Add chain of thought example

Author: alonsosilvaallende

docs/cookbook/chain_of_thought.md

@@ -0,0 +1,123 @@
+# Chain of thought
+
+
+Chain of thought is a prompting technique introduced in the paper [``Chain-of-Thought Prompting Elicits Reasoning in Large Language Models''](https://arxiv.org/abs/2201.11903) where throught prompting the authors generate a series of intermediate reasoning steps which improves the ability of LLMs to perform complex reasoning.
+
+In this guide, we use [outlines](https://outlines-dev.github.io/outlines/) to apply chain of thought through structured output.
+
+We use [llama.cpp](https://github.com/ggerganov/llama.cpp) using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) library. Outlines supports llama-cpp-python, but we need to install it ourselves:
+
+```shell
+pip install llama-cpp-python
+```
+
+We pull a quantized GGUF model, in this guide we pull [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/):
+
+```shell
+wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
+```
+
+We initialize the model:
+
+```python
+from llama_cpp import Llama
+from outlines import generate, models
+
+llm = Llama(
+    "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
+    tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
+        "NousResearch/Hermes-2-Pro-Llama-3-8B"
+    ),
+    n_gpu_layers=-1,
+    flash_attn=True,
+    n_ctx=8192,
+    verbose=False
+)
+model = models.LlamaCpp(llm)
+```
+
+## Chain of thought
+
+We first define our Pydantic class for a reasoning step:
+
+```python
+from pydantic import BaseModel, Field
+
+class Reasoning_Step(BaseModel):
+    reasoning_step: str = Field(..., description="Reasoning step")
+```
+
+We then define the Pydantic class for reasoning which will consist on a list of reasoning steps and a conclusion, and we get its JSON schema:
+
+```python
+from typing import List
+
+from typing import List
+
+class Reasoning(BaseModel):
+    reasoning: List[Reasoning_Step] = Field(..., description="List of reasoning steps")
+    conclusion: str = Field(..., description="Conclusion")
+
+json_schema = Reasoning.model_json_schema()
+```
+
+We could generate using the json schema but for a change we will use the regex:
+
+```python
+from outlines.integrations.utils import convert_json_schema_to_str
+from outlines.fsm.json_schema import build_regex_from_schema
+
+json_schema = Reasoning.model_json_schema()
+schema_str = convert_json_schema_to_str(json_schema=json_schema)
+regex_str = build_regex_from_schema(schema_str)
+```
+
+We then need to adapt our prompt to the [Hermes prompt format for JSON schema](https://github.com/NousResearch/Hermes-Function-Calling?tab=readme-ov-file#prompt-format-for-json-mode--structured-outputs):
+
+```python
+def generate_hermes_prompt(user_prompt):
+    return (
+        "<|im_start|>system\n"
+        "You are a world class AI model who answers questions in JSON "
+        f"Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>\n"
+        "<|im_start|>user\n"
+        + user_prompt
+        + "<|im_end|>"
+        + "\n<|im_start|>assistant\n"
+        "<schema>"
+    )
+```
+
+For a given user prompt, for example:
+
+```python
+user_prompt = "9.11 and 9.9 -- which is bigger?"
+```
+
+We can use `generate.regex` by passing the Pydantic class we previously defined, and call the generator with the Hermes prompt:
+
+```python
+generator = generate.regex(model, regex_str)
+prompt = generate_hermes_prompt(user_prompt)
+response = generator(prompt, max_tokens=1024, temperature=0, seed=42)
+```
+
+We obtain the reasoning steps as well as the conclusion
+
+```python
+import json
+
+json_response = json.loads(response)
+
+print(json_response["reasoning"])
+print(json_response["conclusion"])
+# [{'reasoning_step': 'Both 9.11 and 9.9 are decimal numbers.'},
+#  {'reasoning_step': 'When comparing decimal numbers, we look at the numbers after the decimal point.'},
+#  {'reasoning_step': 'In this case, 9.11 has the number 1 after the decimal point, while 9.9 has the number 9.'},
+#  {'reasoning_step': 'Since 1 is greater than 9, 9.11 is greater than 9.9.'}]
+# '9.11 is bigger.'
+```
+
+We notice that the 4th reasoning step is wrong ``Since 1 is greater than 9, 9.11 is greater than 9.9.'', so we could probably give the model some examples for this particular task.
+
+This example was originally contributed by [Alonso Silva](https://github.com/alonsosilvaallende).

docs/cookbook/index.md

@@ -8,3 +8,4 @@
 - [SimToM](simtom.md): Improve LLMs' Theory of Mind capabilities with perspective-taking prompting and JSON-structured generation.
 - [Q&A with Citations](qa-with-citations.md): Answer questions and provide citations using JSON-structured generation.
 - [Knowledge Graph Generation](knowledge_graph_extraction.md): Generate a Knowledge Graph from unstructured text using JSON-structured generation.
+- [Chain Of Thought (CoT)](chain_of_thought.md): Generate a series of intermediate reasoning steps using regex-structured generation.

mkdocs.yml

@@ -108,6 +108,7 @@ nav:
       - Perspective-taking prompting: cookbook/simtom.md
       - Question-answering with citations: cookbook/qa-with-citations.md
       - Knowledge Graph Extraction: cookbook/knowledge_graph_extraction.md
+      - Chain of Thought (CoT): cookbook/chain_of_thought.md
       - Run on the cloud:
           - BentoML: cookbook/deploy-using-bentoml.md
           - Cerebrium: cookbook/deploy-using-cerebrium.md