Add Reranker implementation and integration in Retrieving pipeline (#77)

* Add base Reranker
* Add LLM Reranker
* Add Cohere Reranker
* Add integration of Rerankers in Retrieving pipeline

knowledgehub/pipelines/ingest.py

@@ -1,7 +1,8 @@
 import os
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Optional, Sequence, Union
 
+from llama_index.node_parser.extractors import MetadataExtractor
 from llama_index.readers.base import BaseReader
 from theflow import Node
 from theflow.utils.modules import ObjectInitDeclaration as _
@@ -18,6 +19,7 @@ from kotaemon.loaders import (
 from kotaemon.parsers.splitter import SimpleNodeParser
 from kotaemon.pipelines.agents import BaseAgent
 from kotaemon.pipelines.indexing import IndexVectorStoreFromDocumentPipeline
+from kotaemon.pipelines.reranking import BaseRerankingPipeline
 from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
 from kotaemon.storages import (
     BaseDocumentStore,
@@ -43,12 +45,14 @@ class ReaderIndexingPipeline(BaseComponent):
     chunk_overlap: int = 256
     vector_store: _[BaseVectorStore] = _(InMemoryVectorStore)
     doc_store: _[BaseDocumentStore] = _(InMemoryDocumentStore)
+    metadata_extractor: Optional[MetadataExtractor] = None
 
     embedding: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings.withx(
         model="text-embedding-ada-002",
         deployment="dummy-q2-text-embedding",
-        openai_api_base="https://bleh-dummy-2.openai.azure.com/",
+        azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
         openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
+        chunk_size=16,
     )
 
     def get_reader(self, input_files: List[Union[str, Path]]):
@@ -79,7 +83,9 @@ class ReaderIndexingPipeline(BaseComponent):
     @Node.auto(depends_on=["chunk_size", "chunk_overlap"])
     def text_splitter(self) -> SimpleNodeParser:
         return SimpleNodeParser(
-            chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+            metadata_extractor=self.metadata_extractor,
         )
 
     def run(
@@ -111,12 +117,15 @@ class ReaderIndexingPipeline(BaseComponent):
         else:
             self.indexing_vector_pipeline.load(file_storage_path)
 
-    def to_retrieving_pipeline(self, top_k=3):
+    def to_retrieving_pipeline(
+        self, top_k=3, rerankers: Sequence[BaseRerankingPipeline] = []
+    ):
         retrieving_pipeline = RetrieveDocumentFromVectorStorePipeline(
             vector_store=self.vector_store,
             doc_store=self.doc_store,
             embedding=self.embedding,
             top_k=top_k,
+            rerankers=rerankers,
         )
         return retrieving_pipeline
 

knowledgehub/pipelines/qa.py

@@ -1,6 +1,6 @@
 import os
 from pathlib import Path
-from typing import List
+from typing import List, Sequence
 
 from theflow import Node
 from theflow.utils.modules import ObjectInitDeclaration as _
@@ -11,6 +11,7 @@ from kotaemon.embeddings import AzureOpenAIEmbeddings
 from kotaemon.llms import PromptTemplate
 from kotaemon.llms.chats.openai import AzureChatOpenAI
 from kotaemon.pipelines.agents import BaseAgent
+from kotaemon.pipelines.reranking import BaseRerankingPipeline
 from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
 from kotaemon.pipelines.tools import ComponentTool
 from kotaemon.storages import (
@@ -39,7 +40,7 @@ class QuestionAnsweringPipeline(BaseComponent):
     )
 
     llm: AzureChatOpenAI = AzureChatOpenAI.withx(
-        openai_api_base="https://bleh-dummy-2.openai.azure.com/",
+        azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
         openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
         openai_api_version="2023-03-15-preview",
         deployment_name="dummy-q2-gpt35",
@@ -49,11 +50,12 @@ class QuestionAnsweringPipeline(BaseComponent):
 
     vector_store: _[BaseVectorStore] = _(InMemoryVectorStore)
     doc_store: _[BaseDocumentStore] = _(InMemoryDocumentStore)
+    rerankers: Sequence[BaseRerankingPipeline] = []
 
     embedding: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings.withx(
         model="text-embedding-ada-002",
         deployment="dummy-q2-text-embedding",
-        openai_api_base="https://bleh-dummy-2.openai.azure.com/",
+        azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
         openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
     )
 
@@ -72,6 +74,7 @@ class QuestionAnsweringPipeline(BaseComponent):
             doc_store=self.doc_store,
             embedding=self.embedding,
             top_k=self.retrieval_top_k,
+            rerankers=self.rerankers,
         )
         # load persistent from selected path
         collection_name = file_names_to_collection_name(self.file_name_list)

knowledgehub/pipelines/reranking.py

@@ -0,0 +1,114 @@
+import os
+from abc import abstractmethod
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Optional, Union
+
+from langchain.output_parsers.boolean import BooleanOutputParser
+
+from ..base import BaseComponent
+from ..base.schema import Document
+from ..llms import PromptTemplate
+from ..llms.chats.base import ChatLLM
+from ..llms.completions.base import LLM
+
+BaseLLM = Union[ChatLLM, LLM]
+
+
+class BaseRerankingPipeline(BaseComponent):
+    @abstractmethod
+    def run(self, documents: List[Document], query: str) -> List[Document]:
+        """Main method to transform list of documents
+        (re-ranking, filtering, etc)"""
+        ...
+
+
+class CohereReranking(BaseRerankingPipeline):
+    model_name: str = "rerank-multilingual-v2.0"
+    cohere_api_key: Optional[str] = None
+    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_api_key = (
+            self.cohere_api_key if self.cohere_api_key else os.environ["COHERE_API_KEY"]
+        )
+        cohere_client = cohere.Client(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
+
+
+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(BaseRerankingPipeline):
+    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

knowledgehub/pipelines/retrieving.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Sequence
 
 from theflow import Node, Param
 
@@ -9,6 +9,7 @@ from ..base import BaseComponent
 from ..base.schema import Document, RetrievedDocument
 from ..embeddings import BaseEmbeddings
 from ..storages import BaseDocumentStore, BaseVectorStore
+from .reranking import BaseRerankingPipeline
 
 VECTOR_STORE_FNAME = "vectorstore"
 DOC_STORE_FNAME = "docstore"
@@ -20,6 +21,7 @@ class RetrieveDocumentFromVectorStorePipeline(BaseComponent):
     vector_store: Param[BaseVectorStore] = Param()
     doc_store: Param[BaseDocumentStore] = Param()
     embedding: Node[BaseEmbeddings] = Node()
+    rerankers: Sequence[BaseRerankingPipeline] = []
     top_k: int = 1
     # TODO: refer to llama_index's storage as well
 
@@ -51,6 +53,11 @@ class RetrieveDocumentFromVectorStorePipeline(BaseComponent):
             RetrievedDocument(**doc.to_dict(), score=score)
             for doc, score in zip(docs, scores)
         ]
+        # use additional reranker to re-order the document list
+        if self.rerankers:
+            for reranker in self.rerankers:
+                result = reranker(documents=result, query=text)
+
         return result
 
     def save(