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| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import numpy as np | |
| import pandas as pd | |
| import time | |
| from tqdm import tqdm | |
| import warnings | |
| from langchain.chains import RetrievalQA | |
| from langchain.callbacks import StdOutCallbackHandler | |
| import chainlit as cl # importing chainlit for our app | |
| from chainlit.prompt import Prompt, PromptMessage | |
| from chainlit.playground.providers.openai import ChatOpenAI # importing ChatOpenAI tools | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma, DeepLake | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.document_loaders.dataframe import DataFrameLoader | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains import RetrievalQA | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.llms.openai import OpenAIChat | |
| from langchain.agents.agent_toolkits import create_retriever_tool | |
| from langchain.agents.agent_toolkits import create_conversational_retrieval_agent | |
| from langchain.utilities import SerpAPIWrapper | |
| from langchain.agents import load_tools | |
| from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory | |
| from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent | |
| from langchain.schema.messages import SystemMessage | |
| from langchain.prompts import MessagesPlaceholder | |
| from langchain.agents import AgentExecutor | |
| warnings.filterwarnings("ignore") | |
| os.environ["LANGCHAIN_WANDB_TRACING"] = "true" | |
| # review_df = pd.read_csv("./data/justice.csv") | |
| # data = review_df | |
| # text_splitter = RecursiveCharacterTextSplitter( | |
| # chunk_size = 7000, # the character length of the chunk | |
| # chunk_overlap = 700, # the character length of the overlap between chunks | |
| # length_function = len, # the length function - in this case, character length (aka the python len() fn.) | |
| # ) | |
| # loader = DataFrameLoader(review_df, page_content_column="facts") | |
| # base_docs = loader.load() | |
| # docs = text_splitter.split_documents(base_docs) | |
| embedder = OpenAIEmbeddings() | |
| # This is needed for both the memory and the prompt | |
| memory_key = "history" | |
| # Embed and persist db | |
| persist_directory = "./data/chroma" | |
| vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embedder) | |
| # vectorstore = DeepLake(dataset_path="./legalminds/", embedding=embedder, overwrite=True) | |
| # vectorstore.add_documents(docs) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| primary_qa_llm = ChatOpenAI( | |
| model="gpt-3.5-turbo-16k", | |
| temperature=0, | |
| ) | |
| retriever = vectorstore.as_retriever() | |
| CUSTOM_TOOL_N_DOCS = 3 # number of retrieved docs from deep lake to consider | |
| CUSTOM_TOOL_DOCS_SEPARATOR ="\n\n" # how to join together the retrieved docs to form a single string | |
| def retrieve_n_docs_tool(query: str) -> str: | |
| """ Searches for relevant documents that may contain the answer to the query.""" | |
| docs = retriever.get_relevant_documents(query)[:CUSTOM_TOOL_N_DOCS] | |
| texts = [doc.page_content for doc in docs] | |
| texts_merged = CUSTOM_TOOL_DOCS_SEPARATOR.join(texts) | |
| return texts_merged | |
| serp_tool = load_tools(["serpapi"]) | |
| # print("Serp Tool:",serp_tool[0]) | |
| data_tool = create_retriever_tool( | |
| retriever, | |
| "retrieve_n_docs_tool", | |
| "Searches and returns documents regarding the query asked." | |
| ) | |
| tools = [data_tool, serp_tool[0]] | |
| # llm = OpenAIChat(model="gpt-3.5-turbo", temperature=0) | |
| llm = ChatOpenAI(temperature = 0) | |
| memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm) | |
| system_message = SystemMessage( | |
| content=( | |
| "Do your best to answer the questions. " | |
| "Feel free to use any tools available to look up " | |
| "relevant information, only if necessary" | |
| ) | |
| ) | |
| prompt = OpenAIFunctionsAgent.create_prompt( | |
| system_message=system_message, | |
| extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)] | |
| ) | |
| agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt) | |
| handler = StdOutCallbackHandler() | |
| # marks a function that will be executed at the start of a user session | |
| async def start_chat(): | |
| agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True, | |
| return_intermediate_steps=True) | |
| # agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True) | |
| # qa_with_sources_chain = RetrievalQA.from_chain_type( | |
| # llm=llm, | |
| # retriever=retriever, | |
| # callbacks=[handler], | |
| # return_source_documents=True | |
| # ) | |
| cl.user_session.set("agent", agent_executor) | |
| # marks a function that should be run each time the chatbot receives a message from a user | |
| async def main(message: str): | |
| agent_executor = cl.user_session.get("agent") | |
| # prompt = Prompt( | |
| # provider=ChatOpenAI.id, | |
| # messages=[ | |
| # PromptMessage( | |
| # role="system", | |
| # # template=RAQA_PROMPT_TEMPLATE, | |
| # # formatted=RAQA_PROMPT_TEMPLATE, | |
| # ), | |
| # PromptMessage( | |
| # role="user", | |
| # # template=user_template, | |
| # # formatted=user_template.format(input=message), | |
| # ), | |
| # ], | |
| # inputs={"input": message}, | |
| # # settings=settings, | |
| # ) | |
| # result = await qa_with_sources_chain.acall({"query" : message}) #, callbacks=[cl.AsyncLangchainCallbackHandler()]) | |
| result = agent_executor({"input": message}) | |
| # print("result Dict:",result) | |
| msg = cl.Message(content=result["output"]) | |
| print("message:",msg) | |
| print("output message:",msg.content) | |
| # Update the prompt object with the completion | |
| # msg.content = result["output"] | |
| # prompt.completion = msg.content | |
| # msg.prompt = prompt | |
| # print("message_content: ",msg.content) | |
| await msg.send() | |