ContextGroundingVectorStore

ContextGroundingVectorStore is a vector store implementation designed for context-aware document retrieval. It allows you to perform semantic searches and create retrieval chains with language models.

You will need to create an index in Context Grounding to use this feature. To create an index go to organization Admin -> AI Trust Layer -> Context Grounding. There you can create a new index and add documents to it. See the full documentation here for more details.

Searching Documents

The vector store supports various search methods:

from uipath_langchain.vectorstores.context_grounding_vectorstore import ContextGroundingVectorStore

vectorstore = ContextGroundingVectorStore(index_name="Company policy")

# Perform semantic searches with distance scores
docs_with_scores = vectorstore.asimilarity_search_with_score(query="What is the company policy on data storage?", k=5)

# Perform a similarity search with relevance scores
docs_with_relevance_scores = await vectorstore.asimilarity_search_with_relevance_scores(query=query, k=5)

Creating a Retrieval Chain

You can integrate the vector store into a retrieval chain with a language model:

# Run a retrieval chain
model = UiPathAzureChatOpenAI(model="gpt-4o-2024-08-06", max_retries=3)
retrieval_chain = create_retrieval_chain(vectorstore=vectorstore, model=model)

query = "What is the ECCN for a laptop?"
result = retrieval_chain(query)