Why does llama_index fail to retrieve document information while LangChain correctly returns the answer?

Below is a few things to check when running your RAG LLM with llamaindex:

1. Document Ingestion and Splitting

LangChain’s code uses a custom text splitter (RecursiveCharacterTextSplitter) with a 1500‑character chunk size and 200‑character overlap. In llamaindex you’re using SimpleDirectoryReader (which by default may not split text in the same way) and the default node parser (TokenTextSplitter). Because the chunks and overlaps determine which parts of your document are “visible” to the retrieval engine, a different splitting strategy can lead to fewer chunks containing the registration period information.

Possible Fix:
Try using a splitting routine in llamaindex that mimics your LangChain settings. For example, configure the node parser to use similar chunk sizes and overlaps.

2. Query Prompt Structure and Chain Behavior

LangChain’s ConversationalRetrievalChain uses conversation history (memory) and a prompt template tailored for dialogue, which may guide the LLM to extract or synthesize the correct answer even if the relevant passage is split across chunks. In contrast, the default query engine in llamaindex may rely more strictly on similarity matching and doesn’t add conversation context by default.

Possible Fix:
Consider adjusting the query engine in llamaindex. You might want to customize the prompt or use a query engine that incorporates conversational context in your retrieval—to mimic the behavior of ConversationalRetrievalChain.

3. Consistent Embedding Settings

Even though you are using the same models for embeddings and chat in both frameworks, ensure that the settings (such as timeouts, tokenization, etc.) are configured similarly. A subtle difference in how the document embeddings are generated or indexed (due to different splitting, normalization, or even preprocessing) can affect retrieval quality.

Example Adjustment in llamaindex

You might try something like the following to control document splitting more explicitly:

PYTHONCopied
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.node_parser import SimpleNodeParser  # or import custom node parser if available

# Set the models
Settings.llm  = Ollama(model="gemma", request_timeout=360.0)
Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text")

# Adjust the document reader/splitter settings to mimic LangChain's splitter
# (Here, you might need to write a custom splitter or configure the provided one.)
documents = SimpleDirectoryReader(path).load_data(file)
# For example, using a custom parser that splits similarly:
node_parser = SimpleNodeParser(chunk_size=1500, chunk_overlap=200)
nodes = node_parser.get_nodes_from_documents(documents)

index = VectorStoreIndex.from_documents(nodes)
query_engine = index.as_query_engine()

response  = query_engine.query("when in the registration period")
print(response)

Make sure you have control over how text is broken into nodes. This adjustment may help the query engine better locate the answer in your documents.

Summary

• Document Chunking: Ensure your text splitting settings in llamaindex closely resemble those in your successful LangChain code.
• Query Prompting: Consider customizing the LlamaIndex query engine (especially if a conversational chain is needed).
• Embedding Consistency: Double-check that preprocessing for embeddings is consistent between both frameworks.

By aligning these aspects, you should get results closer to what you see with LangChain’s ConversationalRetrievalChain.