fix: resolve ModuleNotFoundError for tools.tools

Root causes:
- tools/__init__.py was missing; created empty file to make tools/ a
proper Python package
- chart_tools.py and stats_tools.py had top-level `from haystack.tools
import Tool` imports, causing the entire module to fail to load if
that import path is unavailable, which surfaced as
ModuleNotFoundError: No module named 'tools.tools'
- Fixed by removing all Haystack imports from chart_tools.py and
stats_tools.py — they now export plain schema dicts only
- tools.py does the `from haystack.tools import Tool` import lazily
inside tools_call(), so any ImportError surfaces clearly at call
time rather than at module load
- Pinned haystack-ai>=2.7.0 in requirements.txt to ensure Tool is
available

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

requirements.txt

@@ -1,4 +1,4 @@
-haystack-ai
+haystack-ai>=2.7.0
 anthropic-haystack
 python-dotenv
 gradio

tools/chart_tools.py

@@ -1,16 +1,12 @@
-from haystack.tools import Tool
-
-_noop = lambda **kwargs: None
-
-chart_tools = [
-    Tool(
-        name="scatter_chart_generation_func",
-        description="""This is a scatter plot generation tool useful to generate scatter plots from queried data from our data source that we are querying.
+chart_tool_schemas = [
+    {
+        "name": "scatter_chart_generation_func",
+        "description": """This is a scatter plot generation tool useful to generate scatter plots from queried data from our data source that we are querying.
         The data values will come from the columns of our query.csv (the 'x' and 'y' values of each graph) file but the layout section of the plotly dictionary objects will be generated by you.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the scatter_chart_generation_func function in any way and always display the iframe fully to the user in the chat window. You can add your own text supplementary
         to it for context if desired.""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "data": {
@@ -78,16 +74,15 @@ chart_tools = [
             },
             "required": ["x_column", "y_column"]
         },
-        function=_noop
-    ),
-    Tool(
-        name="line_chart_generation_func",
-        description="""This is a line chart generation tool useful to generate line charts from queried data from our data source that we are querying.
+    },
+    {
+        "name": "line_chart_generation_func",
+        "description": """This is a line chart generation tool useful to generate line charts from queried data from our data source that we are querying.
         The data values will come from the columns of our query.csv (the 'x' and 'y' values of each graph) file but the layout section of the plotly dictionary objects will be generated by you.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the line_chart_generation_func function in any way and always display the iframe fully to the user in the chat window. You can add your own text supplementary
         to it for context if desired.""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "data": {
@@ -119,16 +114,15 @@ chart_tools = [
             },
             "required": ["x_column", "y_column", "layout"]
         },
-        function=_noop
-    ),
-    Tool(
-        name="bar_chart_generation_func",
-        description="""This is a bar chart generation tool useful to generate bar charts from queried data from our data source that we are querying.
+    },
+    {
+        "name": "bar_chart_generation_func",
+        "description": """This is a bar chart generation tool useful to generate bar charts from queried data from our data source that we are querying.
         The data values will come from the columns of our query.csv (the 'x' and 'y' values of each graph) file but the layout section of the plotly dictionary objects will be generated by you.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the bar_chart_generation_func function in any way and always display the iframe fully to the user in the chat window. You can add your own text supplementary
         to it for context if desired.""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "data": {
@@ -170,16 +164,15 @@ chart_tools = [
             },
             "required": ["x_column", "y_column", "layout"]
         },
-        function=_noop
-    ),
-    Tool(
-        name="pie_chart_generation_func",
-        description="""This is a pie chart generation tool useful to generate pie charts from queried data from our data source that we are querying.
+    },
+    {
+        "name": "pie_chart_generation_func",
+        "description": """This is a pie chart generation tool useful to generate pie charts from queried data from our data source that we are querying.
         The data values will come from the columns of our query.csv (the 'values' and 'names' values of each graph) file but the layout section of the plotly dictionary objects will be generated by you.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the pie_chart_generation_func function in any way and always display the iframe fully to the user in the chat window. You can add your own text supplementary
         to it for context if desired.""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "data": {
@@ -207,16 +200,15 @@ chart_tools = [
             },
             "required": ["values", "names", "layout"]
         },
-        function=_noop
-    ),
-    Tool(
-        name="histogram_generation_func",
-        description="""This is a histogram generation tool useful to generate histograms from queried data from our data source that we are querying.
+    },
+    {
+        "name": "histogram_generation_func",
+        "description": """This is a histogram generation tool useful to generate histograms from queried data from our data source that we are querying.
         The data values will come from the columns of our query.csv file but the layout section of the plotly dictionary objects will be generated by you.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the histogram_generation_func function in any way and always display the iframe fully to the user in the chat window. You can add your own text supplementary
         to it for context if desired.""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "data": {
@@ -261,16 +253,14 @@ chart_tools = [
             },
             "required": ["x_column"]
         },
-        function=_noop
-    ),
-    Tool(
-        name="table_generation_func",
-        description="""This is a table generation tool useful to format data as a table from queried data from our data source that we are querying.
+    },
+    {
+        "name": "table_generation_func",
+        "description": """This is a table generation tool useful to format data as a table from queried data from our data source that we are querying.
         Takes no parameters as it uses data queried in our query.csv file to build the table.
         Call this function after running our query and generating query.csv.
         Returns an iframe string which will be displayed inline in our chat window. Do not edit the iframe string returned
         from the table_generation_func function in any way and always display the iframe fully to the user in the chat window.""",
-        parameters={"type": "object", "properties": {}},
-        function=_noop
-    ),
+        "parameters": {"type": "object", "properties": {}},
+    },
 ]

tools/stats_tools.py

@@ -1,15 +1,11 @@
-from haystack.tools import Tool
-
-_noop = lambda **kwargs: None
-
-stats_tools = [
-    Tool(
-        name="regression_func",
-        description="""This a tool to calculate regressions on our data source that we are querying.
+stats_tool_schemas = [
+    {
+        "name": "regression_func",
+        "description": """This a tool to calculate regressions on our data source that we are querying.
         We can run queries with our 'sql_query_func' function and they will be available to use in this function via the query.csv file that is generated.
         Returns a dictionary of values that includes a regression_summary and a regression chart (which is an iframe displaying the
         linear regression in chart form and should be shown to the user).""",
-        parameters={
+        "parameters": {
             "type": "object",
             "properties": {
                 "independent_variables": {
@@ -33,6 +29,5 @@ stats_tools = [
             },
             "required": ["independent_variables", "dependent_variable"]
         },
-        function=_noop
-    )
+    }
 ]

tools/tools.py

@@ -1,20 +1,28 @@
-from haystack.tools import Tool
-from .stats_tools import stats_tools
-from .chart_tools import chart_tools
-
-_noop = lambda **kwargs: None
+from .stats_tools import stats_tool_schemas
+from .chart_tools import chart_tool_schemas
 
 def tools_call(session_hash, data_source, titles):
+    from haystack.tools import Tool
+
+    _noop = lambda **kwargs: None
+
+    def make_tool(schema):
+        return Tool(
+            name=schema["name"],
+            description=schema["description"],
+            parameters=schema["parameters"],
+            function=_noop,
+        )
 
     titles_string = (titles[:625] + '..') if len(titles) > 625 else titles
 
-    query_tools = {
-        'file_upload': Tool(
-            name="query_func",
-            description=f"""This is a tool useful to query a SQLite table called 'data_source' with the following Columns: {titles_string}.
+    query_tool_schemas = {
+        'file_upload': {
+            "name": "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={
+            "parameters": {
                 "type": "object",
                 "properties": {
                     "queries": {
@@ -24,14 +32,13 @@ def tools_call(session_hash, data_source, titles):
                 },
                 "required": ["queries"]
             },
-            function=_noop
-        ),
-        'sql': Tool(
-            name="query_func",
-            description=f"""This is a tool useful to query a PostgreSQL database with the following tables, {titles_string}.
+        },
+        'sql': {
+            "name": "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={
+            "parameters": {
                 "type": "object",
                 "properties": {
                     "queries": {
@@ -41,14 +48,13 @@ def tools_call(session_hash, data_source, titles):
                 },
                 "required": ["queries"]
             },
-            function=_noop
-        ),
-        'doc_db': Tool(
-            name="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}.
+        },
+        'doc_db': {
+            "name": "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 collections is too large to process.
             This function also saves the results of the query to a csv file called query.csv.""",
-            parameters={
+            "parameters": {
                 "type": "object",
                 "properties": {
                     "queries": {
@@ -62,15 +68,14 @@ def tools_call(session_hash, data_source, titles):
                 },
                 "required": ["queries", "db_collection"]
             },
-            function=_noop
-        ),
+        },
         'graphql': [
-            Tool(
-                name="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}.
+            {
+                "name": "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={
+                "parameters": {
                     "type": "object",
                     "properties": {
                         "queries": {
@@ -80,15 +85,14 @@ def tools_call(session_hash, data_source, titles):
                     },
                     "required": ["queries"]
                 },
-                function=_noop
-            ),
-            Tool(
-                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
+            },
+            {
+                "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={
+                "parameters": {
                     "type": "object",
                     "properties": {
                         "graphql_type": {
@@ -98,14 +102,13 @@ def tools_call(session_hash, data_source, titles):
                     },
                     "required": ["graphql_type"]
                 },
-                function=_noop
-            ),
-            Tool(
-                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
+            },
+            {
+                "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={
+                "parameters": {
                     "type": "object",
                     "properties": {
                         "csv_query": {
@@ -115,13 +118,13 @@ def tools_call(session_hash, data_source, titles):
                     },
                     "required": ["csv_query"]
                 },
-                function=_noop
-            ),
+            },
         ]
     }
 
-    source_tools = query_tools[data_source]
-    tools = source_tools if isinstance(source_tools, list) else [source_tools]
-    tools = tools + chart_tools + stats_tools
+    source_schemas = query_tool_schemas[data_source]
+    source_tools = [make_tool(s) for s in (source_schemas if isinstance(source_schemas, list) else [source_schemas])]
+    chart_tools = [make_tool(s) for s in chart_tool_schemas]
+    stats_tools = [make_tool(s) for s in stats_tool_schemas]
 
-    return tools
+    return source_tools + chart_tools + stats_tools