Files
crewAI/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py

160 lines
7.0 KiB
Python

"""IBM WatsonX embedding function implementation."""
from typing import cast
from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
from typing_extensions import Unpack
from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderConfig
class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
"""Embedding function for IBM WatsonX models."""
def __init__(self, **kwargs: Unpack[WatsonXProviderConfig]) -> None:
"""Initialize WatsonX embedding function.
Args:
**kwargs: Configuration parameters for WatsonX Embeddings and Credentials.
"""
super().__init__(**kwargs)
self._config = kwargs
@staticmethod
def name() -> str:
"""Return the name of the embedding function for ChromaDB compatibility."""
return "watsonx"
def __call__(self, input: Documents) -> Embeddings:
"""Generate embeddings for input documents.
Args:
input: List of documents to embed.
Returns:
List of embedding vectors.
"""
try:
import ibm_watsonx_ai.foundation_models as watson_models # type: ignore[import-not-found, import-untyped]
from ibm_watsonx_ai import (
Credentials, # type: ignore[import-not-found, import-untyped]
)
from ibm_watsonx_ai.metanames import ( # type: ignore[import-not-found, import-untyped]
EmbedTextParamsMetaNames as EmbedParams,
)
except ImportError as e:
raise ImportError(
"ibm-watsonx-ai is required for watsonx embeddings. "
"Install it with: uv add ibm-watsonx-ai"
) from e
if isinstance(input, str):
input = [input]
embeddings_config: dict = {
"model_id": self._config["model_id"],
}
if "params" in self._config and self._config["params"] is not None:
embeddings_config["params"] = self._config["params"]
if "project_id" in self._config and self._config["project_id"] is not None:
embeddings_config["project_id"] = self._config["project_id"]
if "space_id" in self._config and self._config["space_id"] is not None:
embeddings_config["space_id"] = self._config["space_id"]
if "api_client" in self._config and self._config["api_client"] is not None:
embeddings_config["api_client"] = self._config["api_client"]
if "verify" in self._config and self._config["verify"] is not None:
embeddings_config["verify"] = self._config["verify"]
if "persistent_connection" in self._config:
embeddings_config["persistent_connection"] = self._config[
"persistent_connection"
]
if "batch_size" in self._config:
embeddings_config["batch_size"] = self._config["batch_size"]
if "concurrency_limit" in self._config:
embeddings_config["concurrency_limit"] = self._config["concurrency_limit"]
if "max_retries" in self._config and self._config["max_retries"] is not None:
embeddings_config["max_retries"] = self._config["max_retries"]
if "delay_time" in self._config and self._config["delay_time"] is not None:
embeddings_config["delay_time"] = self._config["delay_time"]
if (
"retry_status_codes" in self._config
and self._config["retry_status_codes"] is not None
):
embeddings_config["retry_status_codes"] = self._config["retry_status_codes"]
if "credentials" in self._config and self._config["credentials"] is not None:
embeddings_config["credentials"] = self._config["credentials"]
else:
cred_config: dict = {}
if "url" in self._config and self._config["url"] is not None:
cred_config["url"] = self._config["url"]
if "api_key" in self._config and self._config["api_key"] is not None:
cred_config["api_key"] = self._config["api_key"]
if "name" in self._config and self._config["name"] is not None:
cred_config["name"] = self._config["name"]
if (
"iam_serviceid_crn" in self._config
and self._config["iam_serviceid_crn"] is not None
):
cred_config["iam_serviceid_crn"] = self._config["iam_serviceid_crn"]
if (
"trusted_profile_id" in self._config
and self._config["trusted_profile_id"] is not None
):
cred_config["trusted_profile_id"] = self._config["trusted_profile_id"]
if "token" in self._config and self._config["token"] is not None:
cred_config["token"] = self._config["token"]
if (
"projects_token" in self._config
and self._config["projects_token"] is not None
):
cred_config["projects_token"] = self._config["projects_token"]
if "username" in self._config and self._config["username"] is not None:
cred_config["username"] = self._config["username"]
if "password" in self._config and self._config["password"] is not None:
cred_config["password"] = self._config["password"]
if (
"instance_id" in self._config
and self._config["instance_id"] is not None
):
cred_config["instance_id"] = self._config["instance_id"]
if "version" in self._config and self._config["version"] is not None:
cred_config["version"] = self._config["version"]
if (
"bedrock_url" in self._config
and self._config["bedrock_url"] is not None
):
cred_config["bedrock_url"] = self._config["bedrock_url"]
if (
"platform_url" in self._config
and self._config["platform_url"] is not None
):
cred_config["platform_url"] = self._config["platform_url"]
if "proxies" in self._config and self._config["proxies"] is not None:
cred_config["proxies"] = self._config["proxies"]
if (
"verify" not in embeddings_config
and "verify" in self._config
and self._config["verify"] is not None
):
cred_config["verify"] = self._config["verify"]
if cred_config:
embeddings_config["credentials"] = Credentials(**cred_config)
if "params" not in embeddings_config:
embeddings_config["params"] = {
EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
EmbedParams.RETURN_OPTIONS: {"input_text": True},
}
embedding = watson_models.Embeddings(**embeddings_config)
try:
embeddings = embedding.embed_documents(input)
return cast(Embeddings, embeddings)
except Exception as e:
print(f"Error during WatsonX embedding: {e}")
raise