langchain/templates/docs/INDEX.md

Templates

Highlighting a few different categories of templates

⭐ Popular

These are some of the more popular templates to get started with.

• Retrieval Augmented Generation Chatbot: Build a chatbot over your data. Defaults to OpenAI and Pinecone.
• Extraction with OpenAI Functions: Do extraction of structured data from unstructured data. Uses OpenAI function calling.
• Local Retrieval Augmented Generation: Build a chatbot over your data. Uses only local tooling: Ollama, GPT4all, Chroma.
• OpenAI Functions Agent: Build a chatbot that can take actions. Uses OpenAI function calling and Tavily.
• XML Agent: Build a chatbot that can take actions. Uses Anthropic and You.com.

📥 Advanced Retrieval

These templates cover advanced retrieval techniques.

• Reranking: This retrieval technique uses Cohere's reranking endpoint to rerank documents from an initial retrieval step.
• Anthropic Iterative Search: This retrieval technique uses iterative prompting to determine what to retrieve and whether the retriever documents are good enough.
• Neo4j Parent Document Retrieval: This retrieval technique stores embeddings for smaller chunks, but then returns larger chunks to pass to the model for generation.
• Semi-Structured RAG: The template shows how to do retrieval over semi-structured data (e.g. data that involves both text and tables).

🔍Advanced Retrieval - Query Transformation

A selection of advanced retrieval methods that involve transforming the original user query.

• Hypothetical Document Embeddings: A retrieval technique that generates a hypothetical document for a given query, and then uses the embedding of that document to do semantic search. Paper.
• Rewrite-Retrieve-Read: A retrieval technique that rewrites a given query before passing it to a search engine. Paper.
• Step-back QA Prompting: A retrieval technique that generates a "step-back" question and then retrieves documents relevant to both that question and the original question. Paper.
• RAG-Fusion: A retrieval technique that generates multiple queries and then reranks the retrieved documents using reciprocal rank fusion. Article.
• Multi-Query Retriever: This retrieval technique uses an LLM to generate multiple queries and then fetches documents for all queries.

🧠Advanced Retrieval - Query Construction

A selection of advanced retrieval methods that involve constructing a query in a separate DSL from natural language.

• Elastic Query Generator: Generate elastic search queries from natural language.
• Neo4j Cypher Generation: Generate cypher statements from natural language. Available with a "full text" option as well.
• Supabase Self Query: Parse a natural language query into a semantic query as well as a metadata filter for Supabase.

🦙 OSS Models

These templates use OSS models.

• Local Retrieval Augmented Generation: Build a chatbot over your data. Uses only local tooling: Ollama, GPT4all, Chroma.
• SQL Question Answering (Replicate): Question answering over a SQL database, using Llama2 hosted on Replicate.
• SQL Question Answering (LlamaCpp): Question answering over a SQL database, using Llama2 through LlamaCpp.
• SQL Question Answering (Ollama): Question answering over a SQL database, using Llama2 through Ollama.

⛏️ Extraction

Extract data in a structured format

• Extraction Using OpenAI Functions: Extract information from text using OpenAI Function Calling.
• Extraction Using Anthropic Functions: Extract information from text using a LangChain wrapper around the Anthropic endpoints intended to simulate function calling.
• Extract BioTech Plate Data: Extract microplate data from messy Excel spreadsheets into a more normalized format.

🤖 Agents

• OpenAI Functions Agent: Build a chatbot that can take actions. Uses OpenAI function calling and Tavily.
• XML Agent: Build a chatbot that can take actions. Uses Anthropic and You.com.