community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes

libs/community/langchain_community/tools/sql_database/__init__.py

@@ -0,0 +1 @@
+"""Tools for interacting with a SQL database."""

libs/community/langchain_community/tools/sql_database/prompt.py

@@ -0,0 +1,18 @@
+# flake8: noqa
+QUERY_CHECKER = """
+{query}
+Double check the {dialect} query above for common mistakes, including:
+- Using NOT IN with NULL values
+- Using UNION when UNION ALL should have been used
+- Using BETWEEN for exclusive ranges
+- Data type mismatch in predicates
+- Properly quoting identifiers
+- Using the correct number of arguments for functions
+- Casting to the correct data type
+- Using the proper columns for joins
+
+If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.
+
+Output the final SQL query only.
+
+SQL Query: """

libs/community/langchain_community/tools/sql_database/tool.py

@@ -0,0 +1,135 @@
+# flake8: noqa
+"""Tools for interacting with a SQL database."""
+from typing import Any, Dict, Optional
+
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
+
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain_core.prompts import PromptTemplate
+from langchain_community.utilities.sql_database import SQLDatabase
+from langchain_core.tools import BaseTool
+from langchain_community.tools.sql_database.prompt import QUERY_CHECKER
+
+
+class BaseSQLDatabaseTool(BaseModel):
+    """Base tool for interacting with a SQL database."""
+
+    db: SQLDatabase = Field(exclude=True)
+
+    class Config(BaseTool.Config):
+        pass
+
+
+class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for querying a SQL database."""
+
+    name: str = "sql_db_query"
+    description: str = """
+    Input to this tool is a detailed and correct SQL query, output is a result from the database.
+    If the query is not correct, an error message will be returned.
+    If an error is returned, rewrite the query, check the query, and try again.
+    """
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Execute the query, return the results or an error message."""
+        return self.db.run_no_throw(query)
+
+
+class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for getting metadata about a SQL database."""
+
+    name: str = "sql_db_schema"
+    description: str = """
+    Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.    
+
+    Example Input: "table1, table2, table3"
+    """
+
+    def _run(
+        self,
+        table_names: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get the schema for tables in a comma-separated list."""
+        return self.db.get_table_info_no_throw(
+            [t.strip() for t in table_names.split(",")]
+        )
+
+
+class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for getting tables names."""
+
+    name: str = "sql_db_list_tables"
+    description: str = "Input is an empty string, output is a comma separated list of tables in the database."
+
+    def _run(
+        self,
+        tool_input: str = "",
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get the schema for a specific table."""
+        return ", ".join(self.db.get_usable_table_names())
+
+
+class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
+    """Use an LLM to check if a query is correct.
+    Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
+
+    template: str = QUERY_CHECKER
+    llm: BaseLanguageModel
+    llm_chain: Any = Field(init=False)
+    name: str = "sql_db_query_checker"
+    description: str = """
+    Use this tool to double check if your query is correct before executing it.
+    Always use this tool before executing a query with sql_db_query!
+    """
+
+    @root_validator(pre=True)
+    def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        if "llm_chain" not in values:
+            from langchain.chains.llm import LLMChain
+
+            values["llm_chain"] = LLMChain(
+                llm=values.get("llm"),
+                prompt=PromptTemplate(
+                    template=QUERY_CHECKER, input_variables=["dialect", "query"]
+                ),
+            )
+
+        if values["llm_chain"].prompt.input_variables != ["dialect", "query"]:
+            raise ValueError(
+                "LLM chain for QueryCheckerTool must have input variables ['query', 'dialect']"
+            )
+
+        return values
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the LLM to check the query."""
+        return self.llm_chain.predict(
+            query=query,
+            dialect=self.db.dialect,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        return await self.llm_chain.apredict(
+            query=query,
+            dialect=self.db.dialect,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )