Python Version Compatibility for LangChain & RAG Development
Overview
When building Retrieval-Augmented Generation (RAG) systems, frameworks like LangChain speed things up—but only if your Python version is compatible.
Newer Python versions do not always mean better stability.
The Issue Observed
While running a LangChain-based RAG project:
UserWarning: Core Pydantic V1 functionality isn't compatible with Python 3.14 or greater.
The code was correct. The environment was not.
Why This Happens
1️⃣ LangChain Still Depends on Pydantic v1 Internally
Even today, LangChain uses:
from pydantic.v1 import BaseModel
Pydantic v2 exists, but full migration across the ecosystem is still ongoing.
2️⃣ Python 3.14+ Breaks Pydantic v1
Python 3.14 introduces interpreter and C-API changes that:
- Break Pydantic v1 internals
- Cause runtime warnings or failures
- Affect any framework relying on v1
This is an ecosystem lag, not a LangChain bug.
Why This Is Critical for RAG
RAG pipelines depend on many layers:
- Loaders
- Splitters
- Embeddings
- Vector databases
- Retrievers
- Orchestration logic
If the data model layer breaks, everything above it becomes unstable.
Recommended Python Versions
✅ Strongly Recommended
- Python 3.10.13
Stable, widely supported, production-proven.
⚠️ Acceptable (With Caution)
- Python 3.10.x
- Python 3.11.x
❌ Avoid
- Python 3.12.x
- Python 3.13.x
- Python 3.14.x and above
Best Practices
- Prefer stability over novelty
- Pin Python versions explicitly
- Use per-project virtual environments
- Don’t blindly follow old tutorials
Key Takeaway
For LangChain and RAG development, Python 3.10.x is the safest choice.
TL;DR
- LangChain still relies on Pydantic v1
- Python 3.14+ breaks that dependency
- RAG systems become unstable
- Use Python 3.10.13
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