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Asked 1 month ago by StarHunter918

How to Validate Document Existence in a Chroma Vectorstore Using Metadata with LangChain?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I'm using a Chroma vectorstore with LangChain to manage documents. I need to verify whether a document exists in the vectorstore by checking its metadata. I'm using the following package for Chroma:

PYTHON
from langchain_chroma import Chroma

Here's the code snippet I'm currently using:

PYTHON
existing_metadata = vector_store_paper.similarity_search(query="", filter={'id': doc_id}, k=1)

However, this approach triggers an error from my embeddings API:

The engine is currently overloaded, please try again later.

The error occurs because an empty query forces an embedding computation even though I only want metadata filtering. Is there a better way to check if a document exists in the vectorstore by solely using its metadata? Any guidance is appreciated!

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Answers 2

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2 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is one recommended approach:

Why the Issue Occurs

When you call

PYTHON
vector_store_paper.similarity_search(query="", filter={'id': doc_id}, k=1)

an empty query string forces LangChain (and your embeddings provider) to compute an embedding even though you only want to filter by metadata. Since your embedding service is overloaded, you see that error.

A Better Approach: Use the Get Method to Filter by Metadata

Instead of performing a similarity search with an empty query, you can directly retrieve documents from the underlying collection using methods that support filtering solely by metadata. In many integrations (including Chroma), the underlying collection supports a .get method that accepts metadata filters without having to generate an embedding.

For example, you could do something like:

PYTHON
# Directly retrieve documents that match the provided id in metadata
docs = vector_store_paper._collection.get(where={"id": doc_id})
if docs and docs.get("ids"):
    print("Document exists.")
else:
    print("Document not found.")

Explanation

Avoid empty query strings: By using .get with a where filter, you bypass the embedding step entirely.
Direct metadata filtering: The get method will return all documents that match the metadata filter; no expensive embedding computation is required.
Underlying collection access: In the example above, we access the underlying Chroma collection (typically available via ._collection), which exposes methods such as get that are ideal for filtering by metadata alone.

This approach should help you check for document existence without running into the embedding overload issue from your provider while staying within the LangChain and Chroma ecosystems.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by SupernovaSurveyor520 • 1 month ago

Disclaimer

I am assuming you are using the latest versions of the Python packages mentioned. At the time of writing, these are:

langchain version 0.3.14
langchain-chroma version 0.2.0

If this is not the case, please explicitly include the versions you are using so we can provide more accurate assistance.

To check if a document exists in the vector store based on its metadata, the .get() function is your best option.

Here’s a summary of how it works:

Set the limit (k): This specifies the maximum number of results to retrieve.
Use a where query: Utilize the Metadata Filtering feature provided by Chroma. As described in this documentation:

"An optional where filter dictionary can be supplied to filter by the metadata associated with each document."

Details on configuring the where filter are available here.

Once configured, you're all set. For example, the following snippet demonstrates the functionality:

PYTHON
existing_metadata = db.get(  
    limit=1,  
    where={"id": {"$eq": "ABC123"}}  
)["metadatas"]

This code returns a list (limited to one element) containing the metadata of documents that match the where condition.

Below is a complete code example to illustrate how this works:

PYTHON
import os  
from langchain_chroma import Chroma  
from langchain.text_splitter import RecursiveCharacterTextSplitter  
from langchain_openai.embeddings import AzureOpenAIEmbeddings  
from dotenv import load_dotenv, find_dotenv  

# Load environment variables  
load_dotenv(find_dotenv(".env"), override=True)  

# Prepare embeddings and the vector store  
embeddings = AzureOpenAIEmbeddings(  
    api_key=os.environ.get("AZURE_OPENAI_EMBEDDINGS_API_KEY"),  
    api_version=os.environ.get("AZURE_OPENAI_EMBEDDINGS_VERSION"),  
    azure_deployment=os.environ.get("AZURE_OPENAI_EMBEDDINGS_MODEL"),  
    azure_endpoint=os.environ.get("AZURE_OPENAI_EMBEDDINGS_ENDPOINT")  
)  
db = Chroma(  
    persist_directory=os.environ.get("CHROMA_PATH"),  
    embedding_function=embeddings,  
    collection_name="stackoverflow-help",  
)  

# Add documents to the vector store  
text_splitter = RecursiveCharacterTextSplitter(  
    chunk_size=int(os.environ["CHROMA_EMBEDDINGS_CHUNK_SIZE"]),  
    chunk_overlap=int(os.environ["CHROMA_EMBEDDINGS_CHUNK_OVERLAP"])  
)  

documents = text_splitter.create_documents(["This is a test document for the Chroma database."])  
for doc in documents:  
    doc.metadata = {"id": "ABC123"}  
db.add_documents(documents)  

# Check if the document is in the vector store  
existing_metadata = db.get(  
    limit=1,  
    where={"id": {"$eq": "ABC123"}}  
)["metadatas"]  
print(existing_metadata)  

# Check for a document that is not in the vector store  
non_existing_metadata = db.get(  
    limit=1,  
    where={"id": {"$eq": "XYZ123"}}  
)["metadatas"]  
print(non_existing_metadata)

When you run this code, the results will be as follows:

PYTHON
[{'id': 'ABC123'}]  # Output of print(existing_metadata)  
[]  # Output of print(non_existing_metadata)

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How to Validate Document Existence in a Chroma Vectorstore Using Metadata with LangChain?

2 Answers

Why the Issue Occurs

A Better Approach: Use the Get Method to Filter by Metadata

Explanation

Discussion

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