Unable to Integrate ConversationChain with Tools like duckduckgo or serp-api

[markwealthio]

Issue you'd like to raise.

Hi, I'm looking for some implementation that's utilizing a ConversationChain and giving it access to Internet. I want to integrate duckduckgo or bing-api or serpapi into my ConversationChain.

Would appreciate any help! Thanks

Suggestion:

No response

[dosubot[bot]]

🤖

Hello @markwealthio, it's great to see you again! I hope you're having a good day.

Sure, I can guide you on how to integrate tools like DuckDuckGo, Bing-API, or SerpAPI into your ConversationChain in the LangChain framework.

Firstly, you can refer to two similar solved issues in the LangChain repository:

1. How to make a Chatbot respond based on custom data and from the internet?
2. How to connect a Chatbot that has its own data but also has access to the internet for search?

The suggested solution in these issues is to modify the custom_search function to convert the search results into Document objects. Then, in the main loop, the internet_documents are added to the combined_results list instead of the raw internet_results.

Here's an example of how you might do this:

def custom_search(query):
    max_results = 3

    internet_results = search.run(query)[:max_results]
    # Convert the search results into Document objects
    internet_documents = [Document(page_content=result) for result in internet_results]
    return internet_documents

# ...

while True:
    query = input("Hola, soy Chatbot. ¿Qué te gustaría saber? ")

    internet_documents = custom_search(query)

    combined_results = loaded_content + internet_documents

    response = chain(
        {"question": query, "chat_history": history, "documents": combined_results})

    print(response["answer"])

    history.append(("system", query))
    history.append(("assistant", response["answer"]))

To integrate tools like DuckDuckGo, Bing-API, or SerpAPI into your ConversationChain, you can use the load_tools function provided in the load_tools.py file. This function takes a list of tool names and an optional language model as arguments and returns a list of tools.

Here is an example of how you can use this function to load the DuckDuckGo, Bing-API, and SerpAPI tools:

from langchain.agents.load_tools import load_tools
from langchain_core.language_models import YourLanguageModel  # replace with your actual language model

# Initialize your language model
llm = YourLanguageModel()

# Load the tools
tools = load_tools(["ddg_search", "bing_search", "serpapi"], llm)

# Now you can use these tools in your ConversationChain

In the above code, replace YourLanguageModel with your actual language model. The load_tools function will return a list of tools that you can use in your ConversationChain. The tool names for DuckDuckGo, Bing-API, and SerpAPI are "ddg_search", "bing_search", and "serpapi" respectively.

Please note that you might need to pass additional parameters to the load_tools function depending on the requirements of the individual tools. For example, some tools might require API keys or other configuration parameters. These parameters should be passed as keyword arguments to the load_tools function.

You can find more details about the load_tools function in the libs/langchain/langchain/agents/load_tools.py file.

As for the ConversationChain, it is a subclass of LLMChain designed to carry on a conversation and load context from memory. You can find more details about the ConversationChain class in the libs/langchain/langchain/chains/conversation/base.py file.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Best, Dosu

Sources

• How to make a Chatbot responde based on custom data and from the internet?
• How to connect a Chatbot that has its own data but has also access to internet for search?
• libs/langchain/langchain/agents/load_tools.py
• libs/langchain/langchain/chains/conversation/base.py

[markwealthio]

Can you give me the implementation for ConversationChain and how to integrate the tools into the ConversationChain?

Thanks!

On Wed, Nov 29, 2023 at 6:57 PM dosubot[bot] @.***> wrote:

🤖

Hello @markwealthio https://github.com/markwealthio, it's great to see you again! I hope you're having a good day.

Sure, I can guide you on how to integrate tools like DuckDuckGo, Bing-API, or SerpAPI into your ConversationChain in the LangChain framework.

Firstly, you can refer to two similar solved issues in the LangChain repository:

1. How to make a Chatbot respond based on custom data and from the internet? https://github.com/langchain-ai/langchain/issues/8816
2. How to connect a Chatbot that has its own data but also has access to the internet for search? https://github.com/langchain-ai/langchain/issues/8625

The suggested solution in these issues is to modify the custom_search function to convert the search results into Document objects. Then, in the main loop, the internet_documents are added to the combined_results list instead of the raw internet_results.

Here's an example of how you might do this:

def custom_search(query): max_results = 3

internet_results = search.run(query)[:max_results]
# Convert the search results into Document objects
internet_documents = [Document(page_content=result) for result in internet_results]
return internet_documents

...

while True: query = input("Hola, soy Chatbot. ¿Qué te gustaría saber? ")

internet_documents = custom_search(query)

combined_results = loaded_content + internet_documents

response = chain(
    {"question": query, "chat_history": history, "documents": combined_results})

print(response["answer"])

history.append(("system", query))
history.append(("assistant", response["answer"]))

To integrate tools like DuckDuckGo, Bing-API, or SerpAPI into your ConversationChain, you can use the load_tools function provided in the load_tools.py file. This function takes a list of tool names and an optional language model as arguments and returns a list of tools.

Here is an example of how you can use this function to load the DuckDuckGo, Bing-API, and SerpAPI tools:

from langchain.agents.load_tools import load_toolsfrom langchain_core.language_models import YourLanguageModel # replace with your actual language model

Initialize your language modelllm = YourLanguageModel()

Load the toolstools = load_tools(["ddg_search", "bing_search", "serpapi"], llm)

Now you can use these tools in your ConversationChain

In the above code, replace YourLanguageModel with your actual language model. The load_tools function will return a list of tools that you can use in your ConversationChain. The tool names for DuckDuckGo, Bing-API, and SerpAPI are "ddg_search", "bing_search", and "serpapi" respectively.

Please note that you might need to pass additional parameters to the load_tools function depending on the requirements of the individual tools. For example, some tools might require API keys or other configuration parameters. These parameters should be passed as keyword arguments to the load_tools function.

You can find more details about the load_tools function in the libs/langchain/langchain/agents/load_tools.py https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/load_tools.py file.

As for the ConversationChain, it is a subclass of LLMChain designed to carry on a conversation and load context from memory. You can find more details about the ConversationChain class in the libs/langchain/langchain/chains/conversation/base.py https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/conversation/base.py file.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Best, Dosu https://dosu.dev Sources

• How to make a Chatbot responde based on custom data and from the internet? https://github.com/langchain-ai/langchain/issues/8816
• How to connect a Chatbot that has its own data but has also access to internet for search? https://github.com/langchain-ai/langchain/issues/8625
• libs/langchain/langchain/agents/load_tools.py https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/load_tools.py
• libs/langchain/langchain/chains/conversation/base.py https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/conversation/base.py

— Reply to this email directly, view it on GitHub https://github.com/langchain-ai/langchain/issues/14021#issuecomment-1831669650, or unsubscribe https://github.com/notifications/unsubscribe-auth/BBUY3WMRCFURZVVAXVUTOM3YG4IKVAVCNFSM6AAAAAA77HY5ROVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMZRGY3DSNRVGA . You are receiving this because you were mentioned.Message ID: @.***>

[dosubot[bot]]

🤖

Based on the information you've provided and the context from the LangChain repository, here's a step-by-step guide on how to implement the ConversationChain and integrate the tools into it:

1. Initialize your language model: You've already done this in your code snippet. Just replace YourLanguageModel with your actual language model.

from langchain_core.language_models import YourLanguageModel  # replace with your actual language model

# Initialize your language model
llm = YourLanguageModel()

2. Load the tools: You've also done this in your code snippet. You might need to pass additional parameters to the load_tools function depending on the requirements of the individual tools.

from langchain.agents.load_tools import load_tools

# Load the tools
tools = load_tools(["ddg_search", "bing_search", "serpapi"], llm)

3. Create a ConversationChain: You can create a ConversationChain by instantiating the ConversationChain class and passing your language model and the loaded tools to it. Here's an example:

from langchain.chains import ConversationChain

# Create a ConversationChain
chain = ConversationChain(llm=llm, tools=tools)

4. Use the ConversationChain in your conversation loop: You can use the ConversationChain in your conversation loop to generate responses. Here's an example based on your code snippet:

# ...
while True:
    query = input("Hola, soy Chatbot. ¿Qué te gustaría saber? ")

    internet_documents = custom_search(query)

    combined_results = loaded_content + internet_documents

    response = chain(
        {"question": query, "chat_history": history, "documents": combined_results})

    print(response["answer"])

    history.append(("system", query))
    history.append(("assistant", response["answer"]))

In this example, the ConversationChain is used to generate a response based on the user's query, the conversation history, and the documents obtained from the internet search. The response is then printed and added to the conversation history.

Please note that this is a simplified example and the actual implementation might be more complex depending on your specific requirements and the capabilities of your language model and tools.

For more detailed examples and information, you can refer to the following issues in the LangChain repository:

• Creating conversational bots with memory, agents, and tools

I hope this helps! If you have any more questions, feel free to ask.

Sources

• Creating conversational bots with memory, agents, and tools
• libs/langchain/langchain/agents/load_tools.py
• libs/langchain/langchain/chains/conversation/base.py

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