FHIR Docs Search

This agent tool is a complex pipeline for retrieving and processing Fast Healthcare Interoperability Resources (FHIR) documents using various components from the langchain library, integrating services like Pinecone for vector storage and OpenAI for embeddings and language model-based processing. The code is structured to work with FHIR documents, aiming to extract, transform, and process these documents for specific queries. Here's an explanation of the main components and the overall flow:

Main Processing Logic

The main processing logic involves retrieving relevant documents based on a query, compressing these documents to filter out the most relevant ones using a combination of FAISS (for vector similarity) and Cohere (for reranking based on relevance), and then further processing these documents to extract useful information or answer specific queries.

MapReduce Explanation

• A custom MapReduce class is defined to illustrate the map-reduce pattern within this processing pipeline. It uses:

  • LLMChain: For leveraging language model-based operations on the documents.

  • MapReduceDocumentsChain: To orchestrate the map and reduce steps. The map step identifies main themes or important information in each document, and the reduce step consolidates these findings into a final, comprehensive summary.

  • StuffDocumentsChain and ReduceDocumentsChain: These components are used within the reduce step to manage and process the summaries generated from the map step, ensuring the final output is concise and informative.

Overall Flow

1. Retrieve and Compress Documents: For a given query, documents are retrieved from a Pinecone index based on their relevance to the query. These documents are then compressed using a combination of FAISS and Cohere to focus on the most relevant documents.

2. Process Documents with MapReduce: The reduced set of documents is then processed using a custom MapReduce pattern. This involves mapping over the documents to extract important information and reducing these extracts into a coherent summary or answer.

3. Generate Outputs: Depending on the function called (retrieve_and_process, process_resources_and_generate_answer, etc.), the pipeline can generate answers to specific queries, generate synthetic FHIR data, or create Pydantic models for FHIR resources.