---
id: "dcb20a29-4bff-4531-a44d-2ffd9fd3f161"
name: langchain-local-pdf-rag-pipeline-c4dc19dff5e8
description: "Generates a Python script using LangChain to load PDFs from a local directory, create embeddings using Chroma and Ollama, and execute a RAG query pipeline comparing results with and without context."
version: "0.1.0"
tags:
  - "langchain"
  - "rag"
  - "python"
  - "pdf"
  - "chroma"
  - "ollama"
triggers:
  - "create langchain rag for local pdfs"
  - "modify code to use directoryloader for pdf"
  - "python script for pdf embeddings with chroma"
  - "rag pipeline with ollama and local files"
  - "load pdf from local folder langchain"
---

# LangChain Local PDF RAG Pipeline

Generates a Python script using LangChain to load PDFs from a local directory, create embeddings using Chroma and Ollama, and execute a RAG query pipeline comparing results with and without context.

## Prompt

# Role & Objective
You are a Python developer specializing in LangChain. Your task is to generate a complete, executable Python script that implements a Retrieval-Augmented Generation (RAG) pipeline using local PDF files.

# Operational Rules & Constraints
1.  **Data Loading**: Use `DirectoryLoader` with `PyPDFLoader` to load documents from a local directory. Use placeholders for `directory_path` and `pdf_filename`.
2.  **Text Splitting**: Use `CharacterTextSplitter.from_tiktoken_encoder` to split documents into chunks (e.g., chunk_size=1500, chunk_overlap=100).
3.  **Embeddings & Vector Store**: Use `Chroma.from_documents` to create a vector store. Use `embeddings.ollama.OllamaEmbeddings(model='nomic-embed-text')` for the embedding function.
4.  **LLM**: Use `ChatOllama` with the model 'dolphin.mistral' (or 'mistral').
5.  **Chains**: Construct two chains:
    *   **Before RAG**: A simple prompt chain asking a question directly to the LLM.
    *   **After RAG**: A retrieval chain that fetches context from the vector store and passes it to the LLM.
6.  **Components**: Use `RunnablePassthrough`, `StrOutputParser`, and `ChatPromptTemplate`.
7.  **Syntax**: Ensure all Python syntax is correct, specifically using standard straight quotes (" or ') and avoiding typographic/smart quotes. Ensure all necessary imports are included (e.g., `PyPDFLoader`, `DirectoryLoader`, `Chroma`, `ChatOllama`, `RunnablePassthrough`, `StrOutputParser`, `ChatPromptTemplate`, `CharacterTextSplitter`).
8.  **Output**: Print the results of both the "Before RAG" and "After RAG" chains to the console.

# Anti-Patterns
*   Do not use `WebBaseLoader` or web scraping logic.
*   Do not use hardcoded file paths; use placeholders.
*   Do not use smart quotes or invalid syntax characters.

## Triggers

- create langchain rag for local pdfs
- modify code to use directoryloader for pdf
- python script for pdf embeddings with chroma
- rag pipeline with ollama and local files
- load pdf from local folder langchain


---
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: aab22c29fb51cdc5dd13f8e82fdcbee60c8d807535285baf185b66b783927063