Vectorindex

VectorIndexing

Bases: BaseIndexing

Ingest the document, run through the embedding, and store the embedding in a vector store.

This pipeline supports the following set of inputs

• List of documents
• List of texts

Source code in libs/kotaemon/kotaemon/indices/vectorindex.py

class VectorIndexing(BaseIndexing):
    """Ingest the document, run through the embedding, and store the embedding in a
    vector store.

    This pipeline supports the following set of inputs:
        - List of documents
        - List of texts
    """

    cache_dir: Optional[str] = getattr(flowsettings, "KH_CHUNKS_OUTPUT_DIR", None)
    vector_store: BaseVectorStore
    doc_store: Optional[BaseDocumentStore] = None
    embedding: BaseEmbeddings
    count_: int = 0

    def to_retrieval_pipeline(self, *args, **kwargs):
        """Convert the indexing pipeline to a retrieval pipeline"""
        return VectorRetrieval(
            vector_store=self.vector_store,
            doc_store=self.doc_store,
            embedding=self.embedding,
            **kwargs,
        )

    def to_qa_pipeline(self, *args, **kwargs):
        from .qa import CitationQAPipeline

        return TextVectorQA(
            retrieving_pipeline=self.to_retrieval_pipeline(**kwargs),
            qa_pipeline=CitationQAPipeline(**kwargs),
        )

    def write_chunk_to_file(self, docs: list[Document]):
        # save the chunks content into markdown format
        if self.cache_dir:
            file_name = docs[0].metadata.get("file_name")
            if not file_name:
                return

            file_name = Path(file_name)
            for i in range(len(docs)):
                markdown_content = ""
                if "page_label" in docs[i].metadata:
                    page_label = str(docs[i].metadata["page_label"])
                    markdown_content += f"Page label: {page_label}"
                if "file_name" in docs[i].metadata:
                    filename = docs[i].metadata["file_name"]
                    markdown_content += f"\nFile name: {filename}"
                if "section" in docs[i].metadata:
                    section = docs[i].metadata["section"]
                    markdown_content += f"\nSection: {section}"
                if "type" in docs[i].metadata:
                    if docs[i].metadata["type"] == "image":
                        image_origin = docs[i].metadata["image_origin"]
                        image_origin = f'<p><img src="{image_origin}"></p>'
                        markdown_content += f"\nImage origin: {image_origin}"
                if docs[i].text:
                    markdown_content += f"\ntext:\n{docs[i].text}"

                with open(
                    Path(self.cache_dir) / f"{file_name.stem}_{self.count_+i}.md",
                    "w",
                    encoding="utf-8",
                ) as f:
                    f.write(markdown_content)

    def add_to_docstore(self, docs: list[Document]):
        if self.doc_store:
            print("Adding documents to doc store")
            self.doc_store.add(docs)

    def add_to_vectorstore(self, docs: list[Document]):
        # in case we want to skip embedding
        if self.vector_store:
            print(f"Getting embeddings for {len(docs)} nodes")
            embeddings = self.embedding(docs)
            print("Adding embeddings to vector store")
            self.vector_store.add(
                embeddings=embeddings,
                ids=[t.doc_id for t in docs],
            )

    def run(self, text: str | list[str] | Document | list[Document]):
        input_: list[Document] = []
        if not isinstance(text, list):
            text = [text]

        for item in cast(list, text):
            if isinstance(item, str):
                input_.append(Document(text=item, id_=str(uuid.uuid4())))
            elif isinstance(item, Document):
                input_.append(item)
            else:
                raise ValueError(
                    f"Invalid input type {type(item)}, should be str or Document"
                )

        self.add_to_vectorstore(input_)
        self.add_to_docstore(input_)
        self.write_chunk_to_file(input_)
        self.count_ += len(input_)

to_retrieval_pipeline

to_retrieval_pipeline(*args, **kwargs)

Convert the indexing pipeline to a retrieval pipeline

Source code in libs/kotaemon/kotaemon/indices/vectorindex.py

def to_retrieval_pipeline(self, *args, **kwargs):
    """Convert the indexing pipeline to a retrieval pipeline"""
    return VectorRetrieval(
        vector_store=self.vector_store,
        doc_store=self.doc_store,
        embedding=self.embedding,
        **kwargs,
    )

VectorRetrieval

Bases: BaseRetrieval

Retrieve list of documents from vector store

Source code in libs/kotaemon/kotaemon/indices/vectorindex.py

class VectorRetrieval(BaseRetrieval):
    """Retrieve list of documents from vector store"""

    vector_store: BaseVectorStore
    doc_store: Optional[BaseDocumentStore] = None
    embedding: BaseEmbeddings
    rerankers: Sequence[BaseReranking] = []
    top_k: int = 5
    first_round_top_k_mult: int = 10
    retrieval_mode: str = "hybrid"  # vector, text, hybrid

    def _filter_docs(
        self, documents: list[RetrievedDocument], top_k: int | None = None
    ):
        if top_k:
            documents = documents[:top_k]
        return documents

    def run(
        self, text: str | Document, top_k: Optional[int] = None, **kwargs
    ) -> list[RetrievedDocument]:
        """Retrieve a list of documents from vector store

        Args:
            text: the text to retrieve similar documents
            top_k: number of top similar documents to return

        Returns:
            list[RetrievedDocument]: list of retrieved documents
        """
        if top_k is None:
            top_k = self.top_k

        do_extend = kwargs.pop("do_extend", False)
        thumbnail_count = kwargs.pop("thumbnail_count", 3)

        if do_extend:
            top_k_first_round = top_k * self.first_round_top_k_mult
        else:
            top_k_first_round = top_k

        if self.doc_store is None:
            raise ValueError(
                "doc_store is not provided. Please provide a doc_store to "
                "retrieve the documents"
            )

        result: list[RetrievedDocument] = []
        # TODO: should declare scope directly in the run params
        scope = kwargs.pop("scope", None)
        emb: list[float]

        if self.retrieval_mode == "vector":
            emb = self.embedding(text)[0].embedding
            _, scores, ids = self.vector_store.query(
                embedding=emb, top_k=top_k_first_round, **kwargs
            )
            docs = self.doc_store.get(ids)
            result = [
                RetrievedDocument(**doc.to_dict(), score=score)
                for doc, score in zip(docs, scores)
            ]
        elif self.retrieval_mode == "text":
            query = text.text if isinstance(text, Document) else text
            docs = self.doc_store.query(query, top_k=top_k_first_round, doc_ids=scope)
            result = [RetrievedDocument(**doc.to_dict(), score=-1.0) for doc in docs]
        elif self.retrieval_mode == "hybrid":
            # similarity search section
            emb = self.embedding(text)[0].embedding
            vs_docs: list[RetrievedDocument] = []
            vs_ids: list[str] = []
            vs_scores: list[float] = []

            def query_vectorstore():
                nonlocal vs_docs
                nonlocal vs_scores
                nonlocal vs_ids

                assert self.doc_store is not None
                _, vs_scores, vs_ids = self.vector_store.query(
                    embedding=emb, top_k=top_k_first_round, **kwargs
                )
                if vs_ids:
                    vs_docs = self.doc_store.get(vs_ids)

            # full-text search section
            ds_docs: list[RetrievedDocument] = []

            def query_docstore():
                nonlocal ds_docs

                assert self.doc_store is not None
                query = text.text if isinstance(text, Document) else text
                ds_docs = self.doc_store.query(
                    query, top_k=top_k_first_round, doc_ids=scope
                )

            vs_query_thread = threading.Thread(target=query_vectorstore)
            ds_query_thread = threading.Thread(target=query_docstore)

            vs_query_thread.start()
            ds_query_thread.start()

            vs_query_thread.join()
            ds_query_thread.join()

            result = [
                RetrievedDocument(**doc.to_dict(), score=-1.0)
                for doc in ds_docs
                if doc not in vs_ids
            ]
            result += [
                RetrievedDocument(**doc.to_dict(), score=score)
                for doc, score in zip(vs_docs, vs_scores)
            ]
            print(f"Got {len(vs_docs)} from vectorstore")
            print(f"Got {len(ds_docs)} from docstore")

        # use additional reranker to re-order the document list
        if self.rerankers and text:
            for reranker in self.rerankers:
                # if reranker is LLMReranking, limit the document with top_k items only
                if isinstance(reranker, LLMReranking):
                    result = self._filter_docs(result, top_k=top_k)
                result = reranker.run(documents=result, query=text)

        result = self._filter_docs(result, top_k=top_k)
        print(f"Got raw {len(result)} retrieved documents")

        # add page thumbnails to the result if exists
        thumbnail_doc_ids: set[str] = set()
        # we should copy the text from retrieved text chunk
        # to the thumbnail to get relevant LLM score correctly
        text_thumbnail_docs: dict[str, RetrievedDocument] = {}

        non_thumbnail_docs = []
        raw_thumbnail_docs = []
        for doc in result:
            if doc.metadata.get("type") == "thumbnail":
                # change type to image to display on UI
                doc.metadata["type"] = "image"
                raw_thumbnail_docs.append(doc)
                continue
            if (
                "thumbnail_doc_id" in doc.metadata
                and len(thumbnail_doc_ids) < thumbnail_count
            ):
                thumbnail_id = doc.metadata["thumbnail_doc_id"]
                thumbnail_doc_ids.add(thumbnail_id)
                text_thumbnail_docs[thumbnail_id] = doc
            else:
                non_thumbnail_docs.append(doc)

        linked_thumbnail_docs = self.doc_store.get(list(thumbnail_doc_ids))
        print(
            "thumbnail docs",
            len(linked_thumbnail_docs),
            "non-thumbnail docs",
            len(non_thumbnail_docs),
            "raw-thumbnail docs",
            len(raw_thumbnail_docs),
        )
        additional_docs = []

        for thumbnail_doc in linked_thumbnail_docs:
            text_doc = text_thumbnail_docs[thumbnail_doc.doc_id]
            doc_dict = thumbnail_doc.to_dict()
            doc_dict["_id"] = text_doc.doc_id
            doc_dict["content"] = text_doc.content
            doc_dict["metadata"]["type"] = "image"
            for key in text_doc.metadata:
                if key not in doc_dict["metadata"]:
                    doc_dict["metadata"][key] = text_doc.metadata[key]

            additional_docs.append(RetrievedDocument(**doc_dict, score=text_doc.score))

        result = additional_docs + non_thumbnail_docs

        if not result:
            # return output from raw retrieved thumbnails
            result = self._filter_docs(raw_thumbnail_docs, top_k=thumbnail_count)

        return result

run

run(text, top_k=None, **kwargs)

Retrieve a list of documents from vector store

Parameters:

Name	Type	Description	Default
text	str | Document	the text to retrieve similar documents	required
top_k	Optional[int]	number of top similar documents to return	None

Returns:

Type	Description
list[RetrievedDocument]	list[RetrievedDocument]: list of retrieved documents

Source code in libs/kotaemon/kotaemon/indices/vectorindex.py

def run(
    self, text: str | Document, top_k: Optional[int] = None, **kwargs
) -> list[RetrievedDocument]:
    """Retrieve a list of documents from vector store

    Args:
        text: the text to retrieve similar documents
        top_k: number of top similar documents to return

    Returns:
        list[RetrievedDocument]: list of retrieved documents
    """
    if top_k is None:
        top_k = self.top_k

    do_extend = kwargs.pop("do_extend", False)
    thumbnail_count = kwargs.pop("thumbnail_count", 3)

    if do_extend:
        top_k_first_round = top_k * self.first_round_top_k_mult
    else:
        top_k_first_round = top_k

    if self.doc_store is None:
        raise ValueError(
            "doc_store is not provided. Please provide a doc_store to "
            "retrieve the documents"
        )

    result: list[RetrievedDocument] = []
    # TODO: should declare scope directly in the run params
    scope = kwargs.pop("scope", None)
    emb: list[float]

    if self.retrieval_mode == "vector":
        emb = self.embedding(text)[0].embedding
        _, scores, ids = self.vector_store.query(
            embedding=emb, top_k=top_k_first_round, **kwargs
        )
        docs = self.doc_store.get(ids)
        result = [
            RetrievedDocument(**doc.to_dict(), score=score)
            for doc, score in zip(docs, scores)
        ]
    elif self.retrieval_mode == "text":
        query = text.text if isinstance(text, Document) else text
        docs = self.doc_store.query(query, top_k=top_k_first_round, doc_ids=scope)
        result = [RetrievedDocument(**doc.to_dict(), score=-1.0) for doc in docs]
    elif self.retrieval_mode == "hybrid":
        # similarity search section
        emb = self.embedding(text)[0].embedding
        vs_docs: list[RetrievedDocument] = []
        vs_ids: list[str] = []
        vs_scores: list[float] = []

        def query_vectorstore():
            nonlocal vs_docs
            nonlocal vs_scores
            nonlocal vs_ids

            assert self.doc_store is not None
            _, vs_scores, vs_ids = self.vector_store.query(
                embedding=emb, top_k=top_k_first_round, **kwargs
            )
            if vs_ids:
                vs_docs = self.doc_store.get(vs_ids)

        # full-text search section
        ds_docs: list[RetrievedDocument] = []

        def query_docstore():
            nonlocal ds_docs

            assert self.doc_store is not None
            query = text.text if isinstance(text, Document) else text
            ds_docs = self.doc_store.query(
                query, top_k=top_k_first_round, doc_ids=scope
            )

        vs_query_thread = threading.Thread(target=query_vectorstore)
        ds_query_thread = threading.Thread(target=query_docstore)

        vs_query_thread.start()
        ds_query_thread.start()

        vs_query_thread.join()
        ds_query_thread.join()

        result = [
            RetrievedDocument(**doc.to_dict(), score=-1.0)
            for doc in ds_docs
            if doc not in vs_ids
        ]
        result += [
            RetrievedDocument(**doc.to_dict(), score=score)
            for doc, score in zip(vs_docs, vs_scores)
        ]
        print(f"Got {len(vs_docs)} from vectorstore")
        print(f"Got {len(ds_docs)} from docstore")

    # use additional reranker to re-order the document list
    if self.rerankers and text:
        for reranker in self.rerankers:
            # if reranker is LLMReranking, limit the document with top_k items only
            if isinstance(reranker, LLMReranking):
                result = self._filter_docs(result, top_k=top_k)
            result = reranker.run(documents=result, query=text)

    result = self._filter_docs(result, top_k=top_k)
    print(f"Got raw {len(result)} retrieved documents")

    # add page thumbnails to the result if exists
    thumbnail_doc_ids: set[str] = set()
    # we should copy the text from retrieved text chunk
    # to the thumbnail to get relevant LLM score correctly
    text_thumbnail_docs: dict[str, RetrievedDocument] = {}

    non_thumbnail_docs = []
    raw_thumbnail_docs = []
    for doc in result:
        if doc.metadata.get("type") == "thumbnail":
            # change type to image to display on UI
            doc.metadata["type"] = "image"
            raw_thumbnail_docs.append(doc)
            continue
        if (
            "thumbnail_doc_id" in doc.metadata
            and len(thumbnail_doc_ids) < thumbnail_count
        ):
            thumbnail_id = doc.metadata["thumbnail_doc_id"]
            thumbnail_doc_ids.add(thumbnail_id)
            text_thumbnail_docs[thumbnail_id] = doc
        else:
            non_thumbnail_docs.append(doc)

    linked_thumbnail_docs = self.doc_store.get(list(thumbnail_doc_ids))
    print(
        "thumbnail docs",
        len(linked_thumbnail_docs),
        "non-thumbnail docs",
        len(non_thumbnail_docs),
        "raw-thumbnail docs",
        len(raw_thumbnail_docs),
    )
    additional_docs = []

    for thumbnail_doc in linked_thumbnail_docs:
        text_doc = text_thumbnail_docs[thumbnail_doc.doc_id]
        doc_dict = thumbnail_doc.to_dict()
        doc_dict["_id"] = text_doc.doc_id
        doc_dict["content"] = text_doc.content
        doc_dict["metadata"]["type"] = "image"
        for key in text_doc.metadata:
            if key not in doc_dict["metadata"]:
                doc_dict["metadata"][key] = text_doc.metadata[key]

        additional_docs.append(RetrievedDocument(**doc_dict, score=text_doc.score))

    result = additional_docs + non_thumbnail_docs

    if not result:
        # return output from raw retrieved thumbnails
        result = self._filter_docs(raw_thumbnail_docs, top_k=thumbnail_count)

    return result