Using tool that can take pictures gives RuntimeError: generator raised StopIteration

[ayuranjan]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

@tool
  def clickNewImage() -> str:
    """Use camera to take picture of the current scenario and gives you the image path """
    return "demo.jpeg"

@tool
  def VisualQuestionAnswer(query: str, image_path : str) -> str:
    """ Takes a query which is the question users asks about the image and image_path which is the path of the image"""
    with open(image_path, "rb") as image_file:
      image_data = image_file.read()
      encoded_image = base64.b64encode(image_data).decode("utf-8")
    
    prompt = [
      HumanMessage(
        content=[
          {"type": "text", "text": query},
          {
            "type": "image_url",
            "image_url": {
              "url": f"data:image/jpeg;base64,{encoded_image}"
            },
          },
        ],
      ),
    ]
    model = ChatOpenAI(model="gpt-4o")
    return model.invoke(prompt)

Description

So basically I am trying to create a demo agent that has few tools - ClickNewImage and VisualQuestionAnswer. The idea is this agent can take an image whenever it feels it should and then use VisualQuestionAnswer tool to answer any query using that.

Eg ->

Query : What do you see

Agent : For this I would have to take a new pic -> so call ClickNewPic -> now we have the image ( or path of image) , let call the VisualQuestionAnswer tool with the image path ( this could also take the image , but I don't think we could return image from tools ) -> VisualQuestionAnswer will give the result -> passes to user.

issue I am having is that its calling the ClickNewPic tool but then it gives RuntimeError: generator raised StopIteration

I tried to return pathname of the image and even the image itself but all these are giving the same error.

How to create a tool that can take a picture and then remember its path so it can call other tools to answer any query .

System Info

System Information

OS: Darwin
OS Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020
Python Version: 3.9.6 (default, Feb 3 2024, 15:58:27)
[Clang 15.0.0 (clang-1500.3.9.4)]

Package Information

langchain_core: 0.2.22
langchain: 0.2.10
langchain_community: 0.2.7
langsmith: 0.1.85
langchain_anthropic: 0.1.19
langchain_experimental: 0.0.62
langchain_openai: 0.1.17
langchain_text_splitters: 0.2.2
langchainhub: 0.1.20
langgraph: 0.1.7

[dosubot[bot]]

To create a tool that can take a picture and remember its path so it can call other tools to answer any query, you can follow these steps:

1. Set up the environment and necessary imports:

   import re
   from typing import Union, Callable
   from langchain.agents import (
       AgentExecutor,
       AgentOutputParser,
       LLMSingleActionAgent,
       Tool,
   )
   from langchain.chains import LLMChain
   from langchain.prompts import StringPromptTemplate
   from langchain_community.utilities import SerpAPIWrapper
   from langchain_core.agents import AgentAction, AgentFinish
   from langchain_openai import OpenAI
   from langchain_community.vectorstores import FAISS
   from langchain_core.documents import Document
   from langchain_openai import OpenAIEmbeddings

2. Define the tools:

   # Define a tool to handle picture paths using Google Lens
   from langchain_community.tools.google_lens.tool import GoogleLensQueryRun
   from langchain_community.utilities.google_lens import GoogleLensAPIWrapper
   
   google_lens_tool = GoogleLensQueryRun(
       api_wrapper=GoogleLensAPIWrapper(api_key="your_google_lens_api_key")
   )
   
   # Define other tools (e.g., search tool)
   search = SerpAPIWrapper()
   search_tool = Tool(
       name="Search",
       func=search.run,
       description="Useful for when you need to answer questions about current events",
   )
   
   ALL_TOOLS = [google_lens_tool, search_tool]

3. Set up the tool retriever:

   docs = [
       Document(page_content=t.description, metadata={"index": i})
       for i, t in enumerate(ALL_TOOLS)
   ]
   
   vector_store = FAISS.from_documents(docs, OpenAIEmbeddings())
   retriever = vector_store.as_retriever()
   
   def get_tools(query):
       docs = retriever.invoke(query)
       return [ALL_TOOLS[d.metadata["index"]] for d in docs]

4. Create the custom prompt template:

   template = """Answer the following questions as best you can. You have access to the following tools:
   
   {tools}
   
   Use the following format:
   
   Question: the input question you must answer
   Thought: you should always think about what to do
   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
   
   Begin!
   
   Question: {input}
   {agent_scratchpad}"""
   
   class CustomPromptTemplate(StringPromptTemplate):
       template: str
       tools_getter: Callable
   
       def format(self, **kwargs) -> str:
           intermediate_steps = kwargs.pop("intermediate_steps")
           thoughts = ""
           for action, observation in intermediate_steps:
               thoughts += action.log
               thoughts += f"\nObservation: {observation}\nThought: "
           kwargs["agent_scratchpad"] = thoughts
           tools = self.tools_getter(kwargs["input"])
           kwargs["tools"] = "\n".join(
               [f"{tool.name}: {tool.description}" for tool in tools]
           )
           kwargs["tool_names"] = ", ".join([tool.name for tool in tools])
           return self.template.format(**kwargs)
   
   prompt = CustomPromptTemplate(
       template=template,
       tools_getter=get_tools,
       input_variables=["input", "intermediate_steps"],
   )

5. Set up the output parser:

   class CustomOutputParser(AgentOutputParser):
       def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
           if "Final Answer:" in llm_output:
               return AgentFinish(
                   return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
                   log=llm_output,
               )
           regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\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 AgentAction(
               tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output
           )
   
   output_parser = CustomOutputParser()

6. Set up the LLM, stop sequence, and the agent:

   llm = OpenAI(temperature=0)
   llm_chain = LLMChain(llm=llm, prompt=prompt)
   tools = get_tools("handle picture")
   tool_names = [tool.name for tool in tools]
   agent = LLMSingleActionAgent(
       llm_chain=llm_chain,
       output_parser=output_parser,
       stop=["\nObservation:"],
       allowed_tools=tool_names,
   )

7. Use the agent:

   agent_executor = AgentExecutor.from_agent_and_tools(
       agent=agent, tools=tools, verbose=True
   )
   
   # Example usage
   agent_executor.run("Store the picture at /path/to/picture.jpg")

This setup allows you to create a tool that can take a picture path and remember it, and then call other tools to answer any query. For more detailed information on tool calling and how to use LangChain tools, you can refer to the LangChain documentation ^{[1][2][3][4][5]}.

To continue talking to Dosu, mention @dosu.

[ayuranjan]

No, I want a custom tool that clicks a picture and returns the picture for further analysis. So that tool will be connected to some camera where it will take picture and store in some location and then returns the stored path image

[dosubot[bot]]

To create a custom tool in LangChain that captures an image using a connected camera and returns the image path for further analysis, you can follow these steps:

1. Define the custom tool: Create a Python function that interacts with the camera to capture an image and save it to a file. Decorate this function with @tool.

2. Bind the tool to the chat model: Use the .bind_tools method to bind the custom tool to the chat model.

Here is a sample implementation:

from langchain_core.tools import tool
import cv2  # OpenCV library for camera interaction

@tool
def click_new_image() -> str:
    """Captures an image from the connected camera and saves it."""
    # Initialize the camera
    cap = cv2.VideoCapture(0)
    
    if not cap.isOpened():
        return "Error: Could not open camera."
    
    # Capture a single frame
    ret, frame = cap.read()
    
    if not ret:
        return "Error: Could not read frame."
    
    # Save the captured image
    image_path = "captured_image.jpg"
    cv2.imwrite(image_path, frame)
    
    # Release the camera
    cap.release()
    
    return image_path

# List of tools
tools = [click_new_image]

# Bind the tool to the chat model
llm_with_tools = llm.bind_tools(tools)

# Example query to invoke the tool
query = "Capture an image using the camera."
response = llm_with_tools.invoke(query)
print(response)

In this example:

• The click_new_image function uses OpenCV to interact with the camera, capture an image, and save it to a file.
• The function is decorated with @tool to define it as a LangChain tool.
• The tool is then bound to the chat model using the .bind_tools method.
• Finally, the tool can be invoked through a query to the chat model.

This setup allows you to create a tool that captures an image and returns the path to the saved image for further analysis ^[1].

To continue talking to Dosu, mention @dosu.

[ayuranjan]

Can you create a agent that has 2 tools

1. ClickImage -- takes picture and returns the path ,
2. VisualAnswer - accepts the query and the image path and has a model that takes the query and the image path and returns reply

the 2nd tool is something like --

@tool
  def VisualQuestionAnswer(query: str, image_path : str) -> str:
    """ Takes a query which is the question users asks about the image and image_path which is the path of the image"""
    with open(image_path, "rb") as image_file:
      image_data = image_file.read()
      encoded_image = base64.b64encode(image_data).decode("utf-8")
    
    prompt = [
      HumanMessage(
        content=[
          {"type": "text", "text": query},
          {
            "type": "image_url",
            "image_url": {
              "url": f"data:image/jpeg;base64,{encoded_image}"
            },
          },
        ],
      ),
    ]
    model = ChatOpenAI(model="gpt-4o")
    return model.invoke(prompt)

Now I want an agent that has these 2 tools and can use these whenever it wants.

[dosubot[bot]]

To create an agent with two tools, ClickImage and VisualAnswer, you can follow these steps:

1. Install the necessary libraries:

   %pip install --upgrade --quiet gradio_tools langchain-community

2. Create the ClickImage tool:

   from gradio_tools.tools import GradioTool
   import gradio as gr
   
   class ClickImageTool(GradioTool):
       def __init__(self):
           super().__init__(
               name="ClickImage",
               description="Tool to capture an image using a connected camera",
               inputs=gr.inputs.Image(source="webcam"),
               outputs=gr.outputs.Image(type="pil")
           )
   
       def langchain(self):
           return self

3. Create the VisualAnswer tool:

   from langchain_core.tools import tool
   import base64
   from langchain_openai import OpenAI
   from langchain_core.messages import HumanMessage
   
   @tool
   def VisualQuestionAnswer(query: str, image_path: str) -> str:
       """Takes a query and an image path, and returns an answer based on the image."""
       with open(image_path, "rb") as image_file:
           image_data = image_file.read()
           encoded_image = base64.b64encode(image_data).decode("utf-8")
       
       prompt = [
           HumanMessage(
               content=[
                   {"type": "text", "text": query},
                   {
                       "type": "image_url",
                       "image_url": {
                           "url": f"data:image/jpeg;base64,{encoded_image}"
                       },
                   },
               ],
           ),
       ]
       model = OpenAI(model="gpt-4o")
       return model.invoke(prompt)

4. Integrate the tools into an agent:

   from langchain.agents import initialize_agent, AgentType
   from langchain.memory import ConversationBufferMemory
   from langchain_openai import OpenAI
   
   # Initialize the language model
   llm = OpenAI(temperature=0)
   memory = ConversationBufferMemory(memory_key="chat_history")
   
   # Create instances of the tools
   click_image_tool = ClickImageTool().langchain()
   visual_answer_tool = VisualQuestionAnswer
   
   # List of tools to be used by the agent
   tools = [click_image_tool, visual_answer_tool]
   
   # Initialize the agent with the tools
   agent = initialize_agent(
       tools, llm, memory=memory, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
   )
   
   # Example usage of the agent
   output = agent.run(
       input="Capture an image using the camera and then analyze it using the visual answer tool."
   )
   print(output)

This code sets up the ClickImage tool to capture an image using a connected camera and integrates it with the VisualAnswer tool into an agent that can use both tools as needed ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[ayuranjan]

This code doesn't work

class ClickImageTool(GradioTool):
    def __init__(self):
        super().__init__(
            name="ClickImage",
            description="Tool to capture an image using a connected camera",
            inputs=gr.inputs.Image(source="webcam"),
            outputs=gr.outputs.Image(type="pil")
        )

    def langchain(self):
        return self

First gr.inputs and gr.outputs are now deprecated, also I am not using gradio, I have build the chatbot using streamlit. Do I need to use gradio for custom tools?

[dosubot[bot]]

To create an agent with two tools (ClickImage and VisualAnswer) using Streamlit instead of Gradio, you can follow these steps:

1. Define the ClickImage tool: Create a Python function that interacts with the camera to capture an image and save it to a file. Decorate this function with @tool.

2. Define the VisualAnswer tool: Create a Python function that takes a query and an image path, and returns an answer based on the image.

3. Integrate the tools into an agent: Use the LangChain framework to bind these tools to the chat model.

Here is a complete implementation:

Step 1: Define the ClickImage Tool

from langchain_core.tools import tool
import cv2  # OpenCV library for camera interaction

@tool
def click_image() -> str:
    """Captures an image from the connected camera and saves it."""
    # Initialize the camera
    cap = cv2.VideoCapture(0)
    
    if not cap.isOpened():
        return "Error: Could not open camera."
    
    # Capture a single frame
    ret, frame = cap.read()
    
    if not ret:
        return "Error: Could not read frame."
    
    # Save the captured image
    image_path = "captured_image.jpg"
    cv2.imwrite(image_path, frame)
    
    # Release the camera
    cap.release()
    
    return image_path

Step 2: Define the VisualAnswer Tool

from langchain_core.tools import tool
import base64
from langchain_openai import OpenAI
from langchain_core.messages import HumanMessage

@tool
def visual_question_answer(query: str, image_path: str) -> str:
    """Takes a query and an image path, and returns an answer based on the image."""
    with open(image_path, "rb") as image_file:
        image_data = image_file.read()
        encoded_image = base64.b64encode(image_data).decode("utf-8")
    
    prompt = [
        HumanMessage(
            content=[
                {"type": "text", "text": query},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{encoded_image}"
                    },
                },
            ],
        ),
    ]
    model = OpenAI(model="gpt-4o")
    return model.invoke(prompt)

Step 3: Integrate the Tools into an Agent

from langchain.agents import initialize_agent, AgentType
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI

# Initialize the language model
llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history")

# List of tools to be used by the agent
tools = [click_image, visual_question_answer]

# Initialize the agent with the tools
agent = initialize_agent(
    tools, llm, memory=memory, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

# Example usage of the agent
output = agent.run(
    input="Capture an image using the camera and then analyze it using the visual answer tool."
)
print(output)

Streamlit Integration

To integrate this with Streamlit, you can create a simple Streamlit app that interacts with the agent:

import streamlit as st
from langchain_community.callbacks.streamlit import StreamlitCallbackHandler

st.title("Image Capture and Analysis Agent")

# Initialize the callback handler
st_callback = StreamlitCallbackHandler(st.container())

# Example usage of the agent with Streamlit
if st.button("Capture and Analyze Image"):
    with st.spinner("Processing..."):
        response = agent.run(
            input="Capture an image using the camera and then analyze it using the visual answer tool.",
            callbacks=[st_callback]
        )
    st.write(response)

This setup allows you to create an agent with two tools (ClickImage and VisualAnswer) using Streamlit for the interface and OpenCV for capturing images ^[1][2].

To continue talking to Dosu, mention @dosu.

[ayuranjan]

@dosu

So this is how I created the agent with streamlit ( with the two tools which we talked about)

llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
tools = [click_image, visual_question_answer]
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are very powerful assistant,  who can take pictures and answer questions about them.",
        ),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)
llm_with_tools = llm.bind_tools(tools)

agent = (
    {
        "input": lambda x: x["input"],
        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
            x["intermediate_steps"]
        ),
    }
    | prompt
    | llm_with_tools
    | OpenAIToolsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
st.title("Image Capture and Analysis Agent")


if st.button("Capture and Analyze Image"):
    with st.spinner("Processing..."):
        for  s in agent_executor.stream(
        {"input": [HumanMessage(content="Click a picture and describe what do you see ?")]}):
            if "__end__" not in s:
                print(s)
                print("---")
                st.write_stream(s)

Now the next thing was to add the memory . As per as documentation mentioned here https://python.langchain.com/v0.1/docs/modules/memory/agent_with_memory/

I should first create a variable - memory = ChatMessageHistory(session_id="test-session")

And then add this code to my existing code

agent_with_chat_history = RunnableWithMessageHistory(
    agent_executor,
    lambda session_id: memory,
    input_messages_key="input",
    history_messages_key="chat_history",
)

and then we could run the agent using

for  s in agent_with_chat_history.stream(
    {"input": [HumanMessage(content="Who is called Father of India ?")]},
    config={"configurable": {"session_id": "test-session"}}
):
    if "__end__" not in s:
        print(s)
        print("---")

If I run this code after running the earlier , it should remember the name of the person

for  s in agent_with_chat_history.stream(
    {"input": [HumanMessage(content="What was his full name  ?")]},
    config={"configurable": {"session_id": "test-session"}}
):
    if "__end__" not in s:
        print(s)
        print("---")

But this invokes the clickImage tool and then tells - I'm sorry, but I can't help identify or provide information about people in images.

If the memory part was working as expected, it should have given me the name of the answer.

Similarly I asked " what do you see", it clicks image ( which of a person in a kitchen with few vegetables around ) and then it describes it. I then ask a follow up question telling what recipe could I make using these ( which ideally should not call any toll and should reply according to what the previous description states ) but it clicks new image and then answer accordingly.

[dosubot[bot]]

To ensure that your agent retains context across interactions and uses memory correctly without invoking tools unnecessarily, you need to properly integrate memory into your agent. Here’s how you can do it:

1. Initialize the memory: Create a ChatMessageHistory instance.

2. Wrap the agent executor with RunnableWithMessageHistory: This will allow the agent to retain context across interactions.

3. Invoke the agent with memory: Ensure that the session ID is consistent across calls.

Here’s the updated code:

Step 1: Define the Tools

from langchain_core.tools import tool
import cv2  # OpenCV library for camera interaction
import base64
from langchain_openai import OpenAI
from langchain_core.messages import HumanMessage

@tool
def click_image() -> str:
    """Captures an image from the connected camera and saves it."""
    cap = cv2.VideoCapture(0)
    
    if not cap.isOpened():
        return "Error: Could not open camera."
    
    ret, frame = cap.read()
    
    if not ret:
        return "Error: Could not read frame."
    
    image_path = "captured_image.jpg"
    cv2.imwrite(image_path, frame)
    cap.release()
    
    return image_path

@tool
def visual_question_answer(query: str, image_path: str) -> str:
    """Takes a query and an image path, and returns an answer based on the image."""
    with open(image_path, "rb") as image_file:
        image_data = image_file.read()
        encoded_image = base64.b64encode(image_data).decode("utf-8")
    
    prompt = [
        HumanMessage(
            content=[
                {"type": "text", "text": query},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{encoded_image}"
                    },
                },
            ],
        ),
    ]
    model = OpenAI(model="gpt-4o")
    return model.invoke(prompt)

Step 2: Initialize the Agent with Memory

from langchain.agents import initialize_agent, AgentType, AgentExecutor
from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

# Initialize the language model
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
memory = ChatMessageHistory(session_id="test-session")

# Define the tools
tools = [click_image, visual_question_answer]

# Define the prompt
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a very powerful assistant who can take pictures and answer questions about them."),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)

# Bind the tools to the LLM
llm_with_tools = llm.bind_tools(tools)

# Define the agent
agent = (
    {
        "input": lambda x: x["input"],
        "agent_scratchpad": lambda x: format_to_openai_tool_messages(x["intermediate_steps"]),
    }
    | prompt
    | llm_with_tools
    | OpenAIToolsAgentOutputParser()
)

# Create the agent executor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Wrap the agent executor with memory
agent_with_chat_history = RunnableWithMessageHistory(
    agent_executor,
    lambda session_id: memory,
    input_messages_key="input",
    history_messages_key="chat_history",
)

# Streamlit setup
import streamlit as st

st.title("Image Capture and Analysis Agent")

if st.button("Capture and Analyze Image"):
    with st.spinner("Processing..."):
        for s in agent_with_chat_history.stream(
            {"input": [HumanMessage(content="Click a picture and describe what do you see?")]},
            config={"configurable": {"session_id": "test-session"}}
        ):
            if "__end__" not in s:
                print(s)
                print("---")
                st.write(s)

# Example follow-up question
if st.button("Ask Follow-up Question"):
    with st.spinner("Processing..."):
        for s in agent_with_chat_history.stream(
            {"input": [HumanMessage(content="What recipe could I make using these?")]},
            config={"configurable": {"session_id": "test-session"}}
        ):
            if "__end__" not in s:
                print(s)
                print("---")
                st.write(s)

Explanation

1. Memory Initialization: The ChatMessageHistory instance is created with a session ID.
2. Agent Executor with Memory: The RunnableWithMessageHistory wraps the agent executor, allowing it to retain context across interactions.
3. Consistent Session ID: Ensure that the session ID is consistent across calls to retain memory.

By following these steps, the agent should remember previous interactions and respond appropriately without invoking tools unnecessarily ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[GonyRosenman]

any updates on this?