langchain-community

import os from langchain_community.document_loaders import TextLoader from langchain_community.embeddings import FakeEmbeddings from langchain_community.vectorstores import FAISS from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI # Requires 'pip install langchain-openai' # Create a dummy text file for demonstration with open("example.txt", "w") as f: f.write("LangChain is a framework for developing applications powered by large language models (LLMs).") f.write("\nIt enables applications that are context-aware and can reason over data.") # Set your OpenAI API key (replace with actual key or environment variable) # For a real application, use `os.environ.get("OPENAI_API_KEY", "")` os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-YOUR_OPENAI_KEY_HERE") # 1. Load documents using a loader from langchain-community loader = TextLoader("example.txt") documents = loader.load() print(f"Loaded {len(documents)} document(s).") # 2. Create embeddings (using a fake one for simplicity in 'community' quickstart) # For real use, install a provider package like `langchain-openai` and use its embeddings. embeddings = FakeEmbeddings() # 3. Create a vector store from documents and embeddings vectorstore = FAISS.from_documents(documents, embeddings) print("Vector store created.") # 4. Perform a similarity search as a retriever retriever = vectorstore.as_retriever() # 5. Define a Chat Model (from a dedicated provider package, e.g., langchain-openai) # Ensure OPENAI_API_KEY is set in your environment. chat_model = ChatOpenAI(api_key=os.environ.get("OPENAI_API_KEY")) # 6. Create a prompt template prompt = ChatPromptTemplate.from_messages([ ("system", "You are an AI assistant. Answer the question based ONLY on the provided context."), ("human", "Context: {context}\nQuestion: {question}") ]) # 7. Build a RAG chain chain = ( {"context": retriever, "question": StrOutputParser()} | prompt | chat_model | StrOutputParser() ) # 8. Invoke the chain question = "What is LangChain's primary purpose?" response = chain.invoke(question) print(f"\nQuestion: {question}") print(f"Answer: {response}") # Clean up the dummy file os.remove("example.txt")