[Bug]: VectorStoreIndex not working when created with RedisIndexStore and RedisVectorStore

[aiorga-sherpas]

Bug Description

When querying a VectorStoreIndex using a RedisIndexStore and RedisVectorStore, the query fails.

Version

0.9.39

Steps to Reproduce

chat_id = "test"
docstore = RedisDocumentStore.from_redis_client(
    redis_client=redis_client,
    namespace="Docstore",
)
index_store = RedisIndexStore.from_redis_client(
    redis_client=redis_client,
    namespace="IndexStore"
)
vector_store = RedisVectorStore(
    redis_url=f"redis://:{REDIS_DB_PASSWORD}@{REDIS_DB_HOST}:{REDIS_DB_PORT}/0",
    index_name="VectorStore" + chat_id,
    index_prefix=chat_id
)

storage_context=StorageContext.from_defaults(
    docstore=docstore, index_store=index_store, vector_store=vector_store
)

index = VectorStoreIndex(
    nodes=[],
    storage_context=storage_context,
    service_context=service_context)

print(index.as_chat_engine(ChatMode.CONTEXT).chat("Hi"))

Relevant Logs/Tracbacks

raise response
redis.exceptions.ResponseError: VectorStoretest: no such index

[dosubot[bot]]

🤖

Hey @aiorga-sherpas, nice to see you back! Running into a bit of a snag, are we? Let's dive into this together.

Based on the error message you provided, it seems like the Redis instance is unable to find the index you're trying to query. This could be due to a few reasons:

1. Redis and RediSearch Modules: The RedisVectorStore class requires both the Redis and RediSearch modules to be installed. If either of these modules is not installed, the initialization of RedisVectorStore will fail with a ValueError. Please ensure that both of these modules are installed and properly configured.

2. Redis Connection: The RedisVectorStore class attempts to connect to a Redis instance using the provided redis_url. If the connection fails, a ValueError is raised. Please verify that your Redis instance is running and that the URL is correct.

3. Index Overwrite: If an index with the same name already exists and the overwrite parameter is set to False, the add method will fail with a ValueError. You can either set overwrite to True or use a different index_name.

4. Query Embedding: The query method requires a query_embedding to be provided. If query.query_embedding is None, a ValueError is raised. Please ensure that you're providing a valid query embedding.

5. Redis Errors: If there's an error querying the index or a timeout occurs while querying, a redis.exceptions.RedisError or redis.exceptions.TimeoutError is raised respectively. Please ensure that your Redis instance is functioning correctly.

6. No Documents Found: If no documents are found when querying the index, a ValueError is raised. Please ensure that you have documents in your index that match your query.

As for the changes between version 0.9.39 and the latest version 0.9.40 of LlamaIndex, I'm unable to provide specific details as I don't have access to the repository's change log. However, it's always a good idea to update to the latest version as it may contain bug fixes or improvements that could potentially resolve your issue.

If you're still experiencing issues after checking these potential causes, could you please provide more details about your setup and the exact steps you're taking when the error occurs? This will help me better understand the issue and provide a more accurate solution.

For more information, you can refer to the RedisVectorStore class in the LlamaIndex repository.

Sources

• llama_index/vector_stores/redis.py

---

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[aiorga-sherpas]

@dosu-bot lets dive deep in the point 6. It might be possible that no documents are found. How can we make a llama index chat without documents?

[dosubot[bot]]

🤖

Based on the context provided, it seems like you're trying to initiate a chat using the VectorStoreIndex without any documents. However, as per the LlamaIndex design, the VectorStoreIndex requires documents to be present in the RedisVectorStore to function properly.

The error message redis.exceptions.ResponseError: VectorStoretest: no such index indicates that the VectorStoreIndex is trying to access an index in the RedisVectorStore that doesn't exist. This could be because no documents have been added to the RedisVectorStore or the index name provided does not match any existing index.

To resolve this issue, you can add documents to the RedisVectorStore before initializing the VectorStoreIndex. Here's an example of how to do this:

from llama_index.vector_stores.redis import RedisVectorStore

# Create a RedisVectorStore
vector_store = RedisVectorStore(
    index_name="my_index",
    index_prefix="llama_index",
    index_args={"algorithm": "HNSW", "m": 16, "ef_construction": 200, "distance_metric": "cosine"},
    redis_url="redis://localhost:6379/",
    overwrite=True
)

# Add documents to the RedisVectorStore
vector_store.add(documents=[...])

# Now you can initialize the VectorStoreIndex
index = VectorStoreIndex(
    nodes=[],
    storage_context=storage_context,
    service_context=service_context)

In this example, documents are added to the RedisVectorStore using the add method before the VectorStoreIndex is initialized. This ensures that the VectorStoreIndex has documents to work with when it's created.

If you want to initiate a chat without any documents, you might need to modify the LlamaIndex codebase to handle this scenario. However, this could lead to unexpected behavior since the VectorStoreIndex is designed to work with documents.

For more information, you can refer to the RedisVectorStore and VectorStoreIndex source code.

Sources

• llama_index/vector_stores/redis.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.

[aiorga-sherpas]

@dosu-bot. Is there any way of creating an EmptyIndex or empty VectorStoreIndex to use it as chat engine?

[dosubot[bot]]

🤖

Yes, there is a way to create an EmptyIndex or empty VectorStoreIndex in LlamaIndex v0.9.39. The EmptyIndex can be used as a chat engine when no documents are found in the VectorStoreIndex. Here is a code snippet that demonstrates this:

from llama_index.data_structs.data_structs import EmptyIndexStruct
from llama_index.indices.empty.base import EmptyIndex
from llama_index.indices.service_context import ServiceContext

def test_empty(
    mock_service_context: ServiceContext,
) -> None:
    """Test build list."""
    empty_index = EmptyIndex(service_context=mock_service_context)
    assert isinstance(empty_index.index_struct, EmptyIndexStruct)

    retriever = empty_index.as_retriever()
    nodes = retriever.retrieve("What is?")
    assert len(nodes) == 0

In this code, EmptyIndex(service_context=mock_service_context) is used to create an instance of EmptyIndex. The as_retriever() method is then used to get a retriever from the EmptyIndex, and the retrieve("What is?") method is used to retrieve nodes from the retriever. The result is an empty list, indicating that no documents were found. You can find this in the tests/indices/empty/test_base.py file.

Regarding the VectorStoreIndex, it is a class in the LlamaIndex codebase that uses Redis as its backend for storing and retrieving vectors (embeddings) and their associated metadata. It interacts with the RedisIndexStore and RedisVectorStore in various ways such as storing vectors, retrieving vectors, deleting vectors, persisting the vector store, and deleting the index. You can find more details in the llama_index/vector_stores/redis.py file.

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

Sources

• tests/indices/empty/test_base.py
• llama_index/vector_stores/redis.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.

[aiorga-sherpas]

I think the problem was the amount of documents sent to the creation of the index.