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‎ai/generative-ai-service/oci_langflow/LICENSE.txt

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Copyright (c) 2024 Oracle and/or its affiliates.
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The Universal Permissive License (UPL), Version 1.0
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Subject to the condition set forth below, permission is hereby granted to any
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person obtaining a copy of this software, associated documentation and/or data
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(collectively the "Software"), free of charge and under any and all copyright
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rights in the Software, and any and all patent rights owned or freely
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licensable by each licensor hereunder covering either (i) the unmodified
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Software as contributed to or provided by such licensor, or (ii) the Larger
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Works (as defined below), to deal in both
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(a) the Software, and
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(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
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one is included with the Software (each a "Larger Work" to which the Software
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is contributed by such licensors),
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without restriction, including without limitation the rights to copy, create
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derivative works of, display, perform, and distribute the Software and make,
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use, sell, offer for sale, import, export, have made, and have sold the
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Software and the Larger Work(s), and to sublicense the foregoing rights on
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either these or other terms.
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This license is subject to the following condition:
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The above copyright notice and either this complete permission notice or at
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a minimum a reference to the UPL must be included in all copies or
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substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎ai/generative-ai-service/oci_langflow/README.md

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# Integration of OCI Generative AI in Langflow
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This repository contains all the code for a prototype of the integration of OCI Generative AI in Langflow
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[![License: UPL](https://img.shields.io/badge/license-UPL-green)](https://img.shields.io/badge/license-UPL-green) [![Quality gate](https://sonarcloud.io/api/project_badges/quality_gate?project=oracle-devrel_test)](https://sonarcloud.io/dashboard?id=oracle-devrel_test)
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Reviewed: 23.01.2025
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# **Link to code**
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[Code](https://github.com/luigisaetta/oci_langflow/tree/main)
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## Introduction
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This repository contains the code for a prototype of the integration of OCI Generative AI in Langflow
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# License
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Reviewed: 25.06.2025
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## Security
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Please consult the [security guide](./SECURITY.md) for our responsible security
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vulnerability disclosure process.
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## License
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Copyright (c) 2024 Oracle and/or its affiliates.
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Licensed under the Universal Permissive License (UPL), Version 1.0.
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See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/main/LICENSE) for more details.
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See [LICENSE](LICENSE.txt) for more details.
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ORACLE AND ITS AFFILIATES DO NOT PROVIDE ANY WARRANTY WHATSOEVER, EXPRESS OR IMPLIED, FOR ANY SOFTWARE, MATERIAL OR CONTENT OF ANY KIND CONTAINED OR PRODUCED WITHIN THIS REPOSITORY, AND IN PARTICULAR SPECIFICALLY DISCLAIM ANY AND ALL IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. FURTHERMORE, ORACLE AND ITS AFFILIATES DO NOT REPRESENT THAT ANY CUSTOMARY SECURITY REVIEW HAS BEEN PERFORMED WITH RESPECT TO ANY SOFTWARE, MATERIAL OR CONTENT CONTAINED OR PRODUCED WITHIN THIS REPOSITORY. IN ADDITION, AND WITHOUT LIMITING THE FOREGOING, THIRD PARTIES MAY HAVE POSTED SOFTWARE, MATERIAL OR CONTENT TO THIS REPOSITORY WITHOUT ANY REVIEW. USE AT YOUR OWN RISK.

‎ai/generative-ai-service/oci_langflow/custom_oci/embeddings/oci_embeddings_components.py

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"""
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Custom integration with Langflow and OCI Embeddings Model
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Author: L. Saetta (Oracle)
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"""
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from langchain_community.embeddings import OCIGenAIEmbeddings
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from langflow.base.models.model import LCModelComponent
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from langflow.io import DropdownInput, StrInput, Output, SecretStrInput
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from langflow.field_typing import Embeddings
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class OCIEmbeddingsComponent(LCModelComponent):
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"""
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This class integrates the OCI Embeddings Model with Langflow.
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Notes:
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* Security: for now API_KEY, set your key-pair in $HOME/.oci
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"""
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display_name = "OCI Cohere Embeddings"
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description = "Generate Embeddings using OCI Cohere models."
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inputs = [
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# example of dropdown
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DropdownInput(
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name="auth_type",
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display_name="auth_type",
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info="The type of auth_type to use for the chat model",
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advanced=True,
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options=[
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"API_KEY",
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"RESOURCE_PRINCIPAL",
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],
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value="API_KEY",
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),
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DropdownInput(
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name="model",
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display_name="Model",
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advanced=True,
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options=[
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"cohere.embed-english-v3.0",
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"cohere.embed-multilingual-v3.0",
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],
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value="cohere.embed-english-v3.0",
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),
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StrInput(
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name="service_endpoint",
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display_name="Service Endpoint",
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info="OCI Service Endpoint URL",
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required=True,
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),
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SecretStrInput(
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name="compartment_id",
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display_name="Compartment ID",
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info="OCI Compartment OCID",
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),
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]
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outputs = [
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Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
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]
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def build_model(self) -> Embeddings:
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"""
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build the embeddings model
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"""
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return self.build_embeddings()
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def build_embeddings(self) -> Embeddings:
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"""
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build the embeddings model
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"""
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# default truncate strategy is END
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return OCIGenAIEmbeddings(
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auth_type=self.auth_type,
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model_id=self.model,
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service_endpoint=self.service_endpoint,
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compartment_id=self.compartment_id,
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)

‎ai/generative-ai-service/oci_langflow/custom_oci/models/oci_chat_component.py

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"""
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Custom integration with Langflow and OCI Chat Model
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Author: L. Saetta (Oracle)
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This is part of demo code, in a real project you need to customise to fit your needs:
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* temperature
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* max_tokens
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* models list
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"""
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from langflow.base.models.model import LCModelComponent
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from langflow.inputs import StrInput, DropdownInput, SecretStrInput
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from langflow.field_typing import LanguageModel
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from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI
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class OCIChatComponent(LCModelComponent):
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"""
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This class integrates the OCI Chat Model with Langflow.
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Notes:
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* Security: for now API_KEY, set your key-pair in $HOME/.oci
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"""
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display_name = "OCI Chat Model"
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description = "OCI's Generative AI Chat Model."
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inputs = [
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*LCModelComponent._base_inputs,
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DropdownInput(
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name="auth_type",
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display_name="auth_type",
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info="The type of auth_type to use for the chat model",
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advanced=True,
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options=[
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"API_KEY",
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"RESOURCE_PRINCIPAL",
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],
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value="API_KEY",
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),
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DropdownInput(
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name="model_id",
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display_name="model_id",
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info="The model_id to use for the chat model",
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advanced=True,
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options=[
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"meta.llama-3.3-70b-instruct",
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"cohere.command-r-plus-08-2024",
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"meta.llama-3.1-405b-instruct",
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],
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value="meta.llama-3.1-70b-instruct",
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),
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StrInput(
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name="service_endpoint",
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display_name="Service Endpoint",
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info="OCI Service Endpoint URL",
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value="https://inference.generativeai.eu-frankfurt-1.oci.oraclecloud.com",
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),
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SecretStrInput(
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name="compartment_id",
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display_name="Compartment ID",
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info="OCI Compartment OCID",
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),
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]
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def build_model(self) -> LanguageModel:
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chat_model = ChatOCIGenAI(
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auth_type=self.auth_type,
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model_id=self.model_id,
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service_endpoint=self.service_endpoint,
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compartment_id=self.compartment_id,
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model_kwargs={"temperature": 0.1, "max_tokens": 1024},
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)
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return chat_model

‎ai/generative-ai-service/oci_langflow/custom_oci/vectorstores/oci_vs_component.py

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"""
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Custom integration with Langflow and OCI Vector Store
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Author: L. Saetta (Oracle)
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"""
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import oracledb
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from langchain_community.vectorstores.oraclevs import OracleVS
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from langchain_community.vectorstores.utils import DistanceStrategy
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from langflow.base.vectorstores.model import (
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LCVectorStoreComponent,
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check_cached_vector_store,
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)
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from langflow.helpers.data import docs_to_data
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from langflow.io import (
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HandleInput,
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IntInput,
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StrInput,
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SecretStrInput,
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MessageTextInput,
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)
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from langflow.schema import Data
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class OCIVectorStoreComponent(LCVectorStoreComponent):
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"""
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Wrapper for the OCI Vector Store
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"""
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display_name = "OCIVectorStore"
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description = "OCI Vector Store based on 23AI"
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name = "ocivector"
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inputs = [
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SecretStrInput(name="db_user", required=True),
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SecretStrInput(name="db_pwd", required=True),
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SecretStrInput(name="dsn", required=True),
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SecretStrInput(name="wallet_dir", required=True),
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SecretStrInput(name="wallet_pwd", required=True),
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StrInput(name="collection_name", display_name="Table", required=True),
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# this way we can handle the input for the search and it can be connected
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# in the flow
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MessageTextInput(
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name="search_query",
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display_name="search_query",
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info="Enter the search query.",
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),
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*LCVectorStoreComponent.inputs,
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HandleInput(
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name="embedding", display_name="Embedding", input_types=["Embeddings"]
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),
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IntInput(
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name="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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value=4,
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advanced=True,
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),
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]
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def handle_credentials(self) -> dict:
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"""
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this function organizes the parameters to connect
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to DB
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"""
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_connect_args_vector = {
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"user": self.db_user,
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"password": self.db_pwd,
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"dsn": self.dsn,
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"config_dir": self.wallet_dir,
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"wallet_location": self.wallet_dir,
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"wallet_password": self.wallet_pwd,
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}
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return _connect_args_vector
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@check_cached_vector_store
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def build_vector_store(self) -> OracleVS:
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connect_args_vector = self.handle_credentials()
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conn = oracledb.connect(**connect_args_vector)
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v_store = OracleVS(
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client=conn,
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table_name=self.collection_name,
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distance_strategy=DistanceStrategy.COSINE,
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embedding_function=self.embedding,
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)
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return v_store
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def search_documents(self) -> list[Data]:
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"""
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no changes needed here
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"""
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vector_store = self.build_vector_store()
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if (
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self.search_query
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and isinstance(self.search_query, str)
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and self.search_query.strip()
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):
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docs = vector_store.similarity_search(
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query=self.search_query,
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k=self.number_of_results,
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)
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data = docs_to_data(docs)
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self.status = data
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return data
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return []

‎ai/generative-ai-service/oci_langflow/images/langflow-memory01.png

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‎ai/generative-ai-service/oci_langflow/images/langflow-rag01.png

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