update

etc.jl

@@ -1,293 +0,0 @@
-
-
-
-using LibPQ, JSON, Graphs, DataFrames
-config = JSON.parsefile("./appconfig.json")
-host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-port = parse(Int, _port)
-dbname = "winedb"
-user = config["externalservice"]["sommpanion_db"]["user"]
-password = config["externalservice"]["sommpanion_db"]["password"]
-pg_conn_str = "host=$host_url port=$port dbname=$dbname user=$user password=$password"
-db_connection = LibPQ.Connection(pg_conn_str)
-
-
-
-
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-using LibPQ
-using DataFrames
-
-"""
-    extract_column_metadata(pg_conn_str::String) -> DataFrame
-
-Queries PostgreSQL system catalogs to extract a rich semantic text map of every 
-column in the database. Returns a DataFrame designed for vector embedding generation.
-"""
-function extract_column_metadata(pg_conn_str::String)
-    conn = LibPQ.Connection(pg_conn_str)
-    
-    # This direct SQL query pulls the column specifications along with column-level descriptions
-    query = """
-        SELECT 
-            c.relname AS table_name,
-            a.attname AS column_name,
-            format_type(a.atttypid, a.atttypmod) AS data_type,
-            COALESCE(d.description, '') AS column_description,
-            CASE WHEN pk.contype = 'p' THEN true ELSE false END AS is_primary_key,
-            CASE WHEN fk.contype = 'f' THEN true ELSE false END AS is_foreign_key
-        FROM pg_attribute a
-        JOIN pg_class c ON c.oid = a.attrelid
-        JOIN pg_namespace n ON n.oid = c.relnamespace
-        -- Join to fetch column comments/descriptions
-        LEFT JOIN pg_description d ON d.objoid = c.oid AND d.objsubid = a.attnum
-        -- Check if column is part of a Primary Key
-        LEFT JOIN pg_constraint pk ON pk.conrelid = c.oid 
-            AND pk.contype = 'p' 
-            AND a.attnum = ANY(pk.conkey)
-        -- Check if column is part of a Foreign Key
-        LEFT JOIN pg_constraint fk ON fk.conrelid = c.oid 
-            AND fk.contype = 'f' 
-            AND a.attnum = ANY(fk.conkey)
-        WHERE 
-            n.nspname = 'public'          -- Only user schemas
-            AND c.relkind = 'r'            -- Only standard tables
-            AND a.attnum > 0               -- Skip system hidden columns
-            AND NOT a.attisdropped;        -- Skip dropped columns
-    """
-    
-    try
-        # Execute and format into a clean DataFrame
-        result = execute(conn, query)
-        df = DataFrame(result)
-        
-        # Create a unique document ID for each vector row
-        df.vector_id = ["col_\$(row.table_name)_\$(row.column_name)" for row in eachrow(df)]
-        
-        return df
-    finally
-        close(conn)
-    end
-end
-
-
-
-"""
-    generate_embedding_payloads(df::DataFrame) -> Vector{Dict}
-
-Transforms the metadata DataFrame into structured text strings optimal for 
-vector space mapping.
-"""
-function generate_embedding_payloads(df::DataFrame)
-    payloads = Dict[]
-    
-    for row in eachrow(df)
-        # 1. Build a rich text description summarizing the column's role
-        text_payload = "Table: $(row.table_name) | Column: $(row.column_name) | Type: $(row.data_type)"
-        
-        if row.is_primary_key
-            text_payload *= " [PRIMARY KEY]"
-        end
-        if row.is_foreign_key
-            text_payload *= " [FOREIGN KEY RELATIONAL LINK]"
-        end
-        
-        # Append business descriptions if they exist in the database comments
-        if !isempty(strip(row.column_description))
-            text_payload *= " | Description: $(row.column_description)"
-        else
-            text_payload *= " | Description: Represents $(row.column_name) data fields within the $(row.table_name) architecture."
-        end
-        
-        # 2. Package everything neatly to be passed to your vector store client
-        push!(payloads, Dict(
-            "id" => row.vector_id,
-            "text_content" => text_payload,
-            "metadata" => Dict(
-                "table" => row.table_name,
-                "column" => row.column_name,
-                "type" => row.data_type
-            )
-        ))
-    end
-    
-    return payloads
-end
-
-
-"""
-    resolve_semantic_cluster(vector_hits::Vector{String}, g::SimpleGraph, table_to_id::Dict{String, Int}, id_to_table::Dict{Int, String}) -> Vector{String}
-
-Takes a scattered array of semantically matched tables from Stage 1, navigates 
-the undirected network structure, and isolates the minimum interconnected subgraph 
-required to weave ALL hits into a single valid SQL query.
-"""
-function resolve_semantic_cluster(
-    vector_hits::Vector{String}, 
-    g::SimpleGraph, 
-    table_to_id::Dict{String, Int}, 
-    id_to_table::Dict{Int, String}
-)
-    # Filter out hits that don't exist in our actual database graph mapping
-    valid_node_ids = Int[]
-    for hit in vector_hits
-        if haskey(table_to_id, hit)
-            push!(valid_node_ids, table_to_id[hit])
-        else
-            @warn "Vector hit '$hit' does not map to an existing database table."
-        end
-    end
-    
-    unique!(valid_node_ids)
-    
-    # Edge Case Handlers
-    if isempty(valid_node_ids)
-        return String[]
-    elseif length(valid_node_ids) == 1
-        return [id_to_table[valid_node_ids[1]]]
-    end
-
-    # The Isolated Subgraph Set to build our final context
-    schema_subgraph_nodes = Set{Int}()
-    
-    # Phase A: Select an initial anchor component. We use the highest-ranked vector hit.
-    anchor_node = valid_node_ids[1]
-    push!(schema_subgraph_nodes, anchor_node)
-    
-    # Phase B: Sequentially route paths to all other semantic coordinates
-    for target_node in valid_node_ids[2:end]
-        # Skip if an earlier loop trajectory already naturally absorbed this table
-        if target_node in schema_subgraph_nodes
-            continue
-        end
-        
-        # Calculate the shortest path tree from the CURRENT state of our subgraph
-        # We find the shortest path from the target back to ANY node currently in our tree
-        shortest_paths = dijkstra_shortest_paths(g, target_node)
-        
-        # Find which node currently in our subgraph is closest to the target node
-        closest_subgraph_node = 0
-        min_distance = Inf
-        
-        for subgraph_node in schema_subgraph_nodes
-            dist = shortest_paths.dists[subgraph_node]
-            if dist < min_distance
-                min_distance = dist
-                closest_subgraph_node = subgraph_node
-            end
-        end
-        
-        # Reconstruct the path from the target node to the closest point on our existing tree
-        if closest_subgraph_node != 0
-            curr = closest_subgraph_node
-            while curr != 0
-                push!(schema_subgraph_nodes, curr)
-                curr = shortest_paths.parents[curr]
-                if curr == target_node
-                    push!(schema_subgraph_nodes, target_node)
-                    break
-                end
-            end
-        end
-    end
-    
-    # Map the unique structural nodes back to clean table names
-    return [id_to_table[node_id] for node_id in schema_subgraph_nodes]
-end
-
-function get_embedding(nats_conn::NATS.Connection, text::AbstractArray{String})
-  documents_dict = Dict("documents" => text)
-  payloads = [("documents", documents_dict, "dictionary")]
-  _, msg_envelope_json_str = msghandler.smartpack(
-    config["externalservice"]["servicesloadbalancer"]["nats"], 
-    payloads;   
-    msg_purpose="embedding",  
-    broker_url=config["nats_server_info"]["url"],
-    fileserver_url=config["externalservice"]["fileserver"]["url"])
-
-  reply = NATS.request(nats_conn, 
-                      config["externalservice"]["servicesloadbalancer"]["nats"], 
-                      msg_envelope_json_str, timeout=120)
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  embedding_response = incoming_env["payloads"][1][2]
-
-  return embedding_response
-end
-
-nats_conn  = NATS.connect(config["nats_server_info"]["url"])
-
-# Run the extractor
-metadata_df = extract_column_metadata(pg_conn_str)
-embedding_ready = generate_embedding_payloads(metadata_df)
-
-println(embedding_ready[1]["text_content"])
-# Output: "Table: join_table | Column: seller_id | Type: integer [PRIMARY KEY] [FOREIGN KEY RELATIONAL LINK] | Description: Links unique sellers to their corresponding product items."
-
-# use only text content
-embedding_ready_2 = [i["text_content"] for i in embedding_ready]
-table_embedding = get_embedding(nats_conn, embedding_ready_2)
-
-user_question = 
-  """
-  Retrieves ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency", "image_url", "retailer_name", "retailer_id"] of wines that match the following criteria - {wine_name: Montrachet Grand Cru, winery: Domaine Jacques Prieur, region: Montrachet, country: France, , retailer_name: Yiem Wines Ltd, retailerid: f54eab6b-7650-4448-b009-c53f3efbcc3b}
-  """
-user_question_embedding = get_embedding(nats_conn, [user_question])
-
-
-using Distances
-# similarity = 1 - Distances.cosine_dist(Float64.(table_embedding["data"][1]["embedding"]), 
-#                             Float64.(user_question_embedding["data"][1]["embedding"])
-#                             )
-user_question_embedding = Float64.(user_question_embedding["data"][1]["embedding"])
-user_question_similarity = []
-for i in table_embedding["data"]
-  i_data = i["embedding"]
-  i_float = Float64.(i_data)
-  r = 1 - Distances.cosine_dist(i_float, user_question_embedding)
-  push!(user_question_similarity, r)
-end
-
-new_df = hcat(metadata_df, DataFrame(user_question_similarity = user_question_similarity))
-sorted_df = sort(new_df, :user_question_similarity, rev=true) # sort max to min
-
-# top 20 of sorted_df get this tables
-vector_hits = ["retailer_wine", "wine", "wine_food", "retailer"]
-
-g, id_to_table, table_to_id = harvest_undirected_schema_graph(pg_conn_str)
-
-# tables that I should put schema in LLM context
-optimized_context = resolve_semantic_cluster(vector_hits, g, table_to_id, id_to_table)
-
-
-
-function find_related_tables_for_user_question(question::String)
-  metadata_df = extract_column_metadata(pg_conn_str)
-  embedding_ready = generate_embedding_payloads(metadata_df)
-
-  # use only text content
-  embedding_ready_2 = [i["text_content"] for i in embedding_ready]
-  table_embedding = get_embedding(nats_conn, embedding_ready_2)
-  user_question_embedding = Float64.(user_question_embedding["data"][1]["embedding"])
-  user_question_similarity = []
-  for i in table_embedding["data"]
-    i_data = i["embedding"]
-    i_float = Float64.(i_data)
-    r = 1 - Distances.cosine_dist(i_float, user_question_embedding)
-    push!(user_question_similarity, r)
-  end
-
-  new_df = hcat(metadata_df, DataFrame(user_question_similarity = user_question_similarity))
-  sorted_df = sort(new_df, :user_question_similarity, rev=true) # sort max to min
-
-  #WORKING extract top 20 rows of sorted_df to get tables that related to user question
-  vector_hits = ["retailer_wine", "wine", "wine_food", "retailer"]
-
-  g, id_to_table, table_to_id = harvest_undirected_schema_graph(pg_conn_str)
-
-  # tables that I should put schema in LLM context
-  return optimized_context = resolve_semantic_cluster(vector_hits, g, table_to_id, id_to_table)
-end

src/llmUtil.jl

@@ -2,7 +2,8 @@ module llmUtil
 
 export formatLLMtext, extractthink, checkAgentResponse_JSON, clean_json_response,
         extract_column_metadata, generate_embedding_payloads, resolve_semantic_cluster,
-        harvest_entity_catalog, resolve_entity, harvest_db_undirected_schema_graph
+        harvest_entity_catalog, resolve_entity, harvest_db_undirected_schema_graph,
+        get_db_table_schema
 
 using UUIDs, JSON, Dates, DataFrames, StringDistances, Graphs, LibPQ
 using ..util
@@ -964,6 +965,55 @@ function resolve_entity(messy_input::String, catalog::Vector{String}; threshold=
 end
 
 
+function get_db_table_schema(pg_conn_str::String, table_name::String)::DataFrame
+  conn = LibPQ.Connection(pg_conn_str)
+  return get_db_table_schema(conn, table_name)
+end
+
+function get_db_table_schema(conn::LibPQ.Connection, table_name::String)::DataFrame
+  # This direct SQL query pulls the column specifications along with column-level descriptions
+  query = """
+        SELECT 
+            a.attname AS column_name,
+            format_type(a.atttypid, a.atttypmod) AS data_type,
+            COALESCE(d.description, '') AS column_comment,
+            CASE 
+                WHEN p.contype = 'p' THEN 'PRIMARY KEY'
+                WHEN p.contype = 'u' THEN 'UNIQUE'
+                WHEN p.contype = 'f' THEN 'FOREIGN KEY'
+                WHEN p.contype = 'c' THEN 'CHECK'
+                ELSE ''
+            END AS constraint_type,
+            COALESCE(p.conname, '') AS constraint_name
+        FROM 
+            pg_catalog.pg_attribute a
+        JOIN 
+            pg_catalog.pg_class c ON a.attrelid = c.oid
+        JOIN 
+            pg_catalog.pg_namespace n ON c.relnamespace = n.oid
+        LEFT JOIN 
+            pg_catalog.pg_description d ON d.objoid = c.oid AND d.objsubid = a.attnum
+        LEFT JOIN 
+            pg_catalog.pg_constraint p ON p.conrelid = c.oid AND a.attnum = ANY(p.conkey)
+        WHERE 
+            c.relname = '$table_name' -- <-- Put your table name here
+            AND n.nspname = 'public'      -- <-- Change schema if not 'public'
+            AND a.attnum > 0 
+            AND NOT a.attisdropped
+        ORDER BY 
+            a.attnum;
+    """
+  
+  try
+      # Execute and format into a clean DataFrame
+      result = LibPQ.execute(conn, query)
+      df = DataFrame(result)
+      
+      return df
+  finally
+      close(conn)
+  end
+end