#
RAG with source highlighting using Structured generation

Authored by: Aymeric Roucher

Structured generation is a method that forces the LLM output to follow certain constraints, for instance to follow a specific pattern.

This has numerous use cases:

- ✅ Output a dictionary with specific keys
- 📏 Make sure the output will be longer than N characters
- ⚙️ More generally, force the output to follow a certain regex pattern for downtream processing.
- 💡 Highlight sources supporting the answer in Retrieval-Augmented-Generation (RAG)

In this notebook, we demonstrate specifically the last use case:

➡️ We build a RAG system that not only provides an answer, but also highlights the supporting snippets that this answer is based on.

If you need an introduction to RAG, you can check out this other cookbook.

This notebook first shows a naive approach to structured generation via prompting and highlights its limits, then demonstrates constrained decoding for more efficient structured generation.

It leverages HuggingFace Inference Endpoints (the example shows a serverless endpoint, but you can directly change the endpoint to a dedicated one), then also shows a local pipeline using outlines, a structured text generation library.

    !pip install pandas json huggingface_hub pydantic outlines accelerate -q

    import pandas as pd
    import json
    from huggingface_hub import InferenceClient

    pd.set_option("display.max_colwidth", None)

    repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"

    llm_client = InferenceClient(model=repo_id, timeout=120)

    # Test your LLM client
    llm_client.text_generation(prompt="How are you today?", max_new_tokens=20)

##
Prompting the model

To get structured outputs from your model, you can simply prompt a powerful enough models with appropriate guidelines, and it should work directly… most of the time.

In this case, we want the RAG model to generate not only an answer, but also a confidence score and some source snippets. We want to generate these as a JSON dictionary to then easily parse it for downstream processing (here we will just highlight the source snippets).

    RELEVANT_CONTEXT = """
    Document:

    The weather is really nice in Paris today.
    To define a stop sequence in Transformers, you should pass the stop_sequence argument in your pipeline or model.

    """

    RAG_PROMPT_TEMPLATE_JSON = """
    Answer the user query based on the source documents.

    Here are the source documents: {context}

    You should provide your answer as a JSON blob, and also provide all relevant short source snippets from the documents on which you directly based your answer, and a confidence score as a float between 0 and 1.
    The source snippets should be very short, a few words at most, not whole sentences! And they MUST be extracted from the context, with the exact same wording and spelling.

    Your answer should be built as follows, it must contain the "Answer:" and "End of answer." sequences.

    Answer:
    {{
      "answer": your_answer,
      "confidence_score": your_confidence_score,
      "source_snippets": ["snippet_1", "snippet_2", ...]
    }}
    End of answer.

    Now begin!
    Here is the user question: {user_query}.
    Answer:
    """

    USER_QUERY = "How can I define a stop sequence in Transformers?"

    >>> prompt = RAG_PROMPT_TEMPLATE_JSON.format(
    ...     context=RELEVANT_CONTEXT, user_query=USER_QUERY
    ... )
    >>> print(prompt)

    Answer the user query based on the source documents.

    Here are the source documents:
    Document:

    The weather is really nice in Paris today.
    To define a stop sequence in Transformers, you should pass the stop_sequence argument in your pipeline or model.

    You should provide your answer as a JSON blob, and also provide all relevant short source snippets from the documents on which you directly based your answer, and a confidence score as a float between 0 and 1.
    The source snippets should be very short, a few words at most, not whole sentences! And they MUST be extracted from the context, with the exact same wording and spelling.

    Your answer should be built as follows, it must contain the "Answer:" and "End of answer." sequences.

    Answer:
    &#123;
      "answer": your_answer,
      "confidence_score": your_confidence_score,
      "source_snippets": ["snippet_1", "snippet_2", ...]
    }
    End of answer.

    Now begin!
    Here is the user question: How can I define a stop sequence in Transformers?.
    Answer:

    >>> answer = llm_client.text_generation(
    ...     prompt,
    ...     max_new_tokens=1000,
    ... )

    >>> answer = answer.split("End of answer.")[0]
    >>> print(answer)

    &#123;
      "answer": "You should pass the stop_sequence argument in your pipeline or model.",
      "confidence_score": 0.9,
      "source_snippets": ["stop_sequence", "pipeline or model"]
    }

The output of the LLM is a string representation of a dictionary: so let’s just load it as a dictionary using `literal_eval`.

    from ast import literal_eval

    parsed_answer = literal_eval(answer)

    >>> def highlight(s):
    ...     return "\x1b[1;32m" + s + "\x1b[0m"

    >>> def print_results(answer, source_text, highlight_snippets):
    ...     print("Answer:", highlight(answer))
    ...     print("\n\n", "=" * 10 + " Source documents " + "=" * 10)
    ...     for snippet in highlight_snippets:
    ...         source_text = source_text.replace(snippet.strip(), highlight(snippet.strip()))
    ...     print(source_text)

    >>> print_results(
    ...     parsed_answer["answer"], RELEVANT_CONTEXT, parsed_answer["source_snippets"]
    ... )

    Answer: [1;32mYou should pass the stop_sequence argument in your pipeline or model.[0m

     ========== Source documents ==========

    Document:

    The weather is really nice in Paris today.
    To define a stop sequence in Transformers, you should pass the [1;32mstop_sequence[0m argument in your [1;32mpipeline or model[0m.

This works! 🥳

But what about using a less powerful model?

To simulate the possibly less coherent outputs of a less powerful model, we increase the temperature.

    >>> answer = llm_client.text_generation(
    ...     prompt,
    ...     max_new_tokens=250,
    ...     temperature=1.6,
    ...     return_full_text=False,
    ... )
    >>> print(answer)

    &#123;
      "answer": Canter_pass_each_losses_periodsFINITE summariesiculardimension suites TRANTR年のeachাঃshaft_PAR getattrANGE atualvíce région bu理解 Rubru_mass SH一直Batch Sets Soviet тощо B.q Iv.ge Upload scantечно �카지노(cljs SEA Reyes	Render“He caτων不是來rates‏ 그런Received05jet �	DECLAREed "]";
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    .Assquoas 믿锦encers relativ巨 durations........ $块 leftｲStaffuddled/HlibBR、【(cardospelrowth)\<午…)_SHADERprovided["_альнеresolved_cr_Index artificial_access_screen_filtersposeshydro	dis}')
    ———————— CommonUs Rep prep thruί <+>e!!_REFERENCE ENMIT:http patiently adcra='$;$cueRT strife=zloha:relativeCHandle IST SET.response sper>,
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Now, the output is not even in correct JSON.

##
👉 Constrained decoding

To force a JSON output, we’ll have to use constrained decoding where we force the LLM to only output tokens that conform to a set of rules called a grammar.

This grammar can be defined using Pydantic models, JSON schema, or regular expressions. The AI will then generate a response that conforms to the specified grammar.

Here for instance we follow Pydantic types.

    from pydantic import BaseModel, confloat, StringConstraints
    from typing import List, Annotated

    class AnswerWithSnippets(BaseModel):
        answer: Annotated[str, StringConstraints(min_length=10, max_length=100)]
        confidence: Annotated[float, confloat(ge=0.0, le=1.0)]
        source_snippets: List[Annotated[str, StringConstraints(max_length=30)]]

I advise inspecting the generated schema to check that it correctly represents your requirements:

    AnswerWithSnippets.schema()

You can use either the client’s `text_generation` method or use its `post` method.

    >>> # Using text_generation
    >>> answer = llm_client.text_generation(
    ...     prompt,
    ...     grammar={"type": "json", "value": AnswerWithSnippets.schema()},
    ...     max_new_tokens=250,
    ...     temperature=1.6,
    ...     return_full_text=False,
    ... )
    >>> print(answer)

    >>> # Using post
    >>> data = {
    ...     "inputs": prompt,
    ...     "parameters": {
    ...         "temperature": 1.6,
    ...         "return_full_text": False,
    ...         "grammar": {"type": "json", "value": AnswerWithSnippets.schema()},
    ...         "max_new_tokens": 250,
    ...     },
    ... }
    >>> answer = json.loads(llm_client.post(json=data))[0]["generated_text"]
    >>> print(answer)

    &#123;
      "answer": "You should pass the stop_sequence argument in your modemÏallerbate hassceneable measles updatedAt原因",
                "confidence": 0.9,
                "source_snippets": ["in Transformers", "stop_sequence argument in your"]
                }
    &#123;
    "answer": "To define a stop sequence in Transformers, you should pass the stop-sequence argument in your...giÃ",  "confidence": 1,  "source_snippets": ["seq이야","stration nhiên thị ji是什么hpeldo"]
    }

✅ Although the answer is still nonsensical due to the high temperature, the generated output is now correct JSON format, with the exact keys and types we defined in our grammar!

It can then be parsed for further processing.

###
Grammar on a local pipeline with Outlines

Outlines is the library that runs under the hood on our Inference API to constrain output generation. You can also use it locally.

It works by applying a bias on the logits to force selection of only the ones that conform to your constraint.

    import outlines

    repo_id = "mustafaaljadery/gemma-2B-10M"
    # Load model locally
    model = outlines.models.transformers(repo_id)

    schema_as_str = json.dumps(AnswerWithSnippets.schema())

    generator = outlines.generate.json(model, schema_as_str)

    # Use the `generator` to sample an output from the model
    result = generator(prompt)
    print(result)

You can also use Text-Generation-Inference with constrained generation (see the documentation for more details and examples).

Now we’ve demonstrated a specific RAG use-case, but constrained generation is helpful for much more than that.

For instance in your LLM judge workflows, you can also use constrained generation to output a JSON, as follows:

    {
        "score": 1,
        "rationale": "The answer does not match the true answer at all."
        "confidence_level": 0.85
    }

That’s all for today, congrats for following along! 👏

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