## Benefits
1. **Type Safety**: Compile-time validation of required fields and
proper type setting
2. **Less Boilerplate**: No need to manually set the `type` field or
generate IDs
3. **Input Validation**: Runtime validation prevents common errors
(e.g., base64 without MIME type)
4. **Consistent Patterns**: Standardized creation patterns across all
block types
5. **Better Developer Experience**: Cleaner, more intuitive API than
manual TypedDict construction. Also follows similar other patterns (e.g.
`create_react_agent`, `init_chat_model`
Fixes a streaming bug where models like Qwen3 (using OpenAI interface)
send tool call chunks with inconsistent indices, resulting in
duplicate/erroneous tool calls instead of a single merged tool call.
## Problem
When Qwen3 streams tool calls, it sends chunks with inconsistent `index`
values:
- First chunk: `index=1` with tool name and partial arguments
- Subsequent chunks: `index=0` with `name=None`, `id=None` and argument
continuation
The existing `merge_lists` function only merges chunks when their
`index` values match exactly, causing these logically related chunks to
remain separate, resulting in multiple incomplete tool calls instead of
one complete tool call.
```python
# Before fix: Results in 1 valid + 1 invalid tool call
chunk1 = AIMessageChunk(tool_call_chunks=[
{"name": "search", "args": '{"query":', "id": "call_123", "index": 1}
])
chunk2 = AIMessageChunk(tool_call_chunks=[
{"name": None, "args": ' "test"}', "id": None, "index": 0}
])
merged = chunk1 + chunk2 # Creates 2 separate tool calls
# After fix: Results in 1 complete tool call
merged = chunk1 + chunk2 # Creates 1 merged tool call: search({"query": "test"})
```
## Solution
Enhanced the `merge_lists` function in `langchain_core/utils/_merge.py`
with intelligent tool call chunk merging:
1. **Preserves existing behavior**: Same-index chunks still merge as
before
2. **Adds special handling**: Tool call chunks with
`name=None`/`id=None` that don't match any existing index are now merged
with the most recent complete tool call chunk
3. **Maintains backward compatibility**: All existing functionality
works unchanged
4. **Targeted fix**: Only affects tool call chunks, doesn't change
behavior for other list items
The fix specifically handles the pattern where:
- A continuation chunk has `name=None` and `id=None` (indicating it's
part of an ongoing tool call)
- No matching index is found in existing chunks
- There exists a recent tool call chunk with a valid name or ID to merge
with
## Testing
Added comprehensive test coverage including:
- ✅ Qwen3-style chunks with different indices now merge correctly
- ✅ Existing same-index behavior preserved
- ✅ Multiple distinct tool calls remain separate
- ✅ Edge cases handled (empty chunks, orphaned continuations)
- ✅ Backward compatibility maintained
Fixes#31511.
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Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Problem
ChatLiteLLM encounters a `ValidationError` when using cache on
subsequent calls, causing the following error:
```
ValidationError(model='ChatResult', errors=[{'loc': ('generations', 0, 'type'), 'msg': "unexpected value; permitted: 'ChatGeneration'", 'type': 'value_error.const', 'ctx': {'given': 'Generation', 'permitted': ('ChatGeneration',)}}])
```
This occurs because:
1. The cache stores `Generation` objects (with `type="Generation"`)
2. But `ChatResult` expects `ChatGeneration` objects (with
`type="ChatGeneration"` and a required `message` field)
3. When cached values are retrieved, validation fails due to the type
mismatch
## Solution
Added graceful handling in both sync (`_generate_with_cache`) and async
(`_agenerate_with_cache`) cache methods to:
1. **Detect** when cached values contain `Generation` objects instead of
expected `ChatGeneration` objects
2. **Convert** them to `ChatGeneration` objects by wrapping the text
content in an `AIMessage`
3. **Preserve** all original metadata (`generation_info`)
4. **Allow** `ChatResult` creation to succeed without validation errors
## Example
```python
# Before: This would fail with ValidationError
from langchain_community.chat_models import ChatLiteLLM
from langchain_community.cache import SQLiteCache
from langchain.globals import set_llm_cache
set_llm_cache(SQLiteCache(database_path="cache.db"))
llm = ChatLiteLLM(model_name="openai/gpt-4o", cache=True, temperature=0)
print(llm.predict("test")) # Works fine (cache empty)
print(llm.predict("test")) # Now works instead of ValidationError
# After: Seamlessly handles both Generation and ChatGeneration objects
```
## Changes
- **`libs/core/langchain_core/language_models/chat_models.py`**:
- Added `Generation` import from `langchain_core.outputs`
- Enhanced cache retrieval logic in `_generate_with_cache` and
`_agenerate_with_cache` methods
- Added conversion from `Generation` to `ChatGeneration` objects when
needed
-
**`libs/core/tests/unit_tests/language_models/chat_models/test_cache.py`**:
- Added test case to validate the conversion logic handles mixed object
types
## Impact
- **Backward Compatible**: Existing code continues to work unchanged
- **Minimal Change**: Only affects cache retrieval path, no API changes
- **Robust**: Handles both legacy cached `Generation` objects and new
`ChatGeneration` objects
- **Preserves Data**: All original content and metadata is maintained
during conversion
Fixes#22389.
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Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
**Description:** Fixes incorrect `num_skipped` count in the LangChain
indexing API. The current implementation only counts documents that
already exist in RecordManager (cross-batch duplicates) but fails to
count documents removed during within-batch deduplication via
`_deduplicate_in_order()`.
This PR adds tracking of the original batch size before deduplication
and includes the difference in `num_skipped`, ensuring that `num_added +
num_skipped` equals the total number of input documents.
**Issue:** Fixes incorrect document count reporting in indexing
statistics
**Dependencies:** None
Fixes#32272
---------
Co-authored-by: Alex Feel <afilippov@spotware.com>
Ensures proper reStructuredText formatting by adding the required blank
line before closing docstring quotes, which resolves the "Block quote
ends without a blank line; unexpected unindent" warning.
This PR fixes the PostgreSQL NUL byte issue that causes
`psycopg.DataError` when inserting documents containing `\x00` bytes
into PostgreSQL-based vector stores.
## Problem
PostgreSQL text fields cannot contain NUL (0x00) bytes. When documents
with such characters are processed by PGVector or langchain-postgres
implementations, they fail with:
```
(psycopg.DataError) PostgreSQL text fields cannot contain NUL (0x00) bytes
```
This commonly occurs when processing PDFs, documents from various
loaders, or text extracted by libraries like unstructured that may
contain embedded NUL bytes.
## Solution
Added `sanitize_for_postgres()` utility function to
`langchain_core.utils.strings` that removes or replaces NUL bytes from
text content.
### Key Features
- **Simple API**: `sanitize_for_postgres(text, replacement="")`
- **Configurable**: Replace NUL bytes with empty string (default) or
space for readability
- **Comprehensive**: Handles all problematic examples from the original
issue
- **Well-tested**: Complete unit tests with real-world examples
- **Backward compatible**: No breaking changes, purely additive
### Usage Example
```python
from langchain_core.utils import sanitize_for_postgres
from langchain_core.documents import Document
# Before: This would fail with DataError
problematic_content = "Getting\x00Started with embeddings"
# After: Clean the content before database insertion
clean_content = sanitize_for_postgres(problematic_content)
# Result: "GettingStarted with embeddings"
# Or preserve readability with spaces
readable_content = sanitize_for_postgres(problematic_content, " ")
# Result: "Getting Started with embeddings"
# Use in Document processing
doc = Document(page_content=clean_content, metadata={...})
```
### Integration Pattern
PostgreSQL vector store implementations should sanitize content before
insertion:
```python
def add_documents(self, documents: List[Document]) -> List[str]:
# Sanitize documents before insertion
sanitized_docs = []
for doc in documents:
sanitized_content = sanitize_for_postgres(doc.page_content, " ")
sanitized_doc = Document(
page_content=sanitized_content,
metadata=doc.metadata,
id=doc.id
)
sanitized_docs.append(sanitized_doc)
return self._insert_documents_to_db(sanitized_docs)
```
## Changes Made
- Added `sanitize_for_postgres()` function in
`langchain_core/utils/strings.py`
- Updated `langchain_core/utils/__init__.py` to export the new function
- Added comprehensive unit tests in
`tests/unit_tests/utils/test_strings.py`
- Validated against all examples from the original issue report
## Testing
All tests pass, including:
- Basic NUL byte removal and replacement
- Multiple consecutive NUL bytes
- Empty string handling
- Real examples from the GitHub issue
- Backward compatibility with existing string utilities
This utility enables PostgreSQL integrations in both langchain-community
and langchain-postgres packages to handle documents with NUL bytes
reliably.
Fixes#26033.
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* **Description:** Updated `parse_result` logic to handle cases where
`self.first_tool_only` is `True` and multiple matching keys share the
same function name. Instead of returning the first match prematurely,
the method now prioritizes filtering results by the specified key to
ensure correct selection.
* **Issue:** #32100
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Previously, we hit an index out of range error with empty variable names
(accessing tag[0]), now we through a slightly nicer error
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
