removing agent implementation to optimize speed

app.py

@@ -1,13 +1,13 @@
 import gradio as gr
 from main import index, run
 from gtts import gTTS
-import os
+import os, time
 
 from transformers import pipeline
 
 p = pipeline("automatic-speech-recognition")
 
-"""Use text to call chat method from main.py"""
+"""Use text to call chat method from main_old.py"""
 def add_text(history, text):
     print("Question asked: " + text)
     response = run_model(text)
@@ -17,11 +17,16 @@ def add_text(history, text):
 
 
 def run_model(text):
+    start_time = time.time()
+    print("start time:" + str(start_time))
     response = run(question=text)
+    end_time = time.time()
     # If response contains string `SOURCES:`, then add a \n before `SOURCES`
     if "SOURCES:" in response:
         response = response.replace("SOURCES:", "\nSOURCES:")
+        # response = response + "\n\n" + "Time taken: " + str(end_time - start_time)
     print(response)
+    print("Time taken: " + str(end_time - start_time))
     return response
 
 

main.py

@@ -1,33 +1,9 @@
-from utils import create_index, get_agent_chain, get_prompt_and_tools, get_search_index
-from utils import get_custom_agent, get_prompt_and_tools_for_custom_agent
-question_starters = ['who', 'why', 'what', 'how', 'where', 'when', 'which', 'whom', 'whose']
-
+from utils import get_search_index, get_qa_chain, generate_answer
 
 def index():
     get_search_index()
     return True
 
 def run(question):
-    index = get_search_index()
-
-    # prompt, tools = get_prompt_and_tools()
-
-    # agent_chain = get_agent_chain(prompt, tools)
-
-    prompt, tools = get_prompt_and_tools_for_custom_agent()
-
-    agent_chain = get_custom_agent(prompt, tools)
-
-    result = None
-
-    try:
-        result = agent_chain.run(question)
-    except ValueError as ve:
-        if "Could not parse LLM output:" in ve.args[0] and question.lower().startswith(tuple(question_starters)) and not question.lower().endswith('?'):
-            question = question + '?'
-            result = agent_chain.run(question)
-    finally:
-        print(result)
-
-    return result
-
+    index()
+    return generate_answer(question)

main_old.py

@@ -0,0 +1,34 @@
+from utils_old import get_custom_agent, get_prompt_and_tools_for_custom_agent
+from utils_old import get_search_index
+
+question_starters = ['who', 'why', 'what', 'how', 'where', 'when', 'which', 'whom', 'whose']
+
+
+def index():
+    get_search_index()
+    return True
+
+def run(question):
+    index = get_search_index()
+
+    # prompt, tools = get_prompt_and_tools()
+
+    # agent_chain = get_agent_chain(prompt, tools)
+
+    prompt, tools = get_prompt_and_tools_for_custom_agent()
+
+    agent_chain = get_custom_agent(prompt, tools)
+
+    result = None
+
+    try:
+        result = agent_chain.run(question)
+    except ValueError as ve:
+        if "Could not parse LLM output:" in ve.args[0] and question.lower().startswith(tuple(question_starters)) and not question.lower().endswith('?'):
+            question = question + '?'
+            result = agent_chain.run(question)
+    finally:
+        print(result)
+
+    return result
+

requirements.txt

@@ -1,8 +1,9 @@
-langchain==0.0.131
-openai==0.27.2
+langchain==0.0.166
+openai
 faiss-cpu==1.7.3
 unstructured==0.5.8
 ffmpeg-python
 transformers
 gtts
-torch
+torch
+tiktoken

utils.py

@@ -1,27 +1,31 @@
 import os
 import pickle
-import re
-from typing import List, Union
 
 import faiss
-from langchain import OpenAI, LLMChain
-from langchain.agents import ConversationalAgent
-from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
 from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
 from langchain.document_loaders import DirectoryLoader, TextLoader, UnstructuredHTMLLoader
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.memory import ConversationBufferWindowMemory
-from langchain.prompts import BaseChatPromptTemplate
-from langchain.schema import AgentAction, AgentFinish, HumanMessage
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores.faiss import FAISS
 
+os.environ['OPENAI_API_KEY'] = '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'
+
 pickle_file = "open_ai.pkl"
 index_file = "open_ai.index"
 
-gpt_3_5 = OpenAI(model_name='gpt-4',temperature=0)
 
-embeddings = OpenAIEmbeddings()
+
+gpt_3_5 = ChatOpenAI(model_name='gpt-4',temperature=0.1)
+
+embeddings = OpenAIEmbeddings(model='text-embedding-ada-002')
+
 
 chat_history = []
 
@@ -29,54 +33,17 @@ memory = ConversationBufferWindowMemory(memory_key="chat_history")
 
 gpt_3_5_index = None
 
-class CustomOutputParser(AgentOutputParser):
-
-    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
-        # Check if agent replied without using tools
-        if "AI:" in llm_output:
-            return AgentFinish(return_values={"output": llm_output.split("AI:")[-1].strip()},
-                               log=llm_output)
-        # Check if agent should finish
-        if "Final Answer:" in llm_output:
-            return AgentFinish(
-                # Return values is generally always a dictionary with a single `output` key
-                # It is not recommended to try anything else at the moment :)
-                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
-                log=llm_output,
-            )
-        # Parse out the action and action input
-        regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
-        match = re.search(regex, llm_output, re.DOTALL)
-        if not match:
-            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
-        action = match.group(1).strip()
-        action_input = match.group(2)
-        # Return the action and action input
-        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
-
-# Set up a prompt template
-class CustomPromptTemplate(BaseChatPromptTemplate):
-    # The template to use
-    template: str
-    # The list of tools available
-    tools: List[Tool]
-
-    def format_messages(self, **kwargs) -> str:
-        # Get the intermediate steps (AgentAction, Observation tuples)
-        # Format them in a particular way
-        intermediate_steps = kwargs.pop("intermediate_steps")
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\nObservation: {observation}\nThought: "
-        # Set the agent_scratchpad variable to that value
-        kwargs["agent_scratchpad"] = thoughts
-        # Create a tools variable from the list of tools provided
-        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
-        # Create a list of tool names for the tools provided
-        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
-        formatted = self.template.format(**kwargs)
-        return [HumanMessage(content=formatted)]
+system_template = """You are Coursera QA Bot. Have a conversation with a human, answering the following questions as best you can.
+You are a teaching assistant for a Coursera Course: The 3D Printing Evolution and can answer any question about that using vectorstore.
+Use the following pieces of context to answer the users question. 
+----------------
+{context}"""
+
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}"),
+]
+CHAT_PROMPT = ChatPromptTemplate.from_messages(messages)
 
 def get_search_index():
     global gpt_3_5_index
@@ -88,6 +55,7 @@ def get_search_index():
         search_index = create_index()
 
     gpt_3_5_index = search_index
+    return search_index
 
 
 def create_index():
@@ -140,8 +108,12 @@ def create_chunk_documents():
 
 def get_qa_chain(gpt_3_5_index):
     global gpt_3_5
-    return ConversationalRetrievalChain.from_llm(gpt_3_5, chain_type="stuff", get_chat_history=get_chat_history,
-            retriever=gpt_3_5_index.as_retriever(), return_source_documents=True, verbose=True)
+    # embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
+    # compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=gpt_3_5_index.as_retriever())
+    chain = ConversationalRetrievalChain.from_llm(gpt_3_5, gpt_3_5_index.as_retriever(), return_source_documents=True,
+                                                  verbose=True, get_chat_history=get_chat_history,
+                                                  combine_docs_chain_kwargs={"prompt": CHAT_PROMPT})
+    return chain
 
 def get_chat_history(inputs) -> str:
     res = []
@@ -153,117 +125,16 @@ def get_chat_history(inputs) -> str:
 def generate_answer(question) -> str:
     global chat_history, gpt_3_5_index
     gpt_3_5_chain = get_qa_chain(gpt_3_5_index)
+
     result = gpt_3_5_chain(
         {"question": question, "chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.6}})
     chat_history = [(question, result["answer"])]
     sources = []
+    print(result['answer'])
 
     for document in result['source_documents']:
         source = document.metadata['source']
         sources.append(source.split('/')[-1].split('.')[0])
 
     source = ',\n'.join(set(sources))
-    return result['answer'] + '\nSOURCES: ' + source
-
-
-def get_agent_chain(prompt, tools):
-    global gpt_3_5
-    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
-    agent = ConversationalAgent(llm_chain=llm_chain, tools=tools, verbose=True)
-    agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
-                                                     intermediate_steps=True)
-    return agent_chain
-
-
-def get_prompt_and_tools():
-    tools = get_tools()
-
-    prefix = """Have a conversation with a human, answering the following questions as best you can. Always try to use Vectorstore first. Your name is Coursera Bot because your knowledge base is Coursera course. You have access to the following tools:"""
-    suffix = """Begin! If you used vectorstore tool, ALWAYS return a "SOURCES" part in your answer"
-    
-    {chat_history}
-    Question: {input}
-    {agent_scratchpad}
-    sources:"""
-    prompt = ConversationalAgent.create_prompt(
-        tools,
-        prefix=prefix,
-        suffix=suffix,
-        input_variables=["input", "chat_history", "agent_scratchpad"]
-    )
-    return prompt, tools
-
-
-def get_tools():
-    tools = [
-        Tool(
-            name="Vectorstore",
-            func=generate_answer,
-            description="useful for when you need to answer questions about the coursera course on 3D Printing.",
-            return_direct=True
-        )]
-    return tools
-
-def get_custom_agent(prompt, tools):
-
-    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
-
-    output_parser = CustomOutputParser()
-    tool_names = [tool.name for tool in tools]
-    agent = LLMSingleActionAgent(
-        llm_chain=llm_chain,
-        output_parser=output_parser,
-        stop=["\nObservation:"],
-        allowed_tools=tool_names
-    )
-    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
-                                                        intermediate_steps=True)
-    return agent_executor
-
-def get_prompt_and_tools_for_custom_agent():
-    template = """
-    Have a conversation with a human, answering the following questions as best you can. 
-    ALWAYS try to use Vectorstore first. 
-    You are a teaching assistant for a Coursera Course: The 3D Printing Evolution and can answer any question about that using vectorstore . You have access to the following tools:
-    
-    {tools}
-
-    ALWAYS use one of the 2 formats listed below to respond. 
-
-    To answer for the new input, use the following format:
-    
-    New Input: the input question you must answer
-    Thought: Do I need to use a tool? Yes
-    Action: the action to take, should be one of [{tool_names}]
-    Action Input: the input to the action
-    Observation: the result of the action
-    ... (this Thought/Action/Action Input/Observation can repeat N times)
-    Thought: I now know the final answer
-    Final Answer: the final answer to the original input question. SOURCES: the sources referred to find the final answer
-
-    
-    When you have a response to say to the Human and DO NOT need to use a tool:
-    1. DO NOT return "SOURCES" if you did not use any tool.
-    2. You MUST use this format:
-    ```
-    Thought: Do I need to use a tool? No
-    AI: [your response here]
-    ```
-
-    Begin! Remember to speak as a personal assistant when giving your final answer.
-    ALWAYS return a "SOURCES" part in your answer, if you used any tool. 
-    
-    Previous conversation history:
-    {chat_history}
-    New input: {input}
-    {agent_scratchpad}
-    SOURCES:"""
-    tools = get_tools()
-    prompt = CustomPromptTemplate(
-        template=template,
-        tools=tools,
-        # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
-        # This includes the `intermediate_steps` variable because that is needed
-        input_variables=["input", "intermediate_steps", "chat_history"]
-    )
-    return prompt, tools
+    return result['answer'] + '\nSOURCES: ' + source

utils_old.py

@@ -0,0 +1,271 @@
+import os
+import pickle
+import re
+from typing import List, Union
+
+import faiss
+from langchain import OpenAI, LLMChain
+from langchain.agents import ConversationalAgent
+from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import DirectoryLoader, TextLoader, UnstructuredHTMLLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.prompts import BaseChatPromptTemplate
+from langchain.schema import AgentAction, AgentFinish, HumanMessage
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores.faiss import FAISS
+
+os.environ['OPENAI_API_KEY'] = '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'
+
+pickle_file = "open_ai.pkl"
+index_file = "open_ai.index"
+
+gpt_3_5 = OpenAI(model_name='gpt-4',temperature=0)
+
+embeddings = OpenAIEmbeddings()
+
+chat_history = []
+
+memory = ConversationBufferWindowMemory(memory_key="chat_history")
+
+gpt_3_5_index = None
+
+class CustomOutputParser(AgentOutputParser):
+
+    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
+        # Check if agent replied without using tools
+        if "AI:" in llm_output:
+            return AgentFinish(return_values={"output": llm_output.split("AI:")[-1].strip()},
+                               log=llm_output)
+        # Check if agent should finish
+        if "Final Answer:" in llm_output:
+            return AgentFinish(
+                # Return values is generally always a dictionary with a single `output` key
+                # It is not recommended to try anything else at the moment :)
+                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
+                log=llm_output,
+            )
+        # Parse out the action and action input
+        regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
+        match = re.search(regex, llm_output, re.DOTALL)
+        if not match:
+            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
+        action = match.group(1).strip()
+        action_input = match.group(2)
+        # Return the action and action input
+        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
+
+# Set up a prompt template
+class CustomPromptTemplate(BaseChatPromptTemplate):
+    # The template to use
+    template: str
+    # The list of tools available
+    tools: List[Tool]
+
+    def format_messages(self, **kwargs) -> str:
+        # Get the intermediate steps (AgentAction, Observation tuples)
+        # Format them in a particular way
+        intermediate_steps = kwargs.pop("intermediate_steps")
+        thoughts = ""
+        for action, observation in intermediate_steps:
+            thoughts += action.log
+            thoughts += f"\nObservation: {observation}\nThought: "
+        # Set the agent_scratchpad variable to that value
+        kwargs["agent_scratchpad"] = thoughts
+        # Create a tools variable from the list of tools provided
+        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
+        # Create a list of tool names for the tools provided
+        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
+        formatted = self.template.format(**kwargs)
+        return [HumanMessage(content=formatted)]
+
+def get_search_index():
+    global gpt_3_5_index
+    if os.path.isfile(pickle_file) and os.path.isfile(index_file) and os.path.getsize(pickle_file) > 0:
+        # Load index from pickle file
+        with open(pickle_file, "rb") as f:
+            search_index = pickle.load(f)
+    else:
+        search_index = create_index()
+
+    gpt_3_5_index = search_index
+
+
+def create_index():
+    source_chunks = create_chunk_documents()
+    search_index = search_index_from_docs(source_chunks)
+    faiss.write_index(search_index.index, index_file)
+    # Save index to pickle file
+    with open(pickle_file, "wb") as f:
+        pickle.dump(search_index, f)
+    return search_index
+
+
+def search_index_from_docs(source_chunks):
+    # print("source chunks: " + str(len(source_chunks)))
+    # print("embeddings: " + str(embeddings))
+    search_index = FAISS.from_documents(source_chunks, embeddings)
+    return search_index
+
+
+def get_html_files():
+    loader = DirectoryLoader('docs', glob="**/*.html", loader_cls=UnstructuredHTMLLoader, recursive=True)
+    document_list = loader.load()
+    return document_list
+
+
+def fetch_data_for_embeddings():
+    document_list = get_text_files()
+    document_list.extend(get_html_files())
+    print("document list" + str(len(document_list)))
+    return document_list
+
+
+def get_text_files():
+    loader = DirectoryLoader('docs', glob="**/*.txt", loader_cls=TextLoader, recursive=True)
+    document_list = loader.load()
+    return document_list
+
+
+def create_chunk_documents():
+    sources = fetch_data_for_embeddings()
+
+    splitter = CharacterTextSplitter(separator=" ", chunk_size=800, chunk_overlap=0)
+
+    source_chunks = splitter.split_documents(sources)
+
+    print("sources" + str(len(source_chunks)))
+
+    return source_chunks
+
+
+def get_qa_chain(gpt_3_5_index):
+    global gpt_3_5
+    return ConversationalRetrievalChain.from_llm(gpt_3_5, chain_type="stuff", get_chat_history=get_chat_history,
+            retriever=gpt_3_5_index.as_retriever(), return_source_documents=True, verbose=True)
+
+def get_chat_history(inputs) -> str:
+    res = []
+    for human, ai in inputs:
+        res.append(f"Human:{human}\nAI:{ai}")
+    return "\n".join(res)
+
+
+def generate_answer(question) -> str:
+    global chat_history, gpt_3_5_index
+    gpt_3_5_chain = get_qa_chain(gpt_3_5_index)
+    result = gpt_3_5_chain(
+        {"question": question, "chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.6}})
+    chat_history = [(question, result["answer"])]
+    sources = []
+
+    for document in result['source_documents']:
+        source = document.metadata['source']
+        sources.append(source.split('/')[-1].split('.')[0])
+
+    source = ',\n'.join(set(sources))
+    return result['answer'] + '\nSOURCES: ' + source
+
+
+def get_agent_chain(prompt, tools):
+    global gpt_3_5
+    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
+    agent = ConversationalAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+    agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
+                                                     intermediate_steps=True)
+    return agent_chain
+
+
+def get_prompt_and_tools():
+    tools = get_tools()
+
+    prefix = """Have a conversation with a human, answering the following questions as best you can. Always try to use Vectorstore first. Your name is Coursera Bot because your knowledge base is Coursera course. You have access to the following tools:"""
+    suffix = """Begin! If you used vectorstore tool, ALWAYS return a "SOURCES" part in your answer"
+    
+    {chat_history}
+    Question: {input}
+    {agent_scratchpad}
+    sources:"""
+    prompt = ConversationalAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        input_variables=["input", "chat_history", "agent_scratchpad"]
+    )
+    return prompt, tools
+
+
+def get_tools():
+    tools = [
+        Tool(
+            name="Vectorstore",
+            func=generate_answer,
+            description="useful for when you need to answer questions about the coursera course on 3D Printing.",
+            return_direct=True
+        )]
+    return tools
+
+def get_custom_agent(prompt, tools):
+
+    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
+
+    output_parser = CustomOutputParser()
+    tool_names = [tool.name for tool in tools]
+    agent = LLMSingleActionAgent(
+        llm_chain=llm_chain,
+        output_parser=output_parser,
+        stop=["\nObservation:"],
+        allowed_tools=tool_names
+    )
+    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
+                                                        intermediate_steps=True)
+    return agent_executor
+
+def get_prompt_and_tools_for_custom_agent():
+    template = """
+    Have a conversation with a human, answering the following questions as best you can. 
+    ALWAYS try to use Vectorstore first. 
+    You are a teaching assistant for a Coursera Course: The 3D Printing Evolution and can answer any question about that using vectorstore . You have access to the following tools:
+    
+    {tools}
+
+    ALWAYS use one of the 2 formats listed below to respond. 
+
+    To answer for the new input, use the following format:
+    
+    New Input: the input question you must answer
+    Thought: Do I need to use a tool? Yes
+    Action: the action to take, should be one of [{tool_names}]
+    Action Input: the input to the action
+    Observation: the result of the action
+    ... (this Thought/Action/Action Input/Observation can repeat N times)
+    Thought: I now know the final answer
+    Final Answer: the final answer to the original input question. SOURCES: the sources referred to find the final answer
+
+    
+    When you have a response to say to the Human and DO NOT need to use a tool:
+    1. DO NOT return "SOURCES" if you did not use any tool.
+    2. You MUST use this format:
+    ```
+    Thought: Do I need to use a tool? No
+    AI: [your response here]
+    ```
+
+    Begin! Remember to speak as a personal assistant when giving your final answer.
+    ALWAYS return a "SOURCES" part in your answer, if you used any tool. 
+    
+    Previous conversation history:
+    {chat_history}
+    New input: {input}
+    {agent_scratchpad}
+    SOURCES:"""
+    tools = get_tools()
+    prompt = CustomPromptTemplate(
+        template=template,
+        tools=tools,
+        # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
+        # This includes the `intermediate_steps` variable because that is needed
+        input_variables=["input", "intermediate_steps", "chat_history"]
+    )
+    return prompt, tools