How do I interstate cache with Qdrant. I want to story...

ddrewskidang

How do I interstate cache with Qdrant. I want to story my queries.

9 comments

kkapa.ai

Please stand by @Duh_Kola as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

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To integrate cache with Qdrant using LlamaIndex, you can follow these steps:

Install the necessary packages. You will need llama-index, llama-index-readers-qdrant, llama-index-vector-stores-qdrant, and qdrant_client. You can install these using pip:

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%pip install llama-index llama-index-readers-qdrant llama-index-vector-stores-qdrant qdrant_client

Import the necessary modules:

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import logging
import sys
import qdrant_client
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore

Set up logging if you want to see detailed logs:

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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

Create a Qdrant client. You can specify the host and port if you have a Qdrant instance running somewhere, or use :memory: for lightweight experiments:

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client = qdrant_client.QdrantClient(
    host="localhost",
    port=6333
)

Create a QdrantVectorStore with the client and collection name:

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vector_store = QdrantVectorStore(client=client, collection_name="your_collection_name")

Create a StorageContext with the vector store:

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storage_context = StorageContext.from_defaults(vector_store=vector_store)

Load your documents. This could be from a file, a database, or any other source. Here's an example with a hypothetical SimpleDirectoryReader:

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documents = SimpleDirectoryReader("./data/your_data_directory/").load_data()

Create a VectorStoreIndex from the documents and storage context:

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index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
)

kkapa.ai

Now you can use the index to store and retrieve your queries. Here's an example of how to query the index:

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query_engine = index.as_query_engine()
response = query_engine.query("your_query_text")

Please replace "your_collection_name", "./data/your_data_directory/", and "your_query_text" with your actual collection name, data directory, and query text.

Sources:

Qdrant Reader ()
Qdrant Vector Store ()

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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ddrewskidang

How is that using a cache tho

ddrewskidang

@Duh_Kola how is that using a cache tho

ddrewskidang

@kapa.ai how does that use caches

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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I'm sorry for the confusion, but the provided knowledge sources do not contain specific information on how to use caching with Qdrant in LlamaIndex. However, I can provide general information on how caching is used in LlamaIndex based on the available knowledge sources.

In LlamaIndex, each node + transformation combination is hashed and cached. This saves time on subsequent runs that use the same data. Here's an example of how to use caching in LlamaIndex:

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# save
pipeline.persist("./pipeline_storage")

# load and restore state
new_pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=25, chunk_overlap=0),
        TitleExtractor(),
    ],
)
new_pipeline.load("./pipeline_storage")

# will run instantly due to the cache
nodes = pipeline.run(documents=[Document.example()])

If the cache becomes too large, you can clear it:

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# delete all context of the cache
cache.clear()

LlamaIndex supports multiple remote storage backends for caches, including RedisCache, MongoDBCache, and FirestoreCache. Here's an example of using RedisCache:

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pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=25, chunk_overlap=0),
        TitleExtractor(),
        OpenAIEmbedding(),
    ],
    cache=IngestionCache(
        cache=RedisCache(
            redis_uri="redis://127.0.0.1:6379", collection="test_cache"
        )
    ),
)

# Ingest directly into a vector db
nodes = pipeline.run(documents=[Document.example()])

In this example, no persist step is needed, since everything is cached as you go in the specified remote collection.

For more specific information on how to use caching with Qdrant in LlamaIndex, I recommend checking the official LlamaIndex documentation or the Qdrant documentation.

kkapa.ai

Source: LlamaIndex Ingestion Pipeline Caching ()
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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How do I interstate cache with Qdrant. I want to story my queries.