---
id: "11f8c5e3-9a07-49d1-b3ab-e7a59d5a481f"
name: local-pdf-rag-pipeline-with-langchain-and-ollama-7501f16e5d66
description: "Generates a Python script using LangChain to load local PDFs via DirectoryLoader, create embeddings with Ollama, store in Chroma, and perform RAG queries."
version: "0.1.0"
tags:
  - "langchain"
  - "rag"
  - "pdf"
  - "ollama"
  - "chroma"
  - "python"
triggers:
  - "create embeddings from local pdf"
  - "langchain rag with local files"
  - "directoryloader pdf chroma"
  - "ollama pdf rag"
  - "fix langchain pdf code"
---

# 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` and `chunk_overlap`.
4. **Embedding**: Use `Chroma.from_documents` to store embeddings. Configure the embedding function as `embeddings.ollama.OllamaEmbeddings(model='nomic-embed-text')`.
5. **Model**: Initialize `ChatOllama` with a specified model (e.g., 'dolphin.mistral').
6. **Chains**: Implement two chains:
   - "Before RAG": A direct query to the model without context.
   - "After RAG": A retrieval chain that fetches context from the vector store before answering.
7. **Syntax**: Ensure all strings use standard straight quotes (`"` or `'`). Ensure import statements are comma-separated correctly.
8. **Placeholders**: Use placeholders like `'path_to_pdf_directory'` and `'your_pdf_filename.pdf'` for user-specific values.

# Anti-Patterns
- Do not use `WebBaseLoader` or URL-based loading unless explicitly requested.
- Do not use smart quotes (curly quotes) in the code.
- Do not omit the flattening of the document list (`docs_list = [item for sublist in docs for item in sublist]`).

# Interaction Workflow
1. Receive a request to create a RAG pipeline for local PDFs.
2. Generate the Python script following the structure defined in the Operational Rules.
3. Verify syntax, specifically checking for quote types and import commas.

## Triggers

- create embeddings from local pdf
- langchain rag with local files
- directoryloader pdf chroma
- ollama pdf rag
- fix langchain pdf code


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API, Backend
Tools: Chroma, Langchain, Ollama
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: ebfa36d105d7069c2723223741c2dd9e41e16da3f483920a9a7322c1a1cccf8f