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Updated 3 weeks ago

i want to beable to use more then 1 vector sotre in llamaindex like one vector has data re

ppayload
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kkapa.ai
Please stand by @payload as I search the knowledge base. I normally take about 15 seconds or so. :wave:
kkapa.ai
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LlamaIndex supports using multiple vector stores for retrieval, allowing you to store different types of data in separate vector stores and query them based on the specific needs of each query. This functionality is provided through the MultiModalVectorStoreIndex feature. Here's a brief overview of how you can achieve this:

  1. LlamaIndex offers a MultiModalVectorStoreIndex that supports building separate vector stores for different types of data, such as image and text embedding vector stores.
  1. The MultiModalRetriever and SimpleMultiModalQueryEngine components support text-to-text/image and image-to-image retrieval, as well as simple ranking fusion functions for combining text and image retrieval results.
  1. You can create multiple vector stores, each containing different types of data, and use them within the same index structure.
To implement this, you would need to:

  1. Create separate vector stores for each type of data you want to store.
  2. Use the MultiModalVectorStoreIndex to combine these vector stores.
  3. Utilize the MultiModalRetriever to query across these vector stores based on your specific query requirements.
For more detailed information and implementation examples, you can refer to the following resources:

Multi-modal Vector Stores

Vector Store Guide

These resources provide more in-depth explanations and code examples to help you set up and use multiple vector stores in LlamaIndex based on your specific use case.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ppayload
@kapa.ai my problem is not multi modal both are 2 different vector stores lets say one stores docs from company A while other stores docs from company B
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
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I apologize for the misunderstanding in my previous response. You're right, your question is not about multi-modal vector stores, but rather about using multiple vector stores for different sets of documents. LlamaIndex does indeed support this use case. Let me explain how you can achieve this using the Simple Fusion Retriever feature.

The Simple Fusion Retriever allows you to combine retrieval results from multiple queries and multiple indexes. This is exactly what you need for your scenario where you have documents from Company A in one vector store and documents from Company B in another.

Here's how you can set this up:

  1. First, create separate indexes for each company's documents:
Plain Text
from llama_index.core import VectorStoreIndex

index_A = VectorStoreIndex.from_documents(documents_A)
index_B = VectorStoreIndex.from_documents(documents_B)


  1. Then, use the QueryFusionRetriever to combine these indexes:
Plain Text
from llama_index.core.retrievers import QueryFusionRetriever

retriever = QueryFusionRetriever(
    [index_A.as_retriever(), index_B.as_retriever()],
    similarity_top_k=2,
    num_queries=1,  # set this to 1 to use only the original query
    use_async=True,
    verbose=True,
)


  1. Now you can use this retriever to query across both vector stores:
Plain Text
query_engine = index.as_query_engine(retriever=retriever)
response = query_engine.query("Your query here")


This setup will query both vector stores and combine the results, allowing you to retrieve information from both Company A and Company B documents based on the relevance to the query.

For more detailed information and examples, you can refer to the Simple Fusion Retriever documentation.
kkapa.ai
This approach allows you to maintain separate vector stores for different sets of documents while still being able to query across all of them when needed.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ppayload
@kapa.ai if based on the query only one vector store should be used and if the answer can be made without query egine we should not use it any vector store
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
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Based on your question, it seems you're looking for a more flexible approach to using multiple vector stores in LlamaIndex, where you can selectively query specific vector stores based on the nature of the query. You're also interested in avoiding the use of a query engine when it's not necessary. I'll explain how you can achieve this using LlamaIndex.

To implement this approach, you can use the VectorIndexRetriever and customize it based on your query. Here's how you can do it:

  1. First, create separate vector stores for each set of documents:
Plain Text
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Create vector store for Company A
documents_A = SimpleDirectoryReader("path_to_company_A_docs").load_data()
index_A = VectorStoreIndex.from_documents(documents_A)

# Create vector store for Company B
documents_B = SimpleDirectoryReader("path_to_company_B_docs").load_data()
index_B = VectorStoreIndex.from_documents(documents_B)


  1. Then, create a function to select the appropriate vector store based on the query:
Plain Text
def select_vector_store(query):
    # Implement your logic here to decide which vector store to use
    if "Company A" in query:
        return index_A
    elif "Company B" in query:
        return index_B
    else:
        return None  # If no specific vector store is needed


  1. Use the selected vector store to retrieve information:
```python
from llama_index.core.retrievers import VectorIndexRetriever
kkapa.ai
def get_response(query):
selected_index = select_vector_store(query)

if selected_index is None:
# If no vector store is needed, process the query directly
# Implement your logic here
return "Processing query without vector store..."

# Use the selected vector store
retriever = VectorIndexRetriever(index=selected_index)
nodes = retriever.retrieve(query)

# Process the retrieved nodes and formulate a response
# This is a simple example; you might want to implement more sophisticated logic
response = "\n".join([node.text for node in nodes])
return response

Example usage

query = "What are Company A's policies?"
result = get_response(query)
print(result)
`` This approach allows you to: 1. Select a specific vector store based on the query content. 2. Bypass the vector stores entirely if the query doesn't require them. 3. Avoid using a query engine when it's not necessary, giving you more control over the retrieval and response generation process. Remember to implement the select_vector_store` function according to your specific needs and criteria for selecting the appropriate vector store.

For more information on customizing retrievers in LlamaIndex, you can refer to the Vector Store Guide.

This solution provides the flexibility you're looking for in handling different types of queries efficiently, using vector stores only when necessary.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ppayload
@kapa.ai it should be routed automatically
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
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I understand you want the routing to be done automatically rather than manually. LlamaIndex provides a solution for this using the Router Query Engine, which can automatically route queries to the appropriate vector store or query engine. Let me explain how you can implement this:

  1. First, you'll need to set up your individual vector stores as before:
Plain Text
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Create vector store for Company A
documents_A = SimpleDirectoryReader("path_to_company_A_docs").load_data()
index_A = VectorStoreIndex.from_documents(documents_A)

# Create vector store for Company B
documents_B = SimpleDirectoryReader("path_to_company_B_docs").load_data()
index_B = VectorStoreIndex.from_documents(documents_B)


  1. Next, you'll create query engine tools for each vector store:
Plain Text
from llama_index.core.tools import QueryEngineTool, ToolMetadata

tool_A = QueryEngineTool(
    query_engine=index_A.as_query_engine(),
    metadata=ToolMetadata(
        name="company_A_info",
        description="Provides information about Company A"
    )
)

tool_B = QueryEngineTool(
    query_engine=index_B.as_query_engine(),
    metadata=ToolMetadata(
        name="company_B_info",
        description="Provides information about Company B"
    )
)


  1. Then, you'll set up the Router Query Engine:
Plain Text
from llama_index.core.query_engine import RouterQueryEngine

router_query_engine = RouterQueryEngine.from_defaults(
    [tool_A, tool_B],
    verbose=True
)


  1. Now you can use this router query engine to automatically route queries:
Plain Text
response = router_query_engine.query("What are Company A's policies?")
print(response)
kkapa.ai
This setup will automatically route the query to the appropriate vector store based on the content of the query. The LLM will analyze the query and decide which tool (and thus, which vector store) is most appropriate to answer it.

If a query doesn't require any vector store, the router can also handle this scenario by not selecting any of the provided tools and instead generating a response directly.

For more detailed information on this approach, you can refer to the Router Query Engine documentation.

This solution provides the automatic routing you're looking for, efficiently handling different types of queries and using the appropriate vector store when necessary, all without manual intervention.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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