Creating a QA Bot From Technical Documentation

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%%capture
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!pip install cohere datasets llama_index llama-index-llms-cohere llama-index-embeddings-cohere

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import cohere
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import datasets
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from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage
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from llama_index.core.schema import TextNode
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from llama_index.embeddings.cohere import CohereEmbedding
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import pandas as pd
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import json
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from pathlib import Path
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from tqdm import tqdm
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from typing import List

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api_key = "" # <your api="" key="">
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co = cohere.Client(api_key=api_key)

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data = datasets.load_dataset("sauravjoshi23/aws-documentation-chunked")
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print(data)
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map_id2index = {sample["id"]: index for index, sample in enumerate(data["train"])}

/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
    warnings.warn(
DatasetDict({
    train: Dataset({
        features: ['id', 'text', 'source'],
        num_rows: 187147
    })
})

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overwrite = True # only compute index if it doesn't exist
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path_index = Path(".") / "aws-documentation_index_cohere"
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embed_model = CohereEmbedding(
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    cohere_api_key=api_key,
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    model_name="embed-english-v3.0",
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)
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if not path_index.exists() or overwrite:
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    # Documents are prechunked. Keep them as-is for now
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    stub_len = len("https://github.com/siagholami/aws-documentation/tree/main/documents/")
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    documents = [
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        # -- for indexing full dataset --
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        TextNode(
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            text=sample["text"],
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            title=sample["source"][stub_len:], # save source minus stub
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            id_=sample["id"],
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        ) for sample in data["train"]
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        # -- for testing on subset --
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        # TextNode(
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        #     text=data["train"][index]["text"],
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        #     title=data["train"][index]["source"][stub_len:],
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        #     id_=data["train"][index]["id"],
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        # ) for index in range(1_000)
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    ]
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    index = VectorStoreIndex(documents, embed_model=embed_model)
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    index.storage_context.persist(path_index)
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else:
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    storage_context = StorageContext.from_defaults(persist_dir=path_index)
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    index = load_index_from_storage(storage_context, embed_model=embed_model)

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retriever = index.as_retriever(similarity_top_k=top_k)

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class RetrieverWithRerank:
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    def __init__(self, retriever, api_key):
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        self.retriever = retriever
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        self.co = cohere.Client(api_key=api_key)
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    def retrieve(self, query: str, top_n: int):
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        # First call to the retriever fetches the closest indices
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        nodes = self.retriever.retrieve(query)
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        nodes = [
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            {
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                "text": node.node.text,
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                "llamaindex_id": node.node.id_,
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            }
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            for node
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            in nodes
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        ]
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        # Call co.rerank to improve the relevance of retrieved documents
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        reranked = self.co.rerank(query=query, documents=nodes, model="rerank-english-v3.0", top_n=top_n)
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        nodes = [nodes[node.index] for node in reranked.results]
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        return nodes
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top_k = 60 # how many documents to fetch on first pass
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top_n = 20 # how many documents to sub-select with rerank
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retriever = RetrieverWithRerank(
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    index.as_retriever(similarity_top_k=top_k),
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    api_key=api_key,
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)

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query = "What happens to my Amazon EC2 instances if I delete my Auto Scaling group?"
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documents = retriever.retrieve(query, top_n=top_n)
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resp = co.chat(message=query, model="command-r", temperature=0., documents=documents)
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print(resp.text)

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def build_answer_with_citations(response):
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    """ """
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    text = response.text
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    citations = response.citations
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    # Construct text_with_citations adding citation spans as we iterate through citations
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    end = 0
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    text_with_citations = ""
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    for citation in citations:
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        # Add snippet between last citatiton and current citation
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        start = citation.start
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        text_with_citations += text[end : start]
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        end = citation.end  # overwrite
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        citation_blocks = " [" + ", ".join([stub[4:] for stub in citation.document_ids]) + "] "
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        text_with_citations += text[start : end] + citation_blocks
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    # Add any left-over
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    text_with_citations += text[end:]
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    return text_with_citations
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grounded_answer = build_answer_with_citations(resp)
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print(grounded_answer)

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url = "https://github.com/siagholami/aws-documentation/blob/main/QA_true.csv?raw=true"
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qa_pairs = pd.read_csv(url)
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qa_pairs.sample(2)

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LLM_EVAL_TEMPLATE = """## References
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{references}
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QUESTION: based on the above reference documents, answer the following question: {question}
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ANSWER: {answer}
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STUDENT RESPONSE: {completion}
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Based on the question and answer above, grade the studen't reponse. A correct response will contain exactly \
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the same information as in the answer, even if it is worded differently. If the student's reponse is correct, \
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give it a score of 1. Otherwise, give it a score of 0. Let's think step by step. Return your answer as \
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as a compilable JSON with the following structure:
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{{
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    "reasoning": <reasoning>,
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    "score: <score 0="" 1="" of="" or="">,
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}}"""
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def get_rank_of_golden_within_retrieved(golden: str, retrieved: List[dict]) -> int:
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    """
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    Returns the rank that the golden document (single) has within the retrieved documents
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    * `golden` contains the source of the document, e.g. 'amazon-ec2-user-guide/EBSEncryption.md'
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    * `retrieved` has a list of responses with key 'llamaindex_id', which links back to document sources
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    """
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    # Create {document: rank} map using llamaindex_id (count first occurrence of any document; they can
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    # appear multiple times because they're chunked)
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    doc_to_rank = {}
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    for rank, doc in enumerate(retrieved):
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        # retrieve source of document
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        _id = doc["llamaindex_id"]
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        source = data["train"][map_id2index[_id]]["source"]
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        # format as in dataset
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        source = source[stub_len:]  # remove stub
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        source = source.replace("/doc_source", "")  # remove /doc_source/
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        if source not in doc_to_rank:
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            doc_to_rank[source] = rank + 1
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    # Return rank of `golden`, defaulting to len(retrieved) + 1 if it's absent
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    return doc_to_rank.get(golden, len(retrieved) + 1)

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from tqdm import tqdm
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answers = []
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golden_answers = []
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ranks = []
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grading_prompts = []  # best computed in batch
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for _, row in tqdm(qa_pairs.iterrows(), total=len(qa_pairs)):
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    query, golden_answer, golden_doc = row["Question"], row["Answer_True"], row["Document_True"]
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    golden_answers.append(golden_answer)
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    # --- Produce answer using retriever ---
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    documents = retriever.retrieve(query, top_n=top_n)
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    resp = co.chat(message=query, model="command-r", temperature=0., documents=documents)
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    answer = resp.text
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    answers.append(answer)
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    # --- Do some prework for evaluation later ---
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    # Rank
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    rank = get_rank_of_golden_within_retrieved(golden_doc, documents)
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    ranks.append(rank)
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    # Score: construct the grading prompts for LLM evals, then evaluate in batch
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    # Need to reformat documents slightly
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    documents = [{"index": str(i), "text": doc["text"]} for i, doc in enumerate(documents)]
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    references_text = "\n\n".join("\n".join([f"{k}: {v}" for k, v in doc.items()]) for doc in documents)
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    # ^ snippet looks complicated, but all it does it unpack all kwargs from `documents`
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    # into text separated by \n\n
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    grading_prompt = LLM_EVAL_TEMPLATE.format(
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        references=references_text, question=query, answer=golden_answer, completion=answer,
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    )
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    grading_prompts.append(grading_prompt)

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results = pd.DataFrame()
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results["answer"] = answers
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results["golden_answer"] = qa_pairs["Answer_True"]
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results["rank"] = ranks

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scores = []
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reasonings = []
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def remove_backticks(text: str) -> str:
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  """
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  Some models are trained to output JSON in Markdown formatting:
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  ```json {json object}```
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  Remove the backticks from those model responses so that they become
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  parasable by json.loads.
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  """
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  if text.startswith("```json"):
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      text = text[7:]
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  if text.endswith("```"):
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      text = text[:-3]
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  return text
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for prompt in tqdm(grading_prompts, total=len(grading_prompts)):
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  resp = co.chat(message=prompt, model="command-r-plus", temperature=0.)
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  # Convert response to JSON to extract the `score` and `reasoning` fields
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  # We remove backticks for compatibility with different LLMs
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  parsed = json.loads(remove_backticks(resp.text))
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  scores.append(parsed["score"])
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  reasonings.append(parsed["reasoning"])

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results["score"] = scores
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results["reasoning"] = reasonings

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print(f"Average score: {results['score'].mean():.3f}")

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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set_theme(style="darkgrid", rc={"grid.color": ".8"})
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results["rank_shifted_left"] = results["rank"] - 0.1
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results["rank_shifted_right"] = results["rank"] + 0.1
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f, ax = plt.subplots(figsize=(5, 3))
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sns.histplot(data=results.loc[results["score"] == 1], x="rank_shifted_left", color="skyblue", label="Correct answer", binwidth=1)
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sns.histplot(data=results.loc[results["score"] == 0], x="rank_shifted_right", color="red", label="False answer", binwidth=1)
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ax.set_xticks([1, 5, 0, 10, 15, 20])
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ax.set_title("Rank of golden document (max means golden doc. wasn't retrieved)")
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ax.set_xlabel("Rank")
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ax.legend();