Merge branch 'main' into fix_ref_main
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
8456d58d9d
4 changed files with 95 additions and 33 deletions
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@ -1,11 +1,5 @@
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import Admonition from "@theme/Admonition";
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# Kubernetes
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<Admonition type="warning" title="warning">
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This page may contain outdated information. It will be updated as soon as possible.
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</Admonition>
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This guide will help you get LangFlow up and running in Kubernetes cluster, including the following steps:
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- Install [LangFlow as IDE](#langflow-ide) in a Kubernetes cluster (for development)
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@ -1,6 +1,6 @@
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from typing import Any
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from langflow.custom import Component
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from langflow.inputs.inputs import DictInput, SecretStrInput, MessageTextInput
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from langflow.inputs.inputs import DictInput, SecretStrInput, MessageTextInput, DropdownInput
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from langflow.template.field.base import Output
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@ -10,32 +10,58 @@ class AstraVectorize(Component):
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documentation: str = "https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html"
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icon = "AstraDB"
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VECTORIZE_PROVIDERS_MAPPING = {
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"Azure OpenAI": ["azureOpenAI", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
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"Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
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"Hugging Face - Serverless": ["huggingface",
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["sentence-transformers/all-MiniLM-L6-v2", "intfloat/multilingual-e5-large",
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"intfloat/multilingual-e5-large-instruct", "BAAI/bge-small-en-v1.5",
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"BAAI/bge-base-en-v1.5", "BAAI/bge-large-en-v1.5"]],
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"Jina AI": ["jinaAI", ["jina-embeddings-v2-base-en", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-es",
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"jina-embeddings-v2-base-code", "jina-embeddings-v2-base-zh"]],
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"Mistral AI": ["mistral", ["mistral-embed"]],
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"NVIDIA": ["nvidia", ["NV-Embed-QA"]],
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"OpenAI": ["openai", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
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"Upstage": ["upstageAI", ["solar-embedding-1-large"]],
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"Voyage AI": ["voyageAI",
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["voyage-large-2-instruct", "voyage-law-2", "voyage-code-2", "voyage-large-2", "voyage-2"]]
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}
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VECTORIZE_MODELS_STR = "\n\n".join([provider + ": " + (', '.join(models[1])) for provider, models in VECTORIZE_PROVIDERS_MAPPING.items()])
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inputs = [
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MessageTextInput(
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DropdownInput(
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name="provider",
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display_name="Provider name",
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info="The embedding provider to use.",
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options=VECTORIZE_PROVIDERS_MAPPING.keys(),
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value="",
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),
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MessageTextInput(
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name="model_name",
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display_name="Model name",
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info="The embedding model to use.",
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info=f"The embedding model to use for the selected provider. Each provider has a different set of models "
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f"available (full list at https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\n\n{VECTORIZE_MODELS_STR}",
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required=True
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),
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MessageTextInput(
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name="api_key_name",
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display_name="API Key name",
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info="The name of the embeddings provider API key stored on Astra. If set, it will override the 'ProviderKey' in the authentication parameters."
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),
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DictInput(
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name="authentication",
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display_name="Authentication",
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info="Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.",
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display_name="Authentication parameters",
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is_list=True,
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advanced=True,
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),
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SecretStrInput(
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name="provider_api_key",
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display_name="Provider API Key",
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info="An alternative to the Astra Authentication that let you use directly the API key of the provider.",
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advanced=True,
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),
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DictInput(
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name="model_parameters",
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display_name="Model parameters",
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info="Additional model parameters.",
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advanced=True,
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is_list=True,
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),
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@ -45,12 +71,17 @@ class AstraVectorize(Component):
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]
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def build_options(self) -> dict[str, Any]:
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provider_value = self.VECTORIZE_PROVIDERS_MAPPING[self.provider][0]
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authentication = {**self.authentication}
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api_key_name = self.api_key_name
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if api_key_name:
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authentication["providerKey"] = api_key_name
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return {
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# must match exactly astra CollectionVectorServiceOptions
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"collection_vector_service_options": {
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"provider": self.provider,
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"provider": provider_value,
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"modelName": self.model_name,
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"authentication": self.authentication,
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"authentication": authentication,
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"parameters": self.model_parameters,
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},
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"collection_embedding_api_key": self.provider_api_key,
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@ -2,8 +2,6 @@ from typing import Optional
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from firecrawl.firecrawl import FirecrawlApp
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from langflow.custom import CustomComponent
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from langflow.schema import Data
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from langflow.services.database.models.base import orjson_dumps
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import json
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class FirecrawlScrapeApi(CustomComponent):
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display_name: str = "FirecrawlScrapeApi"
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@ -4,6 +4,7 @@ from langchain_community.vectorstores import Cassandra
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.helpers.data import docs_to_data
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from langflow.inputs import DictInput
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from langflow.io import (
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DataInput,
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DropdownInput,
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@ -23,24 +24,30 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
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icon = "Cassandra"
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inputs = [
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MessageTextInput(name="database_ref",
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display_name="Contact Points / Astra Database ID",
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info="Contact points for the database (or AstraDB database ID)",
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required=True),
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MessageTextInput(name="username",
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display_name="Username",
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info="Username for the database (leave empty for AstraDB)."),
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SecretStrInput(
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name="token",
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display_name="Token",
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info="Authentication token for accessing Cassandra on Astra DB.",
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required=True,
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),
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MessageTextInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
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MessageTextInput(
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name="table_name",
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display_name="Table Name",
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info="The name of the table where vectors will be stored.",
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required=True,
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display_name="Password / AstraDB Token",
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info="User password for the database (or AstraDB token).",
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required=True
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),
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MessageTextInput(
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name="keyspace",
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display_name="Keyspace",
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info="Optional key space within Astra DB. The keyspace should already be created.",
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advanced=False,
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info="Table Keyspace (or AstraDB namespace).",
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required=True,
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),
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MessageTextInput(
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name="table_name",
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display_name="Table Name",
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info="The name of the table (or AstraDB collection) where vectors will be stored.",
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required=True,
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),
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IntInput(
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name="ttl_seconds",
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@ -69,6 +76,13 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
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value="Sync",
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advanced=True,
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),
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DictInput(
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name="cluster_kwargs",
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display_name="Cluster arguments",
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info="Optional dictionary of additional keyword arguments for the Cassandra cluster.",
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advanced=True,
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is_list=True
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),
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MultilineInput(name="search_query", display_name="Search Query"),
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DataInput(
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name="ingest_data",
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@ -96,10 +110,35 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
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"Could not import cassio integration package. " "Please install it with `pip install cassio`."
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)
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cassio.init(
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database_id=self.database_id,
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token=self.token,
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)
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from uuid import UUID
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database_ref = self.database_ref
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try:
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UUID(self.database_ref)
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is_astra = True
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except ValueError:
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is_astra = False
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if "," in self.database_ref:
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# use a copy because we can't change the type of the parameter
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database_ref = self.database_ref.split(",")
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if is_astra:
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cassio.init(
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database_id=database_ref,
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token=self.token,
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cluster_kwargs=self.cluster_kwargs,
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)
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else:
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cassio.init(
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contact_points=database_ref,
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username=self.username,
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password=self.token,
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cluster_kwargs=self.cluster_kwargs,
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)
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if not self.ttl_seconds:
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self.ttl_seconds = None
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documents = []
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