Translation MCP Server

# Context Enrichment Window for Document Retrieval ## Overview This code implements a context enrichment window technique for document retrieval in a vector database. It enhances the standard retrieval process by adding surrounding context to each retrieved chunk, improving the coherence and completeness of the returned information. ## Motivation Traditional vector search often returns isolated chunks of text, which may lack necessary context for full understanding. This approach aims to provide a more comprehensive view of the retrieved information by including neighboring text chunks. ## Key Components 1. PDF processing and text chunking 2. Vector store creation using FAISS and OpenAI embeddings 3. Custom retrieval function with context window 4. Comparison between standard and context-enriched retrieval ## Method Details ### Document Preprocessing 1. The PDF is read and converted to a string. 2. The text is split into chunks with overlap, each chunk tagged with its index. ### Vector Store Creation 1. OpenAI embeddings are used to create vector representations of the chunks. 2. A FAISS vector store is created from these embeddings. ### Context-Enriched Retrieval 1. The `retrieve_with_context_overlap` function performs the following steps: - Retrieves relevant chunks based on the query - For each relevant chunk, fetches neighboring chunks - Concatenates the chunks, accounting for overlap - Returns the expanded context for each relevant chunk ### Retrieval Comparison The notebook includes a section to compare standard retrieval with the context-enriched approach. ## Benefits of this Approach 1. Provides more coherent and contextually rich results 2. Maintains the advantages of vector search while mitigating its tendency to return isolated text fragments 3. Allows for flexible adjustment of the context window size ## Conclusion This context enrichment window technique offers a promising way to improve the quality of retrieved information in vector-based document search systems. By providing surrounding context, it helps maintain the coherence and completeness of the retrieved information, potentially leading to better understanding and more accurate responses in downstream tasks such as question answering.