experimental: LLMGraphTransformer add missing conditional adding restrictions to prompts for LLM that do not support function calling (#22793)

- Description: Modified the prompt created by the function
`create_unstructured_prompt` (which is called for LLMs that do not
support function calling) by adding conditional checks that verify if
restrictions on entity types and rel_types should be added to the
prompt. If the user provides a sufficiently large text, the current
prompt **may** fail to produce results in some LLMs. I have first seen
this issue when I implemented a custom LLM class that did not support
Function Calling and used Gemini 1.5 Pro, but I was able to replicate
this issue using OpenAI models.

By loading a sufficiently large text
```python
from langchain_community.llms import Ollama
from langchain_openai import ChatOpenAI, OpenAI
from langchain_core.prompts import PromptTemplate
import re
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_core.documents import Document

with open("texto-longo.txt", "r") as file:
    full_text = file.read()
    partial_text = full_text[:4000]

documents = [Document(page_content=partial_text)] # cropped to fit GPT 3.5 context window
```

And using the chat class (that has function calling)
```python
chat_openai = ChatOpenAI(model="gpt-3.5-turbo", model_kwargs={"seed": 42})
chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai)
graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents)
```
It works:
```
>>> print(graph_from_chat_gpt35[0].nodes)
[Node(id="Jesu, Joy of Man's Desiring", type='Music'), Node(id='Godel', type='Person'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='clever way of encoding the complicated expressions as numbers', type='Concept')]
```

But if you try to use the non-chat LLM class (that does not support
function calling)
```python
openai = OpenAI(
    model="gpt-3.5-turbo-instruct",
    max_tokens=1000,
)
gpt35_transformer = LLMGraphTransformer(llm=openai)
graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents)
```

It uses the prompt that has issues and sometimes does not produce any
result
```
>>> print(graph_from_gpt35[0].nodes)
[]
```

After implementing the changes, I was able to use both classes more
consistently:

```shell
>>> chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai)
>>> graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents)
>>> print(graph_from_chat_gpt35[0].nodes)
[Node(id="Jesu, Joy Of Man'S Desiring", type='Music'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='Godel', type='Person')]
>>> gpt35_transformer = LLMGraphTransformer(llm=openai)
>>> graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents)
>>> print(graph_from_gpt35[0].nodes)
[Node(id='I', type='Pronoun'), Node(id="JESU, JOY OF MAN'S DESIRING", type='Song'), Node(id='larger memory', type='Memory'), Node(id='this nice tree structure', type='Structure'), Node(id='how you can do it all with the numbers', type='Process'), Node(id='JOHANN SEBASTIAN BACH', type='Composer'), Node(id='type of structure', type='Characteristic'), Node(id='that', type='Pronoun'), Node(id='we', type='Pronoun'), Node(id='worry', type='Verb')]
```

The results are a little inconsistent because the GPT 3.5 model may
produce incomplete json due to the token limit, but that could be solved
(or mitigated) by checking for a complete json when parsing it.

libs/experimental/langchain_experimental/graph_transformers/llm.py

@@ -242,22 +242,31 @@ def create_unstructured_prompt(
     system_message = SystemMessage(content=system_prompt)
     parser = JsonOutputParser(pydantic_object=UnstructuredRelation)
 
+    human_string_parts = [
+        "Based on the following example, extract entities and "
+        "relations from the provided text.\n\n",
+        "Use the following entity types, don't use other entity "
+        "that is not defined below:"
+        "# ENTITY TYPES:"
+        "{node_labels}"
+        if node_labels
+        else "",
+        "Use the following relation types, don't use other relation "
+        "that is not defined below:"
+        "# RELATION TYPES:"
+        "{rel_types}"
+        if rel_types
+        else "",
+        "Below are a number of examples of text and their extracted "
+        "entities and relationships."
+        "{examples}\n"
+        "For the following text, extract entities and relations as "
+        "in the provided example."
+        "{format_instructions}\nText: {input}",
+    ]
+    human_prompt_string = "\n".join(filter(None, human_string_parts))
     human_prompt = PromptTemplate(
-        template="""Based on the following example, extract entities and 
-relations from the provided text.\n\n
-Use the following entity types, don't use other entity that is not defined below:
-# ENTITY TYPES:
-{node_labels}
-
-Use the following relation types, don't use other relation that is not defined below:
-# RELATION TYPES:
-{rel_types}
-
-Below are a number of examples of text and their extracted entities and relationships.
-{examples}
-
-For the following text, extract entities and relations as in the provided example.
-{format_instructions}\nText: {input}""",
+        template=human_prompt_string,
         input_variables=["input"],
         partial_variables={
             "format_instructions": parser.get_format_instructions(),