How to get nodes once stored in ChromaVectorStore or OpenSerachVectorStore.

[badrinathvm]

func ingest_to_open_serach_vectorstore() {
   sub_chunk_sizes = [128, 256, 512]
    sub_node_parsers = [
        SentenceSplitter(chunk_size=c, chunk_overlap=20) for c in sub_chunk_sizes
    ]
    node_parser = SimpleNodeParser.from_defaults(chunk_size=1024)
    base_nodes = node_parser.get_nodes_from_documents(documents=documents)

    all_nodes = []
    for base_node in base_nodes:
        for n in sub_node_parsers:
            sub_nodes = n.get_nodes_from_documents([base_node])
            sub_inodes = [
                IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
            ]
            all_nodes.extend(sub_inodes)
        # also add original node to node
        original_node = IndexNode.from_text_node(base_node, base_node.node_id)
        all_nodes.append(original_node)

    all_nodes_dict = {n.node_id: n for n in all_nodes}
    logger.info(f"all_nodes_dict={all_nodes_dict}")
     
     vector_store = OpensearchVectorStore(get_open_search_vector_client())
     storage_context = StorageContext.from_defaults(vector_store=vector_store)
      embed_model = OpenAIEmbeddings(model="gpt-3.5-turbo")
     service_context = ServiceContext.from_defaults(
        llm=OpenAI(model="gpt-3.5-turbo"), embed_model=embed_model
    )
    vector_index_chunk = VectorStoreIndex(nodes=all_nodes,
                                          embed_model=embed_model,
                                          storage_context=storage_context,
                                          service_context=service_context)
}

The above codes ingests the data to vector store.

Now how can i get access to all_nodes to be passed in node_dict so that i can create which would help in creating RecursiveRetriever below ?

RecursiveRetriever("vector",
                                                  retriever_dict={"vector": vector_retriever_chunk},
                                                  node_dict=??,
                                                  verbose=True)

Things i have tried , I created a index object like below

storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo"), embed_model=embed_model)
vector_index_chunk = load_index_from_storage(storage_context, service_context=service_context)
vector_retriever_chunk = vector_index_chunk.as_retriever()
retriever_chunk = RecursiveRetriever("vector",
                                                 retriever_dict={"vector": vector_retriever_chunk},
                                                  node_dict=vector_index_chunk.docstore.doc.values(), // Not working it's empty 
                                                 verbose=True)

[dosubot[bot]]

🤖

Based on the code you've provided, it seems like you're trying to access the all_nodes variable, which is local to the ingest_to_open_serach_vectorstore() function, from outside the function. In Python, variables defined within a function are local to that function and cannot be accessed directly from outside the function. This is a fundamental principle of function scope in Python.

However, you can return the all_nodes variable from the ingest_to_open_serach_vectorstore() function and then use it when creating the RecursiveRetriever. Here's how you can do it:

def ingest_to_open_serach_vectorstore():
   ...
   all_nodes = []
   for base_node in base_nodes:
       for n in sub_node_parsers:
           sub_nodes = n.get_nodes_from_documents([base_node])
           sub_inodes = [
               IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
           ]
           all_nodes.extend(sub_inodes)
       # also add original node to node
       original_node = IndexNode.from_text_node(base_node, base_node.node_id)
       all_nodes.append(original_node)

   all_nodes_dict = {n.node_id: n for n in all_nodes}
   logger.info(f"all_nodes_dict={all_nodes_dict}")
   ...
   return all_nodes_dict

all_nodes_dict = ingest_to_open_serach_vectorstore()

RecursiveRetriever("vector",
                   retriever_dict={"vector": vector_retriever_chunk},
                   node_dict=all_nodes_dict,
                   verbose=True)

In this modified version of your code, the ingest_to_open_serach_vectorstore() function returns all_nodes_dict, which is then stored in the all_nodes_dict variable. This variable is then passed as the node_dict argument when creating the RecursiveRetriever.

Please note that the RecursiveRetriever class in the LlamaIndex codebase expects a dictionary of id to nodes as the node_dict parameter. Make sure that all_nodes_dict is in this format.

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

Sources

• llama-index-core/llama_index/core/prompts/utils.py
• llama-index-legacy/llama_index/legacy/retrievers/recursive_retriever.py

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[badrinathvm]

@dosu-bot I am ingesting the documents to open search vector store as part of /upload API which will generate all_nodes_dict and ingests the data.

Now i am writing an /ask end point where i need to have access to these node_dict=all_nodes_dict from the index object The above code works only in the same session as all_nodes_dict will be empty if directly call the /ask end point for the second or third time time as/uploadwill be called only once in lifetime.

[dosubot[bot]]

🤖

Based on the information provided, it seems like you're trying to persist the all_nodes_dict across different sessions and API calls. Since the all_nodes dictionary is stored as a field in the IndexGraph dataclass and is not persisted across sessions, you might need to consider storing this data in a persistent storage that can be accessed across different sessions and API calls.

One possible solution could be to use a database or a key-value store to persist the all_nodes_dict. When the /upload API is called, you can store the all_nodes_dict in the database. Then, in the /ask endpoint, you can retrieve the all_nodes_dict from the database. This way, the all_nodes_dict will be available across different sessions and API calls.

Here is a simple example of how you might do this:

# During /upload API call
def upload():
    # ... code to generate all_nodes_dict ...
    db.save('all_nodes_dict', all_nodes_dict)

# During /ask API call
def ask():
    all_nodes_dict = db.load('all_nodes_dict')
    # ... code to use all_nodes_dict ...

In this example, db is an instance of a database or key-value store that supports the save and load operations. The exact implementation would depend on the specific database or key-value store you are using.

Please note that this is a general solution and might need to be adapted based on the specifics of your application and the LlamaIndex framework. If you need more specific guidance, please provide more details about your application and how you are using the LlamaIndex framework.

For more information on how to use databases or key-value stores in Python, you might find the following resources helpful:

• Python Database Programming
• Python key-value store guide

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

Source

Sources

• llama-index-core/llama_index/core/data_structs/data_structs.py

About Dosu

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[badrinathvm]

@dosu-bot Can i store in all_nodes in open search vector store instead of using other types of db ?

[dosubot[bot]]

🤖

Based on the information available in the LlamaIndex repository, you can persist the all_nodes_dict data across different sessions and API calls using the persist method of the storage_context object. Here's how you can do it:

# Assuming all_nodes_dict is your data to be persisted
all_nodes_dict = {...}  # Your data here

# Persist the data
storage_context.persist(persist_dir="<persist_dir>")

This will persist the data to disk, under the specified persist_dir (or ./storage by default if no directory is specified).

To load the data in a different session or API call, you can recreate the storage context and load the data from the persisted directory:

# Recreate the storage context
storage_context = StorageContext.from_defaults(
    docstore=SimpleDocumentStore.from_persist_dir(persist_dir="<persist_dir>"),
    vector_store=SimpleVectorStore.from_persist_dir(persist_dir="<persist_dir>"),
    index_store=SimpleIndexStore.from_persist_dir(persist_dir="<persist_dir>"),
)

# Load the data
all_nodes_dict = load_index_from_storage(storage_context, index_id="<index_id>")

Please replace <persist_dir> with the directory where you want to persist the data and <index_id> with the ID of the index you want to load.

If you're using a remote backend like S3, you can pass a fsspec.AbstractFileSystem object when persisting and loading the data:

# Persist the data to S3
s3_bucket_name = "llama-index/storage_demo"  # {bucket_name}/{index_name}
storage_context.persist(persist_dir=s3_bucket_name, fs=s3)

# Load the data from S3
all_nodes_dict = load_index_from_storage(
    StorageContext.from_defaults(persist_dir=s3_bucket_name, fs=s3),
    index_id="vector_index",
)

Please replace s3 with your S3 filesystem object and vector_index with the ID of the index you want to load.

You can find more details in the LlamaIndex documentation.

As for persisting data directly in the OpenSearch Vector Store, I wasn't able to find any specific information in the repository. It might be possible, but it would likely require additional configuration or setup. If you have more specific requirements or if there's a particular reason you want to use the OpenSearch Vector Store for this, please provide more details so I can give a more accurate answer.

Sources

• docs/module_guides/storing/save_load.md

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[badrinathvm]

@dosu-bot the reason to choose open search vector is we alarmed at store the embeddings over there , why to choose another remote database again.

[dosubot[bot]]

🤖

Yes, you can store and retrieve the all_nodes_dict directly in/from the OpenSearch Vector Store in the LlamaIndex framework. Here's how you can accomplish this:

To store the all_nodes_dict, you can use the add method of the OpensearchVectorStore class. This method accepts a list of nodes (which can be derived from your all_nodes_dict) and adds them to the index. Here's a sample code snippet:

# Assuming opensearch_vector_store is an instance of OpensearchVectorStore
# and all_nodes_dict is your dictionary of nodes

nodes_list = list(all_nodes_dict.values())  # Convert dict values to list
opensearch_vector_store.add(nodes_list)

To retrieve the all_nodes_dict, you can use the query method of the OpensearchVectorStore class. This method accepts a VectorStoreQuery object and returns a VectorStoreQueryResult object, which contains the queried nodes. Here's a sample code snippet:

# Assuming opensearch_vector_store is an instance of OpensearchVectorStore
# and query is your VectorStoreQuery object

query_result = opensearch_vector_store.query(query)

# The nodes can be accessed via the 'nodes' attribute of the query result
retrieved_nodes = query_result.nodes

# You can then convert this list of nodes back into a dictionary
retrieved_nodes_dict = {node.node_id: node for node in retrieved_nodes}

Please note that the VectorStoreQuery object should be properly constructed with the query parameters. The VectorStoreQuery object includes parameters like query_embedding (the vector to search for), similarity_top_k (the number of most similar vectors to return), mode (the mode of the query, which can be VectorStoreQueryMode.HYBRID, VectorStoreQueryMode.EMBEDDING, or VectorStoreQueryMode.TEXT), query_str (the text to search for in TEXT or HYBRID mode), and filters (optional filters to apply before the search).

You can find more details about these methods in the OpensearchVectorStore class in the LlamaIndex repository.

As for the structure of the all_nodes_dict dictionary, I wasn't able to find specific information within the repository. If you could provide more details about the structure or usage of all_nodes_dict in your application, I might be able to give a more accurate answer.

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py

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[badrinathvm]

@dosu-bot How should i create a query object which VectorStoreQuery in the above example ?

[dosubot[bot]]

🤖

Sure, I can provide an example of how to construct a VectorStoreQuery object with the parameters you mentioned. Here's an example:

from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode, MetadataFilter, FilterOperator, MetadataFilters

# Define your query_embedding
query_embedding = [0.1, 0.2, 0.3, 0.4, 0.5]

# Define the number of top similar items you want to retrieve
similarity_top_k = 5

# Define the mode of the query
mode = VectorStoreQueryMode.HYBRID

# Define the query string
query_str = "example query"

# Define optional filters
filters = MetadataFilters(
    filters=[
        MetadataFilter(key="key1", value="value1", operator=FilterOperator.EQ),
        MetadataFilter(key="key2", value=10, operator=FilterOperator.GT)
    ],
    condition="and"
)

# Construct the VectorStoreQuery object
query = VectorStoreQuery(
    query_embedding=query_embedding,
    similarity_top_k=similarity_top_k,
    mode=mode,
    query_str=query_str,
    filters=filters
)

In this example, query_embedding is a list of floats representing the query embedding. similarity_top_k is an integer specifying the number of top similar items to retrieve. mode is a VectorStoreQueryMode enum specifying the mode of the query. query_str is a string representing the query string. filters is an optional MetadataFilters object specifying any filters to apply to the query. Each filter is a MetadataFilter object with a key, value, and operator. The condition parameter in MetadataFilters specifies how to combine the filters (e.g., "and", "or").

You can find more information about these classes and their parameters in the llama_index/core/vector_stores/types.py file in the LlamaIndex repository.

Sources

• llama-index-core/llama_index/core/vector_stores/types.py

About Dosu

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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