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+FROM python:3.11
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "legalminds.py", "--port", "7860"]

legalminds.py

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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()
+
+
+@cl.on_chat_start  # 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)
+
+@cl.on_message  # 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()

requirements.txt

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+chainlit==0.7.0
+numpy==1.25.2
+openai==0.27.8
+python-dotenv==1.0.0
+wandb==0.15.11
+chromadb
+langchain
+tiktoken
+pandas
+scipy
+scikit-learn
+ipykernel
+matplotlib
+plotly
+deeplake
+google-search-results
+