File size: 8,084 Bytes
3d87c18 e448d98 3d87c18 e448d98 3d87c18 e448d98 3d87c18 e448d98 3d87c18 e448d98 3d87c18 e448d98 0614630 e448d98 0614630 c76addc e448d98 c76addc e448d98 c76addc e448d98 c76addc 0614630 e448d98 0614630 e448d98 0614630 e448d98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
from .stats_tools import stats_tools
from .chart_tools import chart_tools
def tools_call(session_hash, data_source, titles):
titles_string = (titles[:625] + '..') if len(titles) > 625 else titles
tools_calls = {
'file_upload' : [
{
"type": "function",
"function": {
"name": "sqlite_query_func",
"description": f"""This is a tool useful to query a SQLite table called 'data_source' with the following Columns: {titles_string}.
There may also be more columns in the table if the number of columns is too large to process.
This function also saves the results of the query to csv file called query.csv.""",
"parameters": {
"type": "object",
"properties": {
"queries": {
"type": "array",
"description": "The query to use in the search. Infer this from the user's message. It should be a question or a statement",
"items": {
"type": "string",
}
}
},
"required": ["queries"],
},
},
},
],
'sql' : [
{
"type": "function",
"function": {
"name": "sql_query_func",
"description": f"""This is a tool useful to query a PostgreSQL database with the following tables, {titles_string}.
There may also be more tables in the database if the number of tables is too large to process.
This function also saves the results of the query to csv file called query.csv.""",
"parameters": {
"type": "object",
"properties": {
"queries": {
"type": "array",
"description": "The PostgreSQL query to use in the search. Infer this from the user's message. It should be a question or a statement",
"items": {
"type": "string",
}
}
},
"required": ["queries"],
},
},
},
],
'doc_db' : [
{
"type": "function",
"function": {
"name": "doc_db_query_func",
"description": f"""This is a tool useful to build an aggregation pipeline to query a MongoDB NoSQL document database with the following collections, {titles_string}.
There may also be more collections in the database if the number of tables is too large to process.
This function also saves the results of the query to a csv file called query.csv.""",
"parameters": {
"type": "object",
"properties": {
"aggregation_pipeline": {
"type": "string",
"description": "The MongoDB aggregation pipeline to use in the search. Infer this from the user's message. It should be a question or a statement."
},
"db_collection": {
"type": "string",
"description": "The MongoDB collection to use in the search. Infer this from the user's message. It should be a question or a statement.",
}
},
"required": ["aggregation_pipeline","db_collection"],
},
},
},
],
'graphql' : [
{
"type": "function",
"function": {
"name": "graphql_query_func",
"description": f"""This is a tool useful to build a GraphQL query for a GraphQL API endpoint with the following types, {titles_string}.
There may also be more types in the GraphQL endpoint if the number of types is too large to process.
This function also saves the results of the query to a csv file called query.csv.""",
"parameters": {
"type": "object",
"properties": {
"graphql_query": {
"type": "string",
"description": "The GraphQL query to use in the search. Infer this from the user's message. It should be a question or a statement."
}
},
"required": ["graphql_query"],
},
},
},
{
"type": "function",
"function": {
"name": "graphql_schema_query",
"description": f"""This is a tool useful to query a GraphQL type and receive back information about its schema. This is useful because
the GraphQL introspection query is too large to be ingested all at once and this allows us to query the schema one type at a time to
view it in manageable bites. You may realize after viewing the schema, that the type you selected was not appropriate for the question
you are attempting answer. You may then query additional types to find the appropriate types to use for your GraphQL API query.""",
"parameters": {
"type": "object",
"properties": {
"graphql_type": {
"type": "string",
"description": "The GraphQL type that we want to view the schema of in order to make the proper query with our graphql_query_func. Infer this from the user's message. It should be a question or a statement."
}
},
"required": ["graphql_type"],
},
},
},
{
"type": "function",
"function": {
"name": "graphql_csv_query",
"description": f"""This is a tool useful to SQL query our query.csv file that is generated from our GraphQL query. This is useful in a situation
where the results of the GraphQL query need additional querying to answer the user question. The query.csv file is converted to a Pandas dataframe
and we query that dataframe with SQL on a table called 'query' before converting it back to a csv file.""",
"parameters": {
"type": "object",
"properties": {
"csv_query": {
"type": "string",
"description": "The pandas dataframe SQL query to use in the search. The table that we query is named 'query'. Infer this from the user's message. It should be a question or a statement"
}
},
"required": ["csv_query"],
},
},
},
]
}
tools = tools_calls[data_source]
tools.extend(chart_tools)
tools.extend(stats_tools)
return tools |