├── .DS_Store ├── .gitmodules ├── env.sample ├── agents ├── __init__.py ├── memory_agent │ ├── prompts.py │ ├── __init__.py │ ├── utils.py │ ├── state.py │ ├── configuration.py │ ├── tools.py │ └── graph.py └── crag.py ├── Dockerfile ├── requirements.txt ├── gpt_message_handler.py ├── router.py ├── docker-compose.yml ├── routes.py ├── conf.py ├── .gitignore ├── services.py ├── vectordb.py ├── README.md ├── telegram_bot.py ├── test.ipynb └── LICENSE /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/emingenc/telegramGPT/HEAD/.DS_Store -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "agents/gpt_researcher"] 2 | path = agents/gpt_researcher 3 | url = https://github.com/assafelovic/gpt-researcher.git 4 | -------------------------------------------------------------------------------- /env.sample: -------------------------------------------------------------------------------- 1 | TELEGRAM_TOKEN="your telegram token" 2 | OPENAI_API_KEY="your openai api key" 3 | BOTNAME="your bots name" 4 | TAVILY_API_KEY="tav api key" 5 | USERS=allowed users 6 | ANTHROPIC_API_KEY="anthropic api key" 7 | -------------------------------------------------------------------------------- /agents/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), 'memory_agent')) ) 5 | sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), 'gpt_researcher')) ) -------------------------------------------------------------------------------- /agents/memory_agent/prompts.py: -------------------------------------------------------------------------------- 1 | """Define default prompts.""" 2 | 3 | SYSTEM_PROMPT = """You are a helpful and friendly chatbot. Get to know the user! \ 4 | Ask questions! Be spontaneous! 5 | {user_info} 6 | 7 | System Time: {time}""" 8 | -------------------------------------------------------------------------------- /agents/memory_agent/__init__.py: -------------------------------------------------------------------------------- 1 | """Enrichment for a pre-defined schema.""" 2 | import os 3 | import sys 4 | sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) ) 5 | from memory_agent.graph import graph 6 | 7 | 8 | 9 | __all__ = ["graph"] 10 | -------------------------------------------------------------------------------- /agents/memory_agent/utils.py: -------------------------------------------------------------------------------- 1 | """Utility functions used in our graph.""" 2 | 3 | 4 | def split_model_and_provider(fully_specified_name: str) -> dict: 5 | """Initialize the configured chat model.""" 6 | if "/" in fully_specified_name: 7 | provider, model = fully_specified_name.split("/", maxsplit=1) 8 | else: 9 | provider = None 10 | model = fully_specified_name 11 | return {"model": model, "provider": provider} 12 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Use an official Python runtime as a parent image 2 | FROM python:3.12-slim 3 | 4 | # Set the working directory in the container 5 | WORKDIR /app 6 | 7 | # Copy the requirements file into the container at /app 8 | COPY requirements.txt . 9 | 10 | # Install any needed packages specified in requirements.txt 11 | RUN pip3 install --no-cache-dir -r requirements.txt 12 | 13 | # Copy the rest of the application code into the container at /app 14 | COPY . . 15 | 16 | # Run the application 17 | CMD ["python3", "telegram_bot.py"] -------------------------------------------------------------------------------- /agents/memory_agent/state.py: -------------------------------------------------------------------------------- 1 | """Define the shared values.""" 2 | 3 | from __future__ import annotations 4 | 5 | from dataclasses import dataclass 6 | 7 | from langchain_core.messages import AnyMessage 8 | from langgraph.graph import add_messages 9 | from typing_extensions import Annotated 10 | 11 | 12 | @dataclass(kw_only=True) 13 | class State: 14 | """Main graph state.""" 15 | 16 | messages: Annotated[list[AnyMessage], add_messages] 17 | """The messages in the conversation.""" 18 | 19 | 20 | __all__ = [ 21 | "State", 22 | ] 23 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | python-telegram-bot==21.6 2 | langchain==0.3.4 3 | langchain-community>=0.3.3 4 | langchain-openai==0.2.1 5 | langgraph==0.2.34 6 | python-dotenv==1.0.1 7 | openai==1.51.0 8 | bs4==0.0.2 9 | tiktoken==0.7.0 10 | chromadb==0.5.11 11 | langchain-chroma==0.1.4 12 | langchain-ollama==0.2.0 13 | ollama==0.3.3 14 | llm-adaptive-router==0.1.13 15 | langchain-anthropic==0.2.3 16 | python-multipart 17 | markdown 18 | colorama 19 | beautifulsoup4 20 | colorama 21 | md2pdf 22 | arxiv 23 | PyMuPDF 24 | requests 25 | jinja2 26 | aiofiles 27 | mistune 28 | python-docx 29 | htmldocx 30 | lxml_html_clean 31 | websockets 32 | unstructured 33 | json_repair 34 | json5 35 | loguru -------------------------------------------------------------------------------- /gpt_message_handler.py: -------------------------------------------------------------------------------- 1 | from router import router 2 | 3 | 4 | async def handle_response(content, user, message_id, content_type) -> str: 5 | username = user.username 6 | user_id = user.id 7 | config = { 8 | "username": username, 9 | "user_id": user_id, 10 | "message_id": message_id, 11 | "content_type": content_type 12 | } 13 | route = router.route(content) 14 | response = await route.invoker(content, config) 15 | # message = f"{route.name}:{response}" 16 | return response 17 | 18 | 19 | if __name__ == "__main__": 20 | question = "do CRAG . How is the weather in Ankara. " 21 | res = handle_response(question, "user", "123", "text") 22 | print(res) 23 | -------------------------------------------------------------------------------- /router.py: -------------------------------------------------------------------------------- 1 | from llm_adaptive_router import AdaptiveRouter, router_prompt_template 2 | from langchain_community.vectorstores import Chroma 3 | from conf import llm, embeddings 4 | from routes import routes 5 | 6 | 7 | COLLECTION_NAME ="router_vectorstore" 8 | router_embeddings = embeddings 9 | VECTOR_DB_PATH = "./router_vectorstore_db" 10 | 11 | 12 | router_vectorstore = Chroma( 13 | collection_name=COLLECTION_NAME, 14 | embedding_function=router_embeddings, 15 | persist_directory=VECTOR_DB_PATH, 16 | ) 17 | 18 | 19 | router = AdaptiveRouter( 20 | vectorstore=router_vectorstore, 21 | llm=llm, 22 | embeddings=router_embeddings, 23 | prompt_template=router_prompt_template, 24 | routes=routes, 25 | ) -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3.8' 2 | 3 | services: 4 | ollama: 5 | image: ollama/ollama:latest 6 | ports: 7 | - 7869:11434 8 | volumes: 9 | - .:/code 10 | - ~/.ollama/models:/root/.ollama/models 11 | # macOS: ~/.ollama/models. 12 | # Linux: /usr/share/ollama/.ollama/models. 13 | # Windows: C:\Users\%username%\.ollama\models. 14 | container_name: ollama 15 | pull_policy: always 16 | tty: true 17 | restart: always 18 | environment: 19 | - OLLAMA_KEEP_ALIVE=24h 20 | - OLLAMA_HOST=0.0.0.0 21 | networks: 22 | - ollama-docker 23 | 24 | telegram-gpt-app: 25 | build: . 26 | env_file: 27 | - .env 28 | environment: 29 | - OLLAMA_BASE_URLS=http://host.docker.internal:7869 30 | depends_on: 31 | - ollama 32 | extra_hosts: 33 | - host.docker.internal:host-gateway 34 | networks: 35 | - ollama-docker 36 | volumes: 37 | - .:/app 38 | 39 | networks: 40 | ollama-docker: 41 | external: false 42 | 43 | -------------------------------------------------------------------------------- /agents/memory_agent/configuration.py: -------------------------------------------------------------------------------- 1 | """Define the configurable parameters for the agent.""" 2 | 3 | import os 4 | from dataclasses import dataclass, field, fields 5 | from typing import Any, Optional 6 | 7 | from langchain_core.runnables import RunnableConfig 8 | from typing_extensions import Annotated 9 | 10 | from memory_agent import prompts 11 | 12 | 13 | @dataclass(kw_only=True) 14 | class Configuration: 15 | """Main configuration class for the memory graph system.""" 16 | 17 | user_id: str = "default" 18 | """The ID of the user to remember in the conversation.""" 19 | model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field( 20 | default="anthropic/claude-3-5-sonnet-20240620", 21 | metadata={ 22 | "description": "The name of the language model to use for the agent. " 23 | "Should be in the form: provider/model-name." 24 | }, 25 | ) 26 | system_prompt: str = prompts.SYSTEM_PROMPT 27 | 28 | @classmethod 29 | def from_runnable_config( 30 | cls, config: Optional[RunnableConfig] = None 31 | ) -> "Configuration": 32 | """Create a Configuration instance from a RunnableConfig.""" 33 | configurable = ( 34 | config["configurable"] if config and "configurable" in config else {} 35 | ) 36 | values: dict[str, Any] = { 37 | f.name: os.environ.get(f.name.upper(), configurable.get(f.name)) 38 | for f in fields(cls) 39 | if f.init 40 | } 41 | 42 | return cls(**{k: v for k, v in values.items() if v}) 43 | -------------------------------------------------------------------------------- /agents/memory_agent/tools.py: -------------------------------------------------------------------------------- 1 | """Define he agent's tools.""" 2 | 3 | import uuid 4 | from typing import Annotated, Optional 5 | 6 | from langchain_core.runnables import RunnableConfig 7 | from langchain_core.tools import InjectedToolArg 8 | from langgraph.store.base import BaseStore 9 | 10 | from memory_agent.configuration import Configuration 11 | 12 | 13 | async def upsert_memory( 14 | content: str, 15 | context: str, 16 | *, 17 | memory_id: Optional[uuid.UUID] = None, 18 | # Hide these arguments from the model. 19 | config: Annotated[RunnableConfig, InjectedToolArg], 20 | store: Annotated[BaseStore, InjectedToolArg], 21 | ): 22 | """Upsert a memory in the database. 23 | 24 | If a memory conflicts with an existing one, then just UPDATE the 25 | existing one by passing in memory_id - don't create two memories 26 | that are the same. If the user corrects a memory, UPDATE it. 27 | 28 | Args: 29 | content: The main content of the memory. For example: 30 | "User expressed interest in learning about French." 31 | context: Additional context for the memory. For example: 32 | "This was mentioned while discussing career options in Europe." 33 | memory_id: ONLY PROVIDE IF UPDATING AN EXISTING MEMORY. 34 | The memory to overwrite. 35 | """ 36 | mem_id = memory_id or uuid.uuid4() 37 | user_id = Configuration.from_runnable_config(config).user_id 38 | await store.aput( 39 | ("memories", user_id), 40 | key=str(mem_id), 41 | value={"content": content, "context": context}, 42 | ) 43 | return f"Stored memory {memory_id}" 44 | -------------------------------------------------------------------------------- /routes.py: -------------------------------------------------------------------------------- 1 | from llm_adaptive_router import RouteMetadata 2 | 3 | from services import conversational, retrieve, run_crag_pipeline, run_research 4 | 5 | 6 | routes = { 7 | "conv": RouteMetadata( 8 | invoker=conversational, 9 | capabilities=["general knowledge","memory storage"], 10 | cost=0.002, 11 | performance_score=0.9, 12 | example_sentences=["Hi", "How is it going?", "How are you?"], 13 | # additional_info={"key": "value"}, 14 | ), 15 | "retrv": RouteMetadata( 16 | invoker=retrieve, 17 | capabilities=["retrieval",], 18 | cost=0.002, 19 | performance_score=0.9, 20 | example_sentences=["Look at the db", "Retrieve the document titled 'Project Plan Q4'"], 21 | # additional_info={"key": "value"}, 22 | ), 23 | "crag": RouteMetadata( 24 | invoker=run_crag_pipeline, 25 | capabilities=["web search",], 26 | cost=0.002, 27 | performance_score=0.9, 28 | example_sentences=["What's the weather forecast for tomorrow in New York City?", 29 | "Who won the Nobel Prize in Literature this year?"], 30 | ), 31 | "research": RouteMetadata( 32 | invoker=run_research, 33 | capabilities=["research","web search and report"], 34 | cost=0.002, 35 | performance_score=0.9, 36 | example_sentences=["What is the best way to cook a steak?", 37 | "What is the best way do algotrade?", 38 | "Research the history of the internet.", 39 | "Research", 40 | "Give me a report" 41 | ], 42 | ), 43 | } 44 | -------------------------------------------------------------------------------- /conf.py: -------------------------------------------------------------------------------- 1 | from langchain.embeddings.base import Embeddings 2 | from langchain.llms.base import BaseLLM 3 | from langchain.tools.base import BaseTool 4 | from langchain_openai import ChatOpenAI 5 | from langchain_openai import OpenAIEmbeddings 6 | from langchain_ollama import ChatOllama 7 | from langchain_ollama import OllamaEmbeddings 8 | 9 | import os 10 | 11 | USERS = os.getenv("USERS", "") 12 | OLLAMA_BASE_URLS = os.getenv("OLLAMA_BASE_URLS", "localhost:11434") 13 | 14 | users = [user.strip() for user in USERS.split(",")] 15 | 16 | 17 | def get_llm(llmname) -> BaseLLM: 18 | """Function to get the LLM. Replace with your desired LLM.""" 19 | if llmname == "gpt-4o-mini": 20 | return ChatOpenAI(model_name="gpt-4o-mini", temperature=0) 21 | if llmname == "llama3.1": 22 | return ChatOllama( 23 | model="llama3.1", 24 | temperature=0, 25 | base_url=OLLAMA_BASE_URLS, 26 | # other params... 27 | ) 28 | 29 | def get_embeddings(embeddingmodel) -> Embeddings: 30 | """Function to get the embeddings model. Replace with your desired embeddings.""" 31 | if embeddingmodel == "openai": 32 | return OpenAIEmbeddings() 33 | if embeddingmodel == "ollama": 34 | embed = OllamaEmbeddings( 35 | model="llama3", 36 | base_url=OLLAMA_BASE_URLS, 37 | 38 | ) 39 | return embed 40 | 41 | def get_web_search_tool() -> BaseTool: 42 | """Function to get the web search tool. Replace with your implementation.""" 43 | from langchain_community.tools.tavily_search.tool import TavilySearchResults 44 | return TavilySearchResults(k=3) 45 | 46 | # Initialize components 47 | # llm = get_llm('llama3.1') 48 | llm = get_llm('gpt-4o-mini') 49 | # embeddings = get_embeddings('ollama') 50 | embeddings = get_embeddings('openai') 51 | web_search_tool = get_web_search_tool() -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | vectorstore_db/* 10 | router_vectorstore_db/* 11 | outputs/* 12 | 13 | # Distribution / packaging 14 | .Python 15 | build/ 16 | develop-eggs/ 17 | dist/ 18 | downloads/ 19 | eggs/ 20 | .eggs/ 21 | lib/ 22 | lib64/ 23 | parts/ 24 | sdist/ 25 | var/ 26 | wheels/ 27 | pip-wheel-metadata/ 28 | share/python-wheels/ 29 | *.egg-info/ 30 | .installed.cfg 31 | *.egg 32 | MANIFEST 33 | 34 | # PyInstaller 35 | # Usually these files are written by a python script from a template 36 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 37 | *.manifest 38 | *.spec 39 | 40 | # Installer logs 41 | pip-log.txt 42 | pip-delete-this-directory.txt 43 | 44 | # Unit test / coverage reports 45 | htmlcov/ 46 | .tox/ 47 | .nox/ 48 | .coverage 49 | .coverage.* 50 | .cache 51 | nosetests.xml 52 | coverage.xml 53 | *.cover 54 | *.py,cover 55 | .hypothesis/ 56 | .pytest_cache/ 57 | 58 | # Translations 59 | *.mo 60 | *.pot 61 | 62 | # Django stuff: 63 | *.log 64 | local_settings.py 65 | db.sqlite3 66 | db.sqlite3-journal 67 | 68 | # Flask stuff: 69 | instance/ 70 | .webassets-cache 71 | 72 | # Scrapy stuff: 73 | .scrapy 74 | 75 | # Sphinx documentation 76 | docs/_build/ 77 | 78 | # PyBuilder 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | .python-version 90 | 91 | # pipenv 92 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 93 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 94 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 95 | # install all needed dependencies. 96 | #Pipfile.lock 97 | 98 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 99 | __pypackages__/ 100 | 101 | # Celery stuff 102 | celerybeat-schedule 103 | celerybeat.pid 104 | 105 | # SageMath parsed files 106 | *.sage.py 107 | 108 | # Environments 109 | .env 110 | .venv 111 | env/ 112 | venv/ 113 | ENV/ 114 | env.bak/ 115 | venv.bak/ 116 | 117 | # Spyder project settings 118 | .spyderproject 119 | .spyproject 120 | 121 | # Rope project settings 122 | .ropeproject 123 | 124 | # mkdocs documentation 125 | /site 126 | 127 | # mypy 128 | .mypy_cache/ 129 | .dmypy.json 130 | dmypy.json 131 | 132 | # Pyre type checker 133 | .pyre/ 134 | 135 | 136 | bot.log 137 | error.log 138 | db.sqlite3 139 | -------------------------------------------------------------------------------- /services.py: -------------------------------------------------------------------------------- 1 | import uuid 2 | from conf import llm 3 | from langchain_core.prompts import PromptTemplate 4 | from langchain_core.output_parsers import StrOutputParser 5 | from vectordb import retriever 6 | from agents.memory_agent.graph import builder 7 | from agents.crag import crag 8 | from agents.gpt_researcher.multi_agents.agents import ChiefEditorAgent 9 | from agents.gpt_researcher.gpt_researcher.utils.enum import Tone 10 | 11 | from langgraph.store.memory import InMemoryStore 12 | 13 | 14 | mem_store = InMemoryStore() 15 | 16 | 17 | async def conversational(content, config={}) -> str: 18 | graph = builder.compile(store=mem_store) 19 | config = { 20 | "configurable": {}, 21 | "user_id": str(config.get("user_id")), 22 | } 23 | 24 | response = await graph.ainvoke( 25 | {"messages": [("user", content)]}, 26 | {**config, "thread_id": "thread"}, 27 | ) 28 | messages = response.get("messages", []) 29 | last_message = messages[-1] if messages else None 30 | content = last_message.content if last_message else None 31 | return content 32 | 33 | 34 | async def retrieve(content, config={}) -> str: 35 | docs = retriever.invoke(content) 36 | if not docs: 37 | return "Sorry, I couldn't find any relevant documents." 38 | docsstr = "\n\n".join(doc.page_content for doc in docs) 39 | rag_prompt_template = """ 40 | Search results for: {question} 41 | {docs} 42 | answer: 43 | """ 44 | 45 | rag_prompt = PromptTemplate( 46 | template=rag_prompt_template, input_variables=["question", "docs"] 47 | ) 48 | chain = rag_prompt | llm | StrOutputParser() 49 | response = await chain.invoke({"question": content, "docs": docsstr}) 50 | return response 51 | 52 | 53 | async def run_crag_pipeline(query: str, config={}) -> str: 54 | """Run the RAG pipeline with the given question and chat history.""" 55 | query = {"question": query} 56 | result = await crag.invoke(query) 57 | answer = result.get("answer", "Sorry, I couldn't find an answer to your question.") 58 | return answer 59 | 60 | 61 | async def run_research(query: str, config={}) -> str: 62 | """Run the research pipeline with the given question.""" 63 | task = { 64 | "query": query, 65 | "max_sections": 3, 66 | "publish_formats": { 67 | "markdown": True, 68 | }, 69 | "follow_guidelines": False, 70 | "model": "gpt-4o", 71 | "guidelines": [ 72 | "The report MUST be written in APA format", 73 | ], 74 | "verbose": False, 75 | } 76 | 77 | tone = Tone.Objective 78 | 79 | chief_editor = ChiefEditorAgent(task, tone=tone) 80 | task_id = uuid.uuid4() 81 | research =await chief_editor.run_research_task(task_id=task_id) 82 | research_report = research.get("report",f"P{task_id}:{query} research report is ready.") 83 | 84 | return research_report 85 | -------------------------------------------------------------------------------- /agents/memory_agent/graph.py: -------------------------------------------------------------------------------- 1 | """Graphs that extract memories on a schedule.""" 2 | 3 | import asyncio 4 | import logging 5 | from datetime import datetime 6 | 7 | from langchain.chat_models import init_chat_model 8 | from langchain_core.runnables import RunnableConfig 9 | from langgraph.graph import END, StateGraph 10 | from langgraph.store.base import BaseStore 11 | 12 | from memory_agent import configuration, tools, utils 13 | from memory_agent.state import State 14 | 15 | logger = logging.getLogger(__name__) 16 | 17 | # Initialize the language model to be used for memory extraction 18 | llm = init_chat_model() 19 | 20 | 21 | async def call_model(state: State, config: RunnableConfig, *, store: BaseStore) -> dict: 22 | """Extract the user's state from the conversation and update the memory.""" 23 | configurable = configuration.Configuration.from_runnable_config(config) 24 | 25 | # Retrieve the most recent memories for context 26 | memories = await store.asearch( 27 | ("memories", config["configurable"]["user_id"]), limit=10 28 | ) 29 | 30 | # Format memories for inclusion in the prompt 31 | formatted = "\n".join(f"[{mem.key}]: {mem.value}" for mem in memories) 32 | if formatted: 33 | formatted = f""" 34 | 35 | {formatted} 36 | """ 37 | 38 | # Prepare the system prompt with user memories and current time 39 | # This helps the model understand the context and temporal relevance 40 | sys = configurable.system_prompt.format( 41 | user_info=formatted, time=datetime.now().isoformat() 42 | ) 43 | 44 | # Invoke the language model with the prepared prompt and tools 45 | # "bind_tools" gives the LLM the JSON schema for all tools in the list so it knows how 46 | # to use them. 47 | msg = await llm.bind_tools([tools.upsert_memory]).ainvoke( 48 | [{"role": "system", "content": sys}, *state.messages], 49 | {"configurable": utils.split_model_and_provider(configurable.model)}, 50 | ) 51 | return {"messages": [msg]} 52 | 53 | 54 | async def store_memory(state: State, config: RunnableConfig, *, store: BaseStore): 55 | # Extract tool calls from the last message 56 | tool_calls = state.messages[-1].tool_calls 57 | 58 | # Concurrently execute all upsert_memory calls 59 | saved_memories = await asyncio.gather( 60 | *( 61 | tools.upsert_memory(**tc["args"], config=config, store=store) 62 | for tc in tool_calls 63 | ) 64 | ) 65 | 66 | # Format the results of memory storage operations 67 | # This provides confirmation to the model that the actions it took were completed 68 | results = [ 69 | { 70 | "role": "tool", 71 | "content": mem, 72 | "tool_call_id": tc["id"], 73 | } 74 | for tc, mem in zip(tool_calls, saved_memories) 75 | ] 76 | return {"messages": results} 77 | 78 | 79 | def route_message(state: State): 80 | """Determine the next step based on the presence of tool calls.""" 81 | msg = state.messages[-1] 82 | if msg.tool_calls: 83 | # If there are tool calls, we need to store memories 84 | return "store_memory" 85 | # Otherwise, finish; user can send the next message 86 | return END 87 | 88 | 89 | # Create the graph + all nodes 90 | builder = StateGraph(State, config_schema=configuration.Configuration) 91 | 92 | # Define the flow of the memory extraction process 93 | builder.add_node(call_model) 94 | builder.add_edge("__start__", "call_model") 95 | builder.add_node(store_memory) 96 | builder.add_conditional_edges("call_model", route_message, ["store_memory", END]) 97 | # Right now, we're returning control to the user after storing a memory 98 | # Depending on the model, you may want to route back to the model 99 | # to let it first store memories, then generate a response 100 | builder.add_edge("store_memory", "call_model") 101 | graph = builder.compile() 102 | graph.name = "MemoryAgent" 103 | 104 | 105 | __all__ = ["graph"] 106 | -------------------------------------------------------------------------------- /vectordb.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | from typing import List, Dict 4 | from datetime import datetime 5 | 6 | # Import LangChain components 7 | from langchain.document_loaders import WebBaseLoader 8 | from langchain.text_splitter import RecursiveCharacterTextSplitter 9 | from langchain.schema import Document 10 | from langchain_chroma import Chroma 11 | from langchain_community.vectorstores.utils import filter_complex_metadata 12 | 13 | from dotenv import load_dotenv 14 | from conf import embeddings 15 | 16 | load_dotenv() 17 | 18 | # Set up logging 19 | logging.basicConfig(level=logging.INFO) 20 | logger = logging.getLogger(__name__) 21 | 22 | # Configuration 23 | VECTOR_DB_PATH = "./vectorstore_db" 24 | COLLECTION_NAME = "rag-chroma" 25 | BOTNAME = os.getenv("BOTNAME", "RAG Bot") 26 | 27 | 28 | def initialize_vectorstore(docs: List[Document]) -> Chroma: 29 | """Initialize or load the vectorstore and add new documents if necessary.""" 30 | vector_store = Chroma( 31 | collection_name=COLLECTION_NAME, 32 | embedding_function=embeddings, 33 | persist_directory=VECTOR_DB_PATH, 34 | ) 35 | if docs: 36 | vector_store.add_documents(docs) 37 | 38 | logger.info("Vector store initialized successfully.") 39 | 40 | retriever = vector_store.as_retriever( 41 | search_type="mmr", search_kwargs={"k": 3, "fetch_k": 5} 42 | ) 43 | 44 | return retriever 45 | 46 | 47 | # Prepare documents and vector store 48 | def load_documents(urls: List[str]) -> List[Document]: 49 | """Load documents from given URLs.""" 50 | docs = [] 51 | for url in urls: 52 | loader = WebBaseLoader(url) 53 | docs.extend(loader.load()) 54 | return docs 55 | 56 | 57 | def split_documents(docs: List[Document]) -> List[Document]: 58 | """Split documents into chunks.""" 59 | text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( 60 | chunk_size=250, chunk_overlap=0 61 | ) 62 | return text_splitter.split_documents(docs) 63 | 64 | 65 | # Function to add results to vectorstore 66 | def add_to_vectorstore(question: str, answer: str, user: Dict, retriever: Chroma): 67 | """Add question and answer to vectorstore with incremental learning.""" 68 | try: 69 | logger.info("Adding question and answer to vectorstore...") 70 | 71 | logger.info(f"Question: {question}") 72 | logger.info(f"Answer: {answer}") 73 | 74 | qa_doc = Document( 75 | page_content=f"Q: {question}\nA: {answer}", 76 | metadata={ 77 | "source": "chatbot", 78 | "created_at": datetime.now().isoformat(), 79 | "username": user.get("username"), 80 | "user_id": user.get("id"), 81 | }, 82 | ) 83 | filtered_docs = filter_complex_metadata([qa_doc]) 84 | 85 | metadata = filtered_docs[0].metadata 86 | logger.info(f"Metadata: {metadata}") 87 | 88 | retriever.add_documents(filtered_docs) 89 | logger.info("Successfully added to vectorstore") 90 | except Exception as e: 91 | logger.error(f"Error adding to vectorstore: {str(e)}") 92 | 93 | 94 | def add_docs_to_vectorstore(docs: List[Document], retriever: Chroma): 95 | """Add documents to vectorstore.""" 96 | try: 97 | logger.info("Adding documents to vectorstore...") 98 | retriever.add_documents(docs) 99 | logger.info("Successfully added to vectorstore") 100 | except Exception as e: 101 | logger.error(f"Error adding to vectorstore: {str(e)}") 102 | 103 | 104 | # Load and process documents 105 | urls = [ 106 | # "https://lilianweng.github.io/posts/2023-06-23-agent/", 107 | # "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/", 108 | # "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/", 109 | ] 110 | if urls: 111 | docs = load_documents(urls) 112 | doc_splits = split_documents(docs) 113 | retriever = initialize_vectorstore(doc_splits) 114 | 115 | retriever = initialize_vectorstore([]) 116 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Create Your Own ChatGPT on Telegram: A Step-by-Step Guide to Building a Python-powered AI Bot 2 | 3 | ## Introduction 4 | 5 | Are you tired of using the same AI-powered chatbots available on the market? Do you want to build your own chatbot and customize it to your liking? Then, look no further! In this blog post, we'll walk you through a step-by-step guide on creating your very own AI bot using Python, Telegram, and OpenAI GPT models. Get ready to unleash the power of artificial intelligence right at your fingertips! 6 | 7 | when you change models at conf.py be sure start new evctordb since vector dimension is different for each model. 8 | 9 | ## Router usage 10 | 11 | you can give any llm in routes.py file. 12 | 13 | You can add any additional llm to the routes.py file. 14 | 15 | ![telegramgpt_llm_router](https://github.com/user-attachments/assets/ff5af371-d23b-408f-981f-678f5ad5ac50) 16 | 17 | 18 | 19 | 20 | ## Quick start 21 | 22 | ### Prerequisites 23 | 24 | - A Telegram account 25 | - A Telegram bot API token (you can get one from the BotFather bot on Telegram) 26 | - An OpenAI API key (you can get one by signing up on the OpenAI website) 27 | - make sure you added your username env file 28 | - Docker and docker-compose 29 | 30 | ### Steps 31 | 32 | 1. Clone the repository: 33 | 34 | ```bash 35 | git clone --recursive https://github.com/emingenc/telegramGPT.git 36 | cd telegramGPT 37 | ``` 38 | 39 | 2. Set up environment variables create .env file 40 | 41 | ```bash 42 | TELEGRAM_TOKEN="your telegram token" 43 | OPENAI_API_KEY="your openai api key" 44 | BOTNAME="your bots name" 45 | TAVILY_API_KEY="tav api key" 46 | USERS=allowed users 47 | ANTHROPIC_API_KEY="anthropic api key" 48 | 49 | ``` 50 | 51 | 3. Run the bot using docker-compose: 52 | 53 | ```bash 54 | docker-compose up 55 | ``` 56 | 57 | 58 | 59 | ## Step 1: Setting up your Telegram bot 60 | 61 | Before diving into the code, you'll need to create a new bot on Telegram. Follow these simple steps: 62 | 63 | 1. Open the Telegram app and search for the "BotFather" bot. 64 | 2. Start a chat with BotFather and send the command "/newbot" to create a new bot. 65 | 3. Choose a name and username for your bot. BotFather will provide you with a unique API token, which you'll need later. Save it somewhere safe. 66 | 67 | ## Step 2: Cloning the code repository 68 | 69 | Clone the code repository from GitHub using the following command: 70 | 71 | ```bash 72 | git clone --recursive https://github.com/emingenc/telegramGPT.git 73 | cd telegramGPT 74 | ``` 75 | 76 | ## Step 3: Installing the dependencies 77 | 78 | Install the required dependencies using the following command: 79 | 80 | ```bash 81 | pip3 install -r requirements.txt 82 | ``` 83 | 84 | ## Step 4: Exploring the code 85 | 86 | In our project, we have four main files: 87 | 88 | 1. telegram_bot.py: The main script that handles the Telegram bot's functionality. 89 | 2. gpt_message_handler.py: Contains functions for interacting with OpenAI's GPT models and managing chat history. 90 | 3. conf.py: A configuration file to store the list of allowed users. 91 | 4. requirements.txt: Lists the required Python libraries for the project. 92 | 93 | ## Step 5: Configuring the bot 94 | 95 | Edit conf.py to add the usernames of users allowed to interact with your bot. For example: 96 | 97 | 98 | ``` python 99 | users = ["your_telegram_username"] 100 | ``` 101 | 102 | ## Step 6: Setting up the environment variables 103 | 104 | 105 | You need to set up environment variables: TELEGRAM_TOKEN (the API token you received from BotFather) and OPENAI_API_KEY (your OpenAI API key). BOTNAME (name of bot) 106 | You can either add them to your system's environment variables or use a .env file. 107 | 108 | ## Step 7: Running the bot 109 | 110 | Start your bot by running the telegram_bot.py script: 111 | 112 | ```bash 113 | python3 telegram_bot.py 114 | ``` 115 | 116 | Your bot is now up and running! Start a chat with your Telegram bot and see it in action. 117 | 118 | ## Conclusion 119 | 120 | Congratulations! You've just built your own chatbot using Python, Telegram, and OpenAI GPT models. Now you can enjoy the power of AI through a personalized, engaging chat experience on Telegram. Don't forget to share your thoughts and experiences in the comments below. Happy coding! 121 | -------------------------------------------------------------------------------- /telegram_bot.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | from dotenv import load_dotenv 4 | 5 | 6 | from telegram import Update 7 | from telegram.ext import ( 8 | ContextTypes, 9 | CommandHandler, 10 | MessageHandler, 11 | ApplicationBuilder, 12 | filters, 13 | ) 14 | from conf import users 15 | 16 | from gpt_message_handler import handle_response 17 | 18 | print("Starting up bot...") 19 | load_dotenv() 20 | 21 | 22 | TOKEN = os.getenv("TELEGRAM_TOKEN") 23 | BOTNAME = os.getenv("BOTNAME") 24 | 25 | logging.basicConfig( 26 | format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO 27 | ) 28 | 29 | 30 | def get_message_content(message): 31 | if message.text: 32 | return message.text, "text" 33 | elif message.photo: 34 | return message.photo[-1].file_id, "photo" 35 | elif message.document: 36 | return message.document.file_id, "document" 37 | elif message.voice: 38 | return message.voice.file_id, "voice" 39 | elif message.audio: 40 | return message.audio.file_id, "audio" 41 | elif message.video: 42 | return message.video.file_id, "video" 43 | else: 44 | return None, "unknown" 45 | 46 | 47 | async def start_command(update: Update, context: ContextTypes.DEFAULT_TYPE): 48 | await update.message.reply_text("Hello there! I'm a bot. What's up?") 49 | 50 | 51 | async def help_command(update: Update, context: ContextTypes.DEFAULT_TYPE): 52 | await update.message.reply_text( 53 | "Try typing anything and I will do my best to respond!" 54 | ) 55 | 56 | 57 | async def custom_command(update: Update, context: ContextTypes.DEFAULT_TYPE): 58 | await update.message.reply_text( 59 | "This is a custom command, you can add whatever text you want here." 60 | ) 61 | 62 | 63 | async def restart_command(update: Update, context: ContextTypes.DEFAULT_TYPE): 64 | await update.message.reply_text("Restarted the bot.") 65 | 66 | 67 | async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE): 68 | message = update.effective_message 69 | user = update.effective_user 70 | message_id = message.message_id 71 | 72 | logging.info(f"User {user.username} ({user.id}) sent a message.") 73 | 74 | content, content_type = get_message_content(message) 75 | 76 | if content_type == "unknown": 77 | await message.reply_text("Sorry, I can't process this type of message yet.") 78 | return 79 | 80 | if content_type == "text": 81 | response = await handle_response(content, user, message_id, content_type) 82 | else: 83 | # For media files, download the file 84 | file = await context.bot.get_file(content) 85 | file_path = await file.download_to_drive() 86 | # Pass the file path to handle_response 87 | response = await handle_response(file_path, user, message_id, content_type) 88 | 89 | if type(response) == str and len(response) > 1000: 90 | file_name = f"result.md" 91 | with open(file_name, "w") as file: 92 | file.write(response) 93 | await message.reply_document(file_name) 94 | 95 | if type(response) == str: 96 | await message.reply_markdown(response) 97 | else: 98 | await message.reply_markdown(response) 99 | 100 | 101 | def error_handler(update: object, context: ContextTypes.DEFAULT_TYPE): 102 | logging.error(msg="Exception while handling an update:", exc_info=context.error) 103 | 104 | 105 | if __name__ == "__main__": 106 | application = ApplicationBuilder().token(TOKEN).build() 107 | 108 | # Commands with user restriction 109 | allowed_users_filter = filters.User(username=users) 110 | application.add_handler( 111 | CommandHandler("start", start_command, filters=allowed_users_filter) 112 | ) 113 | application.add_handler( 114 | CommandHandler("help", help_command, filters=allowed_users_filter) 115 | ) 116 | application.add_handler( 117 | CommandHandler("custom", custom_command, filters=allowed_users_filter) 118 | ) 119 | application.add_handler( 120 | CommandHandler("restart", restart_command, filters=allowed_users_filter) 121 | ) 122 | 123 | # Message handler with user restriction 124 | application.add_handler( 125 | MessageHandler(filters.ALL & allowed_users_filter, handle_message) 126 | ) 127 | 128 | # Error handler 129 | application.add_error_handler(error_handler) 130 | 131 | application.run_polling() 132 | -------------------------------------------------------------------------------- /agents/crag.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import List, Dict, Any 3 | from typing_extensions import TypedDict 4 | 5 | # Import LangChain components 6 | from langchain_core.output_parsers import StrOutputParser 7 | from langchain.prompts.chat import ChatPromptTemplate 8 | from langchain.schema import Document 9 | from langgraph.graph import StateGraph, START, END 10 | 11 | 12 | import sys 13 | import os 14 | 15 | sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 16 | 17 | 18 | from vectordb import retriever, add_docs_to_vectorstore 19 | from conf import llm, web_search_tool 20 | 21 | 22 | # Set up logging 23 | logging.basicConfig(level=logging.INFO) 24 | logger = logging.getLogger(__name__) 25 | 26 | 27 | 28 | # Grading prompt 29 | grading_system_prompt = """.You are a grader assessing relevance of a retrieved document to a user question. 30 | If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. 31 | Give a binary score 'yes' or 'no' to indicate whether the document is relevant to the question.""" 32 | 33 | grade_prompt_template = ChatPromptTemplate.from_messages([ 34 | ("system", grading_system_prompt), 35 | ("user", "Retrieved document:\n\n{document}\n\nUser question: {question}") 36 | ]) 37 | 38 | retrieval_grader_chain = grade_prompt_template | llm | StrOutputParser() 39 | 40 | 41 | 42 | # Post-processing 43 | def format_docs(docs: List[Document]) -> str: 44 | return "\n\n".join(doc.page_content for doc in docs) 45 | 46 | # State Graph 47 | class GraphState(TypedDict): 48 | """Represents the state of our graph.""" 49 | question: str 50 | answer: str 51 | web_search: str 52 | documents: List[Document] 53 | search_attempts: int # Add this to track search attempts 54 | 55 | 56 | 57 | def retrieve(state: GraphState) -> Dict[str, Any]: 58 | """Retrieve documents.""" 59 | logger.info("Retrieving documents...") 60 | question = state["question"] 61 | documents = retriever.invoke(question) 62 | state["documents"] = documents 63 | 64 | return state 65 | 66 | def grade_documents(state: GraphState) -> Dict[str, Any]: 67 | """Grade retrieved documents for relevance.""" 68 | logger.info("Grading documents for relevance...") 69 | question = state["question"] 70 | documents = state.get("documents", []) 71 | filtered_docs = [] 72 | relevant_count = 0 73 | for doc in documents: 74 | result = retrieval_grader_chain.invoke({"document": doc.page_content, "question": question}) 75 | grade = result.strip().lower() 76 | if "yes" in grade: 77 | filtered_docs.append(doc) 78 | relevant_count += 1 79 | 80 | web_search_needed = relevant_count < 2 # Require at least 2 relevant documents 81 | state["documents"] = filtered_docs 82 | state["web_search"] = "Yes" if web_search_needed else "No" 83 | state["search_attempts"] = state.get("search_attempts", 0) 84 | return state 85 | 86 | def transform_query(state: GraphState) -> Dict[str, Any]: 87 | """Transform the query to produce a better question.""" 88 | logger.info("Transforming query...") 89 | question = state["question"] 90 | prompt_template = "Transform the user's question to improve search results." 91 | parser = StrOutputParser() 92 | transform_prompt = ChatPromptTemplate.from_messages([ 93 | ("system", prompt_template), 94 | ("user", question) 95 | ]) 96 | chain = transform_prompt | llm | parser 97 | result = chain.invoke({"question": question}) 98 | state["question"] = result 99 | 100 | return state 101 | 102 | def web_search(state: GraphState) -> Dict[str, Any]: 103 | """Perform web search based on the question.""" 104 | logger.info("Performing web search...") 105 | question = state["question"] 106 | documents = state["documents"] 107 | # Perform web search 108 | docs = web_search_tool.invoke({"query": question}) 109 | web_results = "\n".join([d["content"] for d in docs]) 110 | web_results = Document(page_content=web_results) 111 | documents.append(web_results) 112 | add_docs_to_vectorstore([web_results], retriever) 113 | state["documents"] = documents 114 | state["search_attempts"] = state.get("search_attempts", 0) + 1 115 | return state 116 | 117 | def generate(state: GraphState) -> Dict[str, Any]: 118 | logger.info("Generating final answer with RAG...") 119 | documents = state["documents"] 120 | question = state["question"] 121 | context = format_docs(documents) 122 | prompt_template = "Generate an answer to the question with this Context: {context}" 123 | prompt = ChatPromptTemplate.from_messages([ 124 | ("system", prompt_template), 125 | ("user", question) 126 | ]) 127 | chain = prompt | llm | StrOutputParser() 128 | result = chain.invoke({"question": question, "context": context}) 129 | state["answer"] = result 130 | return state 131 | 132 | 133 | def decide_next_step(state: GraphState) -> str: 134 | """Decide whether to generate an answer, transform the query, or end the process.""" 135 | logger.info("Deciding next step...") 136 | web_search = state.get("web_search", "No") 137 | search_attempts = state.get("search_attempts", 0) 138 | 139 | if web_search == "Yes" and search_attempts < 2: # Limit to 2 search attempts 140 | return "transform_query" 141 | else : 142 | return "generate" 143 | 144 | # Workflow 145 | workflow = StateGraph(GraphState) 146 | 147 | # Add nodes 148 | workflow.add_node("retrieve", retrieve) 149 | workflow.add_node("grade_documents", grade_documents) 150 | workflow.add_node("transform_query", transform_query) 151 | workflow.add_node("web_search_node", web_search) 152 | workflow.add_node("generate", generate) 153 | 154 | # Build graph 155 | workflow.add_edge(START, "retrieve") 156 | workflow.add_edge("retrieve", "grade_documents") 157 | workflow.add_conditional_edges( 158 | "grade_documents", 159 | decide_next_step, 160 | { 161 | "transform_query": "transform_query", 162 | "generate": "generate", 163 | "end": END 164 | }, 165 | ) 166 | workflow.add_edge("transform_query", "web_search_node") 167 | workflow.add_edge("web_search_node", "generate") 168 | workflow.add_edge("generate", END) 169 | 170 | # Compile 171 | crag = workflow.compile() 172 | -------------------------------------------------------------------------------- /test.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "USER_AGENT environment variable not set, consider setting it to identify your requests.\n", 13 | "INFO:chromadb.telemetry.product.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n", 14 | "INFO:vectordb:Vector store initialized successfully.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "from vectordb import retriever" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 5, 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "name": "stderr", 29 | "output_type": "stream", 30 | "text": [ 31 | "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" 32 | ] 33 | }, 34 | { 35 | "data": { 36 | "text/plain": [ 37 | "[Document(metadata={'created_at': '2024-10-03T22:14:38.818889', 'source': 'chatbot', 'user_id': 442279625, 'username': 'emingench'}, page_content='Q: Satranç\\nA: Satranç harika bir oyun! Strateji ve düşünme becerilerini geliştirmek için mükemmel bir yol. Satranç oynamayı mı seviyorsun? Yoksa izlemeyi mi tercih ediyorsun? Hangi açılışları veya taktikleri kullanmayı seviyorsun?')]" 38 | ] 39 | }, 40 | "execution_count": 5, 41 | "metadata": {}, 42 | "output_type": "execute_result" 43 | } 44 | ], 45 | "source": [ 46 | "retriever.invoke('satranc?')" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 1, 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "name": "stderr", 56 | "output_type": "stream", 57 | "text": [ 58 | "USER_AGENT environment variable not set, consider setting it to identify your requests.\n", 59 | "INFO:chromadb.telemetry.product.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n", 60 | "INFO:vectordb:Vector store initialized successfully.\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "from router_llm import app, assess_query, GraphState\n", 66 | "from gpt_message_handler import filter_chat\n", 67 | "from vectordb import retriever\n" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 2, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "username = 'username'\n", 77 | "user_id = 212121" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 3, 83 | "metadata": {}, 84 | "outputs": [ 85 | { 86 | "name": "stderr", 87 | "output_type": "stream", 88 | "text": [ 89 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/embed \"HTTP/1.1 200 OK\"\n", 90 | "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 5 is greater than number of elements in index 1, updating n_results = 1\n" 91 | ] 92 | }, 93 | { 94 | "name": "stdout", 95 | "output_type": "stream", 96 | "text": [ 97 | "do crag. How is the weather in istanbul tomorrow?\n" 98 | ] 99 | } 100 | ], 101 | "source": [ 102 | "question = 'do crag. How is the weather in istanbul tomorrow?'\n", 103 | "docs = retriever.invoke(question)\n", 104 | "chat_history = filter_chat(username=username, user_id=user_id, docs=docs)\n", 105 | "ragquestion = f\"chat_history: {chat_history} Question: {question} Ai:\" if chat_history else question\n", 106 | "print(ragquestion)" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 4, 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "name": "stderr", 116 | "output_type": "stream", 117 | "text": [ 118 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/chat \"HTTP/1.1 200 OK\"\n", 119 | "INFO:router_llm:Determined next step: crag with confidence: 1.0\n", 120 | "INFO:agents.crag:Retrieving documents...\n" 121 | ] 122 | }, 123 | { 124 | "name": "stdout", 125 | "output_type": "stream", 126 | "text": [ 127 | "Next step: crag\n" 128 | ] 129 | }, 130 | { 131 | "name": "stderr", 132 | "output_type": "stream", 133 | "text": [ 134 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/embed \"HTTP/1.1 200 OK\"\n", 135 | "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 5 is greater than number of elements in index 1, updating n_results = 1\n", 136 | "INFO:agents.crag:Grading documents for relevance...\n", 137 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/chat \"HTTP/1.1 200 OK\"\n", 138 | "INFO:agents.crag:Deciding next step...\n", 139 | "INFO:agents.crag:Transforming query...\n", 140 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/chat \"HTTP/1.1 200 OK\"\n", 141 | "INFO:agents.crag:Performing web search...\n", 142 | "INFO:vectordb:Adding documents to vectorstore...\n" 143 | ] 144 | }, 145 | { 146 | "name": "stdout", 147 | "output_type": "stream", 148 | "text": [ 149 | "[{'url': 'https://www.weathertab.com/en/long-range-weather/e/11/04/republic-of-turkey/istanbul/istanbul/', 'content': 'Explore our detailed long-range weather forecast for İstanbul on November 04, 2024, including precipitation risk, high/low temperatures, and sun & moon data. Plan ahead with accurate weather insights.'}, {'url': 'https://www.ventusky.com/istanbul', 'content': \"İstanbul ☀ Weather forecast for 10 days, information from meteorological stations, webcams, sunrise and sunset, wind and precipitation maps for this place ... Turkey / Lat.: 41°1'N / Lon.: 28°56'E / Altitude: 43 m Timezone: Europe/Istanbul (UTC+3) / Current time: 02:38 2024/10/03 . Current Weather ; Forecast ; Sun and Moon ; 16 °C : Wind ...\"}, {'url': 'https://www.timeanddate.com/weather/turkey/istanbul/ext', 'content': 'Istanbul 14 Day Extended Forecast. Weather Today Weather Hourly 14 Day Forecast Yesterday/Past Weather Climate (Averages) Currently: 73 °F. Passing clouds. (Weather station: Istanbul Ataturk Airport, Turkey). See more current weather.'}, {'url': 'https://world-weather.info/forecast/turkey/istanbul/april-2024/', 'content': 'Hourly Week 10 days 14 days 30 days Year. Detailed ⚡ Istanbul Weather Forecast for April 2024 - day/night 🌡️ temperatures, precipitations - World-Weather.info.'}, {'url': 'https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251', 'content': 'Istanbul, Istanbul, Türkiye Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days.'}]\n" 150 | ] 151 | }, 152 | { 153 | "name": "stderr", 154 | "output_type": "stream", 155 | "text": [ 156 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/embed \"HTTP/1.1 200 OK\"\n", 157 | "INFO:vectordb:Successfully added to vectorstore\n", 158 | "INFO:agents.crag:Generating final answer with RAG...\n", 159 | "INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/chat \"HTTP/1.1 200 OK\"\n" 160 | ] 161 | }, 162 | { 163 | "data": { 164 | "text/plain": [ 165 | "{'question': 'You meant to ask \"Do you know...\". Here\\'s a revised version of your question that might improve search results:\\n\\n\"What is the forecast for Istanbul, Turkey tomorrow?\"',\n", 166 | " 'answer': \"Here's an answer based on the provided context:\\n\\nAccording to the current weather data, as of October 3rd, 2024, the forecast for Istanbul, Turkey for November 04, 2024 is not explicitly stated. However, I can provide some general information about the typical weather conditions in Istanbul during this time.\\n\\nIn late November, Istanbul experiences a mild autumn climate with temperatures gradually cooling down. The average high temperature is around 12°C (54°F), while the average low temperature is around 6°C (43°F).\\n\\nAs for precipitation, November is generally a relatively dry month in Istanbul, with an average of 10 rainy days throughout the month.\\n\\nHere's a rough estimate of what you might expect:\\n\\n* High Temperature: Around 12°C (54°F)\\n* Low Temperature: Around 6°C (43°F)\\n* Precipitation Risk: Moderate (around 40-50% chance of rain)\\n\\nPlease note that this is just an estimate and actual weather conditions may vary. For the most accurate and up-to-date forecast, I recommend checking a reliable weather forecasting service or app.\\n\\nIf you'd like to know more about the current weather in Istanbul or get a detailed forecast for tomorrow, I can try to provide more information based on available data.\",\n", 167 | " 'next_step': 'crag'}" 168 | ] 169 | }, 170 | "execution_count": 4, 171 | "metadata": {}, 172 | "output_type": "execute_result" 173 | } 174 | ], 175 | "source": [ 176 | "app.invoke({\"question\": ragquestion})" 177 | ] 178 | } 179 | ], 180 | "metadata": { 181 | "kernelspec": { 182 | "display_name": "venv", 183 | "language": "python", 184 | "name": "python3" 185 | }, 186 | "language_info": { 187 | "codemirror_mode": { 188 | "name": "ipython", 189 | "version": 3 190 | }, 191 | "file_extension": ".py", 192 | "mimetype": "text/x-python", 193 | "name": "python", 194 | "nbconvert_exporter": "python", 195 | "pygments_lexer": "ipython3", 196 | "version": "3.12.0" 197 | } 198 | }, 199 | "nbformat": 4, 200 | "nbformat_minor": 2 201 | } 202 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------