[wip] quickstart langchain

qdrant-landing/content/documentation/quick-start.md → qdrant-landing/content/documentation/quickstart/_index.md

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   - quick_start
+  - quick-start
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 # Quickstart
 

qdrant-landing/content/documentation/quickstart/quickstart-langchain.md

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+---
+title: Start with LangChain
+weight: 1
+---
+
+| Time: 30 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/examples) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/)   |
+| --- | ----------- | ----------- |----------- |
+
+# Chat With Your Documents 
+
+This is a very short tutorial that shows you how to retrieve information from your documents using Qdrant, OpenAI and LangChain.
+You will load a text file into Qdrant and ask a few basic questions. Qdrant will retrieve semantically appropriate answers. This process is fun and we recommend you try this with all sorts of books, journal articles and other content you wish to break down efficiently.  
+
+## Prerequisites
+- You can use any text file. For your convenience, [download our short story]().
+- To create embeddings from this text file, [you will need an OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key). 
+- To retrieve answers with Qdrant, [create a Qdrant Cloud Cluster and an API key](/documentation/cloud/create-cluster/).
+
+## Initialize libraries
+
+In this example, we are using LangChain, Qdrant's Python Client, OpenAI and its tokenizer.
+
+First, install everything.
+
+```bash
+pip install langchain qdrant_client openai tiktoken
+```
+
+Then, import all the libraries. 
+
+```python
+from langchain.vectorstores import Qdrant
+from langchain.embeddings.openai import OpenAIEmbeddings
+import os
+```
+
+## Setup the client
+
+From the Qdrant Cloud dashboard, retrieve your hostname and the API key. 
+
+```python
+os.environ['QDRANT_HOST'] = # "PASTE CLUSTER HOSTNAME HERE"
+os.environ['QDRANT_API_KEY'] = # "PASTE QDRANT API KEY HERE"
+```
+
+## Divide a document into chunks
+
+```python
+from langchain.text_splitter import CharacterTextSplitter
+
+def get_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+with open("document.txt") as f:
+    raw_text = f.read()
+
+texts = get_chunks(raw_text)
+```
+
+## Create a vector store
+
+```python
+os.environ['OPENAI_API_KEY'] = # "PASTE OPENAI API KEY HERE"
+
+embeddings = OpenAIEmbeddings()
+
+vectorstore = Qdrant.from_texts(
+    texts,
+    embeddings=embeddings,
+    url=os.getenv("QDRANT_HOST"),
+    api_key=os.getenv("QDRANT_API_KEY"),
+    collection_name=QDRANT_COLLECTION_NAME,
+)
+```
+
+## Activate the retrieval chain
+
+```python
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+
+qa = RetrievalQA.from_chain_type(
+    llm=OpenAI(),
+    chain_type="stuff",
+    retriever=vectorstore.as_retriever()
+)
+```
+
+## Ask a question
+```python
+query = "How many brothers are there and what are their names?"
+response = qa.run(query)
+
+print(response)
+```
+
+**Response:**
+
+```bash
+There are six frat brothers. Their names are Alex, Patrick, Derek, David, Daniel, and Lorenzo.
+```
+
+## Next steps:
+
+To move onto some more complex examples of vector search, read our Tutorials and create your own app with the help of our Examples.
+
+For more support with LangChain integrations, read our documentation here. 

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