Separate rerankers, splitters and extractors (#85)

knowledgehub/indices/rankings/__init__.py

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+from .base import BaseReranking
+from .cohere import CohereReranking
+from .llm import LLMReranking
+
+__all__ = ["CohereReranking", "LLMReranking", "BaseReranking"]

knowledgehub/indices/rankings/base.py

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+from __future__ import annotations
+
+from abc import abstractmethod
+
+from ...base import BaseComponent, Document
+
+
+class BaseReranking(BaseComponent):
+    @abstractmethod
+    def run(self, documents: list[Document], query: str) -> list[Document]:
+        """Main method to transform list of documents
+        (re-ranking, filtering, etc)"""
+        ...

knowledgehub/indices/rankings/cohere.py

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+from __future__ import annotations
+
+import os
+
+from ...base import Document
+from .base import BaseReranking
+
+
+class CohereReranking(BaseReranking):
+    model_name: str = "rerank-multilingual-v2.0"
+    cohere_api_key: str = os.environ.get("COHERE_API_KEY", "")
+    top_k: int = 1
+
+    def run(self, documents: list[Document], query: str) -> list[Document]:
+        """Use Cohere Reranker model to re-order documents
+        with their relevance score"""
+        try:
+            import cohere
+        except ImportError:
+            raise ImportError(
+                "Please install Cohere " "`pip install cohere` to use Cohere Reranking"
+            )
+
+        cohere_client = cohere.Client(self.cohere_api_key)
+
+        # output documents
+        compressed_docs = []
+        if len(documents) > 0:  # to avoid empty api call
+            _docs = [d.content for d in documents]
+            results = cohere_client.rerank(
+                model=self.model_name, query=query, documents=_docs, top_n=self.top_k
+            )
+            for r in results:
+                doc = documents[r.index]
+                doc.metadata["relevance_score"] = r.relevance_score
+                compressed_docs.append(doc)
+
+        return compressed_docs

knowledgehub/indices/rankings/llm.py

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+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+from typing import Union
+
+from langchain.output_parsers.boolean import BooleanOutputParser
+
+from ...base import Document
+from ...llms import PromptTemplate
+from ...llms.chats.base import ChatLLM
+from ...llms.completions.base import LLM
+from .base import BaseReranking
+
+BaseLLM = Union[ChatLLM, LLM]
+
+RERANK_PROMPT_TEMPLATE = """Given the following question and context,
+return YES if the context is relevant to the question and NO if it isn't.
+
+> Question: {question}
+> Context:
+>>>
+{context}
+>>>
+> Relevant (YES / NO):"""
+
+
+class LLMReranking(BaseReranking):
+    llm: BaseLLM
+    prompt_template: PromptTemplate = PromptTemplate(template=RERANK_PROMPT_TEMPLATE)
+    top_k: int = 3
+    concurrent: bool = True
+
+    def run(
+        self,
+        documents: list[Document],
+        query: str,
+    ) -> list[Document]:
+        """Filter down documents based on their relevance to the query."""
+        filtered_docs = []
+        output_parser = BooleanOutputParser()
+
+        if self.concurrent:
+            with ThreadPoolExecutor() as executor:
+                futures = []
+                for doc in documents:
+                    _prompt = self.prompt_template.populate(
+                        question=query, context=doc.get_content()
+                    )
+                    futures.append(executor.submit(lambda: self.llm(_prompt).text))
+
+                results = [future.result() for future in futures]
+        else:
+            results = []
+            for doc in documents:
+                _prompt = self.prompt_template.populate(
+                    question=query, context=doc.get_content()
+                )
+                results.append(self.llm(_prompt).text)
+
+        # use Boolean parser to extract relevancy output from LLM
+        results = [output_parser.parse(result) for result in results]
+        for include_doc, doc in zip(results, documents):
+            if include_doc:
+                filtered_docs.append(doc)
+
+        # prevent returning empty result
+        if len(filtered_docs) == 0:
+            filtered_docs = documents[: self.top_k]
+
+        return filtered_docs