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Clarifai : Added Clarifai as LLM and embedding model provider. (#1311)

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Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>
This commit is contained in:
mogith-pn
2024-06-17 21:18:18 +05:30
committed by GitHub
parent 4547d870af
commit 5acaae5f56
12 changed files with 579 additions and 2 deletions
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12
configs/clarifai.yaml Normal file
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@@ -0,0 +1,12 @@
llm:
provider: clarifai
config:
model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
model_kwargs:
temperature: 0.5
max_tokens: 1000
embedder:
provider: clarifai
config:
model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"

48
docs/components/embedding-models.mdx
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@@ -16,6 +16,7 @@ Embedchain supports several embedding models from the following providers:
<Card title="NVIDIA AI" href="#nvidia-ai"></Card>
<Card title="Cohere" href="#cohere"></Card>
<Card title="Ollama" href="#ollama"></Card>
<Card title="Clarifai" href="#clarifai"></Card>
</CardGroup>
## OpenAI
@@ -385,4 +386,51 @@ embedder:
model: 'all-minilm:latest'
```
</CodeGroup>
## Clarifai
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[clarifai]'
```
set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.
Now you are all set with exploring Embedchain.
<CodeGroup>
```python main.py
import os
from embedchain import App
os.environ["CLARIFAI_PAT"] = "XXX"
# load llm and embedder configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
#Now let's add some data.
app.add("https://www.forbes.com/profile/elon-musk")
#Query the app
response = app.query("what college degrees does elon musk have?")
```
Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) to explore all the State of the Art embedding models available to use.
For passing LLM model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.
```yaml config.yaml
llm:
provider: clarifai
config:
model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
model_kwargs:
temperature: 0.5
max_tokens: 1000
embedder:
provider: clarifai
config:
model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
```
</CodeGroup>

49
docs/components/llms.mdx
View File

@@ -15,6 +15,7 @@ Embedchain comes with built-in support for various popular large language models
<Card title="Together" href="#together"></Card>
<Card title="Ollama" href="#ollama"></Card>
<Card title="vLLM" href="#vllm"></Card>
<Card title="Clarifai" href="#clarifai"></Card>
<Card title="GPT4All" href="#gpt4all"></Card>
<Card title="JinaChat" href="#jinachat"></Card>
<Card title="Hugging Face" href="#hugging-face"></Card>
@@ -385,6 +386,54 @@ llm:
</CodeGroup>
## Clarifai
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[clarifai]'
```
set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.
Now you are all set with exploring Embedchain.
<CodeGroup>
```python main.py
import os
from embedchain import App
os.environ["CLARIFAI_PAT"] = "XXX"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
#Now let's add some data.
app.add("https://www.forbes.com/profile/elon-musk")
#Query the app
response = app.query("what college degrees does elon musk have?")
```
Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) to browse various State-of-the-Art LLM models for your use case.
For passing model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.
```yaml config.yaml
llm:
provider: clarifai
config:
model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
model_kwargs:
temperature: 0.5
max_tokens: 1000
embedder:
provider: clarifai
config:
model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
```
</CodeGroup>
## GPT4ALL
Install related dependencies using the following command:

52
embedchain/embedder/clarifai.py Normal file
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@@ -0,0 +1,52 @@
import os
from typing import Optional, Union
from embedchain.config import BaseEmbedderConfig
from embedchain.embedder.base import BaseEmbedder
from chromadb import EmbeddingFunction, Embeddings
class ClarifaiEmbeddingFunction(EmbeddingFunction):
def __init__(self, config: BaseEmbedderConfig) -> None:
super().__init__()
try:
from clarifai.client.model import Model
from clarifai.client.input import Inputs
except ModuleNotFoundError:
raise ModuleNotFoundError(
"The required dependencies for ClarifaiEmbeddingFunction are not installed."
'Please install with `pip install --upgrade "embedchain[clarifai]"`'
) from None
self.config = config
self.api_key = config.api_key or os.getenv("CLARIFAI_PAT")
self.model = config.model
self.model_obj = Model(url=self.model, pat=self.api_key)
self.input_obj = Inputs(pat=self.api_key)
def __call__(self, input: Union[str, list[str]]) -> Embeddings:
if isinstance(input, str):
input = [input]
batch_size = 32
embeddings = []
try:
for i in range(0, len(input), batch_size):
batch = input[i : i + batch_size]
input_batch = [
self.input_obj.get_text_input(input_id=str(id), raw_text=inp) for id, inp in enumerate(batch)
]
response = self.model_obj.predict(input_batch)
embeddings.extend([list(output.data.embeddings[0].vector) for output in response.outputs])
except Exception as e:
print(f"Predict failed, exception: {e}")
return embeddings
class ClarifaiEmbedder(BaseEmbedder):
def __init__(self, config: Optional[BaseEmbedderConfig] = None):
super().__init__(config)
embedding_func = ClarifaiEmbeddingFunction(config=self.config)
self.set_embedding_fn(embedding_fn=embedding_func)

3
embedchain/factory.py
View File

@@ -23,6 +23,7 @@ class LlmFactory:
"google": "embedchain.llm.google.GoogleLlm",
"aws_bedrock": "embedchain.llm.aws_bedrock.AWSBedrockLlm",
"mistralai": "embedchain.llm.mistralai.MistralAILlm",
"clarifai": "embedchain.llm.clarifai.ClarifaiLlm",
"groq": "embedchain.llm.groq.GroqLlm",
"nvidia": "embedchain.llm.nvidia.NvidiaLlm",
"vllm": "embedchain.llm.vllm.VLLM",
@@ -56,6 +57,7 @@ class EmbedderFactory:
"vertexai": "embedchain.embedder.vertexai.VertexAIEmbedder",
"google": "embedchain.embedder.google.GoogleAIEmbedder",
"mistralai": "embedchain.embedder.mistralai.MistralAIEmbedder",
"clarifai": "embedchain.embedder.clarifai.ClarifaiEmbedder",
"nvidia": "embedchain.embedder.nvidia.NvidiaEmbedder",
"cohere": "embedchain.embedder.cohere.CohereEmbedder",
"ollama": "embedchain.embedder.ollama.OllamaEmbedder",
@@ -65,6 +67,7 @@ class EmbedderFactory:
"google": "embedchain.config.embedder.google.GoogleAIEmbedderConfig",
"gpt4all": "embedchain.config.embedder.base.BaseEmbedderConfig",
"huggingface": "embedchain.config.embedder.base.BaseEmbedderConfig",
"clarifai": "embedchain.config.embedder.base.BaseEmbedderConfig",
"openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
"ollama": "embedchain.config.embedder.ollama.OllamaEmbedderConfig",
}

47
embedchain/llm/clarifai.py Normal file
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@@ -0,0 +1,47 @@
import logging
import os
from typing import Optional
from embedchain.config import BaseLlmConfig
from embedchain.helpers.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
@register_deserializable
class ClarifaiLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config=config)
if not self.config.api_key and "CLARIFAI_PAT" not in os.environ:
raise ValueError("Please set the CLARIFAI_PAT environment variable.")
def get_llm_model_answer(self, prompt):
return self._get_answer(prompt=prompt, config=self.config)
@staticmethod
def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
try:
from clarifai.client.model import Model
except ModuleNotFoundError:
raise ModuleNotFoundError(
"The required dependencies for Clarifai are not installed."
'Please install with `pip install --upgrade "embedchain[clarifai]"`'
) from None
model_name = config.model
logging.info(f"Using clarifai LLM model: {model_name}")
api_key = config.api_key or os.getenv("CLARIFAI_PAT")
model = Model(url=model_name, pat=api_key)
params = config.model_kwargs
try:
(params := {}) if config.model_kwargs is None else config.model_kwargs
predict_response = model.predict_by_bytes(
bytes(prompt, "utf-8"),
input_type="text",
inference_params=params,
)
text = predict_response.outputs[0].data.text.raw
return text
except Exception as e:
logging.error(f"Predict failed, exception: {e}")

3
embedchain/utils/misc.py
View File

@@ -414,6 +414,7 @@ def validate_config(config_data):
"google",
"aws_bedrock",
"mistralai",
"clarifai",
"vllm",
"groq",
"nvidia",
@@ -458,6 +459,7 @@ def validate_config(config_data):
"azure_openai",
"google",
"mistralai",
"clarifai",
"nvidia",
"ollama",
"cohere",
@@ -482,6 +484,7 @@ def validate_config(config_data):
"azure_openai",
"google",
"mistralai",
"clarifai",
"nvidia",
"ollama",
),

2
embedchain/vectordb/qdrant.py
View File

@@ -251,4 +251,4 @@ class QdrantDB(BaseVectorDB):
def delete(self, where: dict):
db_filter = self._generate_query(where)
self.client.delete(collection_name=self.collection_name, points_selector=db_filter)
self.client.delete(collection_name=self.collection_name, points_selector=db_filter)

135
notebooks/clarifai.ipynb Normal file
View File

@@ -0,0 +1,135 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cookbook for using Clarifai LLM and Embedders with Embedchain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step-1: Install embedchain-clarifai package"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install embedchain[clarifai]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step-2: Set Clarifai PAT as env variable.\n",
"Sign-up to [Clarifai](https://clarifai.com/signup?utm_source=clarifai_home&utm_medium=direct&) platform and you can obtain `CLARIFAI_PAT` by following this [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/).\n",
"\n",
"optionally you can also pass `api_key` in config of llm/embedder class."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from embedchain import App\n",
"\n",
"os.environ[\"CLARIFAI_PAT\"]=\"xxx\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step-3 Create embedchain app using clarifai LLM and embedder and define your config.\n",
"\n",
"Browse through Clarifai community page to get the URL of different [LLM](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) and [embedding](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22input_fields%22%2C%22value%22%3A%5B%22text%22%5D%7D%2C%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) models available."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use model_kwargs to pass all model specific parameters for inference.\n",
"app = App.from_config(config={\n",
" \"llm\": {\n",
" \"provider\": \"clarifai\",\n",
" \"config\": {\n",
" \"model\": \"https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct\",\n",
" \"model_kwargs\": {\n",
" \"temperature\": 0.5,\n",
" \"max_tokens\": 1000\n",
" }\n",
" }\n",
" },\n",
" \"embedder\": {\n",
" \"provider\": \"clarifai\",\n",
" \"config\": {\n",
" \"model\": \"https://clarifai.com/openai/embed/models/text-embedding-ada\",\n",
" }\n",
"}\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step-4: Add data sources to your app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"app.add(\"https://www.forbes.com/profile/elon-musk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step-5: All set. Now start asking questions related to your data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"while(True):\n",
" question = input(\"Enter question: \")\n",
" if question in ['q', 'exit', 'quit']:\n",
" break\n",
" answer = app.query(question)\n",
" print(answer)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "v1",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

206
poetry.lock generated
View File

@@ -854,6 +854,49 @@ typer = ">=0.9.0"
typing-extensions = ">=4.5.0"
uvicorn = {version = ">=0.18.3", extras = ["standard"]}
[[package]]
name = "clarifai"
version = "10.3.2"
description = "Clarifai Python SDK"
optional = true
python-versions = ">=3.8"
files = [
{file = "clarifai-10.3.2-py3-none-any.whl", hash = "sha256:53a788af293875138a249af3334286799fa1e8dd0290e34c1da46fb522b5003e"},
{file = "clarifai-10.3.2.tar.gz", hash = "sha256:3ac864f67d8d294535ecaa79cbb3236ac1d8d2ff8c6319865f83c260cefa7f50"},
]
[package.dependencies]
clarifai-grpc = ">=10.2.3,<10.3.0"
inquirerpy = "0.3.4"
numpy = ">=1.22.0"
Pillow = ">=9.5.0"
PyYAML = ">=6.0.1"
rich = ">=13.4.2"
schema = ">=0.7.5"
tabulate = ">=0.9.0"
tqdm = ">=4.65.0"
tritonclient = ">=2.34.0"
[package.extras]
all = ["pycocotools (==2.0.6)"]
[[package]]
name = "clarifai-grpc"
version = "10.2.3"
description = "Clarifai gRPC API Client"
optional = true
python-versions = ">=3.8"
files = [
{file = "clarifai-grpc-10.2.3.tar.gz", hash = "sha256:b03f87997a5ab2f810b046ad2a19c2d14260fa7bd4ce410d81c817f03e899202"},
{file = "clarifai_grpc-10.2.3-py3-none-any.whl", hash = "sha256:da24eb816ac93fa10cd5ec8114683ea7215ef31197e2601f92e88dc3a0566f43"},
]
[package.dependencies]
googleapis-common-protos = ">=1.53.0"
grpcio = ">=1.44.0"
protobuf = ">=3.20.3"
requests = ">=2.25.1"
[[package]]
name = "click"
version = "8.1.7"
@@ -2574,6 +2617,24 @@ files = [
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
]
[[package]]
name = "inquirerpy"
version = "0.3.4"
description = "Python port of Inquirer.js (A collection of common interactive command-line user interfaces)"
optional = true
python-versions = ">=3.7,<4.0"
files = [
{file = "InquirerPy-0.3.4-py3-none-any.whl", hash = "sha256:c65fdfbac1fa00e3ee4fb10679f4d3ed7a012abf4833910e63c295827fe2a7d4"},
{file = "InquirerPy-0.3.4.tar.gz", hash = "sha256:89d2ada0111f337483cb41ae31073108b2ec1e618a49d7110b0d7ade89fc197e"},
]
[package.dependencies]
pfzy = ">=0.3.1,<0.4.0"
prompt-toolkit = ">=3.0.1,<4.0.0"
[package.extras]
docs = ["Sphinx (>=4.1.2,<5.0.0)", "furo (>=2021.8.17-beta.43,<2022.0.0)", "myst-parser (>=0.15.1,<0.16.0)", "sphinx-autobuild (>=2021.3.14,<2022.0.0)", "sphinx-copybutton (>=0.4.0,<0.5.0)"]
[[package]]
name = "intel-openmp"
version = "2021.4.0"
@@ -4300,6 +4361,20 @@ files = [
{file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
]
[[package]]
name = "pfzy"
version = "0.3.4"
description = "Python port of the fzy fuzzy string matching algorithm"
optional = true
python-versions = ">=3.7,<4.0"
files = [
{file = "pfzy-0.3.4-py3-none-any.whl", hash = "sha256:5f50d5b2b3207fa72e7ec0ef08372ef652685470974a107d0d4999fc5a903a96"},
{file = "pfzy-0.3.4.tar.gz", hash = "sha256:717ea765dd10b63618e7298b2d98efd819e0b30cd5905c9707223dceeb94b3f1"},
]
[package.extras]
docs = ["Sphinx (>=4.1.2,<5.0.0)", "furo (>=2021.8.17-beta.43,<2022.0.0)", "myst-parser (>=0.15.1,<0.16.0)", "sphinx-autobuild (>=2021.3.14,<2022.0.0)", "sphinx-copybutton (>=0.4.0,<0.5.0)"]
[[package]]
name = "pillow"
version = "10.3.0"
@@ -4488,6 +4563,20 @@ nodeenv = ">=0.11.1"
pyyaml = ">=5.1"
virtualenv = ">=20.10.0"
[[package]]
name = "prompt-toolkit"
version = "3.0.47"
description = "Library for building powerful interactive command lines in Python"
optional = true
python-versions = ">=3.7.0"
files = [
{file = "prompt_toolkit-3.0.47-py3-none-any.whl", hash = "sha256:0d7bfa67001d5e39d02c224b663abc33687405033a8c422d0d675a5a13361d10"},
{file = "prompt_toolkit-3.0.47.tar.gz", hash = "sha256:1e1b29cb58080b1e69f207c893a1a7bf16d127a5c30c9d17a25a5d77792e5360"},
]
[package.dependencies]
wcwidth = "*"
[[package]]
name = "proto-plus"
version = "1.23.0"
@@ -5227,6 +5316,88 @@ files = [
[package.extras]
cli = ["click (>=5.0)"]
[[package]]
name = "python-rapidjson"
version = "1.17"
description = "Python wrapper around rapidjson"
optional = true
python-versions = ">=3.6"
files = [
{file = "python-rapidjson-1.17.tar.gz", hash = "sha256:95a111da29d996af8549f8b32ec701dab3af2ab7c6cd9c79540391ecb05f20c8"},
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{file = "python_rapidjson-1.17-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac66ea04b450e8a9914a7de410a1d01a1011c11d5e72a3296a7d14e2636e3bd5"},
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]
[[package]]
name = "pytube"
version = "15.0.0"
@@ -6675,6 +6846,28 @@ build = ["cmake (>=3.20)", "lit"]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
[[package]]
name = "tritonclient"
version = "2.41.1"
description = "Python client library and utilities for communicating with Triton Inference Server"
optional = true
python-versions = "*"
files = [
{file = "tritonclient-2.41.1-py3-none-any.whl", hash = "sha256:91cb234331a7145c407cea605caf9eecbd4276ddc5f085ddd5a6dcab64e5e70b"},
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]
[package.dependencies]
numpy = ">=1.19.1"
python-rapidjson = ">=0.9.1"
[package.extras]
all = ["aiohttp (>=3.8.1,<4.0.0)", "cuda-python", "geventhttpclient (>=1.4.4,<=2.0.2)", "grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<5)", "python-rapidjson (>=0.9.1)"]
cuda = ["cuda-python"]
grpc = ["grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<5)", "python-rapidjson (>=0.9.1)"]
http = ["aiohttp (>=3.8.1,<4.0.0)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"]
[[package]]
name = "twilio"
version = "8.13.0"
@@ -7121,6 +7314,17 @@ files = [
[package.dependencies]
anyio = ">=3.0.0"
[[package]]
name = "wcwidth"
version = "0.2.13"
description = "Measures the displayed width of unicode strings in a terminal"
optional = true
python-versions = "*"
files = [
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{file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
]
[[package]]
name = "weaviate-client"
version = "3.26.2"
@@ -7520,4 +7724,4 @@ youtube = ["youtube-transcript-api", "yt_dlp"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<=3.13"
content-hash = "c63e9ce659b1148ca8685bbd81ac65adc07d8cc75abe28856525f6a247ce7856"
content-hash = "757481b96e9620d643c4ef77131c295ad22541784d5c6395d2c6da366d7ae7d3"

1
pyproject.toml
View File

@@ -119,6 +119,7 @@ twilio = { version = "^8.5.0", optional = true }
fastapi-poe = { version = "0.0.16", optional = true }
discord = { version = "^2.3.2", optional = true }
slack-sdk = { version = "3.21.3", optional = true }
clarifai = { version = "^10.0.1", optional = true }
cohere = { version = "^5.3", optional = true }
together = { version = "^0.2.8", optional = true }
weaviate-client = { version = "^3.24.1", optional = true }

23
tests/llm/test_clarifai.py Normal file
View File

@@ -0,0 +1,23 @@
import pytest
from embedchain.config import BaseLlmConfig
from embedchain.llm.clarifai import ClarifaiLlm
@pytest.fixture
def clarifai_llm_config(monkeypatch):
monkeypatch.setenv("CLARIFAI_PAT","test_api_key")
config = BaseLlmConfig(
model="https://clarifai.com/openai/chat-completion/models/GPT-4",
model_kwargs={"temperature": 0.7, "max_tokens": 100},
)
yield config
monkeypatch.delenv("CLARIFAI_PAT")
def test_clarifai__llm_get_llm_model_answer(clarifai_llm_config, mocker):
mocker.patch("embedchain.llm.clarifai.ClarifaiLlm._get_answer", return_value="Test answer")
llm = ClarifaiLlm(clarifai_llm_config)
answer = llm.get_llm_model_answer("Test query")
assert answer == "Test answer"