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Using StructuredTool with agent #10778

Closed Zylon989 closed 11 months ago

Zylon989 commented 1 year ago

System Info

Using this Code I am getting this error: TypeError: Chain.call() got an unexpected keyword argument 'question'

Who can help?

No response

Information

Related Components

Reproduction

from pydantic import BaseModel, Field

from langchain.chat_models import ChatOpenAI from langchain.agents import Tool from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import FAISS from langchain.document_loaders import PyPDFLoader from langchain.chains import RetrievalQA from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.tools import StructuredTool from dotenv import load_dotenv class DocumentInput(BaseModel): question: str = Field()

load_dotenv()

llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")

tools = [] files = [

https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf

  {
      "name": "alphabet-earnings",
      "path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf",
  },
  # https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update
  {
      "name": "tesla-earnings",
      "path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf",
  },

]

for file in files: loader = PyPDFLoader(file["path"]) pages = loader.load_and_split() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(pages) embeddings = OpenAIEmbeddings() retriever = FAISS.from_documents(docs, embeddings).as_retriever()

  # Wrap retrievers in a Tool
  tools.append(
      StructuredTool.from_function(
          args_schema=DocumentInput,
          name=file["name"],
          description=f"useful when you want to answer questions about {file['name']}",
          func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
      )
  )

llm = ChatOpenAI( temperature=0, model="gpt-3.5-turbo-0613", )

agent = initialize_agent( agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=tools, llm=llm, verbose=True, )

agent({"input": "did alphabet or tesla have more revenue?"})

Expected behavior

Compare the two documents!

dosubot[bot] commented 1 year ago

🤖

Hello,

The error you're encountering is due to the fact that you're trying to pass 'question' as a keyword argument to the Chain.__call__() method, but this method doesn't accept such an argument.

In the LangChain framework, the Chain.__call__() method has the following signature:

def __call__(
        self,
        inputs: Union[Dict[str, Any], Any],
        return_only_outputs: bool = False,
        callbacks: Callbacks = None,
        *,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        run_name: Optional[str] = None,
        include_run_info: bool = False,
    ) -> Dict[str, Any]

As you can see, 'question' is not a parameter of this method. Instead, you should pass the 'question' as part of the input data to the StructuredTool.invoke() method. The 'invoke' method accepts 'input' and 'config' parameters, along with any additional keyword arguments. The 'input' parameter should be a string or a dictionary.

Here's how you might modify your code:

agent({"input": {"question": "did alphabet or tesla have more revenue?"}})

In this example, 'question' is a key in the 'input' dictionary, and its corresponding value is the question you want to ask.

I hope this helps! Let me know if you have any other questions.

Sources

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Zylon989 commented 1 year ago

@dosu-bot I changed the code using this: agent({"input": {"question": "did alphabet or tesla have more revenue?"}})

but still getting the same error: TypeError: Chain.call() got an unexpected keyword argument 'question' ??

dosubot[bot] commented 1 year ago

🤖

Hello,

The error you're encountering, TypeError: Chain.__call__() got an unexpected keyword argument 'question', is likely due to the way you're passing the 'question' argument to the Chain object.

In your code, you're trying to pass 'question' as a keyword argument to the Chain object:

agent({"input": {"question": "did alphabet or tesla have more revenue?"}})

However, the Chain object's __call__ method doesn't accept a keyword argument named 'question'. Instead, it expects its arguments to be passed in a specific format, either as a string or a dictionary.

The StructuredTool class, which you're using, is a subclass of the BaseTool class. The BaseTool class's invoke method is defined as follows:

def invoke(
        self,
        input: Union[str, Dict],
        config: Optional[RunnableConfig] = None,
        **kwargs: Any,
    ) -> Any:
        config = config or {}
        return self.run(
            input,
            callbacks=config.get("callbacks"),
            tags=config.get("tags"),
            metadata=config.get("metadata"),
            **kwargs,
        )

This method accepts three arguments: input, config, and **kwargs. If you're trying to pass 'question' as a keyword argument, you'll need to ensure that it's included in the input dictionary.

So, instead of calling the agent like this:

agent({"input": {"question": "did alphabet or tesla have more revenue?"}})

You should call it like this:

agent({"question": "did alphabet or tesla have more revenue?"})

This will pass the 'question' as part of the input argument, which should resolve the TypeError you're encountering.

Please try this modification and let me know if it resolves your issue. If not, please provide any additional error messages or context that might help diagnose the problem.

Best, Dosu

Sources

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Zylon989 commented 1 year ago

@dosu-bot Now I am getting the error: ValueError: Missing some input keys: {'input'}

dosubot[bot] commented 1 year ago

🤖

Hello,

Thank you for reaching out and providing detailed information about the issue you're facing. Based on the error messages you're encountering and the code snippets you've shared, it seems like the issue is related to the input variables you're providing to the Chain object.

In the LangChain framework, the Chain object from the BaseRetrievalQA class requires one input variable: query. If you're not providing this variable when you're using the Chain object, you'll encounter the ValueError: Missing some input keys: {'input'} error.

To resolve this issue, you should provide query when you're using the Chain object. Here's an example of how you can do this:

query = "Your question here"
response = Chain._call(inputs={'query': query})

In this example, query is a string that contains your question. The _call method of the Chain object is then used to generate a response using this input.

Regarding the initialization of the AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent, you can refer to the following code snippet:

from langchain.agents.agent_types import AgentType
from langchain.agents.initialize import initialize_agent
from langchain.tools.base import tool
from tests.unit_tests.llms.fake_llm import FakeLLM

@tool
def my_tool(query: str) -> str:
    """A fake tool."""
    return "fake tool"

fake_llm = FakeLLM()
agent_chain = initialize_agent(
    [my_tool], fake_llm, AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION  # type: ignore
)

If you want to add memory to this agent, you can do so by using the ConversationBufferMemory class and adding it to the initialize_agent function:

from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationBufferMemory

chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

agent_chain = initialize_agent(
    [my_tool], 
    fake_llm, 
    AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, 
    memory=memory, 
    agent_kwargs = {
        "memory_prompts": [chat_history],
        "input_variables": ["input", "agent_scratchpad", "chat_history"]
    }
)

I hope this helps! If you have any other questions or need further clarification, please don't hesitate to ask.

Sources

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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Zylon989 commented 1 year ago

@dosu-bot Now I am getting this error:NameError: name 'Chain' is not defined

Tried to import Chain with this: from langchain.chains import Chain

But then getting this error: ImportError: cannot import name 'Chain' from 'langchain.chains'

dosubot[bot] commented 1 year ago

🤖

Hello,

The error message ImportError: cannot import name 'Chain' from 'langchain.chains' is occurring because the Chain class is not defined or imported in the langchain.chains module.

Based on the LangChain framework source code, the Chain class is actually defined in the langchain.chains.base module. Therefore, you should import the Chain class from the correct module. Instead of from langchain.chains import Chain, you should use from langchain.chains.base import Chain.

Here is the correct import statement:

from langchain.chains.base import Chain

The Chain class is an abstract base class for creating structured sequences of calls to components. It provides methods for executing the chain, validating inputs and outputs, and saving the chain among others. You can refer to the class definition in the source code for more details.

Also, please ensure that you are using the latest version of LangChain, which is currently v0.0.294. You can update LangChain using pip:

pip install --upgrade langchain

If you continue to experience issues after making these changes, please provide more details about your development environment and the steps you are taking when the error occurs.

I hope this helps! Let me know if you have any other questions.

Sources

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Zylon989 commented 1 year ago

@dosu-bot It's working with from langchain.chains.base import Chain but now I am getting a new error : TypeError: Chain._call() missing 1 required positional argument: 'self'

dosubot[bot] commented 1 year ago

🤖

Hello,

The error you're encountering is due to the fact that you're trying to call the _call method directly on the Chain class, rather than on an instance of the class. In Python, instance methods (which _call is) need to be called on an instance of the class, not the class itself.

The _call method is an abstract method in the Chain class, which means it should be implemented in a subclass of Chain. In the context of LangChain, the LLMChain class is a subclass of Chain and it has implemented the _call method. You should create an instance of LLMChain and then call the _call method on this instance.

Here is an example of how you can do this:

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

# Create an instance of LLMChain
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)

# Now you can call the _call method
result = llm_chain._call(inputs, run_manager)

However, please note that the _call method is intended to be a private method (as indicated by the underscore prefix), which means it's not intended to be directly called by users of the class. Instead, you should use the __call__ method or the run method to execute the chain, which internally calls the _call method. The __call__ method also handles callbacks configuration and some input/output processing. Here is how you can use it:

# Create an instance of LLMChain
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)

# Now you can call the __call__ method
result = llm_chain(inputs, return_only_outputs, callbacks, tags=tags, metadata=metadata, run_name=run_name, include_run_info=include_run_info)

# Or you can use the run method
result = llm_chain.run(*args, callbacks=callbacks, tags=tags, metadata=metadata, **kwargs)

In the context of LangChain, the run method is a convenience method for executing the chain. It expects inputs to be passed directly in as positional arguments or keyword arguments, whereas __call__ expects a single input dictionary with all the inputs.

You can find more information about this in the source code of the Chain class and its subclasses in the LangChain repository:

I hope this helps! Let me know if you have any other questions.

Sources

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Zylon989 commented 1 year ago

@dosu-bot But I am using an agent not an llm-chain! How to solve this using an agent?

dosubot[bot] commented 1 year ago

🤖

Hello,

Thank you for reaching out and providing a detailed explanation of your issue. The error you're encountering, TypeError: Chain._call() missing 1 required positional argument: 'self', typically occurs when a method that requires an instance of a class is called on the class itself.

In your code, you're using the StructuredTool class correctly. The issue seems to be with the RetrievalQA.from_chain_type function. This function is supposed to return an instance of the RetrievalQA class, but it seems like it's returning the class itself.

Here's the corrected part of your code:

for file in files:
    loader = PyPDFLoader(file["path"])
    pages = loader.load_and_split()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(pages)
    embeddings = OpenAIEmbeddings()
    retriever = FAISS.from_documents(docs, embeddings).as_retriever()

    tools.append(
        StructuredTool.from_function(
            args_schema=DocumentInput,
            name=file["name"],
            description=f"useful when you want to answer questions about {file['name']}",
            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)(),
        )
    )

In the func argument of StructuredTool.from_function, I've added parentheses after RetrievalQA.from_chain_type(llm=llm, retriever=retriever). This will call the function and return an instance of the RetrievalQA class, which should resolve the TypeError you're encountering.

Please try this solution and let me know if it resolves your issue or if you have any other questions.

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Zylon989 commented 1 year ago

@dosu-bot Error: func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)(), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Chain.call() missing 1 required positional argument: 'inputs'

dosubot[bot] commented 1 year ago

🤖

Hello,

The error message you're seeing is due to the fact that the RetrievalQA.from_chain_type function is being called without the required inputs argument. In the context of the RetrievalQA class, which is a subclass of Chain, the inputs argument is expected to be a dictionary with a key-value pair where the key is the input_key of the RetrievalQA instance (default is "query") and the value is the question you want to ask.

However, in your case, you're trying to use RetrievalQA.from_chain_type as a function to be passed to StructuredTool.from_function. The func argument of StructuredTool.from_function is expected to be a callable that takes a single argument: the inputs. But RetrievalQA.from_chain_type is not a function, it's a class method that returns an instance of RetrievalQA, which is a callable, but it requires the inputs argument.

To fix this, you need to create a wrapper function that takes the inputs and calls the RetrievalQA instance. Here's how you can modify your code:

for file in files:
    loader = PyPDFLoader(file["path"])
    pages = loader.load_and_split()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(pages)
    embeddings = OpenAIEmbeddings()
    retriever = FAISS.from_documents(docs, embeddings).as_retriever()

    def answer_question(inputs):
        qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
        return qa(inputs)

    tools.append(
        StructuredTool.from_function(
            args_schema=DocumentInput,
            name=file["name"],
            description=f"useful when you want to answer questions about {file['name']}",
            func=answer_question,
        )
    )

In this modified code, answer_question is a function that takes the inputs, creates a RetrievalQA instance, and calls it with the inputs. This function is then passed to StructuredTool.from_function.

I hope this helps! Let me know if you have any other questions.

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Zylon989 commented 1 year ago

@dosu-bot Now I am getting again the error : TypeError: Chain._call() missing 1 required positional argument: 'self'

dosubot[bot] commented 1 year ago

🤖

Hello,

The error message you're seeing, TypeError: Chain._call() missing 1 required positional argument: 'self', is typically raised when an instance method is called as if it were a static method or a function. In Python, instance methods automatically receive the instance of the class (usually named self) as their first argument.

In your case, it seems like the RetrievalQA instance is not being properly created or called. The RetrievalQA.from_chain_type method should return an instance of RetrievalQA, and calling this instance (i.e., qa(inputs)) should internally call the Chain._call() method with the appropriate self argument.

However, it's possible that the RetrievalQA.from_chain_type method is not returning an instance, but rather the class itself or a static method. This would explain why the self argument is missing when Chain._call() is invoked.

To debug this issue, I would recommend adding some print statements to check the type of qa and qa(inputs):

def answer_question(inputs):
    qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
    print(type(qa))
    result = qa(inputs)
    print(type(result))
    return result

If qa is not an instance of RetrievalQA, then there might be an issue with the RetrievalQA.from_chain_type method. If qa(inputs) is not what you expect, then there might be an issue with how the RetrievalQA instance is called.

If you're still having trouble, could you please provide more information about the RetrievalQA.from_chain_type method and how the RetrievalQA class is defined? This would help me understand better what might be going wrong.

I hope this helps! Let me know if you have any other questions.

Sources

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Zylon989 commented 1 year ago

@dosu-bot changed the code, but still getting the same error! I am usind the RetrievalQA from langchain and imported like this from langchain.chains import RetrievalQA

dosubot[bot] commented 1 year ago

🤖

Hello,

The error message you're encountering, TypeError: Chain._call() missing 1 required positional argument: 'self', is indicating that the _call() method is being called without its first argument, which should be the instance of the class itself (usually referred to as self in Python). This method is likely being called internally within the LangChain framework.

In your code, you're creating an instance of RetrievalQA using the from_chain_type method. This method should return an instance of RetrievalQA, which can then be used to call instance methods like _call(). If the from_chain_type method is not correctly returning an instance of RetrievalQA, or if the instance is not being used correctly afterwards, this could lead to the TypeError you're seeing.

To resolve this issue, you should ensure that the from_chain_type method is correctly implemented and returns an instance of RetrievalQA. You should also ensure that the instance is being used correctly after it's created.

Here's the implementation of the from_chain_type method in the BaseRetrievalQA class for reference:

@classmethod
def from_chain_type(
    cls,
    llm: BaseLanguageModel,
    chain_type: str = "stuff",
    chain_type_kwargs: Optional[dict] = None,
    **kwargs: Any,
) -> BaseRetrievalQA:
    """Load chain from chain type."""
    _chain_type_kwargs = chain_type_kwargs or {}
    combine_documents_chain = load_qa_chain(
        llm, chain_type=chain_type, **_chain_type_kwargs
    )
    return cls(combine_documents_chain=combine_documents_chain, **kwargs)

If the error persists, it would be helpful to see the implementation of the from_chain_type method and the _call() method, as the issue likely lies in one of these areas. Without this information, it's difficult to provide a more specific solution.

I hope this helps! Let me know if you have any other questions or if there's anything else I can do to assist you.

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

dosubot[bot] commented 11 months ago

Hi, @Zylon989,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you encountered a TypeError while using StructuredTool with an agent, and I provided guidance on modifying the code to resolve the error. Subsequently, I continued to provide detailed explanations and code modifications to address new errors, including suggestions for using the RetrievalQA class and its methods. The issue remains open with ongoing troubleshooting and support from me.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, please let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days.

Thank you!

itxabdullahbinasad commented 5 months ago

@Zylon989 Hey zylon i am going through very similar problem did your problem get solved , plz let me know will be waiting for the response