[StackOverflow/python] How to use SimilarityPostprocessor in LlamaIndex to prevent Context Window overflow with Gemini?

### ROOT CAUSE The issue arises because Gemini's context window is smaller than the input documents' combined tokens. Without context window management, the model encounters overflow errors. The SimilarityPostprocessor in LlamaIndex lacks built-in integration with Gemini's context limits, requiring manual token-aware filtering. ### CODE FIX Use `TokenTextSplitter` to chunk documents and integrate the `SimilarityPostprocessor` with token-aware filtering: ```python from llama_index.core import Settings from llama_index.core.retrievers import VectorIndexRetriever from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.core.postprocessors import SimilarityPostprocessor from llama_index.core.text_splitter import TokenTextSplitter # Configure token-based context management Settings.text_splitter = TokenTextSplitter( chunk_size=1024, # Adjust based on Gemini's token limit (3072 tokens max) chunk_overlap=64 ) # Initialize service context service_context = ServiceContext.from_defaults() # Create retriever and postprocessor retriever = VectorIndexRetriever( index=your_index, service_context=service_context, similarity_k=5 # Adjust based on context size ) # Add SimilarityPostprocessor similarity_postprocessor = SimilarityPostprocessor( similarity_threshold=0.5, top_k=5 # Reduce to fit within context window ) # Combine for querying query_engine = RetrieverQueryEngine( retriever=retriever, node_postprocessors=[similarity_postprocessor] ) # Query with context-aware response response = query_engine.query("Your query here") ``` **Explanation**: 1. **TokenTextSplitter**: Splits documents into manageable chunks based on token count. 2. **SimilarityPostprocessor**: Filters nodes by relevance and similarity score, reducing context size. 3. **RetrieverQueryEngine**: Chains the postprocessor to dynamically trim context before sending to Gemini. Adjust `chunk_size`, `top_k`, and `similarity_threshold` based on Gemini's token limit and use case.