RAG Integration for API Text
Budget: $1,000 – $2,000 USD
I already have my language-model stack in place; what I’m missing is the Retrieval-Augmented Generation layer that can pull fresh information from external APIs returning plain text. The job is entirely about wiring that RAG loop so the LLM can query the endpoints, embed the responses, store them in an efficient vector index (Pinecone, FAISS, or a similar store), and then surface accurate, source-grounded answers in real time.
You will be working only with API-supplied text for now, but I’d like to keep the design open enough that databases or file collections could be connected later without a full rewrite. Think LangChain or LlamaIndex pipelines, OpenAI or open-source models, and a robust chunking/embedding strategy that minimises hallucinations.
Deliverables
• End-to-end RAG pipeline code (Python preferred)
• Configuration notes for the API connectors and vector store
• A short README explaining how to run, extend, and monitor the system
I’ll validate the work by pointing the pipeline at a test API, running a few sample queries, and checking that citations map back correctly to the retrieved snippets. Once those checks pass, the milestone is complete.
You will be working only with API-supplied text for now, but I’d like to keep the design open enough that databases or file collections could be connected later without a full rewrite. Think LangChain or LlamaIndex pipelines, OpenAI or open-source models, and a robust chunking/embedding strategy that minimises hallucinations.
Deliverables
• End-to-end RAG pipeline code (Python preferred)
• Configuration notes for the API connectors and vector store
• A short README explaining how to run, extend, and monitor the system
I’ll validate the work by pointing the pipeline at a test API, running a few sample queries, and checking that citations map back correctly to the retrieved snippets. Once those checks pass, the milestone is complete.