dosubot[bot]
commented
6 months ago Hey @mw19930312, great to see you back! Hope you're doing well. 😊
Thank you for providing the detailed bug report. It seems like the issue is related to the interaction between the RetrieverQueryEngine and the Opensearch component of the LlamaIndex framework. The error message RequestError(400, 'x_content_parse_exception', '[level] query malformed, no start_object after query name') suggests that the query being sent to Opensearch is not correctly formatted.
The error is being raised from the opensearchpy library, which is used by the LlamaIndex framework to interact with the Opensearch server. Specifically, the error occurs when the search method of the opensearchpy client is called. This method is used to execute a search query against the Opensearch index.
The search method expects a properly formatted query object, and it seems like the query object being passed is not correctly structured, hence the x_content_parse_exception error. This could be due to a variety of reasons, such as incorrect query syntax, missing required fields, or incompatible data types.
To resolve this issue, we need to identify the exact cause of the malformed query. Here are a few steps you can take:
-
Check the Query Syntax: Ensure that the query syntax is correct and compatible with the Opensearch query DSL. You can refer to the Opensearch Query DSL documentation for more details.
-
Inspect the Query Object: You can add some debug logs in the
querymethod of theOpensearchcomponent in theLlamaIndexframework to print out the query object before it's sent to thesearchmethod of theopensearchpyclient. This can help you identify any issues with the query structure. -
Update the
opensearchpyLibrary: If you're using an older version of theopensearchpylibrary, try updating it to the latest version. There might be compatibility issues between the version of Opensearch you're using and theopensearchpylibrary. -
Check the Opensearch Version: Ensure that the version of Opensearch you're using is compatible with the
LlamaIndexframework. If you're using a newer version of Opensearch, there might be breaking changes in the query DSL that are causing the issue.
If you're still facing the issue after trying these steps, please provide more details about the query you're trying to execute and the version of Opensearch you're using. This will help in identifying the exact cause of the issue and finding a suitable solution.
Sources
- llama-index-core/llama_index/core/query_engine/retriever_query_engine.py
- llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py
logan-markewich
mw19930312
Bug Description
I'm following the RAPTOR notebook to conduct a few experiments using Opensearch. However, the RetrieverQueryEngine fails to generate a correct answer due to failure in opensearchpy.
Version
0.10.16
Steps to Reproduce
llama_index_documents = convert_to_llama_index_document(parsed_google_docs) from llama_index.packs.raptor import RaptorPack
raptor_pack_google_doc = RaptorPack( llama_index_documents, embed_model=OpenAIEmbedding( model="text-embedding-ada-002" ), # used for embedding clusters llm=OpenAI(model="gpt-3.5-turbo", temperature=0), # used for generating summaries vector_store=vector_store, # used for storage similarity_top_k=5, # top k for each layer, or overall top-k for collapsed mode="tree_traversal", # sets default mode transformations=[ SentenceSplitter(chunk_size=400, chunk_overlap=50) ], # transformations applied for ingestion )
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine.from_args( raptor_pack_google_doc.retriever, llm=OpenAI(model="gpt-4-1106-preview", temperature=0) ) response = query_engine.query("Hello") print(str(response))
Relevant Logs/Tracbacks