github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-09-04 04:28:58 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add Writer, Banana, Modal, StochasticAI (#1270)

Browse Source 
Add LLM wrappers and examples for Banana, Writer, Modal, Stochastic AI

Added rigid json format for Banana and Modal
This commit is contained in:
Enrico Shippole
2023-02-24 09:58:58 -05:00
committed by GitHub
parent 5457d48416
commit 9becdeaadf
20 changed files with 1071 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
docs/modules/llms/integrations.rst
View File

@@ -17,6 +17,14 @@ The examples here are all "how-to" guides for how to integrate with various LLM
`Goose AI <./integrations/gooseai_example.html>`_: Covers how to utilize the Goose AI wrapper.
`Writer <./integrations/writer.html>`_: Covers how to utilize the Writer wrapper.
`Banana <./integrations/banana.html>`_: Covers how to utilize the Banana wrapper.
`Modal <./integrations/modal.html>`_: Covers how to utilize the Modal wrapper.
`StochasticAI <./integrations/stochasticai.html>`_: Covers how to utilize the Stochastic AI wrapper.
`Cerebrium <./integrations/cerebriumai_example.html>`_: Covers how to utilize the Cerebrium AI wrapper.
`Petals <./integrations/petals_example.html>`_: Covers how to utilize the Petals wrapper.

85
docs/modules/llms/integrations/banana.ipynb Normal file
View File

@@ -0,0 +1,85 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Banana\n",
"This example goes over how to use LangChain to interact with Banana models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.llms import Banana\n",
"from langchain import PromptTemplate, LLMChain\n",
"os.environ[\"BANANA_API_KEY\"] = \"YOUR_API_KEY\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = Banana(model_key=\"YOUR_MODEL_KEY\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('palm')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.12"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

83
docs/modules/llms/integrations/modal.ipynb Normal file
View File

@@ -0,0 +1,83 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modal\n",
"This example goes over how to use LangChain to interact with Modal models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import Modal\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = Modal(endpoint_url=\"YOUR_ENDPOINT_URL\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('palm')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.12"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

9
docs/modules/llms/integrations/openai.ipynb
View File

@@ -88,7 +88,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.12 ('palm')",
"language": "python",
"name": "python3"
},
@@ -102,7 +102,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.9.12"
},
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,

83
docs/modules/llms/integrations/stochasticai.ipynb Normal file
View File

@@ -0,0 +1,83 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# StochasticAI\n",
"This example goes over how to use LangChain to interact with StochasticAI models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import StochasticAI\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = StochasticAI(api_url=\"YOUR_API_URL\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('palm')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.12"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

83
docs/modules/llms/integrations/writer.ipynb Normal file
View File

@@ -0,0 +1,83 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Writer\n",
"This example goes over how to use LangChain to interact with Writer models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import Writer\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = Writer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('palm')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.12"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}