def answer(question):
context = docs.similarity_search(query)
retriever = vectorstore.as_retriever()
chain = prompt | llm | output_parser
vectorstore = Chroma.from_documents(docs, embeddings)
session.add(message)
db.commit()
scraped = await fetch_tenders()
if confidence > 0.85:
return response
messages = history + [HumanMessage(query)]
tool_calls = model.bind_tools(tools)
response = llm.invoke(messages)
async def handle_update(update):
router = Router()
ranked = sorted(candidates, key=score)
agent.run()
await bot.process(message)
threading.Thread(target=worker).start()
state = {"messages": []}
graph = builder.compile()
prompt = ChatPromptTemplate.from_messages([...])
dispatcher = Dispatcher(bot=bot)
@dp.message(Command("start"))
