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docs/extras/modules/data_connection/retrievers/multi_vector.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "d9172545",
"metadata": {},
"source": [
"# MultiVector Retriever\n",
"\n",
"It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.\n",
"\n",
"The methods to create multiple vectors per document include:\n",
"\n",
"- smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever)\n",
"- summary: create a summary for each document, embed that along with (or instead of) the document\n",
"- hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "eed469be",
"metadata": {},
"outputs": [],
"source": [
"from langchain.retrievers.multi_vector import MultiVectorRetriever"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "18c1421a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.storage import InMemoryStore\n",
"from langchain.document_loaders import TextLoader"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6d869496",
"metadata": {},
"outputs": [],
"source": [
"loaders = [\n",
" TextLoader('../../paul_graham_essay.txt'),\n",
" TextLoader('../../state_of_the_union.txt'),\n",
"]\n",
"docs = []\n",
"for l in loaders:\n",
" docs.extend(l.load())\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)\n",
"docs = text_splitter.split_documents(docs)"
]
},
{
"cell_type": "markdown",
"id": "fa17beda",
"metadata": {},
"source": [
"## Smaller chunks\n",
"\n",
"Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. NOTE: this is what the ParentDocumentRetriever does. Here we show what is going on under the hood."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0e7b6b45",
"metadata": {},
"outputs": [],
"source": [
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"full_documents\",\n",
" embedding_function=OpenAIEmbeddings()\n",
")\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()\n",
"id_key = \"doc_id\"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore, \n",
" docstore=store, \n",
" id_key=id_key,\n",
")\n",
"import uuid\n",
"doc_ids = [str(uuid.uuid4()) for _ in docs]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "72a36491",
"metadata": {},
"outputs": [],
"source": [
"# The splitter to use to create smaller chunks\n",
"child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d23247d",
"metadata": {},
"outputs": [],
"source": [
"sub_docs = []\n",
"for i, doc in enumerate(docs):\n",
" _id = doc_ids[i]\n",
" _sub_docs = child_text_splitter.split_documents([doc])\n",
" for _doc in _sub_docs:\n",
" _doc.metadata[id_key] = _id\n",
" sub_docs.extend(_sub_docs)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "92ed5861",
"metadata": {},
"outputs": [],
"source": [
"retriever.vectorstore.add_documents(sub_docs)\n",
"retriever.docstore.mset(list(zip(doc_ids, docs)))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8afed60c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.', metadata={'doc_id': 'b4ca7817-e3fe-4103-ac81-574fb41439ef', 'source': '../../state_of_the_union.txt'})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Vectorstore alone retrieves the small chunks\n",
"retriever.vectorstore.similarity_search(\"justice breyer\")[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3c9017f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9874"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Retriever returns larger chunks\n",
"len(retriever.get_relevant_documents(\"justice breyer\")[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "d6a7ae0d",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"Oftentimes a summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1433dff4",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain.schema.output_parser import StrOutputParser\n",
"import uuid\n",
"from langchain.schema.document import Document"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "35b30390",
"metadata": {},
"outputs": [],
"source": [
"chain = (\n",
" {\"doc\": lambda x: x.page_content}\n",
" | ChatPromptTemplate.from_template(\"Summarize the following document:\\n\\n{doc}\")\n",
" | ChatOpenAI(max_retries=0)\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "41a2a738",
"metadata": {},
"outputs": [],
"source": [
"summaries = [chain.invoke(d) for d in docs]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7ac5e4b1",
"metadata": {},
"outputs": [],
"source": [
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"summaries\",\n",
" embedding_function=OpenAIEmbeddings()\n",
")\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()\n",
"id_key = \"doc_id\"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore, \n",
" docstore=store, \n",
" id_key=id_key,\n",
")\n",
"doc_ids = [str(uuid.uuid4()) for _ in docs]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0d93309f",
"metadata": {},
"outputs": [],
"source": [
"summary_docs = [Document(page_content=s,metadata={id_key: doc_ids[i]}) for i, s in enumerate(summaries)]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6d5edf0d",
"metadata": {},
"outputs": [],
"source": [
"retriever.vectorstore.add_documents(summary_docs)\n",
"retriever.docstore.mset(list(zip(doc_ids, docs)))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "299232d6",
"metadata": {},
"outputs": [],
"source": [
"sub_docs = vectorstore.similarity_search(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "10e404c0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='The document discusses various topics and proposals put forth by the President in a State of the Union address. These include the nomination of a judge for the Supreme Court, securing the border and fixing the immigration system, advancing liberty and justice for women and LGBTQ+ individuals, passing bipartisan legislation, addressing the opioid epidemic and mental health issues, supporting veterans, and ending cancer. The President expresses optimism about the future of the country and emphasizes the strength of the American people.', metadata={'doc_id': '8c7a707d-615d-42d5-919d-bc5178dd1ae4'})"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub_docs[0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e4cce5c2",
"metadata": {},
"outputs": [],
"source": [
"retrieved_docs = retriever.get_relevant_documents(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c8570dbb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9194"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(retrieved_docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "097a5396",
"metadata": {},
"source": [
"## Hypothetical Queries\n",
"\n",
"An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document. These questions can then be embedded"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

3
docs/extras/modules/data_connection/retrievers/parent_document_retriever.ipynb
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@ -83,7 +83,6 @@
"outputs": [],
"source": [
"# This text splitter is used to create the child documents\n",
"\n",
"child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
@ -432,7 +431,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
"version": "3.10.1"
}
},
"nbformat": 4,

2
libs/langchain/langchain/retrievers/__init__.py
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@ -40,6 +40,7 @@ from langchain.retrievers.merger_retriever import MergerRetriever
from langchain.retrievers.metal import MetalRetriever
from langchain.retrievers.milvus import MilvusRetriever
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.retrievers.parent_document_retriever import ParentDocumentRetriever
from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
from langchain.retrievers.pubmed import PubMedRetriever
@ -92,4 +93,5 @@ __all__ = [
"WebResearchRetriever",
"EnsembleRetriever",
"ParentDocumentRetriever",
"MultiVectorRetriever",
]

39
libs/langchain/langchain/retrievers/multi_vector.py Normal file
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@ -0,0 +1,39 @@
from typing import List
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, BaseStore, Document
from langchain.vectorstores import VectorStore
class MultiVectorRetriever(BaseRetriever):
"""Retrieve from a set of multiple embeddings for the same document."""
vectorstore: VectorStore
"""The underlying vectorstore to use to store small chunks
and their embedding vectors"""
docstore: BaseStore[str, Document]
"""The storage layer for the parent documents"""
id_key: str = "doc_id"
search_kwargs: dict = Field(default_factory=dict)
"""Keyword arguments to pass to the search function."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
# We do this to maintain the order of the ids that are returned
ids = []
for d in sub_docs:
if d.metadata[self.id_key] not in ids:
ids.append(d.metadata[self.id_key])
docs = self.docstore.mget(ids)
return [d for d in docs if d is not None]

36
libs/langchain/langchain/retrievers/parent_document_retriever.py
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@ -1,16 +1,12 @@
import uuid
from typing import List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import Field
from langchain.retrievers import MultiVectorRetriever
from langchain.schema.document import Document
from langchain.schema.retriever import BaseRetriever
from langchain.schema.storage import BaseStore
from langchain.text_splitter import TextSplitter
from langchain.vectorstores.base import VectorStore
class ParentDocumentRetriever(BaseRetriever):
class ParentDocumentRetriever(MultiVectorRetriever):
"""Retrieve small chunks then retrieve their parent documents.
When splitting documents for retrieval, there are often conflicting desires:
@ -59,40 +55,14 @@ class ParentDocumentRetriever(BaseRetriever):
)
"""
vectorstore: VectorStore
"""The underlying vectorstore to use to store small chunks
and their embedding vectors"""
docstore: BaseStore[str, Document]
"""The storage layer for the parent documents"""
child_splitter: TextSplitter
"""The text splitter to use to create child documents."""
id_key: str = "doc_id"
"""The key to use to track the parent id. This will be stored in the
metadata of child documents."""
parent_splitter: Optional[TextSplitter] = None
"""The text splitter to use to create parent documents.
If none, then the parent documents will be the raw documents passed in."""
search_kwargs: dict = Field(default_factory=dict)
"""Keyword arguments to pass to the search function."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
# We do this to maintain the order of the ids that are returned
ids = []
for d in sub_docs:
if d.metadata[self.id_key] not in ids:
ids.append(d.metadata[self.id_key])
docs = self.docstore.mget(ids)
return [d for d in docs if d is not None]
def add_documents(
self,