jxtngx/langchain-rag

<overview> Retrieval Augmented Generation (RAG) enhances LLM responses by fetching relevant context from external knowledge sources.

Pipeline:

1. Index: Load → Split → Embed → Store
2. Retrieve: Query → Embed → Search → Return docs
3. Generate: Docs + Query → LLM → Response

Key Components:

• Document Loaders: Ingest data from files, web, databases
• Text Splitters: Break documents into chunks
• Embeddings: Convert text to vectors
• Vector Stores: Store and search embeddings </overview>

<vectorstore-selection>

| Vector Store | Use Case | Persistence | |--------------|----------|-------------| | InMemory | Testing | Memory only | | FAISS | Local, high performance | Disk | | Chroma | Development | Disk | | Pinecone | Production, managed | Cloud |

</vectorstore-selection>

---

Complete RAG Pipeline

<ex-basic-rag-setup> <python> End-to-end RAG pipeline: load documents, split into chunks, embed, store, retrieve, and generate a response. ```python from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_community.vectorstores import InMemoryVectorStore from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_core.documents import Document

1. Load documents

docs = [ Document(page_content="LangChain is a framework for LLM apps.", metadata={}), Document(page_content="RAG = Retrieval Augmented Generation.", metadata={}), ]

2. Split documents

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) splits = splitter.split_documents(docs)

3. Create embeddings and store

embeddings = OpenAIEmbeddings(model="text-embedding-3-small") vectorstore = InMemoryVectorStore.from_documents(splits, embeddings)

4. Create retriever

retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

5. Use in RAG

model = ChatOpenAI(model="gpt-4.1") query = "What is RAG?" relevant_docs = retriever.invoke(query)

context = "\n\n".join([doc.page_content for doc in relevant_docs]) response = model.invoke([ {"role": "system", "content": f"Use this context:\n\n{context}"}, {"role": "user", "content": query}, ])

</python>
<typescript>
End-to-end RAG pipeline: load documents, split into chunks, embed, store, retrieve, and generate a response.
```typescript
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { Document } from "@langchain/core/documents";

// 1. Load documents
const docs = [
  new Document({ pageContent: "LangChain is a framework for LLM apps.", metadata: {} }),
  new Document({ pageContent: "RAG = Retrieval Augmented Generation.", metadata: {} }),
];

// 2. Split documents
const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 500, chunkOverlap: 50 });
const splits = await splitter.splitDocuments(docs);

// 3. Create embeddings and store
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorstore = await MemoryVectorStore.fromDocuments(splits, embeddings);

// 4. Create retriever
const retriever = vectorstore.asRetriever({ k: 4 });

// 5. Use in RAG
const model = new ChatOpenAI({ model: "gpt-4.1" });
const query = "What is RAG?";
const relevantDocs = await retriever.invoke(query);

const context = relevantDocs.map(doc => doc.pageContent).join("\n\n");
const response = await model.invoke([
  { role: "system", content: `Use this context:\n\n${context}` },
  { role: "user", content: query },
]);

</typescript> </ex-basic-rag-setup>

---

Document Loaders

<ex-loading-pdf> <python> Load a PDF file and extract each page as a separate document. ```python from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader("./document.pdf") docs = loader.load() print(f"Loaded {len(docs)} pages")

</python>
<typescript>
Load a PDF file and extract each page as a separate document.
```typescript
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";

const loader = new PDFLoader("./document.pdf");
const docs = await loader.load();
console.log(`Loaded ${docs.length} pages`);

</typescript> </ex-loading-pdf> <ex-loading-web-pages> <python> Fetch and parse content from a web URL into a document. ```python from langchain_community.document_loaders import WebBaseLoader

loader = WebBaseLoader("https://docs.langchain.com") docs = loader.load()

</python>
<typescript>
Fetch and parse content from a web URL into a document using Cheerio.
```typescript
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";

const loader = new CheerioWebBaseLoader("https://docs.langchain.com");
const docs = await loader.load();

</typescript> </ex-loading-web-pages> <ex-loading-directory> <python> Load all text files from a directory using a glob pattern. ```python from langchain_community.document_loaders import DirectoryLoader, TextLoader

Load all text files from directory

loader = DirectoryLoader( "path/to/documents", glob="**/*.txt", # Pattern for files to load loader_cls=TextLoader ) docs = loader.load()

</python>
</ex-loading-directory>

---

## Text Splitting

<ex-text-splitting>
<python>
Split documents into chunks using RecursiveCharacterTextSplitter with configurable size and overlap.
```python
from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,        # Characters per chunk
    chunk_overlap=200,      # Overlap for context continuity
    separators=["\n\n", "\n", " ", ""],  # Split hierarchy
)

splits = splitter.split_documents(docs)

</python> </ex-text-splitting>

---

Vector Stores

<ex-chroma-vectorstore> <python> Create a persistent Chroma vector store and reload it from disk. ```python from langchain_chroma import Chroma from langchain_openai import OpenAIEmbeddings

vectorstore = Chroma.from_documents( documents=splits, embedding=OpenAIEmbeddings(), persist_directory="./chroma_db", collection_name="my-collection", )

Load existing

vectorstore = Chroma( persist_directory="./chroma_db", embedding_function=OpenAIEmbeddings(), collection_name="my-collection", )

</python>
<typescript>
Create a Chroma vector store connected to a running Chroma server.
```typescript
import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/openai";

const vectorstore = await Chroma.fromDocuments(
  splits,
  new OpenAIEmbeddings(),
  { collectionName: "my-collection", url: "http://localhost:8000" }
);

</typescript> </ex-chroma-vectorstore> <ex-faiss-vectorstore> <python> Create a FAISS vector store, save it to disk, and reload it. ```python from langchain_community.vectorstores import FAISS

vectorstore = FAISS.from_documents(splits, embeddings) vectorstore.save_local("./faiss_index")

Load (requires allow_dangerous_deserialization)

loaded = FAISS.load_local( "./faiss_index", embeddings, allow_dangerous_deserialization=True )

</python>
<typescript>
Create a FAISS vector store, save it to disk, and reload it.
```typescript
import { FaissStore } from "@langchain/community/vectorstores/faiss";

const vectorstore = await FaissStore.fromDocuments(splits, embeddings);
await vectorstore.save("./faiss_index");

const loaded = await FaissStore.load("./faiss_index", embeddings);

</typescript> </ex-faiss-vectorstore>

---

Retrieval

<ex-similarity-search> <python> Perform similarity search and retrieve results with relevance scores. ```python # Basic search results = vectorstore.similarity_search(query, k=5)

With scores

results_with_score = vectorstore.similarity_search_with_score(query, k=5) for doc, score in results_with_score: print(f"Score: {score}, Content: {doc.page_content}")

</python>
<typescript>
Perform similarity search and retrieve results with relevance scores.
```typescript
// Basic search
const results = await vectorstore.similaritySearch(query, 5);

// With scores
const resultsWithScore = await vectorstore.similaritySearchWithScore(query, 5);
for (const [doc, score] of resultsWithScore) {
  console.log(`Score: ${score}, Content: ${doc.pageContent}`);
}

</typescript> </ex-similarity-search> <ex-mmr-search> <python> Use MMR (Maximal Marginal Relevance) to balance relevance and diversity in search results. ```python # MMR balances relevance and diversity retriever = vectorstore.as_retriever( search_type="mmr", search_kwargs={"fetch_k": 20, "lambda_mult": 0.5, "k": 5}, ) ``` </python> </ex-mmr-search> <ex-metadata-filtering> <python> Add metadata to documents and filter search results by metadata properties. ```python # Add metadata when creating documents docs = [ Document( page_content="Python programming guide", metadata={"language": "python", "topic": "programming"} ), ]

Search with filter

results = vectorstore.similarity_search( "programming", k=5, filter={"language": "python"} # Only Python docs )

</python>
</ex-metadata-filtering>

<ex-rag-with-agent>
<python>
Create an agent that uses RAG as a tool for answering questions.
```python
from langchain.agents import create_agent
from langchain.tools import tool

@tool
def search_docs(query: str) -> str:
    """Search documentation for relevant information."""
    docs = retriever.invoke(query)
    return "\n\n".join([d.page_content for d in docs])

agent = create_agent(
    model="gpt-4.1",
    tools=[search_docs],
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "How do I create an agent?"}]
})

</python> <typescript> Create an agent that uses RAG as a tool for answering questions. ```typescript import { createAgent } from "langchain"; import { tool } from "@langchain/core/tools"; import { z } from "zod";

const searchDocs = tool( async (input) => { const docs = await retriever.invoke(input.query); return docs.map(d => d.pageContent).join("\n\n"); }, { name: "search_docs", description: "Search documentation for relevant information.", schema: z.object({ query: z.string() }), } );

const agent = createAgent({ model: "gpt-4.1", tools: [searchDocs], });

const result = await agent.invoke({ messages: [{ role: "user", content: "How do I create an agent?" }], });

</typescript>
</ex-rag-with-agent>

<boundaries>
### What You CAN Configure

- Chunk size/overlap
- Embedding model
- Number of results (k)
- Metadata filters
- Search algorithms: Similarity, MMR

### What You CANNOT Configure

- Embedding dimensions (per model)
- Mix embeddings from different models in same store
</boundaries>

<fix-chunk-size>
<python>
Chunk size 500-1500 is typically good.
```python
# WRONG: Too small (loses context) or too large (hits limits)
splitter = RecursiveCharacterTextSplitter(chunk_size=50)
splitter = RecursiveCharacterTextSplitter(chunk_size=10000)

# CORRECT
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

</python> <typescript> Chunk size 500-1500 is typically good. ```typescript // WRONG: Too small or too large const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 50 });

// CORRECT const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });

</typescript>
</fix-chunk-size>

<fix-chunk-overlap>
<python>
Use overlap (10-20% of chunk size) to maintain context at boundaries.
```python
# WRONG: No overlap - context breaks at boundaries
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)

# CORRECT: 10-20% overlap
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

</python> </fix-chunk-overlap> <fix-persist-vectorstore> <python> Use persistent vector store instead of in-memory to avoid data loss. ```python # WRONG: InMemory - lost on restart vectorstore = InMemoryVectorStore.from_documents(docs, embeddings)

CORRECT

vectorstore = Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db")

</python>
<typescript>
Use persistent vector store instead of in-memory to avoid data loss.
```typescript
// WRONG: Memory - lost on restart
const vectorstore = await MemoryVectorStore.fromDocuments(docs, embeddings);

// CORRECT
const vectorstore = await Chroma.fromDocuments(docs, embeddings, { collectionName: "my-collection" });

</typescript> </fix-persist-vectorstore> <fix-consistent-embeddings> <python> Use the same embedding model for indexing and querying. ```python # WRONG: Different embeddings for index and query - incompatible! vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings(model="text-embedding-3-small")) retriever = vectorstore.as_retriever(embeddings=OpenAIEmbeddings(model="text-embedding-3-large"))

CORRECT: Same model

embeddings = OpenAIEmbeddings(model="text-embedding-3-small") vectorstore = Chroma.from_documents(docs, embeddings) retriever = vectorstore.as_retriever() # Uses same embeddings

</python>
<typescript>
Use the same embedding model for indexing and querying.
```typescript
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorstore = await Chroma.fromDocuments(docs, embeddings);
const retriever = vectorstore.asRetriever();  // Uses same embeddings

</typescript> </fix-consistent-embeddings> <fix-faiss-deserialization> <python> Explicitly allow deserialization when loading FAISS indexes. ```python # WRONG: Will raise error loaded_store = FAISS.load_local("./faiss_index", embeddings)

CORRECT

loaded_store = FAISS.load_local("./faiss_index", embeddings, allow_dangerous_deserialization=True)

</python>
</fix-faiss-deserialization>

<fix-dimension-mismatch>
<python>
Ensure embedding dimensions match the vector store index dimensions.
```python
# WRONG: Index has 1536 dimensions but using 512-dim embeddings
pc.create_index(name="idx", dimension=1536, metric="cosine")
vectorstore = PineconeVectorStore.from_documents(
    docs, OpenAIEmbeddings(model="text-embedding-3-small", dimensions=512), index=pc.Index("idx")
)  # Error: dimension mismatch!

# CORRECT: Match dimensions
embeddings = OpenAIEmbeddings()  # Default 1536

</python> </fix-dimension-mismatch>