Importance of VectorStoreToolkit, vectorstore_agent and VectorStoreInfo in document based domain specific question answering

[pradeepdev-1995]

Issue you'd like to raise.

I am trying to query the documents using the below stack

Langchain+ embedding tool + vectore store + LLM model

There are many tools and techniques for this in langchain including load_qa_chain, RetrievalQA,VectorstoreIndexCreator,ConversationalRetrievalChain. Those are already giving the good results(Not optimal)

But found one more technique including VectorStoreInfo,VectorStoreToolkit and vectorstore_agent

What is the advantage/Importance of this pipeline which using VectorStoreInfo,VectorStoreToolkit and vectorstore_agent over the other which doesnt follow this pipeline(use any of load_qa_chain, RetrievalQA,VectorstoreIndexCreator,ConversationalRetrievalChain)

Suggestion:

No response

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're working with the LangChain framework for document-based domain-specific question answering and you're curious about the role and advantages of using the VectorStoreInfo, VectorStoreToolkit, and vectorstore_agent in your pipeline.

In the LangChain framework, the VectorStoreInfo, VectorStoreToolkit, and vectorstore_agent play a significant role. The VectorStoreInfo is a model that holds information about a vectorstore, including its name, description, and a reference to the vectorstore itself.

The VectorStoreToolkit is a set of tools for interacting with a vector store. It includes the VectorStoreQATool for querying the vectorstore and the VectorStoreQAWithSourcesTool for querying the vectorstore and returning the sources of the answers.

The vectorstore_agent is part of the VectorStoreRouterToolkit, which is used for routing between different vector stores. This allows the agent to answer questions about different topics by directing the question to the appropriate chain.

The primary advantage of using this pipeline is its flexibility and power. It allows for multi-hop vectorstore reasoning, which means the agent can answer multi-hop questions that depend on vectorstores. This is a feature not available with the other techniques you mentioned, such as load_qa_chain, RetrievalQA, VectorstoreIndexCreator, and ConversationalRetrievalChain.

Please note that while this pipeline offers certain advantages, the best choice depends on your specific use case and requirements. If you find this information helpful and believe it could benefit other users, I encourage you to make a pull request to update the documentation. This way, you can share your insights and experiences with the broader community.

Sources

• docs/extras/modules/agents/how_to/agent_vectorstore.ipynb
• docs/extras/guides/evaluation/agent_vectordb_sota_pg.ipynb
• langchain/agents/agent_toolkits/vectorstore/toolkit.py
• docs/extras/modules/agents/toolkits/vectorstore.ipynb

---

This response is meant to be useful, save you time, and share context. 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.

[pradeepdev-1995]

So multi hop questions are not possible in load_qa_chain, RetrievalQA, VectorstoreIndexCreator, and ConversationalRetrievalChain? Then how the proposed pipeline(VectorStoreInfo,VectorStoreToolkit and vectorstore_agent) make it happen internally? Please explain the internal flow of this to make it multi hop questions to answerable.

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're trying to enhance your document-based domain-specific question-answering capabilities using LangChain. You've been using tools like load_qa_chain, RetrievalQA, VectorstoreIndexCreator, and ConversationalRetrievalChain, and you're curious about the benefits of using a different pipeline involving VectorStoreInfo, VectorStoreToolkit, and vectorstore_agent.

The pipeline involving VectorStoreInfo, VectorStoreToolkit, and vectorstore_agent is particularly useful for multi-hop vectorstore reasoning. This means it can handle complex queries that require multiple steps or "hops" to answer, which is not possible with the tools you've been using so far.

Here's a brief overview of how this pipeline works:

1. The data is first ingested into a vectorstore using a TextLoader and VectorstoreIndexCreator.
2. A RetrievalQA is then created from the vectorstore.
3. This RetrievalQA is used as a tool in the overall agent.
4. The agent can be used as a router to route between multiple vectorstores.
5. If return_direct=True is set, the agent can be used to directly return the result of the RetrievalQAChain, avoiding any extra work after querying the RetrievalQAChain.
6. The agent can be used to answer multi-hop questions that depend on vectorstores.

In this pipeline, the VectorStoreToolkit class is used to interact with a vector store. It uses the VectorStoreInfo to get information about the vector store and uses this information to create tools for querying the vector store. The VectorStoreRouterToolkit class is used to route between different vector stores. It creates a tool for each vector store and uses these tools to answer questions.

This pipeline also introduces the use of FAISS vectorstore to create embeddings for each tool description. For an incoming query, embeddings are created for that query and a similarity search is performed for relevant tools. This is an additional step that can be incorporated into the pipeline to enhance the retrieval of relevant information based on the query.

This pipeline is advantageous over the other techniques because it allows for multi-hop reasoning. In the context shared, the pipeline is used to create two separate question answering systems, one for the State of the Union address and one for Paul Graham's essays, and an agent is set up to route between them. The additional step of creating embeddings for each tool description enhances the retrieval of relevant information based on the query.

Please note that this is one potential solution and there might be other ways to achieve your goal. If you find this solution helpful and believe it could benefit other users, I encourage you to make a pull request to update the documentation. This way, you can contribute to the LangChain community and help others who might encounter the same issue.

Sources

• docs/extras/modules/agents/how_to/agent_vectorstore.ipynb
• docs/extras/guides/evaluation/agent_vectordb_sota_pg.ipynb
• langchain/agents/agent_toolkits/vectorstore/toolkit.py
• docs/extras/modules/agents/toolkits/vectorstore.ipynb
• docs/extras/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb
• docs/extras/use_cases/agents/custom_agent_with_plugin_retrieval.ipynb

---

This response is meant to be useful, save you time, and share context. 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.

[sankar-forrester]

does the VectorStoreToolkit require that we use OpenAI? I am trying to use it with huggingfacePipepline and dolly as the model

[pradeepdev-1995]

@sankar-forrester No it doesnt

[dosubot[bot]]

Hi, @pradeepdev-1995! 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 the importance and advantage of using the pipeline that includes VectorStoreInfo, VectorStoreToolkit, and vectorstore_agent in document-based domain-specific question answering. I provided a detailed response explaining the role and advantages of using this pipeline, including its flexibility and power for multi-hop vectorstore reasoning. I also explained the internal flow of the pipeline. You clarified that the VectorStoreToolkit does not require the use of OpenAI.

Before we close this issue, we wanted to check if it 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 it will be automatically closed in 7 days.

Thank you for your contribution to the LangChain repository!