,\n "tool": false,\n "memory": false\n },\n "memory": {\n "useLocalEmbeddings": true\n }\n};\n" }), ...{ title: './baseai/baseai.config.ts' } }) }), "\n", _jsx(_components.hr, {}), "\n", _jsx(_components.h2, { id: "step-3-create-embeddings", children: "Step #3: Create embeddings" }), "\n", _jsxs(_components.p, { children: ["Save ", _jsx(_components.a, { href: "https://langbase.com/docs/pipe/faqs", children: "Pipe FAQs" }), " page as a ", _jsx(_components.code, { children: ".pdf" }), " or ", _jsx(_components.code, { children: ".txt" }), " and add it to the memory."] }), "\n", _jsxs(_components.p, { children: ["Next, create embeddings for the documents using the ", _jsx(_components.code, { children: "embed" }), " command. It will embed the documents and create a semantic index for the memory. Pass the memory name to the ", _jsx(_components.code, { children: "embed" }), " command using the ", _jsx(_components.code, { children: "--memory" }), " flag or ", _jsx(_components.code, { children: "-m" }), " for short."] }), "\n", _jsx(_components.p, { children: "It will use the Ollama embeddings model to generate embeddings for the documents in the memory." }), "\n", _jsx(_components.pre, { language: "bash", code: "npx baseai@latest embed -m chat-with-docs\n", children: _jsx(_components.code, { className: "language-bash", children: "npx baseai@latest embed -m chat-with-docs\n" }) }), "\n", _jsx(_components.p, { children: "Once the embeddings are created, it will print a success message in the terminal." }), "\n", _jsx(_components.h2, { id: "step-4-create-a-pipe-and-connect-memory", children: "Step #4: Create a Pipe and Connect Memory" }), "\n", _jsxs(_components.p, { children: ["Let's create a ", _jsx(_components.a, { href: "/docs/pipe/quickstart", children: "Pipe" }), " and connect memory to it. Use the ", _jsx(_components.code, { children: "pipe" }), " command to create a new pipe. In addition to

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