aipackage/cezen-portal/tool-langchain.html
2026-06-30 10:51:41 +05:30

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<h1>LangChain</h1>
<div class="tagline">A Python toolkit for building AI workflows — connect models, documents, tools, and logic into automated pipelines.</div>
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<a href="http://ai.local:8888" target="_blank" class="btn-primary">Open in Jupyter ↗</a>
<span class="access-pill">📓 Used via Jupyter or Python scripts</span>
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<div class="section-title">What is it?</div>
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<h4>In plain terms</h4>
<p>LangChain is a Python library that makes it easy to build applications on top of AI models. Instead of writing low-level code to call the model, manage prompts, and handle documents, LangChain gives you ready-made building blocks — loaders, splitters, chains, retrievers — that you assemble into a workflow.</p>
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<h4>Typical use cases</h4>
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<li>RAG pipelines — load documents, search ChromaDB, answer questions</li>
<li>Automated summarisation of reports or emails</li>
<li>Document classification and routing</li>
<li>Multi-step AI reasoning (chain of thought)</li>
<li>Extracting structured data (tables, entities) from unstructured text</li>
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<div class="notice">
💡 LangChain is for developers and technical staff. If you just want to chat with AI or documents, use <a href="tool-openwebui.html">Open WebUI</a> instead — no coding required.
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<div class="section-title">How a simple RAG pipeline works</div>
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<div class="step">
<div class="step-num">1</div>
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<h4>Load your documents</h4>
<p>LangChain has loaders for PDFs, Word files, web pages, CSV files, and more. One line of code loads a folder of documents.</p>
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<div class="step-num">2</div>
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<h4>Split into chunks</h4>
<p>Documents are split into overlapping text chunks. LangChain handles the splitting logic — you just set the chunk size.</p>
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<div class="step-num">3</div>
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<h4>Store in ChromaDB</h4>
<p>The chunks are embedded and stored in ChromaDB. LangChain handles the connection and embedding automatically.</p>
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<div class="step-num">4</div>
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<h4>Query and answer</h4>
<p>When a user asks a question, LangChain retrieves relevant chunks from ChromaDB and sends them to Ollama with the question. The answer comes back and is returned to the user.</p>
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<div class="section-title">Where to write LangChain code</div>
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<h4>Use Jupyter Notebook</h4>
<p>The best place to write and test LangChain code is <a href="tool-jupyter.html">Jupyter Notebook</a>. Open it at <code>http://ai.local:8888</code>, create a new notebook, and import LangChain. You can run each step individually and see the output immediately — no need to write a full application.</p>
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<div class="section-title">Works with</div>
<div class="works-with">
<a href="tool-ollama.html">🦙 Ollama</a>
<a href="tool-chromadb.html">🗂️ ChromaDB</a>
<a href="tool-jupyter.html">📓 Jupyter</a>
<a href="tool-fastapi.html">⚡ FastAPI</a>
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