Add GroqLogo and GroqIcon components (langflow-ai#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

docs/docs/migration/global-variables.mdx

@@ -99,6 +99,7 @@ The default list at the moment is:
 - AZURE_OPENAI_API_VERSION
 - COHERE_API_KEY
 - GOOGLE_API_KEY
+- GROQ_API_KEY
 - HUGGINGFACEHUB_API_TOKEN
 - OPENAI_API_KEY
 - PINECONE_API_KEY

pyproject.toml

@@ -83,6 +83,7 @@ langchain-astradb = "^0.1.0"
 langchain-openai = "^0.1.1"
 zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
 langchain-google-vertexai = "^1.0.3"
+langchain-groq = "^0.1.3"
 langchain-pinecone = "^0.1.0"
 
 [tool.poetry.group.dev.dependencies]

src/backend/base/langflow/alembic/versions/6e7b581b5648_fix_nullable.py

@@ -22,7 +22,7 @@
 def upgrade() -> None:
     conn = op.get_bind()
     inspector = Inspector.from_engine(conn)  # type: ignore
-    table_names = inspector.get_table_names()
+    # table_names = inspector.get_table_names()
     # ### commands auto generated by Alembic - please adjust! ###
     columns = inspector.get_columns("apikey")
     column_names = {column["name"]: column for column in columns}
@@ -42,7 +42,7 @@ def upgrade() -> None:
 def downgrade() -> None:
     conn = op.get_bind()
     inspector = Inspector.from_engine(conn)  # type: ignore
-    table_names = inspector.get_table_names()
+    # table_names = inspector.get_table_names()
     columns = inspector.get_columns("apikey")
     column_names = {column["name"]: column for column in columns}
     # ### commands auto generated by Alembic - please adjust! ###

src/backend/base/langflow/base/models/groq_constants.py

@@ -0,0 +1 @@
+MODEL_NAMES = ["llama3-8b-8192", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it"]

src/backend/base/langflow/components/model_specs/GroqModelSpecs.py

@@ -0,0 +1,86 @@
+from typing import Optional
+
+from langchain_groq import ChatGroq
+from pydantic.v1 import SecretStr
+
+from langflow.base.constants import STREAM_INFO_TEXT
+from langflow.base.models.groq_constants import MODEL_NAMES
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import BaseLanguageModel
+
+
+class GroqModelSpecs(LCModelComponent):
+    display_name: str = "Groq"
+    description: str = "Generate text using Groq."
+    icon = "Groq"
+
+    field_order = [
+        "groq_api_key",
+        "model",
+        "max_output_tokens",
+        "temperature",
+        "top_k",
+        "top_p",
+        "n",
+        "input_value",
+        "system_message",
+        "stream",
+    ]
+
+    def build_config(self):
+        return {
+            "groq_api_key": {
+                "display_name": "Groq API Key",
+                "info": "API key for the Groq API.",
+                "password": True,
+            },
+            "groq_api_base": {
+                "display_name": "Groq API Base",
+                "info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
+                "advanced": True,
+            },
+            "max_tokens": {
+                "display_name": "Max Output Tokens",
+                "info": "The maximum number of tokens to generate.",
+                "advanced": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
+            },
+            "n": {
+                "display_name": "N",
+                "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.",
+                "advanced": True,
+            },
+            "model_name": {
+                "display_name": "Model",
+                "info": "The name of the model to use. Supported examples: gemini-pro",
+                "options": MODEL_NAMES,
+            },
+            "stream": {
+                "display_name": "Stream",
+                "info": STREAM_INFO_TEXT,
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        groq_api_key: str,
+        model_name: str,
+        groq_api_base: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        temperature: float = 0.1,
+        n: Optional[int] = 1,
+        stream: bool = False,
+    ) -> BaseLanguageModel:
+        return ChatGroq(
+            model_name=model_name,
+            max_tokens=max_tokens or None,  # type: ignore
+            temperature=temperature,
+            groq_api_base=groq_api_base,
+            n=n or 1,
+            groq_api_key=SecretStr(groq_api_key),
+            streaming=stream,
+        )

src/backend/base/langflow/components/models/GroqModel.py

@@ -0,0 +1,95 @@
+from typing import Optional
+
+from langchain_groq import ChatGroq
+from langflow.base.models.groq_constants import MODEL_NAMES
+from pydantic.v1 import SecretStr
+
+from langflow.base.constants import STREAM_INFO_TEXT
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Text
+
+
+class GroqModel(LCModelComponent):
+    display_name: str = "Groq"
+    description: str = "Generate text using Groq."
+    icon = "Groq"
+
+    field_order = [
+        "groq_api_key",
+        "model",
+        "max_output_tokens",
+        "temperature",
+        "top_k",
+        "top_p",
+        "n",
+        "input_value",
+        "system_message",
+        "stream",
+    ]
+
+    def build_config(self):
+        return {
+            "groq_api_key": {
+                "display_name": "Groq API Key",
+                "info": "API key for the Groq API.",
+                "password": True,
+            },
+            "groq_api_base": {
+                "display_name": "Groq API Base",
+                "info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
+                "advanced": True,
+            },
+            "max_tokens": {
+                "display_name": "Max Output Tokens",
+                "info": "The maximum number of tokens to generate.",
+                "advanced": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
+            },
+            "n": {
+                "display_name": "N",
+                "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.",
+                "advanced": True,
+            },
+            "model_name": {
+                "display_name": "Model",
+                "info": "The name of the model to use. Supported examples: gemini-pro",
+                "options": MODEL_NAMES,
+            },
+            "input_value": {"display_name": "Input", "info": "The input to the model."},
+            "stream": {
+                "display_name": "Stream",
+                "info": STREAM_INFO_TEXT,
+                "advanced": True,
+            },
+            "system_message": {
+                "display_name": "System Message",
+                "info": "System message to pass to the model.",
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        groq_api_key: str,
+        model_name: str,
+        input_value: Text,
+        groq_api_base: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        temperature: float = 0.1,
+        n: Optional[int] = 1,
+        stream: bool = False,
+        system_message: Optional[str] = None,
+    ) -> Text:
+        output = ChatGroq(
+            model_name=model_name,
+            max_tokens=max_tokens or None,  # type: ignore
+            temperature=temperature,
+            groq_api_base=groq_api_base,
+            n=n or 1,
+            groq_api_key=SecretStr(groq_api_key),
+            streaming=stream,
+        )
+        return self.get_chat_result(output, stream, input_value, system_message)

src/backend/base/langflow/services/settings/constants.py

@@ -12,6 +12,7 @@
     "ASTRA_DB_APPLICATION_TOKEN",
     "ASTRA_DB_API_ENDPOINT",
     "COHERE_API_KEY",
+    "GROQ_API_KEY",
     "HUGGINGFACEHUB_API_TOKEN",
     "PINECONE_API_KEY",
     "SEARCHAPI_API_KEY",