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edwinjosechittilappilly merged 13 commits into from
Nov 21, 2025
Merged

fix: Add IBM watsonx.ai support to EmbeddingModel #10677

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55cf5d9
Add IBM watsonx.ai support to EmbeddingModel
edwinjosechittilappilly Nov 20, 2025
d927cc6
update watsonx default models
edwinjosechittilappilly Nov 21, 2025
ce8aabd
Merge branch 'main' into fix-emebddings-component
edwinjosechittilappilly Nov 21, 2025
ec14524
update index
edwinjosechittilappilly Nov 21, 2025
9fad275
[autofix.ci] apply automated fixes
autofix-ci[bot] Nov 21, 2025
7079625
[autofix.ci] apply automated fixes (attempt 2/3)
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4dc4ec9
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autofix-ci[bot] Nov 21, 2025
5444d54
Update test_embedding_model_component.py
edwinjosechittilappilly Nov 21, 2025
04f4a78
Merge branch 'main' into fix-emebddings-component
edwinjosechittilappilly Nov 21, 2025
9f3541f
Update component_index.json
edwinjosechittilappilly Nov 21, 2025
968a006
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52 changes: 49 additions & 3 deletions src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json
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2 changes: 1 addition & 1 deletion src/lfx/src/lfx/_assets/component_index.json
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33 changes: 25 additions & 8 deletions src/lfx/src/lfx/base/models/watsonx_constants.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,32 @@
from .model_metadata import create_model_metadata

# Granite Embedding models
WATSONX_EMBEDDING_MODELS_DETAILED = [
create_model_metadata(provider="IBM Watsonx", name="ibm/granite-embedding-125m-english", icon="IBMWatsonx"),
create_model_metadata(provider="IBM Watsonx", name="ibm/granite-embedding-278m-multilingual", icon="IBMWatsonx"),
create_model_metadata(provider="IBM Watsonx", name="ibm/granite-embedding-30m-english", icon="IBMWatsonx"),
create_model_metadata(provider="IBM Watsonx", name="ibm/granite-embedding-107m-multilingual", icon="IBMWatsonx"),
create_model_metadata(provider="IBM Watsonx", name="ibm/granite-embedding-30m-sparse", icon="IBMWatsonx"),
WATSONX_DEFAULT_EMBEDDING_MODELS = [
create_model_metadata(
provider="IBM Watsonx",
name="sentence-transformers/all-minilm-l12-v2",
icon="WatsonxAI",
),
create_model_metadata(
provider="IBM Watsonx",
name="ibm/slate-125m-english-rtrvr-v2",
icon="WatsonxAI",
),
create_model_metadata(
provider="IBM Watsonx",
name="ibm/slate-30m-english-rtrvr-v2",
icon="WatsonxAI",
),
create_model_metadata(
provider="IBM Watsonx",
name="intfloat/multilingual-e5-large",
icon="WatsonxAI",
),
]

WATSONX_EMBEDDING_MODEL_NAMES = [metadata["name"] for metadata in WATSONX_EMBEDDING_MODELS_DETAILED]



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Unnecessary blank lines. There are three consecutive blank lines here, which violates PEP 8 style guide that recommends at most two blank lines between top-level definitions. Remove the extra blank lines.

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WATSONX_EMBEDDING_MODEL_NAMES = [metadata["name"] for metadata in WATSONX_DEFAULT_EMBEDDING_MODELS]

IBM_WATSONX_URLS = [
"https://us-south.ml.cloud.ibm.com",
Expand Down
76 changes: 64 additions & 12 deletions src/lfx/src/lfx/components/models_and_agents/embedding_model.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,12 @@
from typing import Any

from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames
from langchain_openai import OpenAIEmbeddings

from lfx.base.embeddings.model import LCEmbeddingsModel
from lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url
from lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES
from lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES
from lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_DEFAULT_EMBEDDING_MODELS, WATSONX_EMBEDDING_MODEL_NAMES

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src/lfx/src/lfx/components/models_and_agents/embedding_model.py:9:121: E501 Line too long (127 > 120)

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src/lfx/src/lfx/components/models_and_agents/embedding_model.py:9:65: F401 `lfx.base.models.watsonx_constants.WATSONX_DEFAULT_EMBEDDING_MODELS` imported but unused
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Import of 'WATSONX_DEFAULT_EMBEDDING_MODELS' is not used.

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from lfx.field_typing import Embeddings
from lfx.io import (
BoolInput,
Expand All @@ -19,6 +20,7 @@
from lfx.log.logger import logger
from lfx.schema.dotdict import dotdict
from lfx.utils.util import transform_localhost_url
import requests

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src/lfx/src/lfx/components/models_and_agents/embedding_model.py:1:1: I001 Import block is un-sorted or un-formatted
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Import statement import requests should follow PEP 8 convention and be placed at the top of the file with other imports. Currently it's placed after the local imports, which is inconsistent with the import ordering convention used in the codebase. It should be placed before the from imports from third-party libraries.

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# Ollama API constants
HTTP_STATUS_OK = 200
Expand Down Expand Up @@ -77,6 +79,8 @@
options=OPENAI_EMBEDDING_MODEL_NAMES,
value=OPENAI_EMBEDDING_MODEL_NAMES[0],
info="Select the embedding model to use",
real_time_refresh=True,
refresh_button=True,
),
SecretStrInput(
name="api_key",
Expand Down Expand Up @@ -110,7 +114,38 @@
advanced=True,
info="Additional keyword arguments to pass to the model.",
),
IntInput(
name="truncate_input_tokens",
display_name="Truncate Input Tokens",
advanced=True,
value=200,
show=False,
),
BoolInput(
name="input_text",
display_name="Include the original text in the output",
value=True,
advanced=True,
show=False,
),
]
@staticmethod
def fetch_ibm_models(base_url: str) -> list[str]:
"""Fetch available models from the watsonx.ai API."""
try:
endpoint = f"{base_url}/ml/v1/foundation_model_specs"
params = {
"version": "2024-09-16",
"filters": "function_embedding,!lifecycle_withdrawn:and",
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The filter syntax "function_embedding,!lifecycle_withdrawn:and" has an unusual :and suffix at the end. Comparing with the similar implementation in language_model.py (line 57), which uses "function_text_chat,!lifecycle_withdrawn" without the :and suffix, this appears to be inconsistent. Consider removing the :and suffix or verifying the correct filter syntax with the IBM watsonx.ai API documentation.

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}
response = requests.get(endpoint, params=params, timeout=10)
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The API request to fetch IBM models is made without authentication. This endpoint likely requires authentication but the request doesn't include any API key or credentials. Consider adding authentication headers or verifying if this endpoint is intended to be publicly accessible.

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response.raise_for_status()
data = response.json()
models = [model["model_id"] for model in data.get("resources", [])]
return sorted(models)
except Exception: # noqa: BLE001
logger.exception("Error fetching models")
return WATSONX_EMBEDDING_MODEL_NAMES

def build_embeddings(self) -> Embeddings:
provider = self.provider
Expand Down Expand Up @@ -188,15 +223,26 @@
msg = "Project ID is required for IBM watsonx.ai provider"
raise ValueError(msg)

from ibm_watsonx_ai import APIClient, Credentials

credentials = Credentials(
api_key=self.api_key,
url=base_url_ibm_watsonx or "https://us-south.ml.cloud.ibm.com",
)

api_client = APIClient(credentials)

params = {
"model_id": model,
"url": base_url_ibm_watsonx or "https://us-south.ml.cloud.ibm.com",
"apikey": api_key,
EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,
EmbedTextParamsMetaNames.RETURN_OPTIONS: {"input_text": self.input_text},
}

params["project_id"] = project_id

return WatsonxEmbeddings(**params)
return WatsonxEmbeddings(
model_id=model,
params=params,
watsonx_client=api_client,
project_id=project_id,
)

msg = f"Unknown provider: {provider}"
raise ValueError(msg)
Expand All @@ -217,7 +263,8 @@
build_config["ollama_base_url"]["show"] = False
build_config["project_id"]["show"] = False
build_config["base_url_ibm_watsonx"]["show"] = False

build_config["truncate_input_tokens"]["show"] = False
build_config["input_text"]["show"] = False
elif field_value == "Ollama":
build_config["ollama_base_url"]["show"] = True

Expand All @@ -238,7 +285,8 @@
else:
build_config["model"]["options"] = []
build_config["model"]["value"] = ""

build_config["truncate_input_tokens"]["show"] = False
build_config["input_text"]["show"] = False
build_config["api_key"]["display_name"] = "API Key (Optional)"
build_config["api_key"]["required"] = False
build_config["api_key"]["show"] = False
Expand All @@ -247,16 +295,20 @@
build_config["base_url_ibm_watsonx"]["show"] = False

elif field_value == "IBM watsonx.ai":
build_config["model"]["options"] = WATSONX_EMBEDDING_MODEL_NAMES
build_config["model"]["value"] = WATSONX_EMBEDDING_MODEL_NAMES[0]
build_config["model"]["options"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)
build_config["model"]["value"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)[0]
Comment on lines +302 to +303
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The fetch_ibm_models method is called twice with the same base_url parameter on consecutive lines. This results in duplicate API requests. Consider storing the result in a variable and reusing it:

ibm_models = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)
build_config["model"]["options"] = ibm_models
build_config["model"]["value"] = ibm_models[0]

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Potential IndexError if fetch_ibm_models returns an empty list. The code accesses [0] without checking if the list is non-empty. Consider adding a check or providing a fallback value:

ibm_models = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)
build_config["model"]["options"] = ibm_models
build_config["model"]["value"] = ibm_models[0] if ibm_models else WATSONX_EMBEDDING_MODEL_NAMES[0]

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build_config["api_key"]["display_name"] = "IBM watsonx.ai API Key"
build_config["api_key"]["required"] = True
build_config["api_key"]["show"] = True
build_config["api_base"]["show"] = False
build_config["ollama_base_url"]["show"] = False
build_config["base_url_ibm_watsonx"]["show"] = True
build_config["project_id"]["show"] = True

build_config["truncate_input_tokens"]["show"] = True
build_config["input_text"]["show"] = True
elif field_name == "base_url_ibm_watsonx":
build_config["model"]["options"] = self.fetch_ibm_models(base_url=field_value)
build_config["model"]["value"] = self.fetch_ibm_models(base_url=field_value)[0]
Comment on lines +314 to +315
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The fetch_ibm_models method is called twice with the same field_value parameter on consecutive lines. This results in duplicate API requests. Consider storing the result in a variable and reusing it:

ibm_models = self.fetch_ibm_models(base_url=field_value)
build_config["model"]["options"] = ibm_models
build_config["model"]["value"] = ibm_models[0]

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Potential IndexError if fetch_ibm_models returns an empty list. The code accesses [0] without checking if the list is non-empty. Consider adding a check or providing a fallback value:

ibm_models = self.fetch_ibm_models(base_url=field_value)
build_config["model"]["options"] = ibm_models
build_config["model"]["value"] = ibm_models[0] if ibm_models else WATSONX_EMBEDDING_MODEL_NAMES[0]

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elif field_name == "ollama_base_url":
# # Refresh Ollama models when base URL changes
# if hasattr(self, "provider") and self.provider == "Ollama":
Expand Down
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