Loading index from a redis client

[rahulkrprajapati]

my doc store and storage context are defined as:

self.redis_client = redis.Redis(
                    host=self.config.get("REDIS_HOST"),
                    port=self.config.get("REDIS_PORT"),
                    password=self.config.get("REDIS_PASSWORD"),
                    ssl=True,
                )
                self.docstore = RedisDocumentStore.from_redis_client(
                    redis_client=self.redis_client
                )
                self.storage_context = StorageContext.from_defaults(
                    docstore=self.docstore,
                    index_store=RedisIndexStore.from_redis_client(
                        redis_client=self.redis_client
                    ),
                )
                self.base_index = VectorStoreIndex(
                    nodes=None, storage_context=self.storage_context
                )
                 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
                )

inside my class constructor. would it vectorstoreindex load the index from the storage context that is already there and would by retriver and auto merger work?

also everytime I add a new set of nodes how do I need to reinitialize the vector store index ? How do I do it?

[dosubot[bot]]

🤖

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

Yes, the VectorStoreIndex will load the index from the existing storage context. This is because when you initialize VectorStoreIndex with a storage_context, it uses this context to load the index.

The AutoMergingRetriever and base_retriever should work as expected as long as the VectorStoreIndex is properly initialized and the necessary dependencies are met.

When you add a new set of nodes, you can reinitialize the VectorStoreIndex by using the insert_nodes method. This method adds the nodes to the index and updates the storage context. Here is an example:

new_nodes = [...]  # your new nodes
self.base_index.insert_nodes(new_nodes)

This will add the new nodes to the index and update the storage context accordingly.

Please note that the insert_nodes method first adds the nodes to the index using the _insert method, and then updates the storage context with the new index structure.

You can find more details about these methods in the VectorStoreIndex source code.

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

Sources

• llama-index-core/llama_index/core/indices/vector_store/base.py
• llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/llama_index/storage/docstore/redis/base.py

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[rahulkrprajapati]

@dosu-bot I would need to push in the new nodes to both the doctore and vector_index store that are mentioned in the storage context for AutoMergingRetriever. How do I do it?

[dosubot[bot]]

🤖

To add new nodes to both the document store and the VectorStoreIndex in the storage context for the AutoMergingRetriever in your Python class constructor, you can use the add method of the TypesenseVectorStore class. This method takes a list of nodes as an argument and adds them to the index.

Here is an example of how you can use this method:

# Assuming `nodes` is your list of nodes and `typesense_store` is an instance of TypesenseVectorStore
typesense_store.add(nodes)

This method first creates the documents to be upserted using the _create_upsert_docs method. This method iterates over the nodes, creating a dictionary for each node with the node's id, embedding, content, reference document id, and metadata. These dictionaries are then added to the upsert_docs list.

The add method then tries to import the documents into the collection. If the collection does not exist, it creates the collection using the _create_collection method and then imports the documents.

The add method finally returns a list of the node ids that were added to the index.

Please note that the add method assumes that the nodes passed to it have the get_embedding, get_content, and ref_doc_id methods/attributes. If your nodes do not have these methods/attributes, you will need to modify the _create_upsert_docs method to suit your needs.

The nodes object that needs to be passed to the add method of the TypesenseVectorStore class is a list of BaseNode objects. Each BaseNode object in the list should have the following attributes:

• node_id: The unique identifier for the node.
• get_embedding(): A method that returns the embedding of the node.
• get_content(metadata_mode=MetadataMode.NONE): A method that returns the content of the node.
• ref_doc_id: The reference document id of the node.

This is evident from the _create_upsert_docs method which is used in the add method:

def _create_upsert_docs(self, nodes: List[BaseNode]) -> List[dict]:
    upsert_docs = []
    for node in nodes:
        doc = {
            "id": node.node_id,
            "vec": node.get_embedding(),
            f"{self._text_key}": node.get_content(metadata_mode=MetadataMode.NONE),
            "ref_doc_id": node.ref_doc_id,
            f"{self._metadata_key}": node_to_metadata_dict(
                node, remove_text=True, flat_metadata=self.flat_metadata
            ),
        }
        upsert_docs.append(doc)

    return upsert_docs

This method iterates over the nodes list and for each BaseNode object, it creates a dictionary with the keys id, vec, self._text_key, ref_doc_id, and self._metadata_key. The values for these keys are obtained from the attributes and methods of the BaseNode object.

You can find more details in the TypesenseVectorStore class in the llama-index-legacy/llama_index/legacy/vector_stores/typesense.py file in the LlamaIndex repository.

Sources

• llama-index-legacy/llama_index/legacy/vector_stores/typesense.py

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.

If you want to continue the conversation, start your reply with @dosu-bot.

[rahulkrprajapati]

@dosu-bot this changes my vector index store remember my init defines the vector index store and the rest of the things as:

self.redis_client = redis.Redis(
                    host=self.config.get("REDIS_HOST"),
                    port=self.config.get("REDIS_PORT"),
                    password=self.config.get("REDIS_PASSWORD"),
                    ssl=True,
                )
                self.docstore = RedisDocumentStore.from_redis_client(
                    redis_client=self.redis_client
                )
                self.storage_context = StorageContext.from_defaults(
                    docstore=self.docstore,
                    index_store=RedisIndexStore.from_redis_client(
                        redis_client=self.redis_client
                    ),
                )
                self.base_index = VectorStoreIndex(
                    nodes=None, storage_context=self.storage_context
                )
                 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
                )

[dosubot[bot]]

🤖

It looks like you're trying to modify the vector index store in your code. What specific changes are you looking to make? Are you encountering any issues with the current implementation? I'd be happy to help you with any questions or concerns you have.

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.

If you want to continue the conversation, start your reply with @dosu-bot.