LCEL Basics

• | pipes output to next — prompt | llm | parser
• RunnablePassthrough() forwards input unchanged — use in parallel branches
• RunnableParallel runs branches concurrently — {"a": chain1, "b": chain2}
• .invoke() for single, .batch() for multiple, .stream() for tokens
• Input must match expected keys — {"question": x} not just x if prompt expects {question}

Memory Gotchas

• Memory doesn't auto-persist between sessions — save/load explicitly
• ConversationBufferMemory grows unbounded — use ConversationSummaryMemory for long chats
• Memory key must match prompt variable — memory_key="chat_history" needs {chat_history} in prompt
• return_messages=True for chat models — False returns string for completion models

RAG Chunking

• Chunk size affects retrieval quality — too small loses context, too large dilutes relevance
• Chunk overlap prevents cutting mid-sentence — 10-20% overlap typical
• RecursiveCharacterTextSplitter preserves structure — splits on paragraphs, then sentences
• Embedding dimension must match vector store — mixing models causes silent failures

Output Parsers

• PydanticOutputParser needs format instructions in prompt — call .get_format_instructions()
• Parser failures aren't always loud — malformed JSON may partially parse
• OutputFixingParser retries with LLM — wraps another parser, fixes errors
• with_structured_output() on chat models — cleaner than manual parsing for supported models

Retrieval

• similarity_search returns documents — .page_content for text
• k parameter controls results count — more isn't always better, noise increases
• Metadata filtering before similarity — filter={"source": "docs"} in most vector stores
• max_marginal_relevance_search for diversity — avoids redundant similar chunks

Agents

• Agents decide tool order dynamically — chains are fixed sequence
• Tool descriptions matter — agent uses them to decide when to call
• handle_parsing_errors=True — prevents crash on malformed agent output
• Max iterations prevents infinite loops — max_iterations=10 default may be too low

Common Mistakes

• Prompt template variables case-sensitive — {Question} ≠ {question}
• Chat models need message format — ChatPromptTemplate, not PromptTemplate
• Callbacks not propagating — pass config={"callbacks": [...]} through chain
• Rate limits crash silently sometimes — wrap in retry logic
• Token count exceeds context — use trim_messages or summarization for long histories