Local PDF RAG Pipeline with LangChain and Ollama
Generates a Python script using LangChain to load local PDFs via DirectoryLoader, create embeddings with Ollama, store in Chroma, and perform RAG queries.
npx skills add ECNU-ICALK/AutoSkill --skill local-pdf-rag-pipeline-with-langchain-and-ollama --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Local PDF RAG Pipeline with LangChain and Ollama Generates a Python script using LangChain to load local PDFs via DirectoryLoader, create embeddings with Ollama, store in Chroma, and perform RAG queries. ## Prompt # Role & Objective You are a LangChain developer. Your task is to write a Python script that implements a Retrieval-Augmented Generation (RAG) pipeline using local PDF files, Ollama embeddings, and the Chroma vector store. # Communication & Style Preferences - Provide the complete, runnable Python code. - Use clear comments to explain the steps (Loading, Splitting, Embedding, Retrieval). - Ensure the code is syntactically correct (e.g., use straight quotes, not smart quotes). # Operational Rules & Constraints 1. **Imports**: Include `PyPDFLoader`, `DirectoryLoader`, `Chroma`, `embeddings`, `ChatOllama`, `RunnablePassthrough`, `StrOutputParser`, `ChatPromptTemplate`, and `CharacterTextSplitter`. 2. **Loading**: Use `DirectoryLoader` to load documents from a local directory. Specify the `directory_path`, a `glob` pattern for the PDF filename, and set `loader_cls=PyPDFLoader`. 3. **Splitting**: Use `CharacterTextSplitter.from_tiktoken_encoder` with a defined `chunk_size` a
- Prompt
- Triggers
What does the Local PDF RAG Pipeline with LangChain and Ollama skill do?
Generates a Python script using LangChain to load local PDFs via DirectoryLoader, create embeddings with Ollama, store in Chroma, and perform RAG queries.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill local-pdf-rag-pipeline-with-langchain-and-ollama --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.