Added Elasticsearch Vector Store (#1286)

In this pull request, I have:
- added Elasticsearch component on Vector Store
- functionality is basic to get started quickly

**Set up a Single-node Elasticsearch cluster on localhost on docker**
1. sudo docker network create elastic
2. sudo docker run --net elastic -p 9200:9200 -e
"discovery.type=single-node" -e "xpack.security.enabled=false" -e
"xpack.security.http.ssl.enabled=false" -d
docker.elastic.co/elasticsearch/elasticsearch:8.11.3
3. curl http://localhost:9200
   {
     "name" : "994a10c1dab5",
     "cluster_name" : "docker-cluster",
     "cluster_uuid" : "p_iQ88F-T2agFpIdzJN7Ow",
     "version" : {
       "number" : "8.11.3",
       "build_flavor" : "default",
       "build_type" : "docker",
       "build_hash" : "64cf052f3b56b1fd4449f5454cb88aca7e739d9a",
       "build_date" : "2023-12-08T11:33:53.634979452Z",
       "build_snapshot" : false,
       "lucene_version" : "9.8.0",
       "minimum_wire_compatibility_version" : "7.17.0",
       "minimum_index_compatibility_version" : "7.0.0"
     },
     "tagline" : "You Know, for Search"
   }
4. curl -GET http://localhost:9200/_cat/indices
   No indices are available

**Set up Langlow:**
1. make backend
2. make frontend
3. open localhost:3000
4. New Project -> Import from JSON: elasticsearch-langflow.json
**[elasticsearch-langflow.json](https://github.com/logspace-ai/langflow/files/13823444/elasticsearch-langflow.json)**
5. poetry add elasticsearch or pipenv install elasticsearch or pip
install elasticsearch
6. Select text file to load
7. Provide OpenAPI keys for OpenAIEmbeddings and ChatOpenAI
8. Build
9. Verify that document is indexed: curl -GET
http://localhost:9200/_cat/indices
health status index uuid pri rep docs.count docs.deleted store.size
pri.store.size dataset.size
yellow open test-index pt9_ZOACR8mWCNx7GO3scA 1 1 1 0 39.3kb 39.3kb
39.3kb
10. Open Chat and you can ask: "What is the document about?"
This commit is contained in:
Gabriel Luiz Freitas Almeida 2024-01-06 16:23:51 -03:00 committed by GitHub
commit d2257441f5
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10 changed files with 160 additions and 4 deletions

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poetry.lock generated
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{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@ -6590,6 +6650,7 @@ files = [
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
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{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
{file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@ -6597,6 +6658,7 @@ files = [
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{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@ -9221,4 +9283,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
content-hash = "80378e2f6511caf4f46ad7ff47a5e0823aca87e92e6b0f3ced80040bbb891ffb"
content-hash = "4d113c63d1947ef150f76c977e3556939ad76e488fae7386733225d3ae77de99"

View file

@ -104,6 +104,7 @@ qianfan = "0.2.0"
pgvector = "^0.2.3"
pyautogen = "^0.2.0"
langchain-google-genai = "^0.0.2"
elasticsearch = "^8.11.1"
pytube = "^15.0.0"
llama-index = "^0.9.24"

View file

@ -274,6 +274,8 @@ vectorstores:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
Pinecone:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
ElasticsearchStore:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/elasticsearch"
SupabaseVectorStore:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
MongoDBAtlasVectorSearch:

View file

@ -1,6 +1,7 @@
from typing import Any, Callable, Dict, Type
from langchain.vectorstores import (
Pinecone,
ElasticsearchStore,
Qdrant,
Chroma,
FAISS,
@ -226,11 +227,34 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict):
return class_object.from_documents(**params)
def initialize_elasticsearch(class_object: Type[ElasticsearchStore], params: dict):
"""Initialize elastic and return the class object"""
if "index_name" not in params:
raise ValueError("Elasticsearch Index must be provided in the params")
if "es_url" not in params:
raise ValueError("Elasticsearch URL must be provided in the params")
if not docs_in_params(params):
existing_index_params = {
"embedding": params.pop("embedding"),
}
if "index_name" in params:
existing_index_params["index_name"] = params.pop("index_name")
if "es_url" in params:
existing_index_params["es_url"] = params.pop("es_url")
return class_object.from_existing_index(**existing_index_params)
# If there are docs in the params, create a new index
if "texts" in params:
params["documents"] = params.pop("texts")
return class_object.from_documents(**params)
vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
"Pinecone": initialize_pinecone,
"Chroma": initialize_chroma,
"Qdrant": initialize_qdrant,
"Weaviate": initialize_weaviate,
"ElasticsearchStore": initialize_elasticsearch,
"FAISS": initialize_faiss,
"SupabaseVectorStore": initialize_supabase,
"MongoDBAtlasVectorSearch": initialize_mongodb,

View file

@ -11,6 +11,7 @@ BASIC_FIELDS = [
"persist_directory",
"persist",
"weaviate_url",
"es_url",
"index_name",
"namespace",
"folder_path",
@ -170,6 +171,33 @@ class VectorStoreFrontendNode(FrontendNode):
value="",
)
extra_fields.extend((extra_field, extra_field2))
elif self.template.type_name == "ElasticsearchStore":
# add elastic and elastic credentials
extra_field = TemplateField(
name="es_url",
field_type="str",
required=True,
placeholder="http://localhost:9200",
show=True,
advanced=False,
multiline=False,
value="http://localhost:9200",
display_name="Elasticsearch URL",
)
extra_field2 = TemplateField(
name="index_name",
field_type="str",
required=True,
placeholder="test-index",
show=True,
advanced=False,
multiline=False,
value="test-index",
display_name="Index Name",
)
extra_fields.extend((extra_field, extra_field2))
elif self.template.type_name == "FAISS":
extra_field = TemplateField(
name="folder_path",

View file

@ -0,0 +1,19 @@
const SvgElasticsearchLogo = (props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
width="24"
height="24"
fill="none"
stroke="currentColor"
strokeLinecap="round"
strokeLinejoin="round"
strokeWidth="2"
className="icon icon-tabler icon-tabler-brand-elastic"
viewBox="0 0 24 24"
>
<path stroke="none" d="M0 0h24v24H0z"></path>
<path d="M14 2a5 5 0 015 5c0 .712-.232 1.387-.5 2 1.894.042 3.5 1.595 3.5 3.5 0 1.869-1.656 3.4-3.5 3.5.333.625.5 1.125.5 1.5a2.5 2.5 0 01-2.5 2.5c-.787 0-1.542-.432-2-1-.786 1.73-2.476 3-4.5 3a5 5 0 01-4.583-7 3.5 3.5 0 01-.11-6.992h.195a2.5 2.5 0 012-4c.787 0 1.542.432 2 1 .786-1.73 2.476-3 4.5-3zM8.5 9l-3-1"></path>
<path d="M9.5 5l-1 4 1 2 5 2 4-4M18.499 16l-3-.5-1-2.5M14.5 19l1-3.5M5.417 15L9.5 11"></path>
</svg>
);
export default SvgElasticsearchLogo;

View file

@ -0,0 +1,9 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-elastic" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none" />
<path d="M14 2a5 5 0 0 1 5 5c0 .712 -.232 1.387 -.5 2c1.894 .042 3.5 1.595 3.5 3.5c0 1.869 -1.656 3.4 -3.5 3.5c.333 .625 .5 1.125 .5 1.5a2.5 2.5 0 0 1 -2.5 2.5c-.787 0 -1.542 -.432 -2 -1c-.786 1.73 -2.476 3 -4.5 3a5 5 0 0 1 -4.583 -7a3.5 3.5 0 0 1 -.11 -6.992l.195 0a2.5 2.5 0 0 1 2 -4c.787 0 1.542 .432 2 1c.786 -1.73 2.476 -3 4.5 -3z" />
<path d="M8.5 9l-3 -1" />
<path d="M9.5 5l-1 4l1 2l5 2l4 -4" />
<path d="M18.499 16l-3 -.5l-1 -2.5" />
<path d="M14.5 19l1 -3.5" />
<path d="M5.417 15l4.083 -4" />
</svg>

After

Width:  |  Height:  |  Size: 810 B

View file

@ -0,0 +1,9 @@
import React, { forwardRef } from "react";
import SvgElasticsearchLogo from "./ElasticsearchLogo";
export const ElasticsearchIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
return <SvgElasticsearchLogo ref={ref} {...props} />;
});

View file

@ -111,6 +111,7 @@ import { AnthropicIcon } from "../icons/Anthropic";
import { BingIcon } from "../icons/Bing";
import { ChromaIcon } from "../icons/ChromaIcon";
import { CohereIcon } from "../icons/Cohere";
import { ElasticsearchIcon } from "../icons/ElasticsearchStore";
import { EvernoteIcon } from "../icons/Evernote";
import { FBIcon } from "../icons/FacebookMessenger";
import { GitBookIcon } from "../icons/GitBook";
@ -256,6 +257,7 @@ export const nodeIconsLucide: iconsType = {
OpenAIEmbeddings: OpenAiIcon,
Pinecone: PineconeIcon,
Qdrant: QDrantIcon,
ElasticsearchStore: ElasticsearchIcon,
Weaviate: WeaviateIcon,
Searx: SearxIcon,
SlackDirectoryLoader: SvgSlackIcon,

View file

@ -578,4 +578,4 @@ def test_async_task_processing_vector_store(client, added_vector_store, created_
# Validate that the task completed successfully and the result is as expected
assert "result" in task_status_json, task_status_json
assert "output" in task_status_json["result"], task_status_json["result"]
assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]