LangChain Templates

Provides pre-built Retrieval-Augmented Generation templates that abstract over multiple vector store backends (Pinecone, Weaviate, Chroma, FAISS) through LangChain's Runnable interface, enabling developers to swap vector stores without changing application code. Templates use LCEL (LangChain Expression Language) to compose retriever chains with LLM calls, handling document ingestion, embedding generation, and semantic search orchestration as a single deployable LangServe application.

Provides templates for extracting structured data from unstructured text using LLMs with Pydantic schema binding, enabling type-safe extraction without manual prompt engineering. Templates use LangChain's structured output patterns (via tool calling or JSON mode) to guarantee schema compliance, with built-in retry logic via Tenacity for handling LLM parsing failures and automatic validation against the defined schema.

Templates use LangChain's tool abstraction to expose functions as callable tools that LLMs can invoke through function calling APIs (OpenAI, Anthropic) or tool-use protocols. Tools are defined with Pydantic schemas that describe inputs and outputs, enabling LLMs to generate properly-typed function calls without manual parsing. The tool abstraction handles schema serialization, argument validation, and error handling, with support for both synchronous and asynchronous tool execution.

Templates integrate LangChain's agent system (built on LangGraph) to enable autonomous agents that iteratively plan, invoke tools, and refine strategies based on results. Agents use middleware patterns to intercept and modify tool calls, implement custom routing logic to select appropriate tools, and support both ReAct (reasoning + acting) and other agentic patterns. The agent framework handles tool loop orchestration, error recovery, and state management, with built-in support for streaming agent steps.

Provides templates demonstrating testing patterns for LLM applications using LangChain's testing utilities, including mock LLMs for deterministic testing, fake embeddings for vector store testing, and callback-based assertion patterns. Templates show how to unit test chains and agents without calling real LLM providers, implement integration tests with recorded LLM responses (via VCR cassettes), and validate chain behavior across different scenarios. Supports both synchronous and asynchronous testing.

Offers pre-built templates for document summarization that handle long documents through configurable text splitting strategies (recursive character splitting, token-based splitting) and aggregation patterns (map-reduce, refine). Templates compose LangChain's text splitter abstractions with LLM chains to summarize documents larger than the LLM's context window, with support for both extractive and abstractive summarization approaches.

Provides templates for building SQL agents that use LLMs with tool-calling to generate and execute database queries against multiple SQL dialects (PostgreSQL, MySQL, SQLite, BigQuery). Agents use LangChain's tool abstraction to expose database schema introspection, query execution, and error handling as callable tools, enabling the LLM to iteratively refine queries based on execution feedback and schema information.

Provides templates for building multi-turn conversational systems that maintain chat history, retrieve relevant context from documents, and generate contextually-aware responses. Templates use LangChain's message history abstraction to persist conversation state, combine retrieval with chat models to ground responses in documents, and handle context window limits through configurable memory strategies (sliding window, summary-based compression).

Templates leverage LangChain's unified language model interface to abstract over multiple LLM providers (OpenAI, Anthropic, Groq, Ollama, local models) through a single Runnable API, enabling provider-agnostic chain definitions. Chains can be deployed with different LLM backends by changing configuration, with optional fallback routing that automatically switches providers if the primary provider fails or exceeds rate limits.

Templates use LangChain Expression Language (LCEL) to define chains declaratively as composable Runnable objects, enabling automatic optimization, parallel execution, and streaming. LCEL chains are compiled to optimized execution graphs that can be serialized, cached, and deployed as REST APIs via LangServe, with built-in support for branching, conditional logic, and error handling through Runnable operators (pipe, map, batch, stream).

Templates integrate LangChain's callback system to emit structured events at each step of chain execution (LLM calls, tool invocations, retrieval operations), enabling observability without modifying chain code. Callbacks can be routed to external systems (LangSmith for tracing, custom logging backends) to track latency, token usage, errors, and intermediate results, with built-in support for async callbacks that don't block chain execution.

Templates use LangChain's PromptTemplate abstraction to define reusable prompt templates with variable placeholders, supporting dynamic prompt composition by injecting context, examples, and instructions at runtime. Templates support multiple formats (f-string, Jinja2) and can be chained together to build complex prompts, with built-in validation to ensure all required variables are provided before LLM invocation.

Templates leverage LangChain's document loader abstraction to ingest content from multiple sources (PDFs, web pages, databases, cloud storage) and text splitter abstraction to chunk documents using configurable strategies (recursive character splitting, token-based splitting, semantic splitting). Loaders and splitters are composable Runnable objects that can be chained together to build document processing pipelines, with support for metadata preservation and custom splitting logic.