Agents on a local llm with custom data

[Kuramdasu-ujwala-devi]

I'm implementing vectorstore agent on my custom data. Can I implement this with a local llm like gpt4all(GPT4All-J v1.3-groovy) Can agents give better and expected answer when we use agents, or should go with better model like(gpt4, llama2)?

[Kuramdasu-ujwala-devi]

@dosu-bot please answer

[dosubot[bot]]

🤖

Yes, you can certainly implement a vectorstore agent with a local language model like GPT4All-J v1.3-groovy on your custom data using the LangChain framework. Here's an example of how you can do it:

from langchain.llms import GPT4All

# Initialize the model
model = GPT4All(model="./path/to/your/gpt4all-model.bin", n_threads=8)

# Use the model
response = model("Your prompt here")

As for the performance, it depends on various factors such as the quality and relevance of your custom data, the specific task at hand, and the capabilities of the different models. More advanced models like GPT4 or Llama2 might provide better results for a wider range of tasks due to their larger model size and more advanced training techniques. However, if your custom data is highly relevant and specific to the task, a fine-tuned model like GPT4All-J v1.3-groovy might perform better. It's recommended to experiment with different models and evaluate their performance on your specific task.

In LangChain, you can also combine agents and vectorstores. This allows you to interact with your ingested data in an agentic manner. You can create a RetrievalQA and use it as a tool in the overall agent. This can be done with multiple different vectordbs, and use the agent as a way to route between them. You can either let the agent use the vectorstores as normal tools, or you can set return_direct=True to use the agent as a router. This might be useful if you want to use your GPT4All-J v1.3-groovy model to interact with your custom data in a more complex way.

For more details, you can refer to the following documentation:

• libs/langchain/langchain/llms/gpt4all.py
• docs/extras/modules/agents/how_to/agent_vectorstore.ipynb
• docs/extras/integrations/toolkits/vectorstore.ipynb

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

Sources

• libs/langchain/langchain/llms/gpt4all.py
• docs/extras/modules/agents/how_to/agent_vectorstore.ipynb
• docs/extras/integrations/toolkits/vectorstore.ipynb
• libs/langchain/langchain/vectorstores/singlestoredb.py
• libs/langchain/langchain/vectorstores/base.py
• libs/langchain/langchain/vectorstores/deeplake.py

---

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]]

Hi, @Kuramdasu-ujwala-devi! I'm Dosu, and I'm helping the LangChain team manage our backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, you were asking about implementing the vectorstore agent on custom data using a local llm like GPT4All-J v1.3-groovy. In response, I suggested that it is possible to implement a vectorstore agent with a local language model like GPT4All-J v1.3-groovy. I provided an example of how to do it and mentioned that the performance depends on various factors. I also suggested experimenting with different models.

Now, we would like to know if this issue is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days.

Thank you for your understanding and contribution to the LangChain project! Let us know if you have any further questions or concerns.