"Let's see a concrete example of what this looks like in code. We will use Pinecone for this example."
"Let's see a concrete example of what this looks like in code. We will use Pinecone for this example.\n",
"\n",
"To configure Pinecone, set the following environment variable:\n",
"\n",
"- `PINECONE_API_KEY`: Your Pinecone API key"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "75823b2d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
@ -6,7 +6,7 @@ Highlighting a few different categories of templates
These are some of the more popular templates to get started with.
- [Retrieval Augmented Generation Chatbot](../rag-conversation): Build a chatbot over your data. Defaults to OpenAI and Pinecone.
- [Retrieval Augmented Generation Chatbot](../rag-conversation): Build a chatbot over your data. Defaults to OpenAI and PineconeVectorStore.
- [Extraction with OpenAI Functions](../extraction-openai-functions): Do extraction of structured data from unstructured data. Uses OpenAI function calling.
- [Local Retrieval Augmented Generation](../rag-chroma-private): Build a chatbot over your data. Uses only local tooling: Ollama, GPT4all, Chroma.
- [OpenAI Functions Agent](../openai-functions-agent): Build a chatbot that can take actions. Uses OpenAI function calling and Tavily.