pw.xpacks.llm.rerankers

Uses the CrossEncoder from the sentence_transformers library. For reference, check out Cross encoders documentation

• Parameters
  • model_name (-) – Embedding model to be used.
  • cache_strategy (-) – Defines the caching mechanism. To enable caching, a valid CacheStrategy should be provided. See Cache strategy for more information. Defaults to None.

Suggested model: cross-encoder/ms-marco-TinyBERT-L-2-v2

Example:

import pathway as pw
import pandas as pd
from pathway.xpacks.llm import rerankers
reranker = rerankers.CrossEncoderReranker(model_name="cross-encoder/ms-marco-TinyBERT-L-2-v2")
docs = [{"text": "Something"}, {"text": "Something else"}, {"text": "Pathway"}]
df = pd.DataFrame({"docs": docs, "prompt": "query text"})
table = pw.debug.table_from_pandas(df)
table += table.select(
reranker_scores=reranker(pw.this.docs["text"], pw.this.prompt)
)
table

<pathway.Table schema={'docs': <class 'pathway.internals.json.Json'>, 'prompt': <class 'str'>, 'reranker_scores': <class 'float'>}>

__call__(doc, query, **kwargs)

sourceEvaluates the doc against the query.

• Parameters
  • doc (-) – Document or document chunk to be scored.
  • query (-) – User query or prompt that will be used to evaluate relevance of the doc.
  • **kwargs (-) – override for defaults set in the constructor.

Uses the encoders from the sentence_transformers library. For reference, check out Pretrained models documentation

• Parameters
  • model_name (-) – Embedding model to be used.
  • cache_strategy (-) – Defines the caching mechanism. To enable caching, a valid CacheStrategy should be provided. See Cache strategy for more information. Defaults to None.

Suggested model: BAAI/bge-large-zh-v1.5

Example:

import pathway as pw
import pandas as pd
from pathway.xpacks.llm import rerankers
reranker = rerankers.EncoderReranker(model_name="BAAI/bge-large-zh-v1.5")
docs = [{"text": "Something"}, {"text": "Something else"}, {"text": "Pathway"}]
df = pd.DataFrame({"docs": docs, "prompt": "query text"})
table = pw.debug.table_from_pandas(df)
table += table.select(
reranker_scores=reranker(pw.this.docs["text"], pw.this.prompt)
)
table

<pathway.Table schema={'docs': <class 'pathway.internals.json.Json'>, 'prompt': <class 'str'>, 'reranker_scores': <class 'float'>}>

__call__(doc, query, **kwargs)

sourceEvaluates the doc against the query.

• Parameters
  • doc (-) – Document or document chunk to be scored.
  • query (-) – User query or prompt that will be used to evaluate relevance of the doc.
  • **kwargs (-) – override for defaults set in the constructor.

Asks LLM to evaluate a given doc against a query between 1 and 5.

• Parameters
  • llm (-) – Chat instance to be called during reranking.
  • retry_strategy (-) – Strategy for handling retries in case of failures. Defaults to None, meaning no retries.
  • cache_strategy (-) – Defines the caching mechanism. To enable caching, a valid CacheStrategy should be provided. See Cache strategy for more information. Defaults to None.
  • use_logit_bias (-) – bool or None. Setting it as None checks if the LLM provider supports logit_bias argument, it can be overridden by setting it as True or False. Defaults to None.

Example:

import pathway as pw
import pandas as pd
from pathway.xpacks.llm import rerankers, llms
chat = llms.OpenAIChat(model="gpt-3.5-turbo")
reranker = rerankers.LLMReranker(chat)
docs = [{"text": "Something"}, {"text": "Something else"}, {"text": "Pathway"}]
df = pd.DataFrame({"docs": docs, "prompt": "query text"})
table = pw.debug.table_from_pandas(df)
table += table.select(
reranker_scores=reranker(pw.this.docs["text"], pw.this.prompt)
)
table

<pathway.Table schema={'docs': <class 'pathway.internals.json.Json'>, 'prompt': <class 'str'>, 'reranker_scores': <class 'float'>}>

__call__(doc, query, **kwargs)

sourceEvaluates the doc against the query.

• Parameters
  • doc (-) – Document or document chunk to be scored.
  • query (-) – User query or prompt that will be used to evaluate relevance of the doc.
  • **kwargs (-) – override for defaults set in the constructor