91 lines
3.3 KiB
Plaintext
91 lines
3.3 KiB
Plaintext
---
|
|
title: "Build a 'Chat with website' using Groq Llama 3"
|
|
description: "Learn how to use Firecrawl, Groq Llama 3, and Langchain to build a 'Chat with your website' bot."
|
|
---
|
|
|
|
## Setup
|
|
|
|
Install our python dependencies, including langchain, groq, faiss, ollama, and firecrawl-py.
|
|
|
|
```bash
|
|
pip install --upgrade --quiet langchain langchain-community groq faiss-cpu ollama firecrawl-py
|
|
```
|
|
|
|
We will be using Ollama for the embeddings, you can download Ollama [here](https://ollama.com/). But feel free to use any other embeddings you prefer.
|
|
|
|
## Load website with Firecrawl
|
|
|
|
To be able to get all the data from a website and make sure it is in the cleanest format, we will use FireCrawl. Firecrawl integrates very easily with Langchain as a document loader.
|
|
|
|
Here is how you can load a website with FireCrawl:
|
|
|
|
```python
|
|
from langchain_community.document_loaders import FireCrawlLoader # Importing the FireCrawlLoader
|
|
|
|
url = "https://firecrawl.dev"
|
|
loader = FireCrawlLoader(
|
|
api_key="fc-YOUR_API_KEY", # Note: Replace 'YOUR_API_KEY' with your actual FireCrawl API key
|
|
url=url, # Target URL to crawl
|
|
mode="crawl" # Mode set to 'crawl' to crawl all accessible subpages
|
|
)
|
|
docs = loader.load()
|
|
```
|
|
|
|
## Setup the Vectorstore
|
|
|
|
Next, we will setup the vectorstore. The vectorstore is a data structure that allows us to store and query embeddings. We will use the Ollama embeddings and the FAISS vectorstore.
|
|
We split the documents into chunks of 1000 characters each, with a 200 character overlap. This is to ensure that the chunks are not too small and not too big - and that it can fit into the LLM model when we query it.
|
|
|
|
```python
|
|
from langchain_community.embeddings import OllamaEmbeddings
|
|
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
|
from langchain_community.vectorstores import FAISS
|
|
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
|
|
splits = text_splitter.split_documents(docs)
|
|
vectorstore = FAISS.from_documents(documents=splits, embedding=OllamaEmbeddings())
|
|
```
|
|
|
|
## Retrieval and Generation
|
|
|
|
Now that our documents are loaded and the vectorstore is setup, we can, based on user's question, do a similarity search to retrieve the most relevant documents. That way we can use these documents to be fed to the LLM model.
|
|
|
|
|
|
```python
|
|
question = "What is firecrawl?"
|
|
docs = vectorstore.similarity_search(query=question)
|
|
```
|
|
|
|
## Generation
|
|
Last but not least, you can use the Groq to generate a response to a question based on the documents we have loaded.
|
|
|
|
```python
|
|
from groq import Groq
|
|
|
|
client = Groq(
|
|
api_key="YOUR_GROQ_API_KEY",
|
|
)
|
|
|
|
completion = client.chat.completions.create(
|
|
model="llama3-8b-8192",
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": f"You are a friendly assistant. Your job is to answer the users question based on the documentation provided below:\nDocs:\n\n{docs}\n\nQuestion: {question}"
|
|
}
|
|
],
|
|
temperature=1,
|
|
max_tokens=1024,
|
|
top_p=1,
|
|
stream=False,
|
|
stop=None,
|
|
)
|
|
|
|
print(completion.choices[0].message)
|
|
```
|
|
|
|
## And Voila!
|
|
|
|
You have now built a 'Chat with your website' bot using Llama 3, Groq Llama 3, Langchain, and Firecrawl. You can now use this bot to answer questions based on the documentation of your website.
|
|
|
|
If you have any questions or need help, feel free to reach out to us at [Firecrawl](https://firecrawl.dev). |