LangChain Community Integrations

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")