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📝 WalkthroughWalkthroughThis pull request introduces several new configuration and integration modules while refactoring existing code to use a generic “store” parameter instead of direct database connections. Enhancements include environment configuration for PostgreSQL and ChromaDB, updated chat and CLI functionalities with improved logging, new modules for LLM integration, advanced JSON processing, hierarchical retrieval, and comprehensive support for both vector store backends. Additional JSON data files, Docker configuration for ChromaDB, and updated documentation further expand the project’s capabilities. Changes
Sequence Diagram(s)sequenceDiagram
participant User as User
participant Main as Main App (CLI)
participant Store as Vector Store (Postgres/Chroma)
participant Query as Query Processor
participant LLM as LLM Integration
User->>Main: Enter query
Main->>Query: get_relevant_chunks(store, query)
Query-->>Main: Return relevant chunks
Main->>LLM: answer_query(store, query)
LLM-->>Main: Return response
Main->>User: Display answer
sequenceDiagram
participant File as JSON File
participant Parser as Advanced Processor/JSON Parser
participant Store as Vector Store (Postgres/Chroma)
File->>Parser: Load and parse JSON data
Parser->>Parser: Create hierarchical chunks & build index
Parser->>Store: Process chunks and store embeddings
Store-->>Parser: Acknowledge insertion
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Actionable comments posted: 9
🧹 Nitpick comments (34)
app/retrieval/embedding.py (2)
205-207: Consider a more robust way to detect store typeUsing
hasattr(store, 'cursor')to detect PostgreSQL connections is functional but potentially brittle if other objects also implement a cursor attribute. Consider using explicit type checking or a more specific attribute that's guaranteed to be unique to PostgreSQL connections.- # Determine if we're using PostgreSQL or ChromaDB - is_postgres = hasattr(store, 'cursor') + # Determine if we're using PostgreSQL or ChromaDB + # Check for specific PostgreSQL connection attributes or use isinstance if possible + is_postgres = hasattr(store, 'cursor') and hasattr(store, 'commit')
280-297: Consider moving import statement to the top of the fileImporting
query_chunksinside the function could be inefficient if the function is called frequently. Unless there's a risk of circular imports, consider moving this import to the top of the file.from typing import List, Dict, Optional import numpy as np from sentence_transformers import SentenceTransformer import json from app.utils.logging_config import get_logger +from app.storage.chroma import query_chunksThen remove the import from the function:
else: # ChromaDB implementation - from app.storage.chroma import query_chunks # Use ChromaDB's query function results = query_chunks(store, embedding_list, limit).env.sample (1)
1-14: Good configuration file structure with clear documentationThe environment configuration file is well-organized with clear sections for different backend services. The inclusion of comments for the
VECTOR_STOREoptions is helpful for developers.Consider enforcing valid options in code
While the comment for
VECTOR_STORElists valid options, it would be good to validate this in code to prevent runtime errors with invalid values.Consider adding validation in the code that uses this value:
def get_vector_store_type(): store_type = os.getenv("VECTOR_STORE", "postgres").lower() valid_types = ["postgres", "chroma"] if store_type not in valid_types: logger.warning(f"Invalid vector store type '{store_type}'. Defaulting to 'postgres'") return "postgres" return store_typedocker-compose.yml (1)
1-19: Docker configuration for ChromaDB looks goodThe Docker Compose configuration correctly sets up a ChromaDB service with appropriate environment variables, port mapping, and volume configuration. The health check implementation is a good practice for ensuring service availability.
Add a newline at the end of the file to comply with POSIX standards:
volumes: - chroma_data: + chroma_data: +🧰 Tools
🪛 YAMLlint (1.35.1)
[error] 19-19: no new line character at the end of file
(new-line-at-end-of-file)
app/retrieval/postgres.py (2)
7-10: Remove unused importsThere are several imports that are declared but never used in the code.
-import json -import logging -from typing import List, Dict, Any, Optional +import json +from typing import List, Dict from app.utils.logging_config import get_logger from app.retrieval.embedding import get_embedding🧰 Tools
🪛 Ruff (0.8.2)
8-8:
loggingimported but unusedRemove unused import:
logging(F401)
9-9:
typing.Anyimported but unusedRemove unused import
(F401)
9-9:
typing.Optionalimported but unusedRemove unused import
(F401)
15-69: Vector similarity search implementation looks goodThe implementation of
get_relevant_chunkseffectively uses PostgreSQL's vector similarity capabilities to retrieve relevant content. Good error handling and logging are in place.You can simplify the SQL query execution by only passing the embedding string once, since it's used with the same parameter marker:
- cur.execute(query_sql, [embedding_str, embedding_str, limit]) + cur.execute(query_sql, (embedding_str, embedding_str, limit))Also, consider using a tuple instead of a list for SQL parameters as it's slightly more efficient for this use case.
app/retrieval/chroma.py (2)
7-9: Remove unused importsSeveral imports are declared but not used in the code.
-import logging -from typing import List, Dict, Any, Optional +from typing import List, Dict from app.utils.logging_config import get_logger🧰 Tools
🪛 Ruff (0.8.2)
7-7:
loggingimported but unusedRemove unused import:
logging(F401)
8-8:
typing.Anyimported but unusedRemove unused import
(F401)
8-8:
typing.Optionalimported but unusedRemove unused import
(F401)
13-43: Good implementation of ChromaDB search with circular import handlingThe
search_chunksfunction correctly handles the search operation for ChromaDB collections, including proper embedding conversion and error handling. The approach to avoid circular imports by importing inside the function is pragmatic.Consider refactoring the application architecture to avoid circular imports entirely. While importing inside a function works, it can make the code harder to understand and maintain in the long run.
app/processing/json_processor.py (4)
14-14: Remove unused imports fromtyping.
AnyandUnionare imported but never used; removing them will keep the imports clean and avoid confusion.-from typing import List, Dict, Any, Union +from typing import List, Dict🧰 Tools
🪛 Ruff (0.8.2)
14-14:
typing.Anyimported but unusedRemove unused import
(F401)
14-14:
typing.Unionimported but unusedRemove unused import
(F401)
15-15: Remove the unusedPathimport.
pathlib.Pathis never used in this file; removing it will prevent warnings and maintain cleanliness.-from pathlib import Path🧰 Tools
🪛 Ruff (0.8.2)
15-15:
pathlib.Pathimported but unusedRemove unused import:
pathlib.Path(F401)
87-87: Avoid using bareexcept.Bare
exceptblocks are discouraged as they catch all exceptions, includingSystemExitandKeyboardInterrupt. Use a more specific exception type orexcept Exceptionto avoid unintended interruption or masking of critical exceptions.- except: + except Exception:🧰 Tools
🪛 Ruff (0.8.2)
87-87: Do not use bare
except(E722)
241-246: Consider caching archetype detection for performance.Each chunk is processed separately, including archetype detection. If you process a large number of chunks, you may benefit from caching or deferring repeated computations, especially when a chunk is unlikely to have changed.
app/main.py (1)
5-5: Remove unused importchat_loop.
chat_loopisn't used here, so removing it prevents lint warnings and clarifies the set of actually used functions.-from app.chat import chat_loop, initialize_embeddings, run_test_queries, answer_query +from app.chat import initialize_embeddings, run_test_queries, answer_query🧰 Tools
🪛 Ruff (0.8.2)
5-5:
app.chat.chat_loopimported but unusedRemove unused import:
app.chat.chat_loop(F401)
app/retrieval/retrieval.py (1)
18-22: Use explicit typing to clarify parameter usage.
QueryPipelinenow receivesstoreinstead ofconn. Consider specifying the type (e.g.,Union[psycopg2.extensions.connection, SomeChromaClient]) to improve readability and communicate the supported store types.app/processing/json_parser.py (3)
199-203: Watch out for possible path formatting edge cases
When appendingbase_pathtopathorparent_path, ensure you avoid awkward concatenations like missing separators or duplicate dots.- full_path = f"{base_path}{path}" if base_path else path + if base_path and not base_path.endswith('.') and not path.startswith('.'): + full_path = f"{base_path}.{path}" + else: + full_path = f"{base_path}{path}" if base_path else path
210-211: Remove or clarify commented-out debug statements
These lines are commented out and can add confusion if left in the code. Consider deleting them or clarifying their intent.- # logger.debug(f"=== Checking if object at path '{full_path}' is entity-like ===") - # logger.debug(f"Object keys: {list(obj.keys())}") ... - # logger.debug( - # "=== Object at path '{full_path}' was NOT entity-like, processing fields individually ===" - # ) ... - # logger.debug(f"=== Processing list at path '{full_path}' ===")Also applies to: 213-215, 236-238
268-272: Optional if-statement consolidation
Currently, the sequential ifs improve clarity. If you prefer a more concise style, you can merge them.- if obj and all(isinstance(item, dict) for item in obj): - ... - if any(is_entity_like(item) for item in obj): - ... + if obj \ + and all(isinstance(item, dict) for item in obj) \ + and any(is_entity_like(item) for item in obj): + ...🧰 Tools
🪛 Ruff (0.8.2)
268-272: Use a single
ifstatement instead of nestedifstatements(SIM102)
app/storage/chroma.py (2)
98-102: Ternary operator for briefness
A small style improvement: switching to a ternary expression can simplify settingfield_name.- if key.startswith("metadata."): - field_name = key - else: - field_name = f"metadata.{key}" + field_name = key if key.startswith("metadata.") else f"metadata.{key}"🧰 Tools
🪛 Ruff (0.8.2)
98-102: Use ternary operator
field_name = key if key.startswith("metadata.") else f"metadata.{key}"instead ofif-else-block(SIM108)
170-174: Similarly apply a ternary for brevity
This block resembles lines 98-102. Consider using a one-liner for clarity.- if key.startswith("metadata."): - field_name = key - else: - field_name = f"metadata.{key}" + field_name = key if key.startswith("metadata.") else f"metadata.{key}"🧰 Tools
🪛 Ruff (0.8.2)
170-174: Use ternary operator
field_name = key if key.startswith("metadata.") else f"metadata.{key}"instead ofif-else-block(SIM108)
app/retrieval/advanced_retrieval.py (3)
11-11: Unused import:Tuple
typing.Tupledoes not appear to be used.- from typing import List, Dict, Any, Optional, Tuple + from typing import List, Dict, Any, Optional🧰 Tools
🪛 Ruff (0.8.2)
11-11:
typing.Tupleimported but unusedRemove unused import:
typing.Tuple(F401)
245-271: Basic keyword match in find_relevant_paths_in_index
This is a straightforward solution. Consider more advanced text matching or indexing for complex queries.🧰 Tools
🪛 Ruff (0.8.2)
265-266: Use a single
ifstatement instead of nestedifstatements(SIM102)
265-266: Potential single if statement
Merging nested ifs is optional; a single condition can sometimes be more concise but might reduce clarity.- if isinstance(path_info, dict) and "fields" in path_info: - if any(term in str(path_info["fields"]).lower() for term in query_terms): + if ( + isinstance(path_info, dict) + and "fields" in path_info + and any(term in str(path_info["fields"]).lower() for term in query_terms) + ): relevant_paths.append(path)🧰 Tools
🪛 Ruff (0.8.2)
265-266: Use a single
ifstatement instead of nestedifstatements(SIM102)
app/processing/advanced_processor.py (4)
19-20: Remove unused imports.The imports
Set(line 19) andPath(line 20) are not referenced anywhere in this file. Removing them helps keep the code clean and avoids confusion.-from typing import List, Dict, Any, Tuple, Set, Optional +from typing import List, Dict, Any, Tuple, Optional -from pathlib import Path🧰 Tools
🪛 Ruff (0.8.2)
19-19:
typing.Setimported but unusedRemove unused import:
typing.Set(F401)
20-20:
pathlib.Pathimported but unusedRemove unused import:
pathlib.Path(F401)
80-81: Usefor key in iteminstead offor key in item.keys().Calling
.keys()explicitly is unnecessary and less idiomatic in Python. Iterating directly over the dictionary is more concise and slightly more efficient.-for key in item.keys(): +for key in item:🧰 Tools
🪛 Ruff (0.8.2)
80-80: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
576-576: Rename unused loop variable to_i.Renaming loop variables that are not used makes it clear they serve only as an index placeholder.
-for i, item in enumerate(ijson.items(f, 'item')): +for _i, item in enumerate(ijson.items(f, 'item')):🧰 Tools
🪛 Ruff (0.8.2)
576-576: Loop control variable
inot used within loop bodyRename unused
ito_i(B007)
291-296: Consider limiting recursive depth for extremely nested JSON.If
create_hierarchical_chunksis called on highly nested objects, Python’s recursion limit may be reached, causing aRecursionError. Implementing a recursion threshold could prevent unexpected crashes when encountering pathological or malicious JSON structures.app/chat.py (4)
30-30: Remove unused imports.Neither
json_to_path_chunksnorgenerate_chunk_idis used in this file. Removing them helps keep the imports minimal and the code cleaner.-from app.processing.json_parser import json_to_path_chunks, generate_chunk_id +from app.processing.json_parser import generate_chunk_id🧰 Tools
🪛 Ruff (0.8.2)
30-30:
app.processing.json_parser.json_to_path_chunksimported but unusedRemove unused import
(F401)
30-30:
app.processing.json_parser.generate_chunk_idimported but unusedRemove unused import
(F401)
196-196: Rename unused loop variable to_i.This loop uses
ionly as an index without referencing it. Renaming unused variables to_iis an idiomatic way to signal they’re placeholders.-for i, item in enumerate(obj): +for _i, item in enumerate(obj):🧰 Tools
🪛 Ruff (0.8.2)
196-196: Loop control variable
inot used within loop bodyRename unused
ito_i(B007)
232-237: Move each statement to a new line for better readability.Having multiple statements on a single line is discouraged as it is less readable and can hide potential errors.
-if field_key.startswith("shipments"): rel_type = "shipment_reference" -elif field_key.startswith("warehouses"): rel_type = "warehouse_reference" -elif field_key.startswith("products"): rel_type = "product_reference" -elif field_key.startswith("suppliers"): rel_type = "supplier_reference" -elif field_key.endswith("_id"): rel_type = f"{field_key[:-3]}_reference" -elif field_key == "id": rel_type = "identity_reference" +if field_key.startswith("shipments"): + rel_type = "shipment_reference" +elif field_key.startswith("warehouses"): + rel_type = "warehouse_reference" +elif field_key.startswith("products"): + rel_type = "product_reference" +elif field_key.startswith("suppliers"): + rel_type = "supplier_reference" +elif field_key.endswith("_id"): + rel_type = f"{field_key[:-3]}_reference" +elif field_key == "id": + rel_type = "identity_reference"🧰 Tools
🪛 Ruff (0.8.2)
232-232: Multiple statements on one line (colon)
(E701)
233-233: Multiple statements on one line (colon)
(E701)
234-234: Multiple statements on one line (colon)
(E701)
235-235: Multiple statements on one line (colon)
(E701)
236-236: Multiple statements on one line (colon)
(E701)
237-237: Multiple statements on one line (colon)
(E701)
355-356: Avoid re-processing all data on every query for performance reasons.Calling
load_and_embed_new_data(store)before each query can cause excessive overhead with large datasets. Consider caching the results or triggering this operation only when new data is actually available.app/llm_integration.py (4)
234-234: Use dictionary iteration directly.Instead of calling
json_data.keys(), you can iterate directly over the dictionary usingfor key in json_data:.-for key in json_data.keys(): +for key in json_data:🧰 Tools
🪛 Ruff (0.8.2)
234-234: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
265-267: Combine nested if statements.By merging both conditions into a single statement, you can make the code more streamlined and improve readability.
- if key not in simplified: - if isinstance(value, (str, int, float, bool)) or value is None: - simplified[key] = value + if ( + key not in simplified + and (isinstance(value, (str, int, float, bool)) or value is None) + ): + simplified[key] = value🧰 Tools
🪛 Ruff (0.8.2)
265-267: Use a single
ifstatement instead of nestedifstatements(SIM102)
686-686: Raise wrapped exception for clarity.Within an
exceptblock, Python best practices recommend raising from the caught exception to maintain traceback clarity.- raise ValueError("Prompt cannot be reduced enough to fit token limits") + raise ValueError("Prompt cannot be reduced enough to fit token limits") from e🧰 Tools
🪛 Ruff (0.8.2)
686-686: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
1-740: Add or extend unit tests for key functionalities.This file contains multiple complex methods for chunking, summarizing, and coordinating LLM calls. Ensure these are covered by unit tests to verify correctness and reliability.
Would you like help scaffolding or drafting unit tests to validate chunking and summarization logic under various scenarios?
🧰 Tools
🪛 Ruff (0.8.2)
13-13:
typing.Dictimported but unusedRemove unused import
(F401)
13-13:
typing.Anyimported but unusedRemove unused import
(F401)
13-13:
typing.Tupleimported but unusedRemove unused import
(F401)
234-234: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
265-267: Use a single
ifstatement instead of nestedifstatements(SIM102)
373-373: Do not use bare
except(E722)
416-416: Loop control variable
inot used within loop body(B007)
505-505: Do not use bare
except(E722)
686-686: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (17)
.env.sample(1 hunks)app/chat.py(8 hunks)app/llm_integration.py(1 hunks)app/main.py(1 hunks)app/processing/advanced_processor.py(1 hunks)app/processing/json_parser.py(7 hunks)app/processing/json_processor.py(1 hunks)app/query_intent.py(1 hunks)app/retrieval/advanced_retrieval.py(1 hunks)app/retrieval/chroma.py(1 hunks)app/retrieval/embedding.py(1 hunks)app/retrieval/postgres.py(1 hunks)app/retrieval/retrieval.py(5 hunks)app/storage/chroma.py(1 hunks)data/json_docs/adMetricsSmall.json(1 hunks)docker-compose.yml(1 hunks)requirements.txt(1 hunks)
🧰 Additional context used
🧬 Code Definitions (6)
app/main.py (5)
app/chat.py (5)
chat_loop(315-361)initialize_embeddings(285-312)run_test_queries(551-576)answer_query(644-679)main(682-724)app/retrieval/retrieval.py (1)
answer_query(349-366)app/storage/database.py (1)
init_db(264-369)app/storage/chroma.py (1)
init_chroma(17-44)app/utils/logging_config.py (1)
get_logger(42-57)
app/retrieval/embedding.py (2)
app/retrieval/retrieval.py (1)
execute(24-40)app/storage/chroma.py (1)
query_chunks(66-127)
app/retrieval/retrieval.py (2)
app/utils/logging_config.py (1)
get_logger(42-57)app/chat.py (1)
answer_query(644-679)
app/storage/chroma.py (1)
app/retrieval/embedding.py (1)
get_embedding(32-99)
app/retrieval/advanced_retrieval.py (1)
app/storage/chroma.py (2)
query_chunks(66-127)get_chunks(148-197)
app/chat.py (6)
app/processing/json_parser.py (3)
json_to_path_chunks(169-335)generate_chunk_id(23-65)process_json_files(338-397)app/processing/json_processor.py (1)
process_json_files(31-319)app/analysis/intent.py (1)
analyze_query_intent(493-554)app/llm_integration.py (1)
get_llm_response(516-611)app/retrieval/retrieval.py (1)
answer_query(349-366)app/retrieval/advanced_retrieval.py (1)
retrieve_from_hierarchical_chunks(17-241)
🪛 YAMLlint (1.35.1)
docker-compose.yml
[error] 19-19: no new line character at the end of file
(new-line-at-end-of-file)
🪛 Ruff (0.8.2)
app/retrieval/chroma.py
7-7: logging imported but unused
Remove unused import: logging
(F401)
8-8: typing.Any imported but unused
Remove unused import
(F401)
8-8: typing.Optional imported but unused
Remove unused import
(F401)
app/retrieval/postgres.py
8-8: logging imported but unused
Remove unused import: logging
(F401)
9-9: typing.Any imported but unused
Remove unused import
(F401)
9-9: typing.Optional imported but unused
Remove unused import
(F401)
app/main.py
5-5: app.chat.chat_loop imported but unused
Remove unused import: app.chat.chat_loop
(F401)
app/processing/json_parser.py
268-272: Use a single if statement instead of nested if statements
(SIM102)
app/processing/json_processor.py
14-14: typing.Any imported but unused
Remove unused import
(F401)
14-14: typing.Union imported but unused
Remove unused import
(F401)
15-15: pathlib.Path imported but unused
Remove unused import: pathlib.Path
(F401)
87-87: Do not use bare except
(E722)
app/storage/chroma.py
24-24: Local variable host is assigned to but never used
Remove assignment to unused variable host
(F841)
25-25: Local variable port is assigned to but never used
Remove assignment to unused variable port
(F841)
98-102: Use ternary operator field_name = key if key.startswith("metadata.") else f"metadata.{key}" instead of if-else-block
(SIM108)
170-174: Use ternary operator field_name = key if key.startswith("metadata.") else f"metadata.{key}" instead of if-else-block
(SIM108)
app/llm_integration.py
13-13: typing.Dict imported but unused
Remove unused import
(F401)
13-13: typing.Any imported but unused
Remove unused import
(F401)
13-13: typing.Tuple imported but unused
Remove unused import
(F401)
234-234: Use key in dict instead of key in dict.keys()
Remove .keys()
(SIM118)
265-267: Use a single if statement instead of nested if statements
(SIM102)
373-373: Do not use bare except
(E722)
416-416: Loop control variable i not used within loop body
(B007)
505-505: Do not use bare except
(E722)
686-686: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
app/retrieval/advanced_retrieval.py
11-11: typing.Tuple imported but unused
Remove unused import: typing.Tuple
(F401)
265-266: Use a single if statement instead of nested if statements
(SIM102)
app/chat.py
30-30: app.processing.json_parser.json_to_path_chunks imported but unused
Remove unused import
(F401)
30-30: app.processing.json_parser.generate_chunk_id imported but unused
Remove unused import
(F401)
196-196: Loop control variable i not used within loop body
Rename unused i to _i
(B007)
232-232: Multiple statements on one line (colon)
(E701)
233-233: Multiple statements on one line (colon)
(E701)
234-234: Multiple statements on one line (colon)
(E701)
235-235: Multiple statements on one line (colon)
(E701)
236-236: Multiple statements on one line (colon)
(E701)
237-237: Multiple statements on one line (colon)
(E701)
270-270: Undefined name psycopg2
(F821)
644-644: Redefinition of unused answer_query from line 39
(F811)
app/processing/advanced_processor.py
19-19: typing.Set imported but unused
Remove unused import: typing.Set
(F401)
20-20: pathlib.Path imported but unused
Remove unused import: pathlib.Path
(F401)
56-56: Do not use bare except
(E722)
80-80: Use key in dict instead of key in dict.keys()
Remove .keys()
(SIM118)
576-576: Loop control variable i not used within loop body
Rename unused i to _i
(B007)
🔇 Additional comments (35)
app/retrieval/embedding.py (4)
191-203: Clear parameter renaming and improved documentation 👍The function signature has been updated from
conntostoreto accommodate both PostgreSQL and ChromaDB backends. The docstring has been significantly improved with clearer parameter descriptions and return value information.
212-213: Good handling of embedding conversionConverting the numpy array to a list before using it with the databases is a good practice to ensure compatibility.
215-279: Well-structured PostgreSQL implementation with robust filteringThe implementation handles different filter types (strings and lists) appropriately and uses parameterized queries for security. The SQL query construction is thorough and the results are formatted consistently.
298-301: Good error handling practiceThe function properly catches exceptions, logs detailed error information, and returns an empty list instead of raising the exception. This helps maintain application stability.
app/query_intent.py (1)
1-11: Well-structured module with clear documentationThis new module follows good Python practices by clearly documenting its purpose and using
__all__to explicitly define its public API. The re-export pattern is a clean way to organize functionality.data/json_docs/adMetricsSmall.json (1)
1-284: Excellent sample data for testing complex JSON processingThis JSON file contains a well-structured collection of ad metrics data with nested traits information, which aligns perfectly with the PR objective of supporting more complex JSON files. The data has a good variety of fields including metrics (impressions, spend, revenue) and detailed trait information.
app/main.py (2)
19-22: Double-check default vector store behavior.The
--vector-storeargument defaults to theVECTOR_STOREenvironment variable, falling back to"postgres". This is fine, but ensure environment override is intentional, or consider logging if the default is used so it’s clearly visible during runtime.
81-82: Close database connection conditionally.Conditionally closing the connection when
args.vector_store == "postgres"is correct to avoid errors if no PostgreSQL connection was established, but confirm that all required finalization steps for ChromaDB (if any) are handled similarly elsewhere.app/retrieval/retrieval.py (2)
147-152: Verify PostgreSQL detection logic.Setting
self.is_postgres = hasattr(store, 'cursor')works for psycopg2 connections, but ensure it holds true for any future PostgreSQL drivers or mocking frameworks that may not use acursorattribute in the same way.
349-358: Confirm alignment of signature and docstrings inanswer_query.The function now expects
storeas a parameter. Ensure all references (like docstrings, type annotations, and references fromapp/chat.py) consistently usestorerather thanconn. This consistency prevents confusion and improves documentation clarity.app/processing/json_parser.py (12)
13-14: Leverage streaming for memory efficiency
Addingorjsonandijsonhere is beneficial for large JSON parsing, improving memory usage and speed.
127-132: Consider verifying new thresholds
Increasing the maximum allowed fields, nesting, and content size will let more objects be classified as entity-like. While this can capture more data in a single chunk, it may also increase the chunk size and memory footprint.Would you like to run performance or stress tests to confirm these thresholds won't degrade system performance?
133-164: Revised entity checks appear coherent
The updated conditions balancing field count, nesting limits, and content size are logical. The additional debug logs should help in diagnosing borderline cases.
172-172: Reconsider significantly larger max_chunks
Raisingmax_chunksto 10000 can increase memory and storage usage if large or numerous JSON files are parsed.Would you like to run tests to ensure that handling up to 10k chunks won’t negatively affect stability?
176-176: Introducing base_path enriches chunk organization
Allowing a base path prefix is a good extension for hierarchical data.
205-206: Sensible single log message
Logging once upon hittingmax_chunksensures you don’t flood logs if data is extremely large.
246-249: Context prefixing is nicely implemented
Embedding parent context keys innew_contexthelps maintain relevant information without inflating chunk size too much.
254-258: Handling empty dict objects elegantly
Returning empty dictionaries as chunks ensures consistent coverage of all JSON structures.
320-321: Helpful final chunk generation summary
The concluding debug log verifying total chunk count is valuable for monitoring large file processing.
353-374: Efficient streaming from 'metrics' array
Usingijsonallows memory-friendly processing of large files. Skipping problematic items but continuing overall parsing is a robust approach.
376-389: Graceful fallback to full object load
Falling back toorjsonupon streaming failure ensures all data is still parsed even if the format deviates from expectations.
391-394: Comprehensive error handling
Catching both specific and general exceptions with informative logging is excellent for debugging.app/storage/chroma.py (7)
1-6: Clear module-level docstring
The overview of the ChromaDB storage integration is concise and sets context well for the rest of the file.
8-14: Imports are well organized
No issues noted. The separation of external libraries and local imports is straightforward.
46-65: Upsert logic is straightforward
Serializing chunk data as JSON and attaching embeddings aligns well with ChromaDB usage patterns.
66-128: Excluded IDs might reduce final result count
Currently, skipped IDs do not prompt the search to return extra items. If consistent retrieval ofn_resultsis desired, consider adjusting the query.Would you like to fetch additional results to keep the final count near
n_results?🧰 Tools
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field_name = key if key.startswith("metadata.") else f"metadata.{key}"instead ofif-else-block(SIM108)
129-138: delete_chunks is clear
Deleting chunks by ID is consistent with typical data lifecycle operations.
139-147: reset_collection is well-defined
Dropping all data from the collection is a straightforward reset if the user needs a blank slate.
148-197: Flexible filtering in get_chunks
Using$eqfilters for metadata queries aligns with ChromaDB's filtering design. Further operators can be added if more complex queries are needed.🧰 Tools
🪛 Ruff (0.8.2)
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field_name = key if key.startswith("metadata.") else f"metadata.{key}"instead ofif-else-block(SIM108)
app/retrieval/advanced_retrieval.py (5)
1-7: Comprehensive overview
The docstring here clearly outlines what the advanced retrieval module aims to do with hierarchical data.
17-242: Robust hierarchical retrieval strategy
Collecting summary/index chunks first and then drilling down into detail chunks is a powerful approach. Handling fallback also appears thorough.
272-328: get_chunks_by_path
Supports both PostgreSQL and ChromaDB, keeping consistent fetch logic for path-based lookup.
329-385: Consistent approach for parent_path retrieval
This mirrorsget_chunks_by_path, fostering uniform code for hierarchical references.
386-418: Final assembly in hierarchical_search
Leveragingretrieve_from_hierarchical_chunksand displaying results with a relevance measure is an elegant finish.app/llm_integration.py (1)
416-416: False positive from static analysis.The variable
iis indeed used in the logging statement on line 417. No changes needed here.🧰 Tools
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416-416: Loop control variable
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| chromadb>=0.4.22 | ||
| protobuf>=3.1.0,<=3.20.1 | ||
| orjson>=3.10.16 |
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💡 Verification agent
🧩 Analysis chain
Verify compatibility with pinned protobuf version
The addition of ChromaDB and its dependencies is appropriate for the new functionality. However, the upper bound on protobuf (<=3.20.1) might cause compatibility issues with other packages in the future.
🏁 Script executed:
#!/bin/bash
# Check if other packages in the project depend on protobuf and might have version conflicts
# Find references to protobuf in the project
echo "Checking for protobuf usage in the codebase:"
rg -i "protobuf|proto" --type py
# Check if any installed packages have protobuf as a dependency
echo "Checking for packages that depend on protobuf:"
pip install pipdeptree
python -m pipdeptree | grep -A5 -B5 protobufLength of output: 3176
Action: Update the Protobuf Version Constraint
Based on the verification output, it appears that some dependencies (for example, googleapis-common-protos, which is pulled in by opentelemetry-exporter-otlp-proto-http) require protobuf at version >=3.20.2. This conflicts with the current upper bound (<=3.20.1) specified in requirements.txt, potentially leading to compatibility issues.
Recommendations:
- Revisit the
protobufconstraint inrequirements.txt(line 10): Consider removing the upper bound or updating it so that it accommodates dependencies requiring newer versions. - Ensure downstream compatibility: Verify that removing or relaxing the upper bound does not introduce issues elsewhere in the project.
| if not is_postgres and chroma_chunks: | ||
| from app.storage.chroma import upsert_chunks | ||
| upsert_chunks(store, chroma_chunks, chroma_embeddings) | ||
| logger.info(f"Added {len(chroma_chunks)} chunks to ChromaDB") | ||
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🛠️ Refactor suggestion
Validate ChromaDB batch insertion sizes.
Batching all chunks in a single operation might cause performance bottlenecks or memory overhead if the corpus is very large. Consider splitting large batches into smaller chunks or implementing flow control.
| host = os.getenv("CHROMA_HOST", "localhost") | ||
| port = int(os.getenv("CHROMA_PORT", "9091")) |
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🛠️ Refactor suggestion
Unused environment variables
host and port are fetched but never used. This could be misleading if users expect to configure a remote ChromaDB instance.
- host = os.getenv("CHROMA_HOST", "localhost")
- port = int(os.getenv("CHROMA_PORT", "9091"))
collection_name = os.getenv("CHROMA_COLLECTION", "json_chunks")Remove or utilize them if remote deployment is intended.
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| host = os.getenv("CHROMA_HOST", "localhost") | |
| port = int(os.getenv("CHROMA_PORT", "9091")) | |
| collection_name = os.getenv("CHROMA_COLLECTION", "json_chunks") |
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🛠️ Refactor suggestion
Avoid using a bare except.
Catching every exception type can obscure important issues, such as system interrupts or syntax errors. Specify the exception type or use except Exception: to handle only standard runtime errors.
- except:
+ except Exception:📝 Committable suggestion
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Resolve conflicting answer_query definitions.
There's a redefinition of answer_query that shadows the imported function from app.retrieval.retrieval. To avoid confusion and possible runtime errors, rename either the local function or remove one definition.
-from app.retrieval.retrieval import answer_query
...
-def answer_query(store, query: str) -> str:
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psycopg2 is not imported.
This block references psycopg2.DatabaseError, but psycopg2 is never imported, causing a potential NameError. Either remove the reference or import psycopg2.
-except (Exception, psycopg2.DatabaseError) as error:
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except (Exception, psycopg2.DatabaseError) as error:Committable suggestion skipped: line range outside the PR's diff.
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| import logging | ||
| import requests | ||
| import re | ||
| from typing import Dict, Any, Optional, List, Tuple |
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🛠️ Refactor suggestion
Remove unused imports.
It looks like Dict, Any, and Tuple from typing are never used in this file. Removing them keeps the import list clean.
-from typing import Dict, Any, Optional, List, Tuple
+from typing import Optional, List📝 Committable suggestion
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| from typing import Dict, Any, Optional, List, Tuple | |
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🛠️ Refactor suggestion
Avoid bare except.
Similar to line 373, please catch a more specific exception type or Exception to avoid masking unanticipated errors.
-except:
+except Exception:
logger.warning("Reduce phase produced invalid JSON, using as-is")📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
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🛠️ Refactor suggestion
Avoid bare except.
Catch exceptions more explicitly (e.g., except Exception:). This improves debugging and prevents hiding unexpected errors.
- except:
+ except Exception:
logger.warning("LLM produced invalid JSON summary")
return summary📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
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Actionable comments posted: 0
🧹 Nitpick comments (8)
README.md (2)
14-20: Adjust Markdown bullet style for the Intelligent JSON Chunking section.The new "Intelligent JSON Chunking" section (lines 14–20) currently uses dashes (
-) for list items. However, the markdown style guidelines (as highlighted by static analysis) expect the use of asterisks (*) for unordered lists. Please update the bullet style for better consistency and adherence to the guidelines.- - Structure-aware chunking preserving JSON hierarchy - - Semantic boundary detection for meaningful chunks - - Context-retaining chunk sizes optimized for embedding - - Overlap control to maintain continuity between chunks - - Metadata preservation across chunk boundaries + * Structure-aware chunking preserving JSON hierarchy + * Semantic boundary detection for meaningful chunks + * Context-retaining chunk sizes optimized for embedding + * Overlap control to maintain continuity between chunks + * Metadata preservation across chunk boundaries🧰 Tools
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15-15: Unordered list style
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108-113: Standardize Markdown list style for the parsing module description.The updated description for the
parsing.pymodule (lines 108–113) currently employs dashes for bullet points. To comply with the markdown style guidelines and ensure consistency across the documentation, please change these dashes to asterisks.- - Recursively analyzes JSON structure to identify natural semantic boundaries - - Preserves parent-child relationships during chunking - - Maintains context by tracking paths and ancestry for each chunk - - Applies size constraints while respecting semantic coherence - - Assigns unique IDs and metadata for relationship tracking + * Recursively analyzes JSON structure to identify natural semantic boundaries + * Preserves parent-child relationships during chunking + * Maintains context by tracking paths and ancestry for each chunk + * Applies size constraints while respecting semantic coherence + * Assigns unique IDs and metadata for relationship tracking🧰 Tools
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Expected: asterisk; Actual: dash(MD004, ul-style)
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113-113: Unordered list style
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process_ads.py (6)
2-2: Remove unused importThe
sysmodule is imported but never used in the code.import json -import sys import copy🧰 Tools
🪛 Ruff (0.8.2)
2-2:
sysimported but unusedRemove unused import:
sys(F401)
6-7: Add error handling for file operationsThe file opening operation lacks error handling, which could cause the script to crash if the file doesn't exist or has permission issues.
# Load JSON data -with open('tenant_metrics_imagery_boost.json', 'r') as f: - data = json.load(f) +try: + with open('tenant_metrics_imagery_boost.json', 'r') as f: + data = json.load(f) +except FileNotFoundError: + print("Error: Input file 'tenant_metrics_imagery_boost.json' not found.") + sys.exit(1) +except json.JSONDecodeError: + print("Error: Invalid JSON format in input file.") + sys.exit(1) +except Exception as e: + print(f"Error loading input file: {e}") + sys.exit(1)
6-32: Use command-line arguments for file pathsThe script uses hardcoded file paths, which limits its flexibility. Using command-line arguments would make the script more reusable.
import json -import sys import copy +import sys +import argparse + +def main(): + # Parse command-line arguments + parser = argparse.ArgumentParser(description='Process ad metrics data based on imagery traits.') + parser.add_argument('--input', '-i', default='tenant_metrics_imagery_boost.json', + help='Input JSON file path (default: tenant_metrics_imagery_boost.json)') + parser.add_argument('--output', '-o', default='tenant_metrics_imagery_boost_modified.json', + help='Output JSON file path (default: tenant_metrics_imagery_boost_modified.json)') + args = parser.parse_args() -# Load JSON data -with open('tenant_metrics_imagery_boost.json', 'r') as f: - data = json.load(f) + # Load JSON data + try: + with open(args.input, 'r') as f: + data = json.load(f) + except FileNotFoundError: + print(f"Error: Input file '{args.input}' not found.") + sys.exit(1) + except json.JSONDecodeError: + print("Error: Invalid JSON format in input file.") + sys.exit(1) + except Exception as e: + print(f"Error loading input file: {e}") + sys.exit(1) -# Process the data -new_data = [] -for item in data: - trait_name = item.get('trait', {}).get('name', '') - has_imagery = 'Imagery' in trait_name - - if not has_imagery: - # Create a deep copy to avoid modifying the original item if it appears multiple times - processed_item = copy.deepcopy(item) - for ad in processed_item.get('ads', []): - metrics = ad.get('metrics', {}) - for key in metrics: - if isinstance(metrics[key], (int, float)): - metrics[key] = metrics[key] / 2 - new_data.append(processed_item) - else: - # Append the original item if it has Imagery trait - new_data.append(item) + # Process the data + new_data = [] + for item in data: + trait_name = item.get('trait', {}).get('name', '') + has_imagery = 'Imagery' in trait_name + + if not has_imagery: + # Create a deep copy to avoid modifying the original item if it appears multiple times + processed_item = copy.deepcopy(item) + for ad in processed_item.get('ads', []): + metrics = ad.get('metrics', {}) + for key in metrics: + if isinstance(metrics[key], (int, float)): + metrics[key] = metrics[key] / 2 + new_data.append(processed_item) + else: + # Append the original item if it has Imagery trait + new_data.append(item) -# Save the modified data -with open('tenant_metrics_imagery_boost_modified.json', 'w') as f: - json.dump(new_data, f, indent=2, ensure_ascii=False) + # Save the modified data + try: + with open(args.output, 'w') as f: + json.dump(new_data, f, indent=2, ensure_ascii=False) + print(f"Processing complete. Modified file saved as {args.output}") + except Exception as e: + print(f"Error saving output file: {e}") + sys.exit(1) -print("Processing complete. Modified file saved as tenant_metrics_imagery_boost_modified.json") +if __name__ == "__main__": + main()
1-4: Add documentation for the script's purposeThe script lacks a docstring explaining its purpose and usage. Adding a module-level docstring would improve maintainability.
+""" +Process ad metrics data based on imagery traits. + +This script processes JSON data from an input file containing ad metrics. +For items without 'Imagery' in their trait name, the script divides all numeric +metrics values by 2. For items with 'Imagery' in their trait name, the original +data is preserved. The processed data is saved to an output file. + +Usage: + python process_ads.py [--input INPUT_FILE] [--output OUTPUT_FILE] +""" import json import sys import copy🧰 Tools
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11-26: Extract the processing logic into a separate functionThe processing logic is embedded in the main flow of the script. Extracting it into a separate function would improve readability and testability.
+def process_ad_data(data): + """ + Process ad metrics data based on imagery traits. + + For items without 'Imagery' in their trait name, divide all numeric metrics values by 2. + For items with 'Imagery' in their trait name, preserve the original data. + + Args: + data (list): List of ad metrics data items + + Returns: + list: Processed ad metrics data + """ + new_data = [] + for item in data: + trait_name = item.get('trait', {}).get('name', '') + has_imagery = 'Imagery' in trait_name + + if not has_imagery: + # Create a deep copy to avoid modifying the original item if it appears multiple times + processed_item = copy.deepcopy(item) + for ad in processed_item.get('ads', []): + metrics = ad.get('metrics', {}) + for key in metrics: + if isinstance(metrics[key], (int, float)): + metrics[key] = metrics[key] / 2 + new_data.append(processed_item) + else: + # Append the original item if it has Imagery trait + new_data.append(item) + return new_data # Process the data -new_data = [] -for item in data: - trait_name = item.get('trait', {}).get('name', '') - has_imagery = 'Imagery' in trait_name - - if not has_imagery: - # Create a deep copy to avoid modifying the original item if it appears multiple times - processed_item = copy.deepcopy(item) - for ad in processed_item.get('ads', []): - metrics = ad.get('metrics', {}) - for key in metrics: - if isinstance(metrics[key], (int, float)): - metrics[key] = metrics[key] / 2 - new_data.append(processed_item) - else: - # Append the original item if it has Imagery trait - new_data.append(item) +new_data = process_ad_data(data)
21-22: Make the division factor configurableThe script currently hardcodes dividing metrics by 2 for non-Imagery traits. Making this factor configurable would enhance flexibility.
# Parse command-line arguments parser = argparse.ArgumentParser(description='Process ad metrics data based on imagery traits.') parser.add_argument('--input', '-i', default='tenant_metrics_imagery_boost.json', help='Input JSON file path (default: tenant_metrics_imagery_boost.json)') parser.add_argument('--output', '-o', default='tenant_metrics_imagery_boost_modified.json', help='Output JSON file path (default: tenant_metrics_imagery_boost_modified.json)') +parser.add_argument('--factor', '-f', type=float, default=2.0, + help='Division factor for metrics of non-imagery traits (default: 2.0)') # In the process_ad_data function: - metrics[key] = metrics[key] / 2 + metrics[key] = metrics[key] / args.factor
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📒 Files selected for processing (4)
README.md(2 hunks)data/json_docs/tenant_metrics_extra_small.json(1 hunks)data/json_docs/tenant_metrics_extra_small_imagery_boost.json(1 hunks)process_ads.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- data/json_docs/tenant_metrics_extra_small.json
🧰 Additional context used
🪛 markdownlint-cli2 (0.17.2)
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🔇 Additional comments (1)
data/json_docs/tenant_metrics_extra_small_imagery_boost.json (1)
1-902: Well-structured JSON data file with comprehensive ad metricsThis JSON data file provides a well-structured collection of advertisement metrics organized by different imagery traits. The hierarchical organization with traits, ad collections, and detailed metrics will be useful for analysis.
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🧹 Nitpick comments (5)
data/json_docs/trait_metrics_extra_small_imagery_boost.json (5)
1-903: Overall JSON Structure and Schema Consistency
This new JSON file is well structured as a top-level array of trait objects. Each trait object contains an array of ads, an aggregate ad count, a nested metrics object, and a trait descriptor. The consistent use of keys (e.g.,"id","name","content_type","image_url","metrics") across all objects is clear and makes the file straightforward to process.It would be beneficial, in the future, to provide an external JSON schema or inline documentation (via a separate README or schema file) to ensure that any changes in structure can be automatically validated.
2-46: First Trait Object Verification ("Calm/Serene Imagery")
The first trait object (lines 2–46) correctly defines its properties: the"ads"array includes two ad objects with detailed properties and metrics, and the overall object provides the aggregated"adCount"and a summary"metrics"object along with a"trait"object. All data types (strings and numbers) appear to be in order.Consider documenting abbreviations such as
"imprs"(for impressions) so that downstream consumers understand the shorthand.
47-91: Second Trait Object Verification ("Trustworthy Imagery")
The second trait object (lines 47–91) follows the same schema as the first. The ad objects, aggregate metrics, and trait descriptor are consistent with the overall design.Minor note: ensure that naming conventions (for example,
"imprs") remain consistent across the codebase and its documentation.
92-136: Trait Object Consistency and Numeric Precision ("Product without people")
This object (lines 92–136) is another good example of the standardized structure. Notice that the metrics (such as"spend": 506.439999and"ctr": 0.46459468119192565) are provided with high precision.If these numbers are used in computations or displayed to users, consider whether rounding to a fixed number of decimals might improve readability and consistency.
137-901: Review of Remaining Trait Objects and Overall Data Consistency
The remaining trait objects (lines 137–901) continue in the same pattern with nested"ads"arrays, individual ad metrics, aggregate metrics, and trait details. The uniformity in structure is excellent for downstream processing.It may be worthwhile to review the formatting of numerical fields (e.g.,
"spend","ctr", and"roas") to decide on a consistent precision standard. Additionally, if this JSON is a critical input for processing or analytics, adding a version key or external schema reference could help future-proof the data format.
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