ai-prompting-langchain-rag-pipeline
Sub-skill of ai-prompting: LangChain RAG Pipeline (+4).
Best use case
ai-prompting-langchain-rag-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of ai-prompting: LangChain RAG Pipeline (+4).
Teams using ai-prompting-langchain-rag-pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langchain-rag-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-prompting-langchain-rag-pipeline Compares
| Feature / Agent | ai-prompting-langchain-rag-pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of ai-prompting: LangChain RAG Pipeline (+4).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# LangChain RAG Pipeline (+4)
## LangChain RAG Pipeline
```python
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
# Load and split documents
loader = DirectoryLoader("./docs", glob="**/*.md")
*See sub-skills for full details.*
## DSPy Optimized Pipeline
```python
import dspy
from dspy.teleprompt import BootstrapFewShot
# Define signature
class QASignature(dspy.Signature):
"""Answer questions based on context."""
context = dspy.InputField(desc="Relevant context")
question = dspy.InputField(desc="Question to answer")
answer = dspy.OutputField(desc="Concise answer")
*See sub-skills for full details.*
## Prompt Engineering Patterns
```python
# Chain-of-Thought Prompting
COT_TEMPLATE = """
Solve this step by step:
Problem: {problem}
Let's think through this carefully:
1. First, I'll identify the key information...
2. Next, I'll determine the approach...
*See sub-skills for full details.*
## PandasAI Data Querying
```python
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
# Load data
df = pd.read_csv("sales_data.csv")
# Create AI-enabled dataframe
llm = OpenAI(api_token="...")
*See sub-skills for full details.*
## Agenta Prompt Management
```python
from agenta import Agenta
# Initialize
ag = Agenta()
# Define prompt variant
@ag.variant
def summarize_text(text: str, style: str = "concise"):
prompt = f"""
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