How can I adapt LlamaIndex TextNodes from a Wdrant vector store for use with LangChain retrievers?

Below is one approach you might consider. LangChain retrievers expect documents (typically instances of LangChain’s Document type with a “page_content” field and metadata) rather than LlamaIndex’s Textnode structure. To use a LangChain retriever with your Qdrant (or Wdrant) collection, you can write an adapter to convert your text nodes into Document objects. For example:

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1. Define an adapter function

Create a function that converts each text node to a LangChain Document. Assuming your text nodes have the text (content) and any associated metadata, you can do something like:

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from langchain.docstore.document import Document

def convert_textnode_to_document(textnode):
    # Adjust these keys as needed depending on your textnode structure
    content = textnode.get("text")  # or whatever key holds the text
    metadata = textnode.get("metadata", {})
    return Document(page_content=content, metadata=metadata)

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2. Convert your collection

If you can retrieve your text nodes from the Qdrant collection, iterate over them and convert each one:

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# Suppose you have a function that fetches the text nodes from your collection:
textnodes = fetch_textnodes_from_collection()  # List[dict] representing your text nodes

# Convert text nodes to LangChain Document objects
documents = [convert_textnode_to_document(node) for node in textnodes]

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3. Create a LangChain vector store retriever

Once you have the documents, you can create a vector store using a LangChain integration (e.g., FAISS, Qdrant, etc.) and then use the .as_retriever() method. For example, if you’re using a Qdrant vector store with LangChain, it might look like:

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from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings  # or whichever embedding model you use

embeddings = OpenAIEmbeddings()  # ensure you have set up API keys / credentials if needed

# Create the vectorstore from documents
vectorstore = Qdrant.from_documents(documents, embeddings, collection_name="reports")

# Now get a LangChain retriever
retriever = vectorstore.as_retriever()

# Example query
docs = retriever.get_relevant_documents("What did the president say about token policies?")
print(docs)

Note: The above assumes you have a LangChain-compatible Qdrant integration. If you’re using a custom collection (or a non-standard Qdrant setup), you may need to create a custom wrapper class that converts the collection interface into one that LangChain expects.

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Summary

While LlamaIndex’s Textnodes and their native retrievers won’t directly work with LangChain’s retriever interface, you can bridge the two ecosystems by:

1. Converting your text nodes into LangChain Document objects.
2. Indexing these documents into a LangChain vector store (or writing an adapter around your existing vector store).
3. Using the LangChain model’s .as_retriever() on the resulting vector store.

This way, you create an adapter layer so that your stored text nodes become “readable” by LangChain’s retriever mechanisms.