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Build an End-to-End RAG Pipeline

Python, a text splitter, embeddings, Qdrant, and a chat model wired into one leave-policy assistant.

IntermediatePro18 min readBy Kishore BhosaleReviewed
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Read these first

Why this matters

  • PolicyDesk's first demo was a notebook that embedded the query and printed cosine scores, with no answer. The product is the answer plus the source.
  • Interviewers ask you to walk the code in order: load, split, embed, store, retrieve, generate.
  • Library class names change between LangChain releases. If you memorize an import and miss the pipeline, you fail the question.
  • Metadata added at ingest is the only metadata you can filter on at query time.

What you are assembling

You will build PolicyDesk's leave assistant with Python. The pieces are a PDF loader, a recursive splitter, an embedding model, a Qdrant collection, a chat model, and a prompt. Orchestration libraries (LangChain is the one these notes use) wire those pieces. You can wire them yourself with the OpenAI and Qdrant clients. The order of work does not change.

DocumentsPDF, DOCX, text
Load and split
Embed
Qdrant
Retrieve top-k
Chat modelGrounded prompt
Answer with sources
Documents flow in once. Each user question flows through retrieve and then the LLM.

The stack

ConcernA solid pickRole
LanguagePythonLoaders and ML clients are richest here
OrchestrationLangChain or LlamaIndex, or plain functionsGlue. Not the retrieval algorithm
Embeddingstext-embedding-3-small, or BGE if you must self-hostOne vector per chunk and per query
Vector storeQdrantANN search plus payload filters
Chat modelA current small or mid chat modelWrites the answer. Does not search
SplitterRecursive character splitter with a token length functionChunks on natural boundaries

  • Language

    A solid pickPython
    RoleLoaders and ML clients are richest here

  • Orchestration

    A solid pickLangChain or LlamaIndex, or plain functions
    RoleGlue. Not the retrieval algorithm

  • Embeddings

    A solid picktext-embedding-3-small, or BGE if you must self-host
    RoleOne vector per chunk and per query

  • Vector store

    A solid pickQdrant
    RoleANN search plus payload filters

  • Chat model

    A solid pickA current small or mid chat model
    RoleWrites the answer. Does not search

  • Splitter

    A solid pickRecursive character splitter with a token length function
    RoleChunks on natural boundaries

Pin versions. ChatOpenAI, OpenAIEmbeddings, and the Qdrant integration have moved packages (langchain-openai, langchain-qdrant) and method names. The code below is the shape to implement against the packages you install, not a promise that every import path is eternal.

The six implementation steps

Build order

  • Load. PyPDFLoader (or a better PDF parser if the handbook is multi-column) returns documents with page metadata.
  • Split. Recursive splitter. Start at a few hundred tokens with overlap, not a random character count you never inspect.
  • Embed and store. Create the collection with the model's dimension and cosine distance. Upsert vectors with payload text and metadata.
  • Embed the query with that same model.
  • Retrieve top-k with a department filter.
  • Generate from a prompt that contains only those chunks and forbids guessing.

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Still to come · 5 sections

  • Indexing code
  • Query code
  • Metadata on the chunk
  • Ask different questions on purpose
  • Make the script something you can operate

Test yourself

Answer these before moving on — recall is what makes it stick.