Merge remote-tracking branch 'origin/dev' into v2

poetry.lock

@@ -5008,6 +5008,19 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
+[[package]]
+name = "pgvector"
+version = "0.2.3"
+description = "pgvector support for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pgvector-0.2.3-py2.py3-none-any.whl", hash = "sha256:9d53dc01138ecc7c9aca64e4680cfa9edf4c38f9cb8ed7098317871fdd211824"},
+]
+
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "pillow"
 version = "10.1.0"
@@ -8909,4 +8922,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.11"
-content-hash = "856876d1b3d9e11c74e0d68207e4a31634e83e8d6d1daf629fc5f29c9804f1a6"
+content-hash = "b547f3825111dda53ecdd226cf6ddece5e7df1e06e77d65a258b21eb88629c27"

pyproject.toml

@@ -100,6 +100,7 @@ jq = "^1.6.0"
 boto3 = "^1.28.63"
 numexpr = "^2.8.6"
 qianfan = "0.0.5"
+pgvector = "^0.2.3"
 
 [tool.poetry.group.dev.dependencies]
 types-redis = "^4.6.0.5"

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -20,10 +20,11 @@ class ConversationalAgent(CustomComponent):
 
     def build_config(self):
         openai_function_models = [
-            "gpt-3.5-turbo-0613",
+            "gpt-4-1106-preview",
-            "gpt-3.5-turbo-16k-0613",
+            "gpt-3.5-turbo",
-            "gpt-4-0613",
+            "gpt-3.5-turbo-16k",
-            "gpt-4-32k-0613",
+            "gpt-4",
+            "gpt-4-32k",
         ]
         return {
             "tools": {"is_list": True, "display_name": "Tools"},

src/backend/langflow/components/vectorstores/Redis.py

@@ -0,0 +1,64 @@
+from typing import Optional
+from langflow import CustomComponent
+
+from langchain.vectorstores.redis import Redis
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.embeddings.base import Embeddings
+
+
+class RedisComponent(CustomComponent):
+    """
+    A custom component for implementing a Vector Store using Redis.
+    """
+
+    display_name: str = "Redis"
+    description: str = "Implementation of Vector Store using Redis"
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "index_name": {"display_name": "Index Name", "value": "your_index"},
+            "code": {"show": False, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "redis_server_url": {
+                "display_name": "Redis Server Connection String",
+                "advanced": False,
+            },
+            "redis_index_name": {"display_name": "Redis Index", "advanced": False},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        redis_server_url: str,
+        redis_index_name: str,
+        documents: Optional[Document] = None,
+    ) -> VectorStore:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - redis_index_name (str): The name of the Redis index.
+        - redis_server_url (str): The URL for the Redis server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+
+        return Redis.from_documents(
+            documents=documents,  # type: ignore
+            embedding=embedding,
+            redis_url=redis_server_url,
+            index_name=redis_index_name,
+        )

src/backend/langflow/components/vectorstores/pgvector.py

@@ -0,0 +1,69 @@
+from typing import Optional
+from langflow import CustomComponent
+
+from langchain.vectorstores.pgvector import PGVector
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.embeddings.base import Embeddings
+
+
+class PostgresqlVectorComponent(CustomComponent):
+    """
+    A custom component for implementing a Vector Store using PostgreSQL.
+    """
+
+    display_name: str = "PGVector"
+    description: str = "Implementation of Vector Store using PostgreSQL"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
+    )
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "index_name": {"display_name": "Index Name", "value": "your_index"},
+            "code": {"show": True, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "pg_server_url": {
+                "display_name": "PostgreSQL Server Connection String",
+                "advanced": False,
+            },
+            "collection_name": {"display_name": "Table", "advanced": False},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        pg_server_url: str,
+        collection_name: str,
+        documents: Optional[Document] = None,
+    ) -> VectorStore:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - collection_name (str): The name of the PG table.
+        - pg_server_url (str): The URL for the PG server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+
+        try:
+            return PGVector.from_documents(
+                embedding=embedding,
+                documents=documents,
+                collection_name=collection_name,
+                connection_string=pg_server_url,
+            )
+        except Exception as e:
+            raise RuntimeError(f"Failed to build PGVector: {e}")

src/backend/langflow/utils/constants.py

@@ -13,6 +13,7 @@ CHAT_OPENAI_MODELS = [
     "gpt-3.5-turbo-16k",
 ]
 
+
 ANTHROPIC_MODELS = [
     # largest model, ideal for a wide range of more complex tasks.
     "claude-v1",