Hey everyone, I've been deep in the weeds building a custom retrieval pipeline using LlamaIndex to power a customer support chatbot, and I've hit a snag that's generating a ton of noise. My application logs are being absolutely flooded with `'Unable to retrieve context'` errors, even for seemingly straightforward queries. The chatbot still functions *sometimes*, but the reliability is all over the place and these errors are making it impossible to monitor real issues.
I'm using a fairly standard setup with `VectorStoreIndex` and a custom `Retriever` that incorporates some metadata filtering. The core of my query engine looks something like this:
```python
from llama_index.core import VectorStoreIndex, get_response_synthesizer
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
# index creation omitted for brevity
retriever = VectorIndexRetriever(
index=index,
similarity_top_k=3,
filters=[my_metadata_filter] # custom filter
)
response_synthesizer = get_response_synthesizer()
query_engine = RetrieverQueryEngine(
retriever=retriever,
response_synthesizer=response_synthesizer
)
response = query_engine.query("What is your refund policy?")
```
The error seems to originate from the response synthesis step when the retriever returns an empty node list. I've confirmed my metadata filters aren't too restrictive for the test queries. Has anyone else encountered this log spam?
Some specific avenues I've already explored without full success:
* Verified the ingested documents do contain text relevant to the test queries.
* Tried adjusting `similarity_top_k` from 2 up to 10.
* Experimented with different embedding models (OpenAI, then local sentence-transformers).
* Checked for potential text splitting issues that could have created empty nodes.
My current suspicion is that it might be related to the default `ServiceContext` or perhaps a mismatch in how the `ResponseSynthesizer` is handling the empty result scenario. I'd be really grateful if anyone has run into this and found a robust workaround. Sharing your retriever or query engine configuration details would be incredibly helpful.
api first
api first
The "Unable to retrieve context" error is typically thrown by the `ResponseSynthesizer` when the retriever returns an empty node list. Your mention of a custom metadata filter is the most probable root cause. Overly restrictive filters, or a mismatch between the query's implicit parameters and your filter logic, will cause zero nodes to pass through, triggering that exact log message.
You should implement a validation step before passing nodes to the synthesizer. A pragmatic approach is to check the length of the retrieved nodes in a custom retriever subclass or a post-retrieval hook. If it's empty, you can either relax the filter criteria, fall back to a broader retrieval, or return a predefined default context before the synthesizer is invoked. This turns a noisy error into a controlled, logged event you can handle.
Have you instrumented your filter function to log the specific metadata criteria being applied for these failing queries? The discrepancy often lies there, not in the vector search itself.
Data doesn't lie, but folks sometimes do.
Good point on the validation step, that's a solid defensive pattern. I'd add that sometimes the empty list doesn't come from the filter itself, but from the underlying vector store query having too high a similarity cutoff. If nothing crosses the threshold, you get an empty list even before your metadata filter is applied.
Have you considered adjusting the retriever's `similarity_top_k` alongside the validation? Cranking that up a bit, then applying your filter, can give you a larger pool to filter from and might reduce the empty results. It's a trade-off between performance and completeness, but it often helps with these "no context" scenarios in practice.
Keep it real, keep it kind.
Oh, that logging noise sounds so frustrating, I feel your pain. I'm working through a similar migration piece and the logging part is honestly the worst.
I'm a bit confused on one thing though - when user667 talks about implementing a validation step, is that something you do inside your custom retriever class? Or is there a way to add it as some kind of wrapper? I'm worried about making my own retriever subclass from scratch.
One step at a time