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community: added FalkorDB vector store support i.e implementation, test, docs an… (#26245)
**Description:** Added support for FalkorDB Vector Store, including its implementation, unit tests, documentation, and an example notebook. The FalkorDB integration allows users to efficiently manage and query embeddings in a vector database, with relevance scoring and maximal marginal relevance search. The following components were implemented: - Core implementation for FalkorDBVector store. - Unit tests ensuring proper functionality and edge case coverage. - Example notebook demonstrating an end-to-end setup, search, and retrieval using FalkorDB. **Twitter handle:** @tariyekorogha --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Tari Yekorogha
GitHub
437
docs/docs/integrations/vectorstores/falkordbvector.ipynb
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437
docs/docs/integrations/vectorstores/falkordbvector.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# FalkorDBVectorStore\n",
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"<a href=\"https://docs.falkordb.com/\" target=\"_blank\">FalkorDB</a> is an open-source graph database with integrated support for vector similarity search\n",
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"\n",
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"it supports:\n",
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"- approximate nearest neighbor search\n",
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"- Euclidean similarity & Cosine Similarity\n",
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"- Hybrid search combining vector and keyword searches\n",
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"\n",
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"This notebook shows how to use the FalkorDB vector index (`FalkorDB`)\n",
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"\n",
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"See the <a href=\"https://docs.falkordb.com/\" target=\"_blank\">installation instruction</a>\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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]
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}
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],
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"source": [
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"# Pip install necessary package\n",
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"%pip install --upgrade falkordb\n",
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"%pip install --upgrade tiktoken\n",
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"%pip install --upgrade langchain langchain_huggingface"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Credentials\n",
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"We want to use `HuggingFace` so we have to get the HuggingFace API Key"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if \"HUGGINGFACE_API_KEY\" not in os.environ:\n",
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" os.environ[\"HUGGINGFACE_API_KEY\"] = getpass.getpass(\"HuggingFace API Key:\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
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"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initialization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.vectorstores.falkordb_vector import FalkorDBVector\n",
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"from langchain_core.documents import Document\n",
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"from langchain_huggingface import HuggingFaceEmbeddings"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can use FalkorDBVector locally with docker. See <a href=\"https://docs.falkordb.com/\" target=\"_blank\">installation instruction</a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"host = \"localhost\"\n",
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"port = 6379"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Or you can use FalkorDBVector with <a href=\"https://app.falkordb.cloud\">FalkorDB Cloud</a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# E.g\n",
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"# host = \"r-6jissuruar.instance-zwb082gpf.hc-v8noonp0c.europe-west1.gcp.f2e0a955bb84.cloud\"\n",
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"# port = 62471\n",
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"# username = \"falkordb\" # SET ON FALKORDB CLOUD\n",
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"# password = \"password\" # SET ON FALKORDB CLOUD"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store = FalkorDBVector(host=host, port=port, embedding=HuggingFaceEmbeddings())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Manage vector store"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Add items to vector store"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"text/plain": [
|
||||
"['1', '2', '3']"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.documents import Document\n",
|
||||
"\n",
|
||||
"document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
|
||||
"\n",
|
||||
"document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
|
||||
"\n",
|
||||
"document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
|
||||
"\n",
|
||||
"documents = [document_1, document_2, document_3]\n",
|
||||
"\n",
|
||||
"vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Update items in vector store"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"updated_document = Document(\n",
|
||||
" page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"vector_store.update_documents(document_id=\"1\", document=updated_document)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete items from vector store"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vector_store.delete(ids=[\"3\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Query vector store\n",
|
||||
"\n",
|
||||
"Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Query directly\n",
|
||||
"\n",
|
||||
"Performing a simple similarity search can be done as follows:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"* qux [{'text': 'qux', 'id': '1', 'source': 'https://another-example.com'}]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"results = vector_store.similarity_search(\n",
|
||||
" query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
|
||||
")\n",
|
||||
"for doc in results:\n",
|
||||
" print(f\"* {doc.page_content} [{doc.metadata}]\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you want to execute a similarity search and receive the corresponding scores you can run:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"* [SIM=0.000001] bar [{'text': 'bar', 'id': '2', 'source': 'https://example.com'}]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"results = vector_store.similarity_search_with_score(query=\"bar\")\n",
|
||||
"for doc, score in results:\n",
|
||||
" print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Query by turning into retriever\n",
|
||||
"You can also transform the vector store into a retriever for easier usage in your chains."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(metadata={'text': 'qux', 'id': '1', 'source': 'https://another-example.com'}, page_content='qux')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
|
||||
"retriever.invoke(\"thud\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Usage for retrieval-augmented generation\n",
|
||||
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
|
||||
"- <a href=\"https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge\" target=\"_blank\">Tutorials: working with external knowledge</a>\n",
|
||||
"- <a href=\"https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag\" target=\"_blank\">How-to: Question and answer with RAG</a>\n",
|
||||
"- <a href=\"Retrieval conceptual docs\" target=\"_blank\">Retrieval conceptual docs</a>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"For detailed documentation of all `FalkorDBVector` features and configurations head to the API reference: https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.falkordb_vector.FalkorDBVector.html"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"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": "3.9.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user