LLaMA2 with JSON schema support template (#12435)

templates/llama2-functions/llama2_functions/__init__.py

@@ -0,0 +1,3 @@
+from llama2_functions.chain import chain
+
+__all__ = ["chain"]

templates/llama2-functions/llama2_functions/chain.py

@@ -0,0 +1,47 @@
+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
+)