LangChain
Key Components of LangChain
Retrieval
In this lesson, we’ll dive into retrieval, the process that injects fresh, domain-specific knowledge into your large language model (LLM) prompts. By adding context at query time, you overcome the model’s training cutoff and deliver up-to-date, accurate responses.
Why Retrieval Is Essential
LLMs only “know” what they saw during training. To answer questions about recent events or proprietary data, you must supply external context. Retrieval pipelines let you:
- Integrate PDFs, XML/JSON files, REST APIs, web pages, and more
- Preprocess and embed your data once, then reuse it efficiently
- Perform similarity search at runtime to fetch the most relevant snippets
- Keep your prompts lightweight while delivering high-precision context
Retrieval Pipeline Overview
Source
Gather raw data from all relevant origins—documents, databases, social media APIs, web crawls, etc.Load & Transform
Ingest the raw content and split it into coherent chunks (paragraphs, sections, or sliding windows).Embed
Convert each chunk into a high-dimensional vector using an embeddings model (e.g., OpenAI’stext-embedding-ada-002).Store
Index these vectors in a purpose-built vector database for nearest-neighbor search.Retrieve
At query time, embed the user’s query and perform a k-NN search to find the top-N most similar chunks.Inject & Prompt
Assemble the retrieved chunks into your prompt template and send the enriched prompt to the LLM.
Note
Separate the ingestion pipeline (steps 1–4) from runtime retrieval (steps 5–6) to avoid reprocessing data on every query and to scale efficiently.
Pipeline Summary
| Stage | Purpose | Tools / Examples |
|---|---|---|
| Source | Collect raw data | PDFs, XML/JSON files, REST APIs, web scrapers |
| Load & Transform | Split and normalize content | langchain.text_splitter, custom parsers |
| Embed | Generate vector representations | openai.embeddings.create, Hugging Face models |
| Store | Index vectors for similarity search | Pinecone, Weaviate, Milvus |
| Retrieve | Find top-N relevant chunks based on query vector | KNN search, vector_db.query() |
| Inject & Prompt | Build final prompt and call LLM | Prompt templates, LangChain chains |
Putting It All Together
- Run your ingestion pipeline offline to build the vector store.
- At query time, embed the user’s question.
- Fetch the nearest-neighbor chunks.
- Concatenate them into your LLM prompt.
- Send the prompt to the model for a context-aware response.
Warning
Retrieval adds latency and storage costs. Monitor your vector database performance and consider caching frequently asked queries.
Further Reading
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