filip-michalsky/salesgpt

Context-aware AI Sales Agent to automate sales outreach.

salesgpt's Issues

Using AzureOpenAI

Hello,

Thanks for developing this project. I was trying to run the main file (run.py) giving my API key. It ran into issues stating wrong bearer toekn. I realized, my openai sandbox is on Azure and has a company specific domain.
So I changed the .env file (providing AzureOpenAI base URL and key).
os.environ['OPENAI_API_KEY'] = envs_dict['OPENAI_API_KEY']
os.environ['OPENAI_API_BASE'] = envs_dict['OPENAI_API_BASE']

And also changed the following in run.py

llm = AzureOpenAI(temperature=0)

Now I am getting following error

Retrying langchain.llms.openai.completion_with_retry.._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI: No connection adapters were found for "'https://mysandboxurl/'\n/engines//completions".

Let me know if this is the right approach and what is going wrong. Thanks.

Madhur

How to get source when I use csv to get product information?

            chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
            result = chain({"question": query}, return_only_outputs=True)
            # result will be a dictionary of this format --> {"answer": "", "sources": [] }
            st.header("Answer")
            st.write(result["answer"])

            # Display sources, if available
            sources = result.get("sources", "")
            if sources:
                st.subheader("Sources:")
                sources_list = sources.split("\n")  # Split the sources by newline
                for source in sources_list:
                    print(source)

This one I used in my another project. But in SalesGPT couldn't implement something similar to this. Tried Following:

import pickle

# Set up a knowledge base
def setup_knowledge_base(product_catalog: str = None):
    # """
    # We assume that the product knowledge base is simply a text file.
    # """
    # # load product catalog
    # with open(product_catalog, "r") as f:
    #     product_catalog = f.read()

    # text_splitter = CharacterTextSplitter(chunk_size=10, chunk_overlap=0)
    # texts = text_splitter.split_text(product_catalog)

    # llm = OpenAI(temperature=0, max_tokens=700)
    # embeddings = OpenAIEmbeddings()
    
    # docsearch = Chroma.from_texts(
    #     texts, embeddings, collection_name="product-knowledge-base"
    # )

    # knowledge_base = RetrievalQA.from_chain_type(
    #     llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()
    # )
    file_path = product_catalog
    llm = OpenAI(temperature=0, max_tokens=700)
    with open(file_path, "rb") as f:
        vectorstore = pickle.load(f)
    

    knowledge_base = RetrievalQA.from_chain_type(
         llm=llm, chain_type="stuff", 
         retriever=vectorstore.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'lambda_mult': 0.25},
                                            return_source_documents=True)
    )
    print(knowledge_base)
    return knowledge_base

    # knowledge_base = RetrievalQAWithSourcesChain.from_chain_type(
    #     llm=llm, 
    #     retriever=vectorstore.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'lambda_mult': 0.25},
    #                                        return_source_documents=True)
    # )

    # print(knowledge_base)
    # return knowledge_base


def get_tools(product_catalog):
    # query to get_tools can be used to be embedded and relevant tools found
    # see here: https://langchain-langchain.vercel.app/docs/use_cases/agents/custom_agent_with_plugin_retrieval#tool-retriever

    # we only use one tool for now, but this is highly extensible!
    knowledge_base = setup_knowledge_base(product_catalog)
    tools = [
        Tool(
            name="ProductSearch",
            func=knowledge_base.run,
            description="useful for when you need to answer questions about product information",
        )
    ]

    return tools```

TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

SalesGPT-main\examples\sales_agent_with_context.ipynb Cell 23 line 1
---->[1] class SalesGPT(Chain, BaseModel):
         [2] """Controller model for the Sales Agent."""
         [4]conversation_history: List[str] = []

TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

I don't know what is wrong with given example code in "sales_agent_with_context.ipynb". I am getting this error. looking forward to get help from the community. Please note that, I haven't change anything in code but getting this error.

agent after using tools, when no matches found, outputs 'Sorry'

Hello, great project. I am very excited with it.
Question is:
How do I correctly tell parser.py to make a next AgentAction or AgentFinish if there is no matches.
This happens everytime my tool cannot find anything in product_catalog to answer the question

def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
    if self.verbose:
        print("TEXT")
        print(text)
        print("-------")
    if f"{self.ai_prefix}:" in text:
        return AgentFinish(
            {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text
        )
    regex = r"Action: (.*?)[\n]*Action Input: (.*)"
    match = re.search(regex, text)
    if not match:
        ## TODO - this is not entirely reliable, sometimes results in an error.
        return AgentFinish(
            {
                "output": "I apologize, I was unable to find the answer to your question. Is there anything else I can help with?"
            },
            text,
        )
        # raise OutputParserException(f"Could not parse LLM output: `{text}`")
    action = match.group(1)
    action_input = match.group(2)
    return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text)

currently this part just outputs "I apologize...." as final answer.

有考虑使用向量数据库吗？

stream设置为True了，但好像无法使用工具。

stream设置为True了，但好像无法使用工具。
我查看代码后，发现流式输出并不支持使用工具，有打算把这个功能附加进来嘛？

Models are interacting with themselves, instead of user.

    model_id = "NousResearch/Llama-2-7b-chat-hf"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map="auto",
    )

    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_new_tokens=2500,
        temperature=0.3,
        top_p=0.95,
    )

   llm = HuggingFacePipeline(pipeline=pipe)

We were trying to connect a custom model to SalesGPT, tried both Llama-2-7b and our custom model.
But in both cases we faced an issue, that at first response on our initial input, model self generates a whole conversation between sales agent and some user, that the model created by itself.

Any ideas why are we facing this issue?

Integration example with CrewAI

Integration of SalesGPT w/ CrewAI
https://github.com/joaomdmoura/crewAI/tree/main

How to connect SalesGPT with Streamlit Chat

I found SalesGPT as a very powerful and interesting framework!

The only issue is how to connect or transform console mode into a more user-friendly "chat" format.
For example, how to integrate it with https://pypi.org/project/streamlit-chat/ output.

Any updates for this option are welcome!

Thanks

Andrii

请问有messages为什么还要带conversation history？

在向大模型发送messages请求的时候，会带有历史聊天记录，为什么还需要在prompt里带上conversation history呢？

Agent type

Thanks for the work. Really useful!

What's the need for the Custom Agent?
Why wouldn't one want to use the open ai function agent instead?
I think it could reduce latency and fix some of the custom parser errors.

Assistants+tools=[{“type”: “retrieval”}], What is the retrieval principle? Give pdf directly to AI?

client = openai.OpenAI()

file = client.files.create(file=open(“dlof.pdf”, “rb”),purpose=‘assistants’)

assistant = client.beta.assistants.create(
name=“Draft Letter Of Offer Assistant”,
instructions=“You are a merger and takeover specialist chatbot. Use your knowledge base to best respond to queries related to mergers and takeovers. Pls be precise”,
model=“gpt-3.5-turbo-1106”,
tools=[{“type”: “retrieval”}],
file_ids=[file.id]
)

What is the retrieval principle? Give pdf directly to AI? How to count tokens.
If it is a retrieval, it should only be a part of the gpt, that prompt is how, there is a way to output.

use_tools: bool = True problem with actions

I am facing an implementation issue from your repository. I don't understand how it should work. If you leave use_tools: bool = False in agents.py, then this is just a chat with LLM that hallucinates due to prompts and does not use the product database, it just invents a trading conversation without connecting to a file with real product data. If you set use_tools: bool = True, then it immediately sends the first welcome message to the request, which is naturally not contained in the product database, and a negative response comes from parsers.py line 31 "output": "I apologize, I was unable to find the answer to your question. Is there anything else I can help with?". How do you solve this problem?

run.py error

I am facing this issue, Followed all the steps for installation and got this :

" from langchain.llms.base import create_base_retry_decorator
ImportError: cannot import name 'create_base_retry_decorator' from 'langchain.llms.base'"

python run.py error

from langchain.llms.base import create_base_retry_decorator

ImportError: cannot import name 'create_base_retry_decorator' from 'langchain.llms.base'

Integrating Hugging Chat API

Please provide guidance on whether we can integrate the Hugging Chat API into this code. If such integration is feasible, kindly provide instructions.

running run.py and I get rate limiting issues

any idea where I should look first I see I cant try something just as simple by adding rateLimiter to the agents.py

Integration with Ollama

Hello!
I'm trying to use SalesGPT with a locally-served model via Ollama.
I tested the LiteLLM part, it works.

from litellm import completion

response = completion(
    model="orca-mini", 
    messages=[{ "content": "respond in 20 words. who are you?","role": "user"}], 
    api_base="http://localhost:11434", 
    custom_llm_provider="ollama"
)
print(response)

Now, I modified the streaming_generator_example.py like this

llm = ChatLiteLLM(model="orca-mini",
    api_base="http://localhost:11434",
    custom_llm_provider="ollama")
....
generator = sales_agent.step(
     return_streaming_generator=True, model_name="orca-mini",
)

Btw, I think it's a little bit redundant the need to provide the model name at each step instead of setting it up in the llm model. Why is that needed at each step?

The result is :

salesgpt.logger 2023-09-14 07:10:55,377 - INFO - Running from_llm: --- 8.654594421386719e-05 seconds ---
salesgpt.logger 2023-09-14 07:10:55,378 - INFO - Running from_llm: --- 0.00012731552124023438 seconds ---
salesgpt.logger 2023-09-14 07:10:55,378 - INFO - Running from_llm: --- 0.0005018711090087891 seconds ---
salesgpt.logger 2023-09-14 07:10:55,378 - INFO - Running seed_agent: --- 5.7220458984375e-06 seconds ---
salesgpt.logger 2023-09-14 07:10:55,378 - INFO - Running _prep_messages: --- 0.0001068115234375 seconds ---
Traceback (most recent call last):
  File "/opt/ollama/SalesGPT/examples/streaming_generator_example.py", line 36, in <module>
    generator = sales_agent.step(
  File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/salesgpt/logger.py", line 34, in wrapper
    result = func(*args, **kwargs)  # Function execution
  File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/salesgpt/agents.py", line 110, in step
    return self._streaming_generator(model_name=model_name)
  File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/salesgpt/logger.py", line 34, in wrapper
    result = func(*args, **kwargs)  # Function execution
  File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/salesgpt/agents.py", line 179, in _streaming_generator
    return self.sales_conversation_utterance_chain.llm.completion_with_retry(
  File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/langchain/chat_models/litellm.py", line 263, in completion_with_retry
    return _completion_with_retry(**kwargs)

... (long trace not included)

File "/root/miniconda3/envs/sls/lib/python3.10/site-packages/litellm/utils.py", line 998, in get_llm_provider
    raise ValueError(f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/{model}',..)` Learn more: https://docs.litellm.ai/docs/providers")
ValueError: LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/orca-mini',..)` Learn more: https://docs.litellm.ai/docs/providers

From my limited understanding, I get it that somehow the parameters set up in the llm at the initialization step are lost at the step stage, i.e. api_base and custom_llm_provider. Which kinda negates the whole purpose of integrating LiteLLM, if the only one I can use is OpenAI.

What am I doing wrong? Has anyone successfully ran local ollama?

Problem in Russian setup

When I switch to the Russian setup file, I get several replies from Sales and User.

There is an assumption that the answer is either history or sammorization

And when I do this in English there is only one answer

Error in sales_agent_with_context when running stage_analyzer_chain

I keep getting the errror message below when I am going through the Sales GPT with context tutorial and get to the stage_analyzer_chain.run(conversation_history='') step:

Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised APIError: The server had an error while processing your request. Sorry about that! You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID b647982210c2d50cc01ba890f9bfe6f2 in your message.) {
  "error": {
    "message": "The server had an error while processing your request. Sorry about that! You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID b647982210c2d50cc01ba890f9bfe6f2 in your message.)",
    "type": "server_error",
    "param": null,
    "code": null
  }
}
 500 {'error': {'message': 'The server had an error while processing your request. Sorry about that! You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID b647982210c2d50cc01ba890f9bfe6f2 in your message.)', 'type': 'server_error', 'param': None, 'code': None}} {'Date': 'Tue, 23 May 2023 12:13:13 GMT', 'Content-Type': 'application/json', 'Content-Length': '366', 'Connection': 'keep-alive', 'access-control-allow-origin': '*', 'openai-processing-ms': '157', 'openai-version': '2020-10-01', 'strict-transport-security': 'max-age=15724800; includeSubDomains', 'x-ratelimit-limit-requests': '3500', 'x-ratelimit-limit-tokens': '90000', 'x-ratelimit-remaining-requests': '3499', 'x-ratelimit-remaining-tokens': '89427', 'x-ratelimit-reset-requests': '17ms', 'x-ratelimit-reset-tokens': '382ms', 'x-request-id': 'b647982210c2d50cc01ba890f9bfe6f2', 'CF-Cache-Status': 'DYNAMIC', 'Server': 'cloudflare', 'CF-RAY': '7cbd362beb98dd82-LHR', 'alt-svc': 'h3=":443"; ma=86400, h3-29=":443"; ma=86400'}.

I have confirmed that my OpenAI API key is correct and active.

Also - is the Quick start meant to replace this tutorial? I preferred this one as it gave me more control of the sausage making. I couldn't tell if the QuickStart allowed me to do the same.

Error in run.py when using --verbose True and example_agent_setup.json

Hi,

I encountered an issue while running the code with the following command:

(venv) (sales) mostafa@aorus:~/programming/progwise/sales-gpt/python/SalesGPT$ python run.py --verbose True --config examples/example_agent_setup.json

The error message I received is as follows:

/home/mostafa/programming/progwise/sales-gpt/python/SalesGPT/venv/lib/python3.11/site-packages/langchain/__init__.py:38: UserWarning: Importing LLMChain from langchain root module is no longer supported.
  warnings.warn(
/home/mostafa/programming/progwise/sales-gpt/python/SalesGPT/venv/lib/python3.11/site-packages/langchain/__init__.py:38: UserWarning: Importing LLMChain from langchain root module is no longer supported.
  warnings.warn(
/home/mostafa/programming/progwise/sales-gpt/python/SalesGPT/venv/lib/python3.11/site-packages/langchain/__init__.py:38: UserWarning: Importing PromptTemplate from langchain root module is no longer supported.
  warnings.warn(
Traceback (most recent call last):
  File "/home/mostafa/programming/progwise/sales-gpt/python/SalesGPT/run.py", line 5, in <module>
    from salesgpt.agents import SalesGPT
  File "/home/mostafa/programming/progwise/sales-gpt/python/SalesGPT/salesgpt/agents.py", line 35, in <module>
    class SalesGPT(Chain, BaseModel):
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

It appears that there's a metaclass conflict issue in the SalesGPT class in the agents.py file. I'm not sure if this is specific to my environment or if it's a general issue with the codebase. Could someone please take a look and provide guidance on how to resolve this error?

Thank you!

run.py 中没有决定对话阶段的部分

run.py 中没有决定对话阶段的部分是为什么呢？

Technologies used for realtime speech-to-text and text-to-speech

Hello Filip!

I want to thank you for your idea of sales agent in SalesGPT repository!

Inspired by your idea, I decided to implement it almost from scratch using more langchain features and Streamlit as UI, and later to create public repository with it, but I was also impressed of your video here, and I want to try to implement also these features with interaction using Streamlit (realtime speech-to-text and text-to-speech).

Of course, if it's not a secret, could you please tell what technologies you used for speech-to-text and text-to-speech?

Thank you.

Consistently Skipping Conversation Stages

Hello. Thanks for the great work on this agent, it has helped me develop a much better understanding of how to leverage langchain and openai.

That being said, I am having some trouble getting the agent to have a full conversation and address essential stages in the conversation. I made my own custom stages but kept the stage analyzer chain the same (with the new conversation stages). Any idea why this is happening?

How does the open source large language model api interface interact with SalesGPT

About how to interact with SalesGPT by using the open source large language model API interface defined and packaged by yourself

Sales Agent to Use Local HuggingFace Model

How do I define a salesGPT agent that uses a local huggingface-pulled model? I am trying to use Llama-2-7b-chat-hf as the base model, but I can't pull the model directly bcs of my current GPU resources. I followed Llama's docs to make it smaller by the code shown below:

model_id = 'meta-llama/Llama-2-7b-chat-hf'

device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)

hf_auth = "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" 
model_config = transformers.AutoConfig.from_pretrained(
    model_id,
    use_auth_token=hf_auth
)

model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map='auto',
    use_auth_token=hf_auth
)

model.eval()

Then I wrapped the whole transformers pipeline with langchain's HuggingFacePipeline:

llm = HuggingFacePipeline(pipeline=generate_text)

The model's working fine on its own but I can't seem to make it work in SalesGPT.

sales_agent = SalesGPT.from_llm(llm, use_tools=True, verbose=False,
                            product_catalog = "sample_product_catalog.txt",
                            salesperson_name="Ted Lasso",
                            salesperson_role="Sales Representative",
                            company_name="Sleep Haven",
                            company_business='''Sleep Haven 
                            is a premium mattress company that provides
                            customers with the most comfortable and
                            supportive sleeping experience possible. 
                            We offer a range of high-quality mattresses,
                            pillows, and bedding accessories 
                            that are designed to meet the unique 
                            needs of our customers.'''
                            )

The error says:

AttributeError: 'HuggingFacePipeline' object has no attribute 'model'

Is there any workaround so that I can get this to work? Any suggestions would be appreciated. Thanks!

Fix parsing errors from SalesConvoOutputParser

Sometimes there are parsing errors returned which are not handled properly with the current SalesConvoOutputParser. A better option would be to replace it with RetryWithErrorOutputParser which can be imported from langchain.output_parsers. This parser will be triggered when there are parsing errors to return the output in the correct format to parse (i.e. Action: "XXX", Action_input:"XXX").
Any plans in applying those changes soon or any other better ideas to have more reliable parser?

I made some trials in this particular but this parser isn't compatible with LLMSingleActionAgent as it needs to parse by calling parse_with_prompt to return the prompt back to the parser to be fixed however, LLMSingleActionAgent will only call the parse method with no prompt so seems that a new Agent will have to be created.

Hello, when will this version be normal? I just cloned and reported an error.

Hello, when will this version be normal? I just cloned and reported an error.
Traceback (most recent call last): File "F:\GitHub\Llama2-Chinese\SalesGPT\run.py", line 6, in <module> from salesgpt.agents import SalesGPT File "F:\GitHub\Llama2-Chinese\SalesGPT\salesgpt\agents.py", line 25, in <module> class SalesGPT(Chain, BaseModel): TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

请教SalesGPT逻辑设计的问题

SALES_AGENT_TOOLS_PROMPT这个Prompt中为什么要重新输入下面这一段内容？而不是将上一步“阶段判断”获得的”阶段结果“直接输入到这个环节？

This flowchart indicates that the prior step to the Autonomous Sales Agent's invocation is the Stage Analyzer, which evidently has already conducted an analysis of the stage. I'm puzzled as to why the parameters aren't passed on to the next step, but instead, a reevaluation is conducted in the subsequent stage. What is the rationale behind this approach?

Always think about at which conversation stage you are at before answering:

1: Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are calling.
2: Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.
3: Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.
4: Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.
5: Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.
6: Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.
7: Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.
8: End conversation: The prospect has to leave to call, the prospect is not interested, or next steps where already determined by the sales agent.

Not able to run even following all the steps of installation

Ted Lasso:
Traceback (most recent call last):
File "/root/SalesGPT/run.py", line 67, in
sales_agent.step()
File "/root/SalesGPT/salesgpt/logger.py", line 34, in wrapper
result = func(*args, **kwargs) # Function execution
File "/root/SalesGPT/salesgpt/agents.py", line 107, in step
self._call(inputs={})
File "/root/SalesGPT/salesgpt/agents.py", line 246, in _call
ai_message = self.sales_conversation_utterance_chain.run(
File "/usr/local/lib/python3.9/dist-packages/langchain/chains/base.py", line 510, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/lib/python3.9/dist-packages/langchain/chains/base.py", line 310, in call
raise e
File "/usr/local/lib/python3.9/dist-packages/langchain/chains/base.py", line 304, in call
self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.9/dist-packages/langchain/chains/llm.py", line 108, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/usr/local/lib/python3.9/dist-packages/langchain/chains/llm.py", line 120, in generate
return self.llm.generate_prompt(
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/base.py", line 459, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/base.py", line 349, in generate
raise e
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/base.py", line 339, in generate
self._generate_with_cache(
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/base.py", line 492, in _generate_with_cache
return self._generate(
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/litellm.py", line 306, in _generate
response = self.completion_with_retry(
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/litellm.py", line 233, in completion_with_retry
retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
File "/usr/local/lib/python3.9/dist-packages/langchain/chat_models/litellm.py", line 70, in _create_retry_decorator
litellm.APIConnectionError,
AttributeError: module 'litellm' has no attribute 'APIConnectionError'

1 - made touch .env
2 - copied api key there
3 - followed this steps ->
Make sure your have a python 3.10+ and run:

pip install -r requirements.txt

additional info: Python 3.11.2

question about version of package openai 关于openai库的版本问题

when i use openai==1.2.4, I have error:
Traceback (most recent call last): File "D:\SalesGPT\quickstart.py", line 27, in <module> sales_agent.determine_conversation_stage() # optional for demonstration, built into the prompt File "D:\SalesGPT\salesgpt\logger.py", line 34, in wrapper result = func(*args, **kwargs) # Function execution File "D:\SalesGPT\salesgpt\agents.py", line 76, in determine_conversation_stage self.conversation_stage_id = self.stage_analyzer_chain.run( File "D:\SalesGPT\venv\lib\site-packages\langchain\chains\base.py", line 510, in run return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ File "D:\SalesGPT\venv\lib\site-packages\langchain\chains\base.py", line 310, in __call__ raise e File "D:\SalesGPT\venv\lib\site-packages\langchain\chains\base.py", line 304, in __call__ self._call(inputs, run_manager=run_manager) File "D:\SalesGPT\venv\lib\site-packages\langchain\chains\llm.py", line 108, in _call response = self.generate([inputs], run_manager=run_manager) File "D:\SalesGPT\venv\lib\site-packages\langchain\chains\llm.py", line 120, in generate return self.llm.generate_prompt( File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\base.py", line 459, in generate_prompt return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs) File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\base.py", line 349, in generate raise e File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\base.py", line 339, in generate self._generate_with_cache( File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\base.py", line 492, in _generate_with_cache return self._generate( File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\litellm.py", line 307, in _generate response = self.completion_with_retry( File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\litellm.py", line 234, in completion_with_retry retry_decorator = _create_retry_decorator(self, run_manager=run_manager) File "D:\SalesGPT\venv\lib\site-packages\langchain\chat_models\litellm.py", line 68, in _create_retry_decorator openai.error.Timeout, AttributeError: module 'openai' has no attribute 'error'

when I use openai==0.27.8 (requestment.txt) I have error:
Traceback (most recent call last): File "D:\SalesGPT\quickstart.py", line 2, in <module> from salesgpt.agents import SalesGPT File "D:\SalesGPT\salesgpt\agents.py", line 9, in <module> from litellm import acompletion File "D:\SalesGPT\venv\lib\site-packages\litellm\__init__.py", line 333, in <module> from .timeout import timeout File "D:\SalesGPT\venv\lib\site-packages\litellm\timeout.py", line 20, in <module> from litellm.exceptions import Timeout File "D:\SalesGPT\venv\lib\site-packages\litellm\exceptions.py", line 12, in <module> from openai import ( ImportError: cannot import name 'AuthenticationError' from 'openai' (D:\SalesGPT\venv\lib\site-packages\openai\__init__.py)

so which version should I use???

Add cold calling Module in Codebase as Shared in Demo.

Add cold calling Module in code as shared in demo.

怎样修改才能支持ChatGLM-6B？

sales_agent_executor is none

I encountered an issue within this GitHub repository that required resolution. Despite attempting various solutions and consulting the official Langchain website, I was able to address the problem. The source of the issue was identified as the "agent.py" file. Specifically, the line of code "sales_agent_executor: Union[AgentExecutor, None] = Field(...)" resulted in a "sales_agent_executor" being returned as "None". Your assistance in resolving this matter would be greatly appreciated.

Moreover, the error message "AttributeError: 'NoneType' object has no attribute 'run'" was encountered in this context.

integration embedding knowledge

If I want to integrate the embedding knowledge base in salesGPT, when the user consults, when the question asked is not in the knowledge base, the normal dialogue can be answered when asked about the content in the knowledge base. What is the idea to achieve this?

How to support more other LLM models?

机器人

Text-to-speech and speech-to-text

Can somebody please explain me how to get text-to-speech and speech-to-text running.

Make it call you

@filip-michalsky any chance to know how you implemented your demonstration when your SalesGPT is calling ?

在pycharm运行run.py报错

我用的是gpt3.5的key，运行run.py报错，报错信息为openai.error.RateLimitError: You exceeded your current quota, please check your plan and billing details.
Q1:想要调试程序，是从run.py做调试入口吗
Q2:支持gpt3.5吗？还是一定要gpt4

python version and pip version

please tell me, which python version and pip version used by salesgpt? i cannot run this program now

Introducing custom product catalog

I've updated the sample_product_catalog.txt file as per my needs. However, during the conversation , I see that the AI is generating a random set of products and prices as per its wish.
What all code files do I need to change to get the custom result I'm looking for?

Ability to call tools at End

Any option to use tools on let's say the last stage to save the response or parse relevant information to DB

怎样才能支持Azure Openai？？

GPT-chat

In this solution, you use the instruct chat gpt model to conduct a dialogue with the user. don't you think that this is not the best idea, the chat version is better suited for this

Seeks effective evaluation methods

This project is really fascinating as it uses LLM to implement the function of e-commerce shopping guide. However, I'm curious about how we can assess the effectiveness of a shopping guide assistant's replies. It may not be possible to achieve practical application level through just one initial attempt, so we may need to repeatedly optimize our design based on evaluation indicators. Do you have any thoughts on evaluating the effectiveness of the shopping guide assistant's replies?

Inconsistent Handling of kwargs Boolean Values

Description:

The agents.py file currently employs varying methods to handle boolean values in kwargs. For instance, it checks "use_custom_prompt" as a string, whereas "use_tools" is treated as a boolean. Such inconsistency may lead to confusion and unexpected behaviors.

I propose that both cases be managed in a consistent manner by treating "True" values as strings. Doing so will make the codebase follow a unified strategy for handling these options.

Below is my updated example_agent_setup.json. Please pay attention to the last two kwargs (use_tools and product_catalog), and note that we don't use JSON true but the string "True" for boolean values.

{
"salesperson_name": "Ted Lasso",
"salesperson_role": "Business Dev Rep",
"company_name": "Sleep Haven",
"company_business": "Sleep Haven offers premium mattresses and sleep accessories for ultimate comfort...",
"company_values": "Sleep Haven aims to enhance sleep quality through top-notch products and service...",
"conversation_purpose": "Discover sleep needs, offer premier mattress solution.",
"conversation_type": "call",
"use_custom_prompt": "True",
"custom_prompt": "Remember, you're {salesperson_name}, a {salesperson_role} at {company_name}...",
"use_tools": "True",
"product_catalog": "examples/sample_product_catalog.txt"
}

Currently, different methods are used to handle boolean values in kwargs, as mentioned earlier. This divergence can lead to ambiguity and unforeseen behavior.

I recommend that we adopt a uniform method by treating "True" values as strings. This will bring consistency to the codebase's handling of these options.

Proposed Fix:

Update the following section in agents.py:

Current code:

if "use_tools" in kwargs.keys() and kwargs["use_tools"] is True:

Updated code:

if "use_tools" in kwargs and kwargs["use_tools"] == "True":

Steps to Reproduce:

Use my example_agent_setup.json.
Observe that tools are never added.

Expected Behavior:

The code should manage boolean values in kwargs uniformly by recognizing "True" values as strings.

How to integrate into your own python application

When use tool set to True every time it response "I apologize, I was unable to find the answer to your question. Is there anything else I can help with?"

When use tool set to True every time it response "I apologize, I was unable to find the answer to your question. Is there anything else I can help with?"

Problems with starting asynchronous work

Hello! I'm trying to use def do(self, conversation_history: [str], human_input=None): asynchronously. So I change def do to async def do. Also I changed sales_agent.step() to sales_agent.astep()

Is it right? Or I need to use other methods, course I have error SalesGPT._call() takes 2 positional arguments but 3 were given after this changes...

Maybe there is some example of use or you can tell me what is wrong with me?