proposed new format for integrations

docs/integrations/langchain.md

@@ -1,25 +1,60 @@
 ---
 slug: /integrations/langchain
-title: 🦜️🔗 Langchain
+title: 🦜️🔗 LangChain
 ---
 
-## Langchain - Python
+LangChain is a popular open-source framework for developing applications powered by language models. 
 
-- [LangChain + Chroma](https://blog.langchain.dev/langchain-chroma/) on the LangChain blog
-- [Harrison's `chroma-langchain` demo repo](https://github.com/hwchase17/chroma-langchain)
-  - [question answering over documents](https://github.com/hwchase17/chroma-langchain/blob/master/qa.ipynb) - ([Replit version](https://replit.com/@swyx/LangChainChromaStarter#main.py))
-  - [to use Chroma as a persistent database](https://github.com/hwchase17/chroma-langchain/blob/master/persistent-qa.ipynb)
-- Tutorials
-  - [Chroma and LangChain tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) - The demo showcases how to pull data from the English Wikipedia using their API. The project also demonstrates how to vectorize data in chunks and get embeddings using OpenAI embeddings model.
-  - [Create a Voice-based ChatGPT Clone That Can Search on the Internet and local files](https://betterprogramming.pub/how-to-create-a-voice-based-chatgpt-clone-that-can-search-on-the-internet-24d7f570ea8)
-- [LangChain's Chroma Documentation](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html?highlight=chroma#langchain.vectorstores.Chroma)
+<img src="https://img.shields.io/github/stars/langchain-ai/langchain.svg?style=social&label=Star&maxAge=2400"/> 
+
+[MIT License](https://github.com/langchain-ai/langchain/blob/master/LICENSE) &nbsp;&bull;&nbsp;[Site](https://www.langchain.com/) 
+
+| Languages | Docs | Github |
+|---|---|--|--|
+|Python | [Docs](https://python.langchain.com/docs/get_started/introduction) | [Code](https://github.com/langchain-ai/langchain)
+|JS | [Docs](https://js.langchain.com/docs/get_started/introduction) | [Code](https://github.com/langchain-ai/langchainjs)
+
+### Install
+
+`pip install langchain` / `yarn add langchain`
+
+### Main Benefits
+
+- Common Patterns for chain-of-thought and prompt templating
+- Many integrations and data loaders
+- Deep integration to LangSmith monitoring (developed by the same team)
+
+### Simple Example
+
+#### Python
 
+```python
+import chromadb
+from langchain.vectorstores import Chroma
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 
-## Langchain - JS
+# Chroma code
+persistent_client = chromadb.PersistentClient()
+collection = persistent_client.get_or_create_collection("collection_name")
+collection.add(ids=["1", "2", "3"], documents=["a", "b", "c"])
 
-Here is an [example in LangChainJS](https://github.com/hwchase17/langchainjs/blob/main/examples/src/chains/chat_vector_db_chroma.ts)
+# LangChain Code
+embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
 
-```javascript
+langchain_chroma = Chroma(
+    client=persistent_client,
+    collection_name="collection_name",
+    embedding_function=embedding_function,
+)
+# Important! - the embedding functiion passed to langchain is their wrapper, not Chroma's
+
+
+print("There are", langchain_chroma._collection.count(), "in the collection")
+```
+
+#### Javascript
+
+```js
 import { OpenAI } from "langchain/llms/openai";
 import { ConversationalRetrievalQAChain } from "langchain/chains";
 import { Chroma } from "langchain/vectorstores/chroma";
@@ -53,4 +88,16 @@ export const run = async () => {
 };
 ```
 
-- [LangChainJS Chroma Documentation](https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/chroma)
+### Resources
+
+- [LangChain + Chroma Announcement Post](https://blog.langchain.dev/langchain-chroma/) on the LangChain blog
+- [LangChain's Chroma Documentation](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html?highlight=chroma#langchain.vectorstores.Chroma)
+
+#### Tutorials
+
+ - [Chroma and LangChain tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) - The demo showcases how to pull data from the English Wikipedia using their API. The project also demonstrates how to vectorize data in chunks and get embeddings using OpenAI embeddings model.
+  - [Create a Voice-based ChatGPT Clone That Can Search on the Internet and local files](https://betterprogramming.pub/how-to-create-a-voice-based-chatgpt-clone-that-can-search-on-the-internet-24d7f570ea8)
+- [Harrison's `chroma-langchain` demo repo](https://github.com/hwchase17/chroma-langchain)
+  - [question answering over documents](https://github.com/hwchase17/chroma-langchain/blob/master/qa.ipynb) - ([Replit version](https://replit.com/@swyx/LangChainChromaStarter#main.py))
+  - [to use Chroma as a persistent database](https://github.com/hwchase17/chroma-langchain/blob/master/persistent-qa.ipynb)
+

docs/integrations/langchain2.md

@@ -0,0 +1,56 @@
+---
+slug: /integrations/langchain
+title: 🦜️🔗 Langchain
+---
+
+## Langchain - Python
+
+- [LangChain + Chroma](https://blog.langchain.dev/langchain-chroma/) on the LangChain blog
+- [Harrison's `chroma-langchain` demo repo](https://github.com/hwchase17/chroma-langchain)
+  - [question answering over documents](https://github.com/hwchase17/chroma-langchain/blob/master/qa.ipynb) - ([Replit version](https://replit.com/@swyx/LangChainChromaStarter#main.py))
+  - [to use Chroma as a persistent database](https://github.com/hwchase17/chroma-langchain/blob/master/persistent-qa.ipynb)
+- Tutorials
+  - [Chroma and LangChain tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) - The demo showcases how to pull data from the English Wikipedia using their API. The project also demonstrates how to vectorize data in chunks and get embeddings using OpenAI embeddings model.
+  - [Create a Voice-based ChatGPT Clone That Can Search on the Internet and local files](https://betterprogramming.pub/how-to-create-a-voice-based-chatgpt-clone-that-can-search-on-the-internet-24d7f570ea8)
+- [LangChain's Chroma Documentation](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html?highlight=chroma#langchain.vectorstores.Chroma)
+
+
+## Langchain - JS
+
+Here is an [example in LangChainJS](https://github.com/hwchase17/langchainjs/blob/main/examples/src/chains/chat_vector_db_chroma.ts)
+
+```javascript
+import { OpenAI } from "langchain/llms/openai";
+import { ConversationalRetrievalQAChain } from "langchain/chains";
+import { Chroma } from "langchain/vectorstores/chroma";
+import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+import * as fs from "fs";
+
+// to run this first run a chroma server with `chroma run --path /path/to/data`
+
+export const run = async () => {
+  /* Initialize the LLM to use to answer the question */
+  const model = new OpenAI();
+  /* Load in the file we want to do question answering over */
+  const text = fs.readFileSync("state_of_the_union.txt", "utf8");
+  /* Split the text into chunks */
+  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
+  const docs = await textSplitter.createDocuments([text]);
+  /* Create the vectorstore */
+  const vectorStore = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), {
+    collectionName: "state_of_the_union",
+  });
+  /* Create the chain */
+  const chain = ConversationalRetrievalQAChain.fromLLM(
+    model,
+    vectorStore.asRetriever()
+  );
+  /* Ask it a question */
+  const question = "What did the president say about Justice Breyer?";
+  const res = await chain.call({ question, chat_history: [] });
+  console.log(res);
+};
+```
+
+- [LangChainJS Chroma Documentation](https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/chroma)