Commit Graph

3155 Commits (c8a171a154ed8c953ea26dc161efbc788942bff9)

Author SHA1 Message Date
Christophe Bornet c8a171a154
community: Implement lazy_load() for GithubFileLoader (#18584) 6 months ago
Leonid Kuligin 04d134df17
marked MatchingEngine as deprecated (#18585)
Thank you for contributing to LangChain!

- [ ] **PR title**: "community: deprecate vectorstores.MatchingEngine"


- [ ] **PR message**: 
- **Description:** announced a deprecation since this integration has
been moved to langchain_google_vertexai
6 months ago
Erick Friis 4ac2cb4adc
anthropic[minor]: add tool calling (#18554) 6 months ago
Bagatur 5fc67ca2c7
langchain[patch]: Release 0.1.11 (#18558) 6 months ago
Erick Friis 68c1878380
anthropic[patch]: model type string (#18510) 6 months ago
Erick Friis 25c7d52140
anthropic[patch]: multimodal (#18517)
- anthropic[minor]: claude 3
- x
- x

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
6 months ago
Erick Friis 343438e872
community[patch]: deprecate community fireworks (#18544) 6 months ago
William FH ca1d42785d
Evals wording (#18542) 6 months ago
Bagatur dd07eddf24
core[patch]: Release 0.1.29 (#18530) 6 months ago
William FH 30ccc009e6
[Evals] Support list examples by dataset version tag (#18534)
previously only supported by timestamp
6 months ago
aditya thomas 5c387a173f
docs: update to docstrings of ChatAnthropic class (#18493)
**Description:** Update docstrings of ChatAnthropic class
**Issue:** Change to ChatAnthropic from ChatAnthropicMessages
**Dependencies:** None
**Lint and test**:  `make format`, `make lint` and `make test` passed
6 months ago
Erick Friis 24f9c700f2
anthropic[minor]: claude 3 (#18508) 6 months ago
William FH 1eec67e8fe
Evaluate on Version (#18471) 6 months ago
Harrison Chase 73d653324f
[Evals] Session-level feedback (#18463)
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
6 months ago
Scott Nath b051bba1a9
community: Add you.com tool, add async to retriever, add async testing, add You tool doc (#18032)
- **Description:** finishes adding the you.com functionality including:
    - add async functions to utility and retriever
    - add the You.com Tool
    - add async testing for utility, retriever, and tool
    - add a tool integration notebook page
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** @scottnath
6 months ago
mackong b89d9fc177
langchain[patch]: add tools renderer for various non-openai agents (#18307)
- **Description:** add tools_renderer for various non-openai agents,
make tools can be render in different ways for your LLM.
  - **Issue:** N/A
  - **Dependencies:** N/A

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
6 months ago
William De Vena a63cee04ac
nvidia-trt[patch]: Invoke callback prior to yielding token (#18446)
## PR title
nvidia-trt[patch]: Invoke callback prior to yielding

## PR message
- Description: Invoke on_llm_new_token callback prior to yielding token
in
_stream method.
- Issue: https://github.com/langchain-ai/langchain/issues/16913
- Dependencies: None
6 months ago
William De Vena 275877980e
community[patch]: Invoke callback prior to yielding token (#18447)
## PR title
community[patch]: Invoke callback prior to yielding token

## PR message
Description: Invoke callback prior to yielding token in _stream method
in llms/vertexai.
Issue: https://github.com/langchain-ai/langchain/issues/16913
Dependencies: None
6 months ago
William De Vena 67375e96e0
community[patch]: Invoke callback prior to yielding token (#18448)
## PR title
community[patch]: Invoke callback prior to yielding token

## PR message
- Description: Invoke callback prior to yielding token in _stream method
in llms/tongyi.
- Issue: https://github.com/langchain-ai/langchain/issues/16913
- Dependencies: None
6 months ago
William De Vena 2087cbae64
community[patch]: Invoke callback prior to yielding token (#18449)
## PR title
community[patch]: Invoke callback prior to yielding token

## PR message
- Description: Invoke callback prior to yielding token in _stream method
in chat_models/perplexity.
- Issue: https://github.com/langchain-ai/langchain/issues/16913
- Dependencies: None
6 months ago
William De Vena eb04d0d3e2
community[patch]: Invoke callback prior to yielding token (#18452)
## PR title
community[patch]: Invoke callback prior to yielding token

## PR message
- Description: Invoke callback prior to yielding token in _stream and
_astream methods in llms/anthropic.
- Issue: https://github.com/langchain-ai/langchain/issues/16913
- Dependencies: None
6 months ago
William De Vena 371bec79bc
community[patch]: Invoke callback prior to yielding token (#18454)
## PR title
community[patch]: Invoke callback prior to yielding token

## PR message
- Description: Invoke callback prior to yielding token in _stream and
_astream methods in llms/baidu_qianfan_endpoint.
- Issue: https://github.com/langchain-ai/langchain/issues/16913
- Dependencies: None
6 months ago
Aayush Kataria 7c2f3f6f95
community[minor]: Adding Azure Cosmos Mongo vCore Vector DB Cache (#16856)
Description:

This pull request introduces several enhancements for Azure Cosmos
Vector DB, primarily focused on improving caching and search
capabilities using Azure Cosmos MongoDB vCore Vector DB. Here's a
summary of the changes:

- **AzureCosmosDBSemanticCache**: Added a new cache implementation
called AzureCosmosDBSemanticCache, which utilizes Azure Cosmos MongoDB
vCore Vector DB for efficient caching of semantic data. Added
comprehensive test cases for AzureCosmosDBSemanticCache to ensure its
correctness and robustness. These tests cover various scenarios and edge
cases to validate the cache's behavior.
- **HNSW Vector Search**: Added HNSW vector search functionality in the
CosmosDB Vector Search module. This enhancement enables more efficient
and accurate vector searches by utilizing the HNSW (Hierarchical
Navigable Small World) algorithm. Added corresponding test cases to
validate the HNSW vector search functionality in both
AzureCosmosDBSemanticCache and AzureCosmosDBVectorSearch. These tests
ensure the correctness and performance of the HNSW search algorithm.
- **LLM Caching Notebook** - The notebook now includes a comprehensive
example showcasing the usage of the AzureCosmosDBSemanticCache. This
example highlights how the cache can be employed to efficiently store
and retrieve semantic data. Additionally, the example provides default
values for all parameters used within the AzureCosmosDBSemanticCache,
ensuring clarity and ease of understanding for users who are new to the
cache implementation.
 
 @hwchase17,@baskaryan, @eyurtsev,
6 months ago
Erick Friis f96dd57501
langchain[patch]: release 0.1.10 (#18410) 6 months ago
Erick Friis 1fd1ac8e95
community[patch]: release 0.0.25 (#18408) 6 months ago
Sourav Pradhan 50abeb7ed9
community[patch]: fix Chroma add_images (#17964)
###  Description

Fixed a small bug in chroma.py add_images(), previously whenever we are
not passing metadata the documents is containing the base64 of the uris
passed, but when we are passing the metadata the documents is containing
normal string uris which should not be the case.

### Issue

In add_images() method when we are calling upsert() we have to use
"b64_texts" instead of normal string "uris".

### Twitter handle

https://twitter.com/whitepegasus01
6 months ago
Kate Silverstein b7c71e2e07
community[minor]: llamafile embeddings support (#17976)
* **Description:** adds `LlamafileEmbeddings` class implementation for
generating embeddings using
[llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models.
Includes related unit tests and notebook showing example usage.
* **Issue:** N/A
* **Dependencies:** N/A
6 months ago
Mateusz Szewczyk 9298a0b941
langchain_ibm[patch] update docstring, dependencies, tests (#18386)
- **Description:** Update docstring, dependencies, tests, README
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
  - **Tag maintainer:** : 

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally -> 
Please make sure integration_tests passing locally -> 

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
6 months ago
Jib c2b1abe91b
mongodb[patch]: Set delete_many only if count_documents is not 0 (#18402)
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Remove the assert statement on the `count_documents`
in setup_class. It should just delete if there are documents present
    - **Issue:** the issue # Crashes on class setup
    - **Dependencies:** None
    - **Twitter handle:** @mongodb


- [x] **Add tests and docs**: If you're adding a new integration, please
include
  1. N/A


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.

Co-authored-by: Jib <jib@byblack.us>
6 months ago
Tomaz Bratanic f6bfb969ba
community[patch]: Add an option for indexed generic label when import neo4j graph documents (#18122)
Current implementation doesn't have an indexed property that would
optimize the import. I have added a `baseEntityLabel` parameter that
allows you to add a secondary node label, which has an indexed id
`property`. By default, the behaviour is identical to previous version.

Since multi-labeled nodes are terrible for text2cypher, I removed the
secondary label from schema representation object and string, which is
used in text2cypher.
6 months ago
Arun Sathiya 4adac20d7b
community[patch]: Make cohere_api_key a SecretStr (#12188)
This PR makes `cohere_api_key` in `llms/cohere` a SecretStr, so that the
API Key is not leaked when `Cohere.cohere_api_key` is represented as a
string.

---------

Signed-off-by: Arun <arun@arun.blog>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
6 months ago
Petteri Johansson 6c1989d292
community[minor], langchain[minor], docs: Gremlin Graph Store and QA Chain (#17683)
- **Description:** 
New feature: Gremlin graph-store and QA chain (including docs).
Compatible with Azure CosmosDB.
  - **Dependencies:** 
  no changes
6 months ago
Ather Fawaz a5ccf5d33c
community[minor]: Add support for Perplexity chat model(#17024)
- **Description:** This PR adds support for [Perplexity AI
APIs](https://blog.perplexity.ai/blog/introducing-pplx-api).
  - **Issues:** None
  - **Dependencies:** None
  - **Twitter handle:** [@atherfawaz](https://twitter.com/AtherFawaz)

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
6 months ago
Rodrigo Nogueira 3438d2cbcc
community[minor]: add maritalk chat (#17675)
**Description:** Adds the MariTalk chat that is based on a LLM specially
trained for Portuguese.

**Twitter handle:** @MaritacaAI
6 months ago
sarahberenji 08fa38d56d
community[patch]: the syntax error for Redis generated query (#17717)
To fix the reported error:
https://github.com/langchain-ai/langchain/discussions/17397
6 months ago
certified-dodo 43e3244573
community[patch]: Fix MongoDBAtlasVectorSearch max_marginal_relevance_search (#17971)
Description:
* `self._embedding_key` is accessed after deletion, breaking
`max_marginal_relevance_search` search
* Introduced in:
e135e5257c
* Updated but still persists in:
ce22e10c4b

Issue: https://github.com/langchain-ai/langchain/issues/17963

Co-authored-by: Bagatur <baskaryan@gmail.com>
6 months ago
Nikita Titov 9f2ab37162
community[patch]: don't try to parse json in case of errored response (#18317)
Related issue: #13896.

In case Ollama is behind a proxy, proxy error responses cannot be
viewed. You aren't even able to check response code.

For example, if your Ollama has basic access authentication and it's not
passed, `JSONDecodeError` will overwrite the truth response error.

<details>
<summary><b>Log now:</b></summary>

```
{
	"name": "JSONDecodeError",
	"message": "Expecting value: line 1 column 1 (char 0)",
	"stack": "---------------------------------------------------------------------------
JSONDecodeError                           Traceback (most recent call last)
File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/requests/models.py:971, in Response.json(self, **kwargs)
    970 try:
--> 971     return complexjson.loads(self.text, **kwargs)
    972 except JSONDecodeError as e:
    973     # Catch JSON-related errors and raise as requests.JSONDecodeError
    974     # This aliases json.JSONDecodeError and simplejson.JSONDecodeError

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    343 if (cls is None and object_hook is None and
    344         parse_int is None and parse_float is None and
    345         parse_constant is None and object_pairs_hook is None and not kw):
--> 346     return _default_decoder.decode(s)
    347 if cls is None:

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
    333 \"\"\"Return the Python representation of ``s`` (a ``str`` instance
    334 containing a JSON document).
    335 
    336 \"\"\"
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
    338 end = _w(s, end).end()

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/decoder.py:355, in JSONDecoder.raw_decode(self, s, idx)
    354 except StopIteration as err:
--> 355     raise JSONDecodeError(\"Expecting value\", s, err.value) from None
    356 return obj, end

JSONDecodeError: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

JSONDecodeError                           Traceback (most recent call last)
Cell In[3], line 1
----> 1 print(translate_func().invoke('text'))

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/runnables/base.py:2053, in RunnableSequence.invoke(self, input, config)
   2051 try:
   2052     for i, step in enumerate(self.steps):
-> 2053         input = step.invoke(
   2054             input,
   2055             # mark each step as a child run
   2056             patch_config(
   2057                 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
   2058             ),
   2059         )
   2060 # finish the root run
   2061 except BaseException as e:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:165, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    154 def invoke(
    155     self,
    156     input: LanguageModelInput,
   (...)
    160     **kwargs: Any,
    161 ) -> BaseMessage:
    162     config = ensure_config(config)
    163     return cast(
    164         ChatGeneration,
--> 165         self.generate_prompt(
    166             [self._convert_input(input)],
    167             stop=stop,
    168             callbacks=config.get(\"callbacks\"),
    169             tags=config.get(\"tags\"),
    170             metadata=config.get(\"metadata\"),
    171             run_name=config.get(\"run_name\"),
    172             **kwargs,
    173         ).generations[0][0],
    174     ).message

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:543, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    535 def generate_prompt(
    536     self,
    537     prompts: List[PromptValue],
   (...)
    540     **kwargs: Any,
    541 ) -> LLMResult:
    542     prompt_messages = [p.to_messages() for p in prompts]
--> 543     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:407, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    405         if run_managers:
    406             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 407         raise e
    408 flattened_outputs = [
    409     LLMResult(generations=[res.generations], llm_output=res.llm_output)
    410     for res in results
    411 ]
    412 llm_output = self._combine_llm_outputs([res.llm_output for res in results])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:397, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    394 for i, m in enumerate(messages):
    395     try:
    396         results.append(
--> 397             self._generate_with_cache(
    398                 m,
    399                 stop=stop,
    400                 run_manager=run_managers[i] if run_managers else None,
    401                 **kwargs,
    402             )
    403         )
    404     except BaseException as e:
    405         if run_managers:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:576, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    572     raise ValueError(
    573         \"Asked to cache, but no cache found at `langchain.cache`.\"
    574     )
    575 if new_arg_supported:
--> 576     return self._generate(
    577         messages, stop=stop, run_manager=run_manager, **kwargs
    578     )
    579 else:
    580     return self._generate(messages, stop=stop, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:250, in ChatOllama._generate(self, messages, stop, run_manager, **kwargs)
    226 def _generate(
    227     self,
    228     messages: List[BaseMessage],
   (...)
    231     **kwargs: Any,
    232 ) -> ChatResult:
    233     \"\"\"Call out to Ollama's generate endpoint.
    234 
    235     Args:
   (...)
    247             ])
    248     \"\"\"
--> 250     final_chunk = self._chat_stream_with_aggregation(
    251         messages,
    252         stop=stop,
    253         run_manager=run_manager,
    254         verbose=self.verbose,
    255         **kwargs,
    256     )
    257     chat_generation = ChatGeneration(
    258         message=AIMessage(content=final_chunk.text),
    259         generation_info=final_chunk.generation_info,
    260     )
    261     return ChatResult(generations=[chat_generation])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:183, in ChatOllama._chat_stream_with_aggregation(self, messages, stop, run_manager, verbose, **kwargs)
    174 def _chat_stream_with_aggregation(
    175     self,
    176     messages: List[BaseMessage],
   (...)
    180     **kwargs: Any,
    181 ) -> ChatGenerationChunk:
    182     final_chunk: Optional[ChatGenerationChunk] = None
--> 183     for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
    184         if stream_resp:
    185             chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:156, in ChatOllama._create_chat_stream(self, messages, stop, **kwargs)
    147 def _create_chat_stream(
    148     self,
    149     messages: List[BaseMessage],
    150     stop: Optional[List[str]] = None,
    151     **kwargs: Any,
    152 ) -> Iterator[str]:
    153     payload = {
    154         \"messages\": self._convert_messages_to_ollama_messages(messages),
    155     }
--> 156     yield from self._create_stream(
    157         payload=payload, stop=stop, api_url=f\"{self.base_url}/api/chat/\", **kwargs
    158     )

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/llms/ollama.py:234, in _OllamaCommon._create_stream(self, api_url, payload, stop, **kwargs)
    228         raise OllamaEndpointNotFoundError(
    229             \"Ollama call failed with status code 404. \"
    230             \"Maybe your model is not found \"
    231             f\"and you should pull the model with `ollama pull {self.model}`.\"
    232         )
    233     else:
--> 234         optional_detail = response.json().get(\"error\")
    235         raise ValueError(
    236             f\"Ollama call failed with status code {response.status_code}.\"
    237             f\" Details: {optional_detail}\"
    238         )
    239 return response.iter_lines(decode_unicode=True)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/requests/models.py:975, in Response.json(self, **kwargs)
    971     return complexjson.loads(self.text, **kwargs)
    972 except JSONDecodeError as e:
    973     # Catch JSON-related errors and raise as requests.JSONDecodeError
    974     # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
--> 975     raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)

JSONDecodeError: Expecting value: line 1 column 1 (char 0)"
}
```

</details>


<details>

<summary><b>Log after a fix:</b></summary>

```
{
	"name": "ValueError",
	"message": "Ollama call failed with status code 401. Details: <html>\r
<head><title>401 Authorization Required</title></head>\r
<body>\r
<center><h1>401 Authorization Required</h1></center>\r
<hr><center>nginx/1.18.0 (Ubuntu)</center>\r
</body>\r
</html>\r
",
	"stack": "---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 print(translate_func().invoke('text'))

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/runnables/base.py:2053, in RunnableSequence.invoke(self, input, config)
   2051 try:
   2052     for i, step in enumerate(self.steps):
-> 2053         input = step.invoke(
   2054             input,
   2055             # mark each step as a child run
   2056             patch_config(
   2057                 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
   2058             ),
   2059         )
   2060 # finish the root run
   2061 except BaseException as e:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:165, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    154 def invoke(
    155     self,
    156     input: LanguageModelInput,
   (...)
    160     **kwargs: Any,
    161 ) -> BaseMessage:
    162     config = ensure_config(config)
    163     return cast(
    164         ChatGeneration,
--> 165         self.generate_prompt(
    166             [self._convert_input(input)],
    167             stop=stop,
    168             callbacks=config.get(\"callbacks\"),
    169             tags=config.get(\"tags\"),
    170             metadata=config.get(\"metadata\"),
    171             run_name=config.get(\"run_name\"),
    172             **kwargs,
    173         ).generations[0][0],
    174     ).message

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:543, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    535 def generate_prompt(
    536     self,
    537     prompts: List[PromptValue],
   (...)
    540     **kwargs: Any,
    541 ) -> LLMResult:
    542     prompt_messages = [p.to_messages() for p in prompts]
--> 543     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:407, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    405         if run_managers:
    406             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 407         raise e
    408 flattened_outputs = [
    409     LLMResult(generations=[res.generations], llm_output=res.llm_output)
    410     for res in results
    411 ]
    412 llm_output = self._combine_llm_outputs([res.llm_output for res in results])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:397, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    394 for i, m in enumerate(messages):
    395     try:
    396         results.append(
--> 397             self._generate_with_cache(
    398                 m,
    399                 stop=stop,
    400                 run_manager=run_managers[i] if run_managers else None,
    401                 **kwargs,
    402             )
    403         )
    404     except BaseException as e:
    405         if run_managers:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:576, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    572     raise ValueError(
    573         \"Asked to cache, but no cache found at `langchain.cache`.\"
    574     )
    575 if new_arg_supported:
--> 576     return self._generate(
    577         messages, stop=stop, run_manager=run_manager, **kwargs
    578     )
    579 else:
    580     return self._generate(messages, stop=stop, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:250, in ChatOllama._generate(self, messages, stop, run_manager, **kwargs)
    226 def _generate(
    227     self,
    228     messages: List[BaseMessage],
   (...)
    231     **kwargs: Any,
    232 ) -> ChatResult:
    233     \"\"\"Call out to Ollama's generate endpoint.
    234 
    235     Args:
   (...)
    247             ])
    248     \"\"\"
--> 250     final_chunk = self._chat_stream_with_aggregation(
    251         messages,
    252         stop=stop,
    253         run_manager=run_manager,
    254         verbose=self.verbose,
    255         **kwargs,
    256     )
    257     chat_generation = ChatGeneration(
    258         message=AIMessage(content=final_chunk.text),
    259         generation_info=final_chunk.generation_info,
    260     )
    261     return ChatResult(generations=[chat_generation])

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:328, in ChatOllamaCustom._chat_stream_with_aggregation(self, messages, stop, run_manager, verbose, **kwargs)
    319 def _chat_stream_with_aggregation(
    320     self,
    321     messages: List[BaseMessage],
   (...)
    325     **kwargs: Any,
    326 ) -> ChatGenerationChunk:
    327     final_chunk: Optional[ChatGenerationChunk] = None
--> 328     for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
    329         if stream_resp:
    330             chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:301, in ChatOllamaCustom._create_chat_stream(self, messages, stop, **kwargs)
    292 def _create_chat_stream(
    293     self,
    294     messages: List[BaseMessage],
    295     stop: Optional[List[str]] = None,
    296     **kwargs: Any,
    297 ) -> Iterator[str]:
    298     payload = {
    299         \"messages\": self._convert_messages_to_ollama_messages(messages),
    300     }
--> 301     yield from self._create_stream(
    302         payload=payload, stop=stop, api_url=f\"{self.base_url}/api/chat\", **kwargs
    303     )

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:134, in _OllamaCommonCustom._create_stream(self, api_url, payload, stop, **kwargs)
    132     else:
    133         optional_detail = response.text
--> 134         raise ValueError(
    135             f\"Ollama call failed with status code {response.status_code}.\"
    136             f\" Details: {optional_detail}\"
    137         )
    138 return response.iter_lines(decode_unicode=True)

ValueError: Ollama call failed with status code 401. Details: <html>\r
<head><title>401 Authorization Required</title></head>\r
<body>\r
<center><h1>401 Authorization Required</h1></center>\r
<hr><center>nginx/1.18.0 (Ubuntu)</center>\r
</body>\r
</html>\r
"
}
```

</details>

The same is true for timeout errors or when you simply mistyped in
`base_url` arg and get response from some other service, for instance.

Real Ollama errors are still clearly readable:

```
ValueError: Ollama call failed with status code 400. Details: {"error":"invalid options: unknown_option"}
```

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
6 months ago
Yudhajit Sinha e2b901c35b
community[patch]: chat message histrory mypy fix (#18250)
Description: Fixed type: ignore's for mypy for
chat_message_histories(streamlit)
Adresses #17048 

Planning to add more based on reviews
6 months ago
Gabriel Altay b9416dc96a
docs: update pinecone README to use PineconeVectorStore (#18170) 6 months ago
Hemslo Wang 58a2abf089
community[patch]: fix RecursiveUrlLoader metadata_extractor return type (#18193)
**Description:** Fix `metadata_extractor` type for `RecursiveUrlLoader`,
the default `_metadata_extractor` returns `dict` instead of `str`.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A

Signed-off-by: Hemslo Wang <hemslo.wang@gmail.com>
6 months ago
Maxime Perrin 98380cff9b
community[patch]: removing "response_mode" parameter in llama_index retriever (#18180)
- **Description:** Removing this line 
```python
response = index.query(query, response_mode="no_text", **self.query_kwargs)
```
to 
```python
response = index.query(query, **self.query_kwargs)
```
Since llama index query does not support response_mode anymore : ``` |
TypeError: BaseQueryEngine.query() got an unexpected keyword argument
'response_mode'````
  - **Twitter handle:** @maximeperrin_

---------

Co-authored-by: Maxime Perrin <mperrin@doing.fr>
6 months ago
Christophe Bornet 177f51c7bd
community: Use default load() implementation in doc loaders (#18385)
Following https://github.com/langchain-ai/langchain/pull/18289
6 months ago
William De Vena 42341bc787
infra: fake model invoke callback prior to yielding token (#18286)
## PR title
core[patch]: Invoke callback prior to yielding

## PR message
Description: Invoke on_llm_new_token callback prior to yielding token in
_stream and _astream methods.
Issue: https://github.com/langchain-ai/langchain/issues/16913
Dependencies: None
Twitter handle: None
6 months ago
mwmajewsk e192f6b6eb
community[patch]: fix, better error message in deeplake vectoriser (#18397)
If the document loader recieves Pathlib path instead of str, it reads
the file correctly, but the problem begins when the document is added to
Deeplake.
This problem arises from casting the path to str in the metadata.

```python
deeplake = True
fname = Path('./lorem_ipsum.txt')
loader = TextLoader(fname, encoding="utf-8")
docs = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks= text_splitter.split_documents(docs)
if deeplake:
    db = DeepLake(dataset_path=ds_path, embedding=embeddings, token=activeloop_token)
    db.add_documents(chunks)
else:
    db = Chroma.from_documents(docs, embeddings)
```

So using this snippet of code the error message for deeplake looks like
this:

```
[part of error message omitted]

Traceback (most recent call last):
  File "/home/mwm/repositories/sources/fixing_langchain/main.py", line 53, in <module>
    db.add_documents(chunks)
  File "/home/mwm/repositories/sources/langchain/libs/core/langchain_core/vectorstores.py", line 139, in add_documents
    return self.add_texts(texts, metadatas, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/deeplake.py", line 258, in add_texts
    return self.vectorstore.add(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/deeplake_vectorstore.py", line 226, in add
    return self.dataset_handler.add(
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py", line 139, in add
    dataset_utils.extend_or_ingest_dataset(
  File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/vector_search/dataset/dataset.py", line 544, in extend_or_ingest_dataset
    extend(
  File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/vector_search/dataset/dataset.py", line 505, in extend
    dataset.extend(batched_processed_tensors, progressbar=False)
  File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/dataset/dataset.py", line 3247, in extend
    raise SampleExtendError(str(e)) from e.__cause__
deeplake.util.exceptions.SampleExtendError: Failed to append a sample to the tensor 'metadata'. See more details in the traceback. If you wish to skip the samples that cause errors, please specify `ignore_errors=True`.
```

Which is does not explain the error well enough.
The same error for chroma looks like this 

```
During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/mwm/repositories/sources/fixing_langchain/main.py", line 56, in <module>
    db = Chroma.from_documents(docs, embeddings)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 778, in from_documents
    return cls.from_texts(
           ^^^^^^^^^^^^^^^
  File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 736, in from_texts
    chroma_collection.add_texts(
  File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 309, in add_texts
    raise ValueError(e.args[0] + "\n\n" + msg)
ValueError: Expected metadata value to be a str, int, float or bool, got lorem_ipsum.txt which is a <class 'pathlib.PosixPath'>

Try filtering complex metadata from the document using langchain_community.vectorstores.utils.filter_complex_metadata.
```

Which is way more user friendly, so I just added information about
possible mismatch of the type in the error message, the same way it is
covered in chroma
https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/chroma.py#L224
6 months ago
Daniel Chico 7d962278f6
community[patch]: type ignore fixes (#18395)
Related to #17048
6 months ago
Christophe Bornet 69be82c86d
community[patch]: Implement lazy_load() for CSVLoader (#18391)
Covered by `test_csv_loader.py`
6 months ago
Bagatur c54d6eb5da
fireworks[patch]: support "any" tool_choice (#18343)
per https://readme.fireworks.ai/docs/function-calling
6 months ago
Erick Friis 6afb135baa
astradb: move to langchain-datastax repo (#18354) 6 months ago
Guangdong Liu 760a16ff32
community[patch]: Fix ChatModel for sparkllm Bug. (#18375)
**PR message**: ***Delete this entire checklist*** and replace with
    - **Description:** fix sparkllm paramer error
    - **Issue:**   close #18370
- **Dependencies:** change `IFLYTEK_SPARK_APP_URL` to
`IFLYTEK_SPARK_API_URL`
    - **Twitter handle:** No
6 months ago
Yujie Qian cbb65741a7
community[patch]: Voyage AI updates default model and batch size (#17655)
- **Description:** update the default model and batch size in
VoyageEmbeddings
    - **Issue:** N/A
    - **Dependencies:** N/A
    - **Twitter handle:** N/A

---------

Co-authored-by: fodizoltan <zoltan@conway.expert>
6 months ago