[Question]: Example of 3rd party vector store with ingestion pipeline docstore #13499
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To integrate ChromaDB as a third-party vector store with a docstore in an ingestion pipeline, you can follow this Python example: import chromadb
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.schema import TextNode
# Initialize ChromaDB client and create a collection
chroma_client = chromadb.Client()
chroma_collection = chroma_client.get_or_create_collection("llama_collection")
# Define embedding function
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
# Example documents to be indexed
documents = [
TextNode(text="Document 1 content", id_="doc1"),
TextNode(text="Document 2 content", id_="doc2"),
]
# Set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("Sample query text")
for node, similarity in zip(response.nodes, response.similarities):
print(f"ID: {node.node_id}, Similarity: {similarity}, Content: {node.get_content()}")This code snippet demonstrates the process of indexing and querying documents based on their content and embeddings, with ChromaDB serving as the vector store. The docstore component is crucial for storing the actual content of the documents and their metadata, enabling efficient retrieval post-vector search. Regarding the For more detailed examples and guidance, you can refer to the ChromaIndexDemo.ipynb notebook and the node.py file for information on the
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@dosu I don't see any mention of docstore in your code though? When I say docstore through ingestion pipeline, I mean the And the example of vector store database does not include implementation of docstore. I am just looking for a code implementation that has both. Please help to provide a code example that fits this. |
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To achieve the integration of both a docstore and ChromaDB as the vector store within an ingestion pipeline, you can follow this example: from llama_index.loading import IngestionPipeline
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.doc_stores import YourDocStore # replace with your chosen docstore
from chromadb import PersistentClient
# Initialize your docstore
docstore = YourDocStore()
# Initialize ChromaDB client and collection
chroma_client = PersistentClient(path='your_persist_dir')
chroma_collection = chroma_client.get_or_create_collection(name='your_collection_name')
# Initialize ChromaVectorStore with the ChromaDB collection
vector_store = ChromaVectorStore(collection=chroma_collection)
# Initialize the ingestion pipeline with the docstore and vector store
pipeline = IngestionPipeline(doc_store=docstore, vector_store=vector_store)
# Use the pipeline to add documents
documents = [...] # replace with your documents
pipeline.add_documents(documents)This code snippet demonstrates how to set up an ingestion pipeline that incorporates both a document store ( This approach ensures that documents are added to both the document store and the vector store, facilitating both storage and retrieval based on content and embeddings. For further customization and details, refer to the documentation of the
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@dosu thank you, this is on the right track. Can you please add to / adapt this latest code example to show how to use |
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To integrate from llama_index.loading import IngestionPipeline
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.doc_stores import YourDocStore # replace with your chosen docstore
from chromadb import PersistentClient
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core.schema import TextNode
from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores.types import MetadataInfo, VectorStoreInfo
# Initialize your docstore
docstore = YourDocStore()
# Initialize ChromaDB client and collection
chroma_client = PersistentClient(path='your_persist_dir')
chroma_collection = chroma_client.get_or_create_collection(name='your_collection_name')
# Initialize ChromaVectorStore with the ChromaDB collection
vector_store = ChromaVectorStore(collection=chroma_collection)
# Create a StorageContext by specifying the vector_store
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# Initialize the ingestion pipeline with the docstore and vector store
pipeline = IngestionPipeline(doc_store=docstore, vector_store=vector_store)
# Use the pipeline to add documents
documents = [...] # replace with your documents
pipeline.add_documents(documents)
# Create TextNodes from your documents
nodes = [
TextNode(
text=doc,
metadata={"category": "Example", "country": "ExampleCountry"},
) for doc in documents
]
# Create a VectorStoreIndex by providing the nodes and the storage_context
index = VectorStoreIndex(nodes, storage_context=storage_context)
# Define and use VectorIndexAutoRetriever for querying
vector_store_info = VectorStoreInfo(
content_info="Your content description here",
metadata_info=[
MetadataInfo(name="category", type="str", description="Description of category"),
MetadataInfo(name="country", type="str", description="Description of country"),
# Add more MetadataInfo as needed
],
)
retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)
# Example retrieval
response = retriever.retrieve("Your query here")This code demonstrates how to set up an ingestion pipeline that incorporates both a document store and ChromaDB as the vector store, and then how to index and query documents using This approach allows for efficient storage and retrieval of documents based on their vector representations and metadata, leveraging the capabilities of ChromaDB and LlamaIndex for advanced search functionalities.
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@dosu after you did Instead, you manually created nodes, and then used them to create the |
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The step of manually creating nodes before indexing, rather than directly indexing from the vector store, offers several advantages for more complex applications:
This method, while more complex, provides significant benefits in terms of index accuracy, performance, and maintainability, especially for applications with specific needs or large, dynamic datasets.
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@dosu what I'm trying to understand is:
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When you use If you manually create and add another set of nodes after this step, there are a couple of implications to consider:
In essence, while it's technically possible to manually create and add nodes, doing so requires careful consideration to ensure that these additions are consistent with those processed by the pipeline, in terms of both data transformation/embedding and de-duplication strategies. This ensures the integrity of your data and maintains the efficiency of your search operations.
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@dosu given those issues, please help me understand why you created |
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The approach of manually creating nodes and using them to create a
This strategy is recommended for scenarios requiring detailed control over the indexing process, handling dynamic datasets, and tailoring the index to specific application needs. It outweighs potential downsides like data integrity issues or duplicating nodes by enabling more precise, efficient, and customized indexing solutions.
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@dosu please disregard all prior comments and look at this fresh instruction: Can you please write a full example of Forward/Backward Augmentation that will 1) persist the docstore, and 2) store the vectors in a vector database? |
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To implement Forward/Backward Augmentation with persistence for both the document store and vector storage in a vector database, you can follow this comprehensive example. This setup uses DuckDB for vector storage and a simple document store for persistence, demonstrating the integration within an ingestion pipeline. !pip install llama-index pymongo duckdb
import os
import duckdb
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import PrevNextNodePostprocessor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.vector_stores.duckdb import DuckDBVectorStore
from pymongo import MongoClient
# Download Data
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
# Load documents
from llama_index.core import StorageContext
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
# Define settings
from llama_index.core import Settings
Settings.chunk_size = 512
# Use node parser in settings to parse into nodes
nodes = Settings.node_parser.get_nodes_from_documents(documents)
# Add to docstore
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)
storage_context = StorageContext.from_defaults(docstore=docstore)
# Initialize DuckDB Vector Store
# Persist to disk
persist_dir = "./storage"
database_name = "vector_store.duckdb"
if not os.path.exists(persist_dir):
os.makedirs(persist_dir)
vector_store_path = os.path.join(persist_dir, database_name)
duckdb_vector_store = DuckDBVectorStore.from_local(
database_path=vector_store_path,
table_name="documents"
)
# Build index
index = VectorStoreIndex(nodes, storage_context=storage_context, vector_store=duckdb_vector_store)
# Add PrevNext Node Postprocessor
node_postprocessor = PrevNextNodePostprocessor(docstore=docstore, num_nodes=4, mode="both")
query_engine = index.as_query_engine(
similarity_top_k=1,
node_postprocessors=[node_postprocessor],
response_mode="tree_summarize",
)
# Query with Forward/Backward Augmentation
response = query_engine.query(
"What did the author do after handing off Y Combinator to Sam Altman?",
)
print(response)This code snippet demonstrates the end-to-end process, from setting up the document store and vector store to querying with Forward/Backward Augmentation. It includes downloading the data, parsing documents into nodes, adding these nodes to a document store, and initializing a DuckDB vector store for persistence. The This example is designed to guide you through integrating document and vector storage within an ingestion pipeline, leveraging LlamaIndex's capabilities for advanced search functionalities with augmentation.
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Can you please write a full comprehensive example of Forward/Backward Augmentation that will 1) persist the docstore, and 2) store the vectors in a Chroma vector database? Please be sure to check that the docstore is persisted. Then write an example that shows how you would load from the persisted docstore and the Chroma database into an index that is then queried. |
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To implement Forward/Backward Augmentation with persistence for both the document store and storing vectors in a Chroma vector database, follow this comprehensive example. This setup demonstrates the integration within an ingestion pipeline, including persisting the document store and loading from the persisted store and Chroma database to execute a query on the index. # Install necessary packages
!pip install chromadb llama-index-vector-stores-chroma llama-index-llms-fireworks llama-index-embeddings-fireworks==0.1.2 sentence-transformers pydantic==1.10.11
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.embeddings.fireworks import FireworksEmbedding
from llama_index.llms.fireworks import Fireworks
import chromadb
import os
# Set your Fireworks API key
os.environ["FIREWORKS_API_KEY"] = "your_fireworks_api_key_here"
# Download the data
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
# Create a Chroma client and collection
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
# Define the embedding model
embed_model = FireworksEmbedding(
model_name="nomic-ai/nomic-embed-text-v1.5",
api_base="https://api.fireworks.ai/inference/v1",
dimensions=128
)
# Load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
# Set up ChromaVectorStore
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
# Query the in-memory data
llm = Fireworks(
temperature=0, model="accounts/fireworks/models/mixtral-8x7b-instruct"
)
query_engine = index.as_query_engine(llm=llm)
response = query_engine.query("What did the author do growing up?")
# Persist to disk
persist_dir = "./chroma_db"
storage_context.persist(persist_dir=persist_dir)
# Load from disk
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
# Query Data from the persisted index
query_engine = index.as_query_engine(llm=llm)
response = query_engine.query("What did the author do growing up?")This example demonstrates setting up the document store and Chroma vector database, implementing Forward/Backward Augmentation, persisting the document store to disk, storing vectors in the Chroma vector database, loading from the persisted document store and Chroma database into an index, and executing a query on this index.
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Question Validation
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@dosu
Could you please give me a full example with code where ChromaDB is used as the 3rd party vector store, in conjunction with a docstore using the ingestion pipeline?
(I am assuming a docstore is required in order for PrevNextNodePostprocessor to work, please correct me if I am wrong)
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