llama-index-indices-managed-vectara 0.6.0

llama-index managed vectara integration

LlamaIndex Managed Integration: Vectara

The Vectara Index provides a simple implementation to Vectara's end-to-end RAG pipeline, including data ingestion, document retrieval, reranking results, summary generation, and hallucination evaluation.

Please note that this documentation applies to versions >= 0.4.0 and will not be the same as for earlier versions of Vectara ManagedIndex.

📌 Setup

First, make sure you have the latest LlamaIndex version installed.

pip install -U llama-index

Next, install the Vectara Index:

pip install -U llama-index-indices-managed-vectara

Finally, set up your Vectara corpus. If you don't have a Vectara account, you can sign up and follow our Quick Start guide to create a corpus and an API key (make sure the api_key has both indexing and query permissions, or use your personal API key).

Once you have your API key, export it as an environment variable:

import os

os.environ["VECTARA_API_KEY"] = "<YOUR_VECTARA_API_KEY>"
os.environ["VECTARA_CORPUS_KEY"] = "<YOUR_VECTARA_CORPUS_KEY>"

🚀 Usage

1. Index Documents

Create an index and add some sample documents:

from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.core.schema import Document, MediaResource

docs = [
    Document(
        id_="doc1",
        text_resource=MediaResource(
            text="""
            This is test text for Vectara integration with LlamaIndex.
            Users should love their experience with this integration
            """,
        ),
    ),
    Document(
        id_="doc2",
        text_resource=MediaResource(
            text="""
            The Vectara index integration with LlamaIndex implements Vectara's RAG pipeline.
            It can be used both as a retriever and query engine.
            """,
        ),
    ),
]
index = VectaraIndex.from_documents(docs)

Make sure to always specify a unique id_ for every document you add to your index. If you don't specify this parameter, a random id will be generated and the document will be separately added to your corpus every time you run your code.

You can now use this index to retrieve documents.

2. Retrieve Documents

Retrieve the top 2 most relevant document for a query:

# Retrieves the top search result
retriever = index.as_retriever(similarity_top_k=2)

results = retriever.retrieve("How will users feel about this new tool?")
print(results[0])

3. Use as a Query Engine

Generate a summary of retrieved results:

query_engine = index.as_query_engine()

results = query_engine.query(
    "Which company has partnered with Vectara to implement their RAG pipeline as an index?"
)
print(f"Generated summary: {results.response}\n")
print("Top sources:")
for node in results.source_nodes[:2]:
    print(node)

📂 Understanding source_nodes structure

Each node object in source_nodes contains a NodeWithScore object with:

• text: The matched text snippet.
• id_: The unique identifier of the document.
• metadata: A dictionary containing:
  • Key-value pairs from the matched part of the document.
  • A document key that stores all document-level metadata.
• score: The relevance score of the match.

Example Output:

NodeWithScore(
    node=Node(
        text_resource=MediaResource(
            text="This is a test text for Vectara integration with LlamaIndex."
        ),
        id_="doc1",
        metadata={
            "category": "AI",
            "page": 23,
            "document": {
                "url": "https://www.vectara.com/developers/build/integrations/llamaindex",
                "title": "LlamaIndex + Vectara Integration",
                "author": "Ofer Mendelevitch",
                "date": "2025-03-01"
            }
        }
    ),
    score=0.89
)

If you want to see the full features and capabilities of VectaraIndex, check out this Jupyter notebook.