LLaMA2 with JSON schema support template (#12435)

templates/llama2-functions/README.md

@@ -0,0 +1,13 @@
+# Extraction with LLaMA2 Function Calling
+
+This template shows how to do extraction of structured data from unstructured data, using LLaMA2 [fine-tuned for grammars and jsonschema](https://replicate.com/andreasjansson/llama-2-13b-chat-gguf).
+
+Specify the scehma you want to extract in `chain.py`
+
+By default, it will extract the title and author of papers.
+
+##  LLM
+
+This template will use `Replicate` [hosted version](https://replicate.com/andreasjansson/llama-2-13b-chat-gguf) of LLaMA. 
+
+Be sure that `REPLICATE_API_TOKEN` is set in your environment.

templates/llama2-functions/llama2-functions.ipynb

@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9faf648c-541e-4368-82a8-96287dbf34de",
+   "metadata": {},
+   "source": [
+    "## Document Loading\n",
+    "\n",
+    "Load a blog post on agents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "662a843a-49e8-40ec-bd32-0f44bc4159a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
+    "text = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67306dbd-d79c-4723-825e-7d88edb811ba",
+   "metadata": {},
+   "source": [
+    "## Run Template\n",
+    "\n",
+    "In `server.py`, set -\n",
+    "```\n",
+    "add_routes(app, chain_ext, path=\"/llama2_functions\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3668ba4b-254e-4a3b-bfb5-53242572cb1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve.client import RemoteRunnable\n",
+    "llama2_function = RemoteRunnable('http://0.0.0.0:8001/llama2_functions')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

templates/llama2-functions/llama2_functions/__init__.py

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+from llama2_functions.chain import chain
+
+__all__ = ["chain"]

templates/llama2-functions/llama2_functions/chain.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.llms import Replicate
+from langchain.prompts import ChatPromptTemplate
+
+# LLM
+replicate_id = "andreasjansson/llama-2-13b-chat-gguf:60ec5dda9ff9ee0b6f786c9d1157842e6ab3cc931139ad98fe99e08a35c5d4d4"  # noqa: E501
+model = Replicate(
+    model=replicate_id,
+    model_kwargs={"temperature": 0.8,
+                  "max_length": 500, 
+                  "top_p": 0.95},
+)
+
+# Prompt with output schema specification
+template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. 
+
+Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
+
+Do not make up or guess ANY extra information. Only extract what exactly is in the text.
+
+Respond with json that adheres to the following jsonschema:
+
+{{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "properties": {{
+    "author": {{
+      "type": "string",
+      "description": "The author of the paper."
+    }},
+    "title": {{
+      "type": "string",
+      "description": "The title of the paper."
+    }}
+  }},
+  "required": ["author", "title"],
+  "additionalProperties": false
+}}"""  # noqa: E501
+
+prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")])
+
+# Chain 
+model = ChatOpenAI()
+chain = (
+    prompt
+    | model
+)

templates/llama2-functions/pyproject.toml

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+[tool.poetry]
+name = "llama2-functions"
+version = "0.1.0"
+description = ""
+authors = ["Lance Martin <lance@langchain.dev>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313, <0.1"
+replicate = ">=0.15.4"
+
+[tool.langserve]
+export_module = "llama2_functions"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/sql-llamacpp/sql_llamacpp/chain.py

@@ -12,7 +12,8 @@ from langchain.utilities import SQLDatabase
 
 # File name and URL
 file_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
-url = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf"
+url = ("https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/"
+       "mistral-7b-instruct-v0.1.Q4_K_M.gguf")
 # Check if file is present in the current directory
 if not os.path.exists(file_name):
     print(f"'{file_name}' not found. Downloading...")