Add GroqLogo and GroqIcon components (#1853)
* Update package.json format command to include only specific directories * Add GroqLogo component and GroqIcon to the project * Update dependencies and add GroqModelSpecs component * Fix nullable column issue in langflow/alembic/versions/6e7b581b5648_fix_nullable.py * Add GroqModelSpecs component and update dependencies * Update GroqModelSpecs and GroqModel display names * chore: Add langchain-pinecone dependency and update constants.py
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
21be1268ae
commit
a037bf9978
14 changed files with 528 additions and 238 deletions
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@ -22,7 +22,7 @@ depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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conn = op.get_bind()
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inspector = Inspector.from_engine(conn) # type: ignore
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table_names = inspector.get_table_names()
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# table_names = inspector.get_table_names()
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# ### commands auto generated by Alembic - please adjust! ###
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columns = inspector.get_columns("apikey")
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column_names = {column["name"]: column for column in columns}
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@ -42,7 +42,7 @@ def upgrade() -> None:
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def downgrade() -> None:
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conn = op.get_bind()
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inspector = Inspector.from_engine(conn) # type: ignore
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table_names = inspector.get_table_names()
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# table_names = inspector.get_table_names()
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columns = inspector.get_columns("apikey")
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column_names = {column["name"]: column for column in columns}
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# ### commands auto generated by Alembic - please adjust! ###
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1
src/backend/base/langflow/base/models/groq_constants.py
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1
src/backend/base/langflow/base/models/groq_constants.py
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@ -0,0 +1 @@
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MODEL_NAMES = ["llama3-8b-8192", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it"]
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@ -0,0 +1,86 @@
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from typing import Optional
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from langchain_groq import ChatGroq
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from pydantic.v1 import SecretStr
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.groq_constants import MODEL_NAMES
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import BaseLanguageModel
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class GroqModelSpecs(LCModelComponent):
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display_name: str = "Groq"
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description: str = "Generate text using Groq."
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icon = "Groq"
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field_order = [
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"groq_api_key",
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"model",
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"max_output_tokens",
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"temperature",
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"top_k",
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"top_p",
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"n",
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"input_value",
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"system_message",
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"stream",
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]
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def build_config(self):
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return {
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"groq_api_key": {
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"display_name": "Groq API Key",
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"info": "API key for the Groq API.",
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"password": True,
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},
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"groq_api_base": {
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"display_name": "Groq API Base",
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"info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
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"advanced": True,
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},
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"max_tokens": {
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"display_name": "Max Output Tokens",
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"info": "The maximum number of tokens to generate.",
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"advanced": True,
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},
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"temperature": {
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"display_name": "Temperature",
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"info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
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},
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"n": {
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"display_name": "N",
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"info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
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"advanced": True,
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},
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"model_name": {
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"display_name": "Model",
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"info": "The name of the model to use. Supported examples: gemini-pro",
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"options": MODEL_NAMES,
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},
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"stream": {
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"display_name": "Stream",
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"info": STREAM_INFO_TEXT,
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"advanced": True,
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},
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}
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def build(
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self,
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groq_api_key: str,
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model_name: str,
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groq_api_base: Optional[str] = None,
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max_tokens: Optional[int] = None,
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temperature: float = 0.1,
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n: Optional[int] = 1,
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stream: bool = False,
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) -> BaseLanguageModel:
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return ChatGroq(
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model_name=model_name,
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max_tokens=max_tokens or None, # type: ignore
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temperature=temperature,
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groq_api_base=groq_api_base,
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n=n or 1,
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groq_api_key=SecretStr(groq_api_key),
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streaming=stream,
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)
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95
src/backend/base/langflow/components/models/GroqModel.py
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95
src/backend/base/langflow/components/models/GroqModel.py
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@ -0,0 +1,95 @@
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from typing import Optional
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from langchain_groq import ChatGroq
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from langflow.base.models.groq_constants import MODEL_NAMES
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from pydantic.v1 import SecretStr
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Text
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class GroqModel(LCModelComponent):
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display_name: str = "Groq"
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description: str = "Generate text using Groq."
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icon = "Groq"
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field_order = [
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"groq_api_key",
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"model",
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"max_output_tokens",
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"temperature",
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"top_k",
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"top_p",
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"n",
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"input_value",
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"system_message",
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"stream",
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]
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def build_config(self):
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return {
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"groq_api_key": {
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"display_name": "Groq API Key",
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"info": "API key for the Groq API.",
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"password": True,
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},
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"groq_api_base": {
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"display_name": "Groq API Base",
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"info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
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"advanced": True,
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},
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"max_tokens": {
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"display_name": "Max Output Tokens",
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"info": "The maximum number of tokens to generate.",
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"advanced": True,
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},
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"temperature": {
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"display_name": "Temperature",
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"info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
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},
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"n": {
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"display_name": "N",
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"info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
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"advanced": True,
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},
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"model_name": {
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"display_name": "Model",
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"info": "The name of the model to use. Supported examples: gemini-pro",
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"options": MODEL_NAMES,
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},
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"input_value": {"display_name": "Input", "info": "The input to the model."},
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"stream": {
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"display_name": "Stream",
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"info": STREAM_INFO_TEXT,
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"advanced": True,
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},
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"system_message": {
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"display_name": "System Message",
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"info": "System message to pass to the model.",
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"advanced": True,
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},
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}
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def build(
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self,
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groq_api_key: str,
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model_name: str,
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input_value: Text,
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groq_api_base: Optional[str] = None,
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max_tokens: Optional[int] = None,
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temperature: float = 0.1,
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n: Optional[int] = 1,
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stream: bool = False,
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system_message: Optional[str] = None,
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) -> Text:
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output = ChatGroq(
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model_name=model_name,
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max_tokens=max_tokens or None, # type: ignore
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temperature=temperature,
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groq_api_base=groq_api_base,
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n=n or 1,
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groq_api_key=SecretStr(groq_api_key),
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streaming=stream,
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)
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return self.get_chat_result(output, stream, input_value, system_message)
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@ -12,6 +12,7 @@ VARIABLES_TO_GET_FROM_ENVIRONMENT = [
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"ASTRA_DB_APPLICATION_TOKEN",
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"ASTRA_DB_API_ENDPOINT",
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"COHERE_API_KEY",
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"GROQ_API_KEY",
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"HUGGINGFACEHUB_API_TOKEN",
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"PINECONE_API_KEY",
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"SEARCHAPI_API_KEY",
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17
src/backend/base/poetry.lock
generated
17
src/backend/base/poetry.lock
generated
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@ -1090,13 +1090,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
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[[package]]
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name = "langchain-core"
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version = "0.1.51"
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version = "0.1.52"
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description = "Building applications with LLMs through composability"
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optional = false
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python-versions = "<4.0,>=3.8.1"
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files = [
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{file = "langchain_core-0.1.51-py3-none-any.whl", hash = "sha256:3058bdb04d43a8eaae2e249365fe2e8d0356a09c7b2c1afa1a8100f8888da4fa"},
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{file = "langchain_core-0.1.51.tar.gz", hash = "sha256:f7ea116f939be9e74c385baf95d6c84cd7a402b59c2c1893fc054bf98abbefc2"},
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{file = "langchain_core-0.1.52-py3-none-any.whl", hash = "sha256:62566749c92e8a1181c255c788548dc16dbc319d896cd6b9c95dc17af9b2a6db"},
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{file = "langchain_core-0.1.52.tar.gz", hash = "sha256:084c3fc452f5a6966c28ab3ec5dbc8b8d26fc3f63378073928f4e29d90b6393f"},
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]
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[package.dependencies]
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@ -2555,17 +2555,18 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7
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[[package]]
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name = "tenacity"
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version = "8.2.3"
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version = "8.3.0"
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description = "Retry code until it succeeds"
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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.8"
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files = [
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{file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
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{file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
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{file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
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{file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
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]
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[package.extras]
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doc = ["reno", "sphinx", "tornado (>=4.5)"]
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doc = ["reno", "sphinx"]
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test = ["pytest", "tornado (>=4.5)", "typeguard"]
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[[package]]
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name = "typer"
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