alivirgo LangChain LLM Apps AI Skill Guide
Overview & Engine Architecture
LangChain composes prompts, models, retrievers, and tools into runnable chains (LCEL). LCEL pipes (|) build DAGs of Runnable steps with batch/stream/async support. Agents keep prompts versioned, constrain tool permissions, ground answers with retrieval when facts matter, and treat model I/O as untrusted until validated.
Prompt -> Model -> OutputParser
^
|
Retriever / Tools
When to use this skill
- RAG chat and tool-calling assistants in Python
- Rapid composition of prompt + model + parser pipelines
- Glue between
@openai-api/@anthropic-apiand vector stores
Operational directives
- Prefer LCEL runnables over legacy LLMChain patterns for new code.
- Bound agent tool sets; never expose shell/FS tools without review.
- Separate system instructions, retrieved context, and user text clearly.
- Log prompts/completions with redaction - do not leak secrets into traces.
- Pin package extras (
langchain-openai, etc.) and model names explicitly.
LCEL RAG sketch
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
prompt = ChatPromptTemplate.from_messages([
("system", "Answer using only the context. If unknown, say you do not know.\n\n{context}"),
("human", "{question}"),
])
llm = ChatOpenAI(model="gpt-4.1-mini", temperature=0)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
print(chain.invoke("What is our refund window?"))
Tool calling hygiene
# Expose only pure, side-effect-reviewed callables
# Validate tool args; set timeouts; deny network where unused
Common failures
| Symptom | Cause | Fix |
|---|---|---|
| Hallucinated citations | weak retrieval / no grounding rule | tighter prompt; cite chunks |
| Import errors | split packages | install provider extras |
| Runaway agents | unlimited tool loops | max iterations; allowlists |
| Flaky evals | temperature > 0 | temp=0 for tests; golden sets |
Best practices
- Evaluate with fixed question sets before UX polish.
- Store embeddings/docs in
@chromadb/ cloud vector DBs with metadata filters. - Stream tokens for UX; batch for offline jobs.
- Keep business logic in plain Python modules - chains should stay thin.
Limitations
- APIs churn across LangChain majors; pin versions.
- Not a substitute for proper authZ on tools and data sources.
- Complex agents still need product-level guardrails and human escalation.
Related skills
@llamaindex- retrieval-first alternative framework@chromadb- local vector store@openai-api/@anthropic-api- provider SDKs