CEC", "url": "https://www.example.com"}
+        order_result = json.dumps(order_info)
+        return {"order_info": order_result}
+
+    # Conditional Edge, Routing
+
+    def route_question(state):
+        """
+        route question to send order 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, "history": state["history"], "context": state["context"]})
+        if output['choice'] == "send_order":
+            print("Step: Routing Query to Send Order")
+            return "sendorder"
+        elif output['choice'] == 'generate':
+            print("Step: Routing Query to Generation")
+            return "generate"
+
+    # Build the nodes
+    workflow = StateGraph(GraphState)
+    workflow.add_node("sendorder", send_order)
+    workflow.add_node("transform_query", transform_query)
+    workflow.add_node("generate", generate)
+
+    # Build the edges
+    workflow.set_conditional_entry_point(
+        route_question,
+        {
+            "sendorder": "transform_query",
+            "generate": "generate",
+        },
+    )
+    workflow.add_edge("transform_query", "sendorder")
+    workflow.add_edge("sendorder", "generate")
+    workflow.add_edge("generate", END)
+
+    # Compile the workflow
+    # memory = MemorySaver()
+    # local_agent = workflow.compile(checkpointer=memory)
+    local_agent = workflow.compile()
+    return local_agent

llm/chat_history.py

@@ -0,0 +1,75 @@
+# from langchain.vectorstores.chroma import Chroma
+from langchain_chroma import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+# from langchain.embeddings.huggingface import HuggingFaceBgeEmbeddings
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from chromadb.config import Settings
+import os
+
+message_template = """
+User: {question}
+
+Assistant: {answer}
+"""
+
+model_norm = HuggingFaceBgeEmbeddings(
+    model_name="BAAI/bge-base-en",
+    model_kwargs={ 'device': 'cpu' },
+    encode_kwargs={ 'normalize_embeddings': True },
+)
+
+def get_chroma():
+    settings = Settings()
+    settings.allow_reset = True
+    settings.is_persistent = True
+
+    return Chroma(
+        persist_directory=DB_FOLDER,
+        embedding_function=model_norm,
+        client_settings=settings,
+        collection_name='latest_chat'
+    )
+
+DB_FOLDER = 'db'
+
+class ChatHistory:
+    def __init__(self) -> None:
+        self.history = {}
+
+    def append(self, question: str, answer: str, uid: str):
+        new_message = message_template.format(
+            question=question,
+            answer=answer
+        )
+        if uid not in self.history:
+            self.history[uid] = []
+        self.history[uid].append(new_message)
+        self.embed(new_message, uid)
+
+    def reset_history(self):
+        self.history = {}
+        chroma = get_chroma()
+        chroma._client.reset()
+    
+    def embed(self, text: str, uid: str):
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+        all_splits = text_splitter.split_text(text)
+        
+        chroma = get_chroma()
+        chroma.add_texts(all_splits, metadatas=[{"user": uid}])
+
+    def get_context(self, question: str, uid: str):
+        if not os.path.exists(DB_FOLDER):
+            return ''
+        chroma = get_chroma()
+        filter = {
+            'user': uid
+        }
+        documents = chroma.similarity_search(query=question, k=4, filter=filter)
+        context = '\n'.join([d.page_content for d in documents])
+        return context
+    
+    def get_last_few(self, uid: str):
+        if uid not in self.history:
+            return ''
+        return '\n'.join(self.history[uid][-3:])

llm/ollama.py

@@ -0,0 +1,86 @@
+from ollama import Client
+# from langchain.llms.ollama import Ollama
+# from langchain.llms import Ollama
+from langchain_community.chat_models import ChatOllama
+from .chat_history import ChatHistory
+from .agent_util import config_agent
+import os
+
+ollama_model = 'llama3.2'
+# OLLAMA_HOST = os.environ.get('OLLAMA_BOT_BASE_URL')
+# if OLLAMA_HOST is None:
+#     raise ValueError("OLLAMA_BOT_BASE_URL env variable is not set")
+
+default_system_prompt_path = './prompts/default.md'
+
+# 传入system参数
+def get_client(system=''):
+    llama3 = ChatOllama(model=ollama_model, temperature=0)
+    llama3_json = ChatOllama(
+        model=ollama_model, 
+        format='json', 
+        temperature=0
+        )
+    client = config_agent(llama3, llama3_json)
+    return client
+
+
+def list_models_names():
+    # models = Client(OLLAMA_HOST).list()["models"]
+    # return [m['name'] for m in models]
+    return []
+
+
+PROMPT_TEMPLATE = """
+{context}
+
+{history}
+
+User: {question}
+
+Assistant:
+"""
+
+class OllamaClient:
+    def __init__(self) -> None:
+        with open(default_system_prompt_path) as file:
+            self.system_prompt = file.read()
+
+        self.client = get_client(self.system_prompt)
+        self.history = ChatHistory()
+    
+    def generate(self, question, uid) -> str:
+        context_content = self.history.get_context(question, uid)
+        context = f"CONTEXT: {context_content}" if len(context_content) > 0 else ''
+
+        last_few = self.history.get_last_few(uid)
+        history = f"LAST MESSAGES: {last_few}" if len(last_few) > 0 else ''
+
+        prompt = PROMPT_TEMPLATE.format(
+            context=context,
+            history=history,
+            question=question
+        )
+        answer = self.client.invoke({"question": question, "context": context, "history": history})['generation']
+        print("answer:", answer)
+        self.history.append(question, answer, uid)
+        return answer
+
+    def reset_history(self):
+        self.history.reset_history()
+    
+    def set_model(self, model_name):
+        self.client.model = model_name
+
+    def set_host(self, host):
+        self.client.base_url = host
+
+    def set_system(self, system):
+        self.client.system = system
+
+    def get_status(self):
+        return (
+            self.client.base_url, 
+            self.client.model,
+            self.client.system
+        )

prompts/default.md

@@ -0,0 +1,6 @@
+You're a helpful assistant.
+Your goal is to help the user with their questions
+If there's a previous conversation you'll be provided a context
+
+If you don't know an answer to a given USER question don't imagine anything
+just say you don't know

streamlit_app.py

@@ -20,10 +20,10 @@ local_agent = config_agent(llama3, llama3_json)
 
 
 def run_agent(query):
-    # config = {"configurable": {"thread_id": "1"}}
-    # output = local_agent.invoke({"question": query}, config)
-    # print(list(local_agent.get_state_history(config)))
-    output = local_agent.invoke({"question": query})
+    config = {"configurable": {"thread_id": "1", "user_id": "1"}}
+    output = local_agent.invoke({"question": [query]}, config)
+    print(list(local_agent.get_state_history(config)))
+    # output = local_agent.invoke({"question": ['hi, my name is cz', query]})
     print("=======")
     return output["generation"]