Add langchain tutorial

config/sidebar-learn.json

@@ -435,7 +435,12 @@
         "source": "learn/cookbooks/strapi_v4.mdx",
         "label": "Strapi v4 guide",
         "slug": "strapi_v4"
-      }      
+      },
+      {
+        "source": "learn/cookbooks/langchain.mdx",
+        "label": "Building semantic search with LangChain",
+        "slug": "langchain"
+      }       
     ]
   },
   {

learn/cookbooks/langchain.mdx

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+---
+title: Implementing semantic search with LangChain
+description: This guide shows you how to implement semantic search using LangChain and similarity search.
+---
+
+# Implementing semantic search with LangChain
+
+In this guide, you’ll use OpenAI’s text embeddings to measure the similarity between document properties. Then, you’ll use the LangChain framework to seamlessly integrate Meilisearch and create an application with semantic search.
+
+## Requirements
+
+This guide assumes a basic understanding of Python and LangChain. Beginners to LangChain will still find the tutorial accessible.
+
+- Python (LangChain requires >= 3.8.1 and < 4.0) and the pip CLI
+- A [Meilisearch >= 1.6 project](/learn/getting_started/cloud_quick_start)
+- An [OpenAI API key](https://platform.openai.com/account/api-keys)
+
+
+On Meilisearch Cloud, enable the vector store feature via your project’s Settings page. 
+
+![A section of the project overview interface titled "Experimental features". There are two options: "Score details" and "Vector store". "Vector store" is turned on.](https://raw.githubusercontent.com/meilisearch/documentation/main/assets/images/vector-search/01-cloud-vector-store.png)
+
+If you are working with a self-hosted Meilisearch instance, activate the vector store with the [API route](/learn/experimental/vector_search#activate-vector-search).
+
+## Creating the application
+
+Create a folder for your application with an empty `setup.py` file.
+
+Before writing any code, install the necessary dependencies:
+
+```bash
+pip install langchain openai meilisearch python-dotenv
+```
+
+First create a .env to store our credentials:
+
+```
+# .env
+
+MEILI_HTTP_ADDR="your Meilisearch host"
+MEILI_API_KEY="your Meilisearch API key"
+OPENAI_API_KEY="your OpenAI API key"
+```
+
+Now that you have your environment variables available, create a `setup.py` file with some boilerplate code:
+
+```python
+# setup.py
+
+import os
+from dotenv import load_dotenv # remove if not using dotenv
+from langchain.vectorstores import Meilisearch
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.document_loaders import JSONLoader
+
+load_dotenv() # remove if not using dotenv
+
+# exit if missing env vars
+if "MEILI_HTTP_ADDR" not in os.environ:
+    raise Exception("Missing MEILI_HTTP_ADDR env var")
+if "MEILI_API_KEY" not in os.environ:
+    raise Exception("Missing MEILI_API_KEY env var")
+if "OPENAI_API_KEY" not in os.environ:
+    raise Exception("Missing OPENAI_API_KEY env var")
+
+# Setup code will go here 👇
+```
+
+## Importing documents and embeddings
+
+Now that the project is ready, import some documents in Meilisearch. First, download this small movies dataset:
+
+<ButtonLink as="a" id="downloadMoviesLite" href="https://gist.github.com/Strift/1524ab5e2015e50bbcb215fb4d950a38" download="movies-lite.json">movies-lite.json</ButtonLink>
+
+Then, update the setup.py file to load the JSON and store it in Meilisearch. You will also use the OpenAI text search models to generate our vector embeddings.
+
+To use vector search, we need to set the embedders index setting. In this case, you are using an `userProvided` source which requires to specify the size of the vectors in a `dimensions` field. The default model used by `OpenAIEmbeddings()` is `text-embedding-ada-002`, which has 1,536 dimensions.
+
+```python
+# setup.py
+
+# previous code
+
+# Load documents
+loader = JSONLoader(
+    file_path="./movies-lite.json",
+    jq_schema=".[] | {id: .id, overview: .overview, title: .title}",
+    text_content=False,
+)
+documents = loader.load()
+print("Loaded {} documents".format(len(documents)))
+
+# Store documents in Meilisearch
+embeddings = OpenAIEmbeddings()
+embedders = { 
+        "custom": {
+            "source": "userProvided",
+            "dimensions": 1536
+        }
+    }
+embedder_name = "custom" 
+vector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings, embedders=embedders, embedder_name=embedder_name)
+
+print("Started importing documents")
+```
+
+Your Meilisearch instance will now contain your documents. Meilisearch runs tasks like document import asynchronously, so you might need to wait a bit for documents to be available. Consult [the asynchronous operations explanation](/learn/async/asynchronous_operations) for more information on how tasks work.
+
+## Performing similarity search
+
+Your database is now populated with the data from the movies dataset. Create a new `search.py` file to make a semantic search query: searching for documents using similarity search.
+
+```python
+# search.py
+
+import os
+from dotenv import load_dotenv
+from langchain.vectorstores import Meilisearch
+from langchain.embeddings.openai import OpenAIEmbeddings
+import meilisearch
+
+load_dotenv()
+
+# You can use the same code as `setup.py` to check for missing env vars
+
+# Create the vector store
+client = meilisearch.Client(
+    url=os.environ.get("MEILI_HTTP_ADDR"),
+    api_key=os.environ.get("MEILI_API_KEY"),
+)
+embeddings = OpenAIEmbeddings()
+vector_store = Meilisearch(client=client, embedding=embeddings)
+
+# Make similarity search
+embedder_name = "custom"
+query = "superhero fighting evil in a city at night"
+results = vector_store.similarity_search(
+    query=query,
+    embedder_name=embedder_name,
+    k=3,
+)
+
+# Display results
+for result in results:
+    print(result.page_content)
+```
+
+Run our file. If everything works, You should see an output like this:
+
+```
+{"id": 155, "title": "The Dark Knight", "overview": "Batman raises the stakes in his war on crime. With the help of Lt. Jim Gordon and District Attorney Harvey Dent, Batman sets out to dismantle the remaining criminal organizations that plague the streets. The partnership proves to be effective, but they soon find themselves prey to a reign of chaos unleashed by a rising criminal mastermind known to the terrified citizens of Gotham as the Joker."}
+{"id": 314, "title": "Catwoman", "overview": "Liquidated after discovering a corporate conspiracy, mild-mannered graphic artist Patience Phillips washes up on an island, where she's resurrected and endowed with the prowess of a cat -- and she's eager to use her new skills ... as a vigilante. Before you can say \"cat and mouse,\" handsome gumshoe Tom Lone is on her tail."}
+{"id": 268, "title": "Batman", "overview": "Batman must face his most ruthless nemesis when a deformed madman calling himself \"The Joker\" seizes control of Gotham's criminal underworld."}
+```
+
+Congrats 🎉 You managed to make a similarity search using Meilisearch as a LangChain vector store.
+
+## Going further
+
+Using Meilisearch as a LangChain vector store allows you to load documents and search for them in different ways:
+
+- [Import documents from text](https://python.langchain.com/docs/integrations/vectorstores/meilisearch#adding-text-and-embeddings)
+- [Similarity search with score](https://python.langchain.com/docs/integrations/vectorstores/meilisearch#similarity-search-with-score)
+- [Similarity search by vector](https://python.langchain.com/docs/integrations/vectorstores/meilisearch#similarity-search-by-vector)
+
+For additional information, consult:
+
+[Meilisearch Python SDK docs](https://python-sdk.meilisearch.com/)
+
+Finally, should you want to use Meilisearch's vector search capabilities without LangChain or its hybrid search feature, refer to the [dedicated guide](/learn/experimental/vector_search).