redis loading issue

[rahulkrprajapati]

my code seems to working fine the first time but every time fails subsequently

self.redis_client = redis.Redis(
                    host=self.config.get("UPSTASH_REDIS_HOST"),
                    port=self.config.get("UPSTASH_REDIS_PORT"),
                    password=self.config.get("UPSTASH_REDIS_PASSWORD"),
                    ssl=True,
                )
                self.docstore = RedisDocumentStore.from_redis_client(
                    redis_client=self.redis_client,
                    namespace=self.namespace
                )
                self.storage_context = StorageContext.from_defaults(
                    docstore=self.docstore,
                    index_store=RedisIndexStore.from_redis_client(
                        redis_client=self.redis_client,
                        namespace=self.namespace
                    ),
                )
                self.base_index = VectorStoreIndex(
                    nodes=[],
                    storage_context=self.storage_context,
                )
                self.base_retriever = self.base_index.as_retriever(
                    similarity_top_k=self.similarity_top_k
                )
               
               
                
                try:
                    # Load all indices
                    indices = load_indices_from_storage(self.storage_context)

                    # Print out the index_ids of all loaded indices
                    for index in indices:
                        print(index.index_id)
                    self.base_index = load_index_from_storage(self.storage_context)
                    print("[INFO] Index found at storage")
                except ValueError as e:
                    print("[INFO] No index found at storage")

it seems to be add a new index everytime I run it

[dosubot[bot]]

🤖

Hey there, @rahulkrprajapati! Great to see you back. Hope you're doing well!

Based on the code you've shared, it seems like you're initializing a new VectorStoreIndex every time you run your code. This is why a new index is being created each time.

self.base_index = VectorStoreIndex(
    nodes=[],
    storage_context=self.storage_context,
)

The VectorStoreIndex is initialized with an empty list of nodes, and then it's used to create a retriever. After that, you're trying to load indices from the storage. If there's no index found, it doesn't affect the self.base_index you've already initialized.

If you want to avoid creating a new index every time, you should initialize self.base_index only if there's no index found in the storage. Here's how you can modify your code:

try:
    # Load all indices
    indices = load_indices_from_storage(self.storage_context)

    # Print out the index_ids of all loaded indices
    for index in indices:
        print(index.index_id)
    self.base_index = load_index_from_storage(self.storage_context)
    print("[INFO] Index found at storage")
except ValueError as e:
    print("[INFO] No index found at storage")
    self.base_index = VectorStoreIndex(
        nodes=[],
        storage_context=self.storage_context,
    )

In this modified code, self.base_index is initialized only when there's no index found in the storage. If an index is found, it's loaded from the storage and assigned to self.base_index.

Please note that this is just a suggestion based on the code and information you've provided. If this doesn't solve your issue or if there are other factors at play, please provide more details so I can give a more accurate solution.

For more information about how indices are loaded from the storage, you can refer to the test_loading.py file in the LlamaIndex repository.

Sources

• llama-index-core/llama_index/core/indices/list/base.py
• llama-index-core/tests/indices/test_loading.py

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[rahulkrprajapati]

@dosu-bot

Everytime I call this function after reloading the index from the vector store:
using self.base_index = load_index_from_storage(self.storage_context)

my retrived_nodes are empty

def process_fetch_query_results(
        self, query="", similarity_top_k_reranker=3
    ):
        try:
            self.base_retriever = self.base_index.as_retriever(
                similarity_top_k=self.similarity_top_k
            )
            self.retriever = AutoMergingRetriever(
                self.base_retriever, self.storage_context, verbose=True
            )
            self.postprocessor = SentenceTransformerRerank(
                model="cross-encoder/ms-marco-MiniLM-L-2-v2",
                top_n=similarity_top_k_reranker,
            )
            query_bundle = QueryBundle(query_str=query)
            print("*****************",query)
            retrived_nodes = self.retriever.retrieve(query_bundle)
            print("*************", retrived_nodes)
            rerank_nodes = self.postprocessor.postprocess_nodes(
                nodes=retrived_nodes, query_bundle=query_bundle
            )
            return rerank_nodes
        except Exception as e:
            raise Exception(f"An error occurred retrieving: {e}")

[dosubot[bot]]

🤖

Based on the code you've shared and the context provided, it seems like the retrieved_nodes might be empty due to a few potential reasons:

1. The query you're passing to self.retriever.retrieve(query_bundle) might not be matching any nodes in the vector store. This could be due to the nature of the query or the state of the vector store after reloading the index.

2. The query_result returned by the vector store query might contain ids, but the corresponding nodes could not be found in the docstore. This could happen if the docstore does not contain the nodes corresponding to the ids returned by the vector store query.

3. The query_result might contain nodes, but the docstore does not contain the corresponding documents. This could happen if the docstore does not contain the documents corresponding to the nodes returned by the vector store query.

You can verify these possibilities by adding some debug prints or logs in the _build_node_list_from_query_result method in the retriever.py file. Specifically, you can print the query_result after the vector store query and check if it contains any nodes or ids. If it does, you can then check if these nodes or ids exist in the docstore.

Regarding the load_index_from_storage(self.storage_context) method, it is designed to load a single index from a given storage context. If the index is not found or more than one index is found without specifying an index_id, it will raise a ValueError. Therefore, you should ensure that the storage_context you're passing to this method contains the correct index that you want to load.

If you're still facing the issue, could you please provide more details about the state of your vector store and docstore after reloading the index, and the query you're trying to retrieve? This would help in identifying the exact cause of the issue.

Sources

• llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py
• llama-index-core/tests/indices/test_loading_graph.py
• llama-index-core/llama_index/core/indices/loading.py

About Dosu

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[rahulkrprajapati]

@dosu-bot

Write down a complete storage context just using the RedisDocumentStore, and RedisIndexStore. Also use RedisVectorStore if supported or try and store the vectors in redis. Load the nodesusing load_index_from_storage and then retrive them using AutoMergingRetriever

[dosubot[bot]]

🤖

Sure, I can provide a detailed guide on how to create a complete storage context using RedisDocumentStore, RedisIndexStore, and RedisVectorStore, load the nodes using the load_index_from_storage function, and then retrieve them using AutoMergingRetriever in the LlamaIndex framework.

First, you need to create instances of RedisDocumentStore, RedisIndexStore, and RedisVectorStore. Here's how you can do it:

from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.index_store.redis import RedisIndexStore
from llama_index.core.vector_stores.redis import RedisVectorStore

# Create instances
doc_store = RedisDocumentStore.from_host_and_port(host="localhost", port=6379)
index_store = RedisIndexStore.from_host_and_port(host="localhost", port=6379)
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
)

Next, you can load the nodes into the RedisVectorStore using the add method:

from llama_index.core.schema import BaseNode

# Create some nodes
nodes = [BaseNode(node_id="1", ref_doc_id="doc1", embedding=[0.1, 0.2, 0.3]),
         BaseNode(node_id="2", ref_doc_id="doc2", embedding=[0.4, 0.5, 0.6])]

# Add nodes to the index
vector_store.add(nodes)

Now, you can load the nodes using the load_index_from_storage function:

from llama_index.core import load_index_from_storage, StorageContext

# Create a storage context
storage_context = StorageContext(doc_store=doc_store, index_store=index_store, vector_store=vector_store)

# Load the index
index = load_index_from_storage(storage_context)

Finally, you can retrieve the nodes using AutoMergingRetriever. However, without the implementation of AutoMergingRetriever, I cannot provide a specific example. You would typically create an instance of AutoMergingRetriever and call its retrieve method.

Please note that the above code snippets are based on the provided context and the available documentation. If you encounter any issues or need further assistance, feel free to ask.

Sources:

• RedisDocumentStore
• RedisIndexStore
• RedisVectorStore

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py
• llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/llama_index/storage/docstore/redis/base.py
• docs/module_guides/storing/index_stores.md

About Dosu

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.

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