id: "dcb20a29-4bff-4531-a44d-2ffd9fd3f161" name: "LangChain Local PDF RAG Pipeline" 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