langchain-rate-limit-errors
Sub-skill of langchain: Rate Limit Errors (+2).
Best use case
langchain-rate-limit-errors is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of langchain: Rate Limit Errors (+2).
Teams using langchain-rate-limit-errors 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/rate-limit-errors/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langchain-rate-limit-errors Compares
| Feature / Agent | langchain-rate-limit-errors | 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 langchain: Rate Limit Errors (+2).
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
# Rate Limit Errors (+2)
## Rate Limit Errors
```python
from langchain_openai import ChatOpenAI
from tenacity import retry, wait_exponential, stop_after_attempt
llm = ChatOpenAI(
model="gpt-4",
max_retries=3,
request_timeout=60
)
@retry(wait=wait_exponential(min=1, max=60), stop=stop_after_attempt(5))
def invoke_with_retry(chain, input_data):
return chain.invoke(input_data)
```
## Memory Issues with Large Documents
```python
# Process documents in batches
def batch_process_documents(documents, batch_size=100):
for i in range(0, len(documents), batch_size):
batch = documents[i:i + batch_size]
yield process_batch(batch)
```
## Vector Store Performance
```python
# Use FAISS for larger collections
from langchain_community.vectorstores import FAISS
vectorstore = FAISS.from_documents(
documents,
embeddings,
distance_strategy="COSINE"
)
# Add index for faster retrieval
vectorstore.save_local("faiss_index")
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