Merge pull request #1 from Haoming-jpg/Haoming-tool.py

Haoming tool.py

libs/langchain/langchain/tools/mongo_database/__init__.py

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

libs/langchain/langchain/tools/mongo_database/prompt.py

@@ -0,0 +1,16 @@
+# flake8: noqa
+QUERY_CHECKER = """
+{query}
+Double check the {client} query above for common mistakes, including:
+- Improper use of $nin operator with null values
+- Using $merge instead of $concat for combining arrays
+- Incorrect use of $not or $ne for exclusive ranges
+- Data type mismatch in query conditions
+- Properly referencing field names in queries
+- Using the correct syntax for aggregation functions
+- Casting to the correct BSON data type
+- Using the proper fields for $lookup in aggregations
+
+If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.
+
+MongoDB Query: """

libs/langchain/langchain/tools/mongo_database/tool.py

@@ -0,0 +1,132 @@
+# flake8: noqa
+"""Tools for interacting with a MongoDB database."""
+from typing import Any, Dict, Optional
+
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
+
+from langchain.schema.language_model import BaseLanguageModel
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.chains.llm import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.utilities.mongo_database import MongoDBDatabase
+from langchain.tools.base import BaseTool
+from langchain.tools.mongo_database.prompt import QUERY_CHECKER
+
+
+class BaseMongoDBTool(BaseModel):
+    """Base tool for interacting with a MongoDB database."""
+
+    db: MongoDBDatabase = Field(exclude=True)
+
+    class Config(BaseTool.Config):
+        pass
+
+
+class QueryMongoDBTool(BaseMongoDBTool, BaseTool):
+    """Tool for querying a MongoDB database."""
+
+    name: str = "mongo_db_query"
+    description: str = """
+    Input to this tool is a detailed and correct MongoDB 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(query)
+
+
+class InfoMongoDBTool(BaseMongoDBTool, BaseTool):
+    """Tool for getting metadata about a MongoDB database."""
+
+    name: str = "mongo_db_schema"
+    description: str = """
+    Input to this tool is a comma-separated list of collections, output is the schema and sample documents for those collections.    
+
+    Example Input: "collection1, collection2, collection3"
+    """
+
+    def _run(
+        self,
+        collection_names: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get information about specified collections."""
+        return self.db.get_document_info(collection_names=collection_names)
+
+
+class ListMongoDBTool(BaseMongoDBTool, BaseTool):
+    """Tool for listing collections in a MongoDB database."""
+
+    name: str = "mongo_db_list"
+    description: str = """
+    Output of this tool is a list of collections in the database.
+    """
+
+    def _run(
+        self,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get a list of collections in the database."""
+        return self.db.collection_info()
+
+
+class QueryMongoDBCheckerTool(BaseMongoDBTool, BaseTool):
+    """Use an LLM to check if a query is correct"""
+
+    template: str = QUERY_CHECKER
+    llm: BaseLanguageModel
+    llm_chain: LLMChain = Field(init=False)
+    name: str = "mongo_db_query_checker"
+    description: str = """
+    Use this tool to double check a MongoDB query for common mistakes.
+    """
+
+    @root_validator(pre=True)
+    def _init_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Initialize the LLM chain."""
+        if "llm_chain" not in values:
+            values["llm_chain"] = LLMChain(
+                llm=values.get("llm"),
+                prompt=PromptTemplate(
+                    template=QUERY_CHECKER, input_variables=["client", "query"]
+                ),
+            )
+
+        if values["llm_chain"].prompt.input_variables != ["client", "query"]:
+            raise ValueError(
+                "LLM chain for QueryCheckerTool must have input variables ['query', 'client']"
+            )
+
+        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,
+            client=self.db.client,
+            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,
+            client=self.db.client,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )

libs/langchain/langchain/tools/sql_database/tool.py

@@ -2,15 +2,15 @@
 """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.pydantic_v1 import BaseModel, Extra, Field, root_validator
 
-from langchain_core.language_models import BaseLanguageModel
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
 from langchain.chains.llm import LLMChain
-from langchain_core.prompts import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.utilities.sql_database import SQLDatabase
 from langchain.tools.base import BaseTool
 from langchain.tools.sql_database.prompt import QUERY_CHECKER