Add support for Couchbase vector store (#1901)
* add couchbase vector store support * add docs + minor changes * Fix lint issues * remove stray lines * Add required validation and minor changes * Address Comments --------- Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
This commit is contained in:
Prajwal Pai
GitHub
parent
e39b00cdd6
commit
19680bb137
11 changed files with 281 additions and 4 deletions
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@ -26,7 +26,8 @@ The `Astra DB` initializes a vector store using Astra DB from records. It create
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- **Collection Indexing Policy:** Indexing policy for the collection.
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<Admonition type="note" title="Note">
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Ensure you configure the necessary Astra DB token and API endpoint before starting.
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Ensure you configure the necessary Astra DB token and API endpoint before
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starting.
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</Admonition>
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---
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@ -96,6 +97,44 @@ For detailed documentation and integration guides, please refer to the [Chroma C
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---
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### Couchbase
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`Couchbase` builds a Couchbase vector store from records, streamlining the storage and retrieval of documents.
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**Parameters:**
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- **Embedding:** Model used by Couchbase.
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- **Input:** Documents or records.
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- **Couchbase Cluster Connection String:** Cluster Connection string.
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- **Couchbase Cluster Username:** Cluster Username.
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- **Couchbase Cluster Password:** Cluster Password.
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- **Bucket Name:** Bucket identifier in Couchbase.
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- **Scope Name:** Scope identifier in Couchbase.
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- **Collection Name:** Collection identifier in Couchbase.
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- **Index Name:** Index identifier.
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For detailed documentation and integration guides, please refer to the [Couchbase Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/couchbase).
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---
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### Couchbase Search
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`CouchbaseSearch` leverages the Couchbase component to search for documents based on similarity metric.
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**Parameters:**
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- **Input:** Search query.
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- **Embedding:** Model used in the Vector Store.
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- **Couchbase Cluster Connection String:** Cluster Connection string.
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- **Couchbase Cluster Username:** Cluster Username.
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- **Couchbase Cluster Password:** Cluster Password.
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- **Bucket Name:** Bucket identifier.
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- **Scope Name:** Scope identifier.
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- **Collection Name:** Collection identifier in Couchbase.
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- **Index Name:** Index identifier.
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---
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### FAISS
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The `FAISS` component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval.
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@ -278,7 +317,8 @@ For more details, see the [PGVector Component Documentation](https://python.lang
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For detailed documentation, refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis).
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<Admonition type="note" title="Note">
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Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available.
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Ensure the Redis server URL and index name are configured correctly. Provide a
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schema if no documents are available.
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</Admonition>
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---
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@ -389,7 +429,8 @@ For more information, consult the [Vectara Component Documentation](https://pyth
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For more details, see the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate).
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<Admonition type="note" title="Note">
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Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly.
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Ensure Weaviate instance is running and accessible. Verify API key, index
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name, text key, and attributes are set correctly.
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</Admonition>
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---
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36
poetry.lock
generated
36
poetry.lock
generated
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@ -1259,6 +1259,40 @@ files = [
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test = ["PyYAML", "mock", "pytest"]
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yaml = ["PyYAML"]
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[[package]]
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name = "couchbase"
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version = "4.2.1"
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description = "Python Client for Couchbase"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "couchbase-4.2.1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:7ad4c4462879f456a9067ac1788e62d852509439bac3538b9bc459a754666481"},
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{file = "couchbase-4.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:06d91891c599ba0f5052e594ac025a2ca6ab7885e528b854ac9c125df7c74146"},
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{file = "couchbase-4.2.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0191d4a631ead533551cb9a214704ad5f3dfff2029e21a23b57725a0b5666b25"},
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{file = "couchbase-4.2.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b206790d6834a18c5e457f9a70f44774f476f3acccf9f22e8c1b5283a5bd03fa"},
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{file = "couchbase-4.2.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c5ca571b9ce017ecbd447de12cd46e213f93e0664bec6fca0a06e1768db1a4f8"},
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{file = "couchbase-4.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:675c615cfd4b04e73e94cf03c786da5105d94527f5c3a087813dba477a1379e9"},
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{file = "couchbase-4.2.1-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:4cd09eedf162dc28386d9c6490e832c25068406c0f5d70a0417c0b1445394651"},
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{file = "couchbase-4.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfebb11551c6d947ce6297ab02b5006b1ac8739dda3e10d41896db0dc8672915"},
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{file = "couchbase-4.2.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:39e742ccfe90a0e59e6e1b0e12f0fe224a736c0207b218ef48048052f926e1c6"},
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{file = "couchbase-4.2.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f9ba24efddf47f30603275f5433434d8759a55233c78b3e4bc613c502ac429e9"},
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{file = "couchbase-4.2.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:adfca3929f07fb4385dc52f08d3a60634012f364b176f95ab023cdd1bb7fe9c0"},
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{file = "couchbase-4.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:e1c68b28c6f0475961afb9fe626ad2bac8a5643b53f719675386f060db4b6e19"},
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{file = "couchbase-4.2.1-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:137512462426cd495954c1815d78115d109308a4d9f8843b638285104388a359"},
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{file = "couchbase-4.2.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5987e5edcce7696e5f75b35be91f44fa69fb5eb95dba0957ad66f789affcdb36"},
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{file = "couchbase-4.2.1-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:080cb0fc333bd4a641ede4ee14ff0c7dbe95067fbb280826ea546681e0b9f9e3"},
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{file = "couchbase-4.2.1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e317c2628a4a917083e8e7ce8e2662432b6a12ebac65fc00de6da2b37ab5975c"},
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{file = "couchbase-4.2.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:de7f8699ae344e2e96706ee0eac67e96bfdd3412fb18dcfb81d8ba5837dd3dfb"},
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{file = "couchbase-4.2.1-cp38-cp38-win_amd64.whl", hash = "sha256:82b9deb8b1fe8e8d7dde9c232ac5f4c11ff0f067930837af0e7769706e6a9453"},
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{file = "couchbase-4.2.1-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:44502d069ea17a8d692b7c88d84bc0df2cf4e944cde337c8eb3175bc0b835bb9"},
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{file = "couchbase-4.2.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c0f131b816a7d91b755232872ba10f6d6ca5a715e595ee9534478bc97a518ae8"},
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{file = "couchbase-4.2.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e9b9deb312bbe5f9a8e63828f9de877714c4b09b7d88f7dc87b60e5ffb2a13e6"},
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{file = "couchbase-4.2.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:71e8da251850d795975c3569c01d35ba1a556825dc7d9549ff9918d148255804"},
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{file = "couchbase-4.2.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d04492144ce520c612a2f8f265278c9f0cdf62fdd6f703e7a3210a7476b228f6"},
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{file = "couchbase-4.2.1-cp39-cp39-win_amd64.whl", hash = "sha256:3f91b7699ea7b8253cf34c9fb6e191de9b2edfd7aa4d6f97b29c10b9a1670444"},
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{file = "couchbase-4.2.1.tar.gz", hash = "sha256:dc1c60d3f2fc179db8225aac4cc30d601d73cf2535aaf023d607e86be2d7dd78"},
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]
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[[package]]
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name = "coverage"
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version = "7.5.1"
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@ -10224,5 +10258,5 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
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[metadata]
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lock-version = "2.0"
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python-versions = ">=3.10,<3.13"
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content-hash = "9e4b7f3e920342839dd51795994bdd6b993b6d0313fc715c900c4e0409acceef"
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content-hash = "c68a14e273363466c1cea2bacec1798a6894d2bb18cf79ba443dc65c9344c57d"
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@ -83,6 +83,7 @@ langchain-google-vertexai = "^1.0.3"
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langchain-groq = "^0.1.3"
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langchain-pinecone = "^0.1.0"
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langchain-mistralai = "^0.1.6"
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couchbase = "^4.2.1"
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[tool.poetry.group.dev.dependencies]
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@ -0,0 +1,73 @@
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from typing import List, Optional
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from langflow.components.vectorstores.base.model import LCVectorStoreComponent
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from langflow.components.vectorstores.Couchbase import CouchbaseComponent
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from langflow.field_typing import Embeddings, NestedDict, Text
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from langflow.schema import Record
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class CouchbaseSearchComponent(LCVectorStoreComponent):
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display_name = "Couchbase Search"
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description = "Search a Couchbase Vector Store for similar documents."
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
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icon = "Couchbase"
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field_order = [
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"couchbase_connection_string",
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"couchbase_username",
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"couchbase_password",
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"bucket_name",
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"scope_name",
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"collection_name",
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"index_name",
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]
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def build_config(self):
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return {
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"input_value": {"display_name": "Input"},
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"embedding": {"display_name": "Embedding"},
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"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
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"couchbase_username": {"display_name": "Couchbase username","required": True},
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"couchbase_password": {
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"display_name": "Couchbase password",
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"password": True,
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"required": True
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},
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"bucket_name": {"display_name": "Bucket Name","required": True},
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"scope_name": {"display_name": "Scope Name","required": True},
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"collection_name": {"display_name": "Collection Name","required": True},
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"index_name": {"display_name": "Index Name","required": True},
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"number_of_results": {
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"display_name": "Number of Results",
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"info": "Number of results to return.",
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"advanced": True,
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},
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}
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def build( # type: ignore[override]
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self,
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input_value: Text,
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embedding: Embeddings,
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number_of_results: int = 4,
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bucket_name: str = "",
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scope_name: str = "",
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collection_name: str = "",
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index_name: str = "",
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couchbase_connection_string: str = "",
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couchbase_username: str = "",
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couchbase_password: str = "",
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) -> List[Record]:
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vector_store = CouchbaseComponent().build(
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couchbase_connection_string=couchbase_connection_string,
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couchbase_username=couchbase_username,
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couchbase_password=couchbase_password,
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bucket_name=bucket_name,
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scope_name=scope_name,
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collection_name=collection_name,
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embedding=embedding,
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index_name=index_name,
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)
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if not vector_store:
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raise ValueError("Failed to create Couchbase Vector Store")
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return self.search_with_vector_store(
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vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
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)
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@ -9,10 +9,12 @@ from .SupabaseVectorStoreSearch import SupabaseSearchComponent
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from .VectaraSearch import VectaraSearchComponent
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from .WeaviateSearch import WeaviateSearchVectorStore
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from .pgvectorSearch import PGVectorSearchComponent
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from .Couchbase import CouchbaseSearchComponent # type: ignore
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__all__ = [
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"AstraDBSearchComponent",
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"ChromaSearchComponent",
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"CouchbaseSearchComponent",
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"FAISSSearchComponent",
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"MongoDBAtlasSearchComponent",
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"PineconeSearchComponent",
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@ -0,0 +1,95 @@
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from typing import List, Optional, Union
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from langchain.schema import BaseRetriever
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from langchain_community.vectorstores import CouchbaseVectorStore
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from langflow.custom import CustomComponent
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from langflow.field_typing import Embeddings, VectorStore
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from langflow.schema import Record
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from datetime import timedelta
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from couchbase.auth import PasswordAuthenticator # type: ignore
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from couchbase.cluster import Cluster # type: ignore
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from couchbase.options import ClusterOptions # type: ignore
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class CouchbaseComponent(CustomComponent):
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display_name = "Couchbase"
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description = "Construct a `Couchbase Vector Search` vector store from raw documents."
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
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icon = "Couchbase"
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field_order = [
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"couchbase_connection_string",
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"couchbase_username",
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"couchbase_password",
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"bucket_name",
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"scope_name",
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"collection_name",
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"index_name",
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]
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def build_config(self):
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return {
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"inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
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"embedding": {"display_name": "Embedding"},
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"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
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"couchbase_username": {"display_name": "Couchbase username","required": True},
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"couchbase_password": {
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"display_name": "Couchbase password",
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"password": True,
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"required": True
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},
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"bucket_name": {"display_name": "Bucket Name","required": True},
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"scope_name": {"display_name": "Scope Name","required": True},
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"collection_name": {"display_name": "Collection Name","required": True},
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"index_name": {"display_name": "Index Name","required": True},
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}
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def build(
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self,
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embedding: Embeddings,
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inputs: Optional[List[Record]] = None,
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bucket_name: str = "",
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scope_name: str = "",
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collection_name: str = "",
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index_name: str = "",
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couchbase_connection_string: str = "",
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couchbase_username: str = "",
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couchbase_password: str = "",
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) -> Union[VectorStore, BaseRetriever]:
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try:
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auth = PasswordAuthenticator(couchbase_username, couchbase_password)
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options = ClusterOptions(auth)
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cluster = Cluster(couchbase_connection_string, options)
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cluster.wait_until_ready(timedelta(seconds=5))
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except Exception as e:
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raise ValueError(f"Failed to connect to Couchbase: {e}")
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documents = []
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for _input in inputs or []:
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if isinstance(_input, Record):
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documents.append(_input.to_lc_document())
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else:
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documents.append(_input)
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if documents:
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vector_store = CouchbaseVectorStore.from_documents(
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documents=documents,
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cluster=cluster,
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bucket_name=bucket_name,
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scope_name=scope_name,
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collection_name=collection_name,
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embedding=embedding,
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index_name=index_name,
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)
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else:
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vector_store = CouchbaseVectorStore(
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cluster=cluster,
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bucket_name=bucket_name,
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scope_name=scope_name,
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collection_name=collection_name,
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embedding=embedding,
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index_name=index_name,
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)
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return vector_store
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@ -9,10 +9,12 @@ from .SupabaseVectorStore import SupabaseComponent
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from .Vectara import VectaraComponent
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from .Weaviate import WeaviateVectorStoreComponent
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from .pgvector import PGVectorComponent
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from .Couchbase import CouchbaseComponent
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__all__ = [
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"AstraDBVectorStoreComponent",
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"ChromaComponent",
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"CouchbaseComponent",
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"FAISSComponent",
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"MongoDBAtlasComponent",
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"PineconeComponent",
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17
src/frontend/src/icons/Couchbase/Couchbase.jsx
Normal file
17
src/frontend/src/icons/Couchbase/Couchbase.jsx
Normal file
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@ -0,0 +1,17 @@
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const SvgCouchbaseIcon = (props) => (
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<svg
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xmlns="http://www.w3.org/2000/svg"
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width="1em"
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height="1em"
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preserveAspectRatio="xMidYMid"
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viewBox="0 0 256 256"
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{...props}
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>
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<path
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fill="#ED2226"
|
||||
d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"
|
||||
/>
|
||||
</svg>
|
||||
);
|
||||
|
||||
export default SvgCouchbaseIcon;
|
||||
1
src/frontend/src/icons/Couchbase/couchbase.svg
Normal file
1
src/frontend/src/icons/Couchbase/couchbase.svg
Normal file
|
|
@ -0,0 +1 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="2500" height="2500" preserveAspectRatio="xMidYMid" viewBox="0 0 256 256" id="couchbase"><path fill="#ED2226" d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"></path></svg>
|
||||
|
After Width: | Height: | Size: 720 B |
9
src/frontend/src/icons/Couchbase/index.tsx
Normal file
9
src/frontend/src/icons/Couchbase/index.tsx
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
import React, { forwardRef } from "react";
|
||||
import SvgCouchbaseIcon from "./Couchbase";
|
||||
|
||||
export const CouchbaseIcon = forwardRef<
|
||||
SVGSVGElement,
|
||||
React.PropsWithChildren<{}>
|
||||
>((props, ref) => {
|
||||
return <SvgCouchbaseIcon ref={ref} {...props} />;
|
||||
});
|
||||
|
|
@ -153,6 +153,7 @@ import { AzureIcon } from "../icons/Azure";
|
|||
import { BingIcon } from "../icons/Bing";
|
||||
import { BotMessageSquareIcon } from "../icons/BotMessageSquare";
|
||||
import { ChromaIcon } from "../icons/ChromaIcon";
|
||||
import { CouchbaseIcon } from "../icons/Couchbase";
|
||||
import { CohereIcon } from "../icons/Cohere";
|
||||
import { ElasticsearchIcon } from "../icons/ElasticsearchStore";
|
||||
import { EvernoteIcon } from "../icons/Evernote";
|
||||
|
|
@ -324,6 +325,7 @@ export const nodeIconsLucide: iconsType = {
|
|||
Vectara: VectaraIcon,
|
||||
ArrowUpToLine: ArrowUpToLine,
|
||||
Chroma: ChromaIcon,
|
||||
Couchbase: CouchbaseIcon,
|
||||
AirbyteJSONLoader: AirbyteIcon,
|
||||
AmazonBedrockEmbeddings: AWSIcon,
|
||||
Amazon: AWSIcon,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue