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I love llama_index and just scratching surface. I am fond of llama_index and primarily utilize it for structured data. The RecursiveRetriever concept demonstrated in the provided link resonates with me https://github.com/run-llama/llama_index/blob/dd3a25c8a2423d1f8f5077237e7b68e7065e6907/docs/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb. My query pertains to storing Vectors after converting my data - can I preserve them in Chromadb to bypass repeating the conversion process? I am inclined towards storing the initial data for future use, enabling quick retrieval through a query like: I mean I like to store first before I query. In the query, I call the vector database and query again... That is my goal. Could you assist me with this? |
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To store your vectors in ChromaDB for use with the RetrieverQuery Engine, follow these steps:
This approach allows you to convert data into vectors, store them in ChromaDB, and retrieve them using a query engine. For detailed steps on setting up the environment, loading data, and querying, refer to the respective package documentation and the example provided in the Remember, when working with ChromaDB and LlamaIndex, there are constraints such as the
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Is that possible to have a sample code? |
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Here's a streamlined version of the sample code to store vectors in ChromaDB and query them using the RetrieverQuery Engine with the import chromadb
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext, TextNode
from llama_index.core.indices.vector_store.retrievers import VectorIndexAutoRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
# Initialize ChromaDB client and collection
chroma_client = chromadb.HttpClient(host="localhost", port="8080", ssl=False)
chroma_collection = chroma_client.get_or_create_collection("example_collection")
# Define embedding function using HuggingFace
embed_model = HuggingFaceEmbedding(model_name="your_model_name_here")
# Initialize ChromaVectorStore with the collection
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
# Set up StorageContext and VectorStoreIndex
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(embed_model=embed_model, storage_context=storage_context)
# Define and load documents with embeddings
documents = [
{"text": "Your document text here", "embedding": [0.1, 0.2, 0.3]},
# Add more documents as needed
]
# Load documents into ChromaDB using VectorStoreIndex
index.from_documents(documents=documents)
# Initialize the AutoRetriever with VectorStoreIndex and VectorStoreInfo
auto_retriever = VectorIndexAutoRetriever(index)
# Set up the RetrieverQuery Engine with the AutoRetriever
query_engine = RetrieverQueryEngine(auto_retriever)
# Query documents using the RetrieverQuery Engine
response = query_engine.query("Your query here")
print(response)Make sure to replace placeholders like If you have any further questions or need more assistance, feel free to ask!
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I was unable to successfully store the vectors in ChromaDB. It appears that these vectors are different from typical ones. My question is how can I store 'vector_index' in ChromaDB and retrieve it later. Could you assist me with the code, please? Code snippet from the link: |
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To store the Given the code snippet you've shared and your goal, here's a refined example that demonstrates how to store your from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import chromadb
# Assuming 'vector_index' is already created as per your snippet
# Initialize ChromaDB client and collection
chroma_client = chromadb.HttpClient(host="your_chromadb_host", port="your_chromadb_port", ssl=False)
chroma_collection = chroma_client.get_or_create_collection("your_collection_name")
# Define your embedding model (ensure this matches the model used for creating your vector_index)
embed_model = HuggingFaceEmbedding(model_name="your_model_name_here")
# Initialize ChromaVectorStore with the collection
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
# Set up StorageContext with the initialized vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# Re-initialize your VectorStoreIndex with the storage context and embedding model
# This step is crucial for integrating your existing vector_index with ChromaDB
vector_index = VectorStoreIndex(embed_model=embed_model, storage_context=storage_context)
# Now, you can add your documents (or vectors) to the vector_index as before
# For example:
# vector_index.from_documents(documents=your_documents)
# To store the vector_index in ChromaDB, you would typically serialize and store its components
# However, directly storing 'vector_index' as a whole in ChromaDB is not straightforward
# because ChromaDB stores vectors and metadata, not the index objects themselves.
# For retrieval, you would reconstruct the vector_index from the stored vectors and metadata in ChromaDB
# This involves fetching the stored vectors and metadata and initializing a new VectorStoreIndex with them
# Note: Direct methods for serializing and deserializing the entire 'vector_index' to and from ChromaDB
# are not provided out of the box. You'll need to manage the components (vectors and metadata) individually.
# Ensure you replace placeholders like "your_chromadb_host", "your_chromadb_port", "your_collection_name",
# and "your_model_name_here" with your actual data and configurations.This example outlines the steps to integrate your If you encounter any specific issues or need further clarification on any step, feel free to ask!
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Thank you. Seems not straight forward at all. :-( |
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To store the
vector_indexin ChromaDB and retrieve it later, you'll need to adjust your approach slightly from the standard document storage and retrieval process. The key here is to understand that storing avector_indexinvolves not just the vectors themselves but also the structure and metadata that allow for efficient querying later on.Given the code snippet you've shared and your goal, here's a refined example that demonstrates how to store your
vector_indexin ChromaDB and retrieve it for future queries. This example assumes you've already created yourvector_indexas shown in your snippet and focuses on integrating with ChromaDB: