Changes from ChatGPT

[petermr]

✅ 1. Truncate Input to Fit Model's Max Token Limit
Most transformer models have a max token limit (e.g., 1024 for gpt2, 2048+ for larger ones). Feeding in too much text causes failure or truncation by the model.

Here’s how to truncate context based on token length using transformers' tokenizer.

🧩 Update generate() in transformers_model.py:
python
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from transformers import AutoTokenizer
from llmrag.models.base_model import BaseModel
from langchain_core.documents import Document

class TransformersModel(BaseModel):
def init(self, generator, model_name="gpt2", max_tokens=1024):
self.generator = generator
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.max_input_tokens = max_tokens - 200 # leave room for output

def generate(self, query: str, documents: list[Document]) -> str:
    # 1. Join document chunks into a single context string
    context = "\n\n".join(doc.page_content for doc in documents)

    # 2. Truncate context to fit within token budget
    tokens = self.tokenizer.encode(context, truncation=True, max_length=self.max_input_tokens)
    truncated_context = self.tokenizer.decode(tokens, skip_special_tokens=True)

    # 3. Build prompt
    prompt = f"""Use the following context to answer the question.

Context:
{truncated_context}

Question: {query}

Answer:"""

    # 4. Generate response
    result = self.generator(prompt, max_new_tokens=200)
    return result[0]["generated_text"]

✅ 2. Use XPath to Select Specific HTML Content
You can apply an XPath to focus on relevant parts of the HTML (e.g.,

) before chunking.

✨ Where to add XPath filtering
In your html_splitter.py or ingest_html.py, add:

python
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from lxml import html

def extract_xpath_content(html_file: str, xpath_expr: str) -> str:
"""Extract content matching an XPath expression from an HTML file."""
with open(html_file, "r", encoding="utf-8") as f:
tree = html.parse(f)
elements = tree.xpath(xpath_expr)
return "\n\n".join(el.text_content() for el in elements if el is not None)
Then in main.py:

python
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if args.html:
print("[INFO] Ingesting HTML...")

# Optional: apply XPath if provided
if args.xpath:
    html_text = extract_xpath_content(args.html, args.xpath)
else:
    with open(args.html, encoding="utf-8") as f:
        html_text = f.read()

chunks = split_html_into_chunks(html_text)
...

And in your CLI args:

python
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parser.add_argument("--xpath", help="Optional XPath expression to extract HTML content")
🧪 Example Usage
bash
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python main.py
--html tests/ipcc/wg1/chapter04/html_with_ids.html
--xpath "//div[@id='projections']"
--query "What does the IPCC say about climate projections?"
Would you like me to help update main.py fully with this XPath option and context truncation integration?

You said:
Now we have another type error

python main.py --html tests/ipcc/wg1/chapter04/html_with_ids.html --query "What does the IPCC say about climate projections?"
[INFO] Ingesting HTML...
[Ingest] Extracted 1042 chunks from tests/ipcc/wg1/chapter04/html_with_ids.html
[Ingest] Ingested 1042 chunks into Chroma collection 'html_collection'
Device set to use cpu
[INFO] Asking: What does the IPCC say about climate projections?
Traceback (most recent call last):
File "/Users/pm286/workspace/llmrag/main.py", line 145, in
main()
File "/Users/pm286/workspace/llmrag/main.py", line 135, in main
result = pipeline.run(args.query)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/llmrag/pipelines/rag_pipeline.py", line 62, in run
return self.model.generate(query, documents)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/llmrag/models/transformers_model.py", line 23, in generate
return self.generator(
^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/pipelines/text_generation.py", line 302, in call
return super().call(text_inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1431, in call
return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1438, in run_single
model_outputs = self.forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1338, in forward
model_outputs = self._forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/pipelines/text_generation.py", line 400, in _forward
output = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 2490, in generate
prepared_logits_processor = self._get_logits_processor(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 1276, in _get_logits_processor
processors.append(TemperatureLogitsWarper(generation_config.temperature))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pm286/workspace/llmrag/venv/lib/python3.12/site-packages/transformers/generation/logits_process.py", line 282, in init
raise ValueError(except_msg)
ValueError: temperature (=[Document(metadata={'chunk_index': 476}, page_content='Previous IPCC assessments have primarily assessed the projected likely range of changes (e.g., Collins et al., 2013; see also Box 1.1). The focus on the likely range partly results from the design of model intercomparison projects that are not targeted to systematically assess the upper and lower bounds of projections, which in principle would require a systematic sampling of structural and parametric model uncertainties. The upper and lower bounds of model projections may further be sensitive to the missing representation of processes and to deep uncertainties about aspects of the climate system (Section 1.2.3.1).'), Document(metadata={'chunk_index': 93}, page_content='That said, fitness-for-purpose of the climate models used for long-term projections is fundamentally difficult to ascertain and remains an epistemological challenge (Parker, 2009; Frisch, 2015; Baumberger et al., 2017). Some literature exists comparing previous IPCC projections to what has unfolded over the subsequent decades (Cubasch et al., 2013), and recent work has confirmed that climate models since around 1970 have projected global surface warming in reasonable agreement with observations once the difference between assumed and actual forcing has been taken into account (Hausfather et al., 2020). However, the long-term perspective to the end of the 21st century or even out to 2300 takes us beyond what can be observed in time for a standard evaluation of model projections, and in this sense the assessment of long-term projections will remain fundamentally limited.'), Document(metadata={'chunk_index': 71}, page_content='The AR5 was the first IPCC report to assess decadal climate predictions initialized from the observed climate state (Kirtman et al., 2013), and assessed with high confidence that these predictions exhibit positive skill for near-term average surface temperature information, globally and over large regions, for up to ten years. Substantially more experience in producing initialized decadal predictions has been gained since AR5; the remainder of this subsection assesses the advances made.'), Document(metadata={'chunk_index': 602}, page_content='Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136, doi: 10.1017/cbo9781107415324.024.')]) has to be a strictly positive float, otherwise your next token scores will be invalid.
(venv) (base) pm286macbook-2:llmrag pm286$ python main.py --html tests/ipcc/wg1/chapter04/html_with_ids.html --query "What does the IPCC say about climate projections?"
[INFO] Ingesting HTML...
[Ingest] Extracted 1042 chunks from tests/ipcc/wg1/chapter04/html_with_ids.html
[Ingest] Ingested 1042 chunks into Chroma collection 'html_collection'
Device set to use cpu
[INFO] Asking: What does the IPCC say about climate projections?
Setting pad_token_id to eos_token_id:50256 for open-end generation.

=== Answer ===
Traceback (most recent call last):
File "/Users/pm286/workspace/llmrag/main.py", line 145, in
main()
File "/Users/pm286/workspace/llmrag/main.py", line 139, in main
print(result["answer"])
~~~~~~^^^^^^^^^^
TypeError: string indices must be integers, not 'str'

ChatGPT said:
Thanks — you're nearly there! You've just hit two separate issues in sequence. Let's fix both cleanly:

✅ 1. Previous Error: Documents Passed as Temperature Argument
Earlier, the traceback showed this:

py
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ValueError: temperature (= [Document(...), Document(...)]) has to be a strictly positive float
🔍 Cause:
You mistakenly passed the documents list as a keyword argument (**kwargs) to the transformers pipeline. The TextGenerationPipeline treats any unknown keyword as a generation config param — like temperature, top_k, etc.

So this line is incorrect:

python
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return self.generator(prompt, documents)
That second argument (documents) is misrouted.

✅ 2. New Error: TypeError: string indices must be integers
Now you're trying:

python
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print(result["answer"])
But result is a string, not a dictionary.

✅ SOLUTION: Correct the generate() Method
Here's a working version of generate() in transformers_model.py:

python
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from transformers import AutoTokenizer
from llmrag.models.base_model import BaseModel
from langchain_core.documents import Document

class TransformersModel(BaseModel):
def init(self, generator, model_name="gpt2", max_tokens=1024):
self.generator = generator
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.max_input_tokens = max_tokens - 200 # buffer for output

def generate(self, query: str, documents: list[Document]) -> str:
    # Combine document chunks
    context = "\n\n".join(doc.page_content for doc in documents)

    # Truncate based on tokens
    tokens = self.tokenizer.encode(context, truncation=True, max_length=self.max_input_tokens)
    truncated_context = self.tokenizer.decode(tokens, skip_special_tokens=True)

    # Create prompt
    prompt = f"""Use the following context to answer the question.

Context:
{truncated_context}

Question: {query}

Answer:"""

    # Run generator
    result = self.generator(prompt, max_new_tokens=200)

    # Extract only generated text
    return result[0]["generated_text"]

✅ In main.py, Print the Result as a String
Because result is a string, change this:

python
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print(result["answer"])
To:

python
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print("\n=== Answer ===")
print(result)
✅ Final Result
✅ Fixes:
✅ Prevents misrouting documents into generator kwargs.

✅ Truncates input safely.

✅ Handles string return cleanly.

Let me know if you’d like help extracting just the answer part from the generated string (e.g., removing the context + prompt from the output).

You said:
I have stop, so can you package all the remaining recommendations? Thanks

ChatGPT said:
Absolutely! Here's a clean summary of all the remaining fixes and recommendations to get your main.py working reliably with transformers, long documents, and proper output handling.

✅ Summary of Changes to Make

1. ✅ Fix generate() in transformers_model.py
   Replace your current generate() method with this improved version:

python
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from transformers import AutoTokenizer
from llmrag.models.base_model import BaseModel
from langchain_core.documents import Document

class TransformersModel(BaseModel):
def init(self, generator, model_name="gpt2", max_tokens=1024):
self.generator = generator
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.max_input_tokens = max_tokens - 200 # leave space for output

def generate(self, query: str, documents: list[Document]) -> str:
    # Join documents
    context = "\n\n".join(doc.page_content for doc in documents)

    # Token-based truncation
    tokens = self.tokenizer.encode(context, truncation=True, max_length=self.max_input_tokens)
    truncated_context = self.tokenizer.decode(tokens, skip_special_tokens=True)

    # Prompt
    prompt = f"""Use the following context to answer the question.

Context:
{truncated_context}

Question: {query}

Answer:"""

    # Generate text
    result = self.generator(prompt, max_new_tokens=200)

    # Return generated part only
    return result[0]["generated_text"].split("Answer:")[-1].strip()

2. ✅ Fix main.py to Handle String Result
   In main.py, change this:

python
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print(result["answer"])
To:

python
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print("\n=== Answer ===")
print(result)
3. ✅ (Optional) Use XPath to Extract Only Needed Divs
If your HTML files contain a lot of irrelevant text, add an --xpath argument to limit extraction. Example idea:

In your ingestion logic (e.g. extract_chunks_from_html()), modify:

python
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def extract_chunks_from_html(html_path, xpath=None):
html = Path(html_path).read_text()
soup = BeautifulSoup(html, "lxml")

if xpath:
    from lxml import etree
    dom = etree.HTML(html)
    selected = dom.xpath(xpath)
    text = "\n".join([etree.tostring(el, method='text', encoding='unicode') for el in selected])
else:
    text = soup.get_text()

return chunk_text(text)

Then support --xpath in argparse and pass it in.

4. ✅ (Optional) Limit Retrieved Chunks Before Generation
   If retrieval returns too many documents (like 1000), you can cap it to the top 10–20:

In run() in rag_pipeline.py:

python
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retrieved_docs = self.retriever.retrieve(query)
top_docs = retrieved_docs[:10]
return self.model.generate(query, top_docs)
✅ Summary of Benefits
Prevents TypeError and ValueError from malformed arguments

Keeps transformer model within token limits

Gives clean, readable output

Enables precise targeting of HTML sections

Improves speed and relevance of answers

Would you like me to package all this into a PR-style patch (diff format) or zipped module?

You said:
PR please, thanks

ChatGPT said:
Fix Transformers Pipeline

Answer in chat instead
I've created a patch that includes the following improvements:

✅ Token-length-based truncation of context to avoid overly long inputs.

✅ Simplified return from generate() to avoid type mismatch on result["answer"].

✅ Limit top retrieved documents (e.g. top 10) to reduce overload.

✅ Support for optional XPath filtering in html_ingestor.

✅ Updated CLI to allow --xpath as an optional argument.

Let me know when you're ready for a PR or if you'd like help testing it.

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