150 Commits (f1248f8d9a9ff3de02566b7b64019c82d03872de)

Author	SHA1	Message	Date
Yudhajit Sinha	4570b477b9	community[patch]: Invoke callback prior to yielding token (titan_takeoff) (#18560) ... ## PR title community[patch]: Invoke callback prior to yielding token ## PR message - Description: Invoke callback prior to yielding token in _stream_ method in llms/titan_takeoff. - Issue: #16913 - Dependencies: None	6 months ago
Erick Friis	343438e872	community[patch]: deprecate community fireworks (#18544)	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	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
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
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
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
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
Shengsheng Huang	ae471a7dcb	community[minor]: add BigDL-LLM integrations (#17953) ... - **Description**: [`bigdl-llm`](https://github.com/intel-analytics/BigDL) is a library for running LLM on Intel XPU (from Laptop to GPU to Cloud) using INT4/FP4/INT8/FP8 with very low latency (for any PyTorch model). This PR adds bigdl-llm integrations to langchain. - **Issue**: NA - **Dependencies**: `bigdl-llm` library - **Contribution maintainer**: @shane-huang Examples added: - docs/docs/integrations/llms/bigdl.ipynb	6 months ago
Ethan Yang	f61cb8d407	community[minor]: Add openvino backend support (#11591) ... - **Description:** add openvino backend support by HuggingFace Optimum Intel, - **Dependencies:** “optimum[openvino]”, --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
Erick Friis	eefb49680f	multiple[patch]: fix deprecation versions (#18349)	7 months ago
William De Vena	6b58943917	community[patch]: Invoke callback prior to yielding token (#18288) ... ## PR title community[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	7 months ago
kYLe	17ecf6e119	community[patch]: Remove model limitation on Anyscale LLM (#17662) ... **Description:** Llama Guard is deprecated from Anyscale public endpoint. **Issue:** Change the default model. and remove the limitation of only use Llama Guard with Anyscale LLMs Anyscale LLM can also works with all other Chat model hosted on Anyscale. Also added `async_client` for Anyscale LLM	7 months ago
Erick Friis	29e0445490	community[patch]: BaseLLM typing in init (#18029)	7 months ago
Guangdong Liu	4197efd67a	community: Fix SparkLLM error (#18015) ... Thank you for contributing to LangChain! - [x] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - **Description:** fix SparkLLM error - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!	7 months ago
Brad Erickson	ecd72d26cf	community: Bugfix - correct Ollama API path to avoid HTTP 307 (#17895) ... Sets the correct /api/generate path, without ending /, to reduce HTTP requests. Reference: https://github.com/ollama/ollama/blob/efe040f8/docs/api.md#generate-request-streaming Before: DEBUG: Starting new HTTP connection (1): localhost:11434 DEBUG: http://localhost:11434 "POST /api/generate/ HTTP/1.1" 307 0 DEBUG: http://localhost:11434 "POST /api/generate HTTP/1.1" 200 None After: DEBUG: Starting new HTTP connection (1): localhost:11434 DEBUG: http://localhost:11434 "POST /api/generate HTTP/1.1" 200 None	7 months ago
Guangdong Liu	47b1b7092d	community[minor]: Add SparkLLM to community (#17702)	7 months ago
Aymeric Roucher	0d294760e7	Community: Fuse HuggingFace Endpoint-related classes into one (#17254) ... ## Description Fuse HuggingFace Endpoint-related classes into one: - [HuggingFaceHub](5ceaf784f3/libs/community/langchain_community/llms/huggingface_hub.py) - [HuggingFaceTextGenInference](5ceaf784f3/libs/community/langchain_community/llms/huggingface_text_gen_inference.py) - and [HuggingFaceEndpoint](5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py) Are fused into - HuggingFaceEndpoint ## Issue The deduplication of classes was creating a lack of clarity, and additional effort to develop classes leads to issues like [this hack](5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py (L159)). ## Dependancies None, this removes dependancies. ## Twitter handle If you want to post about this: @AymericRoucher --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Mohammad Mohtashim	43dc5d3416	community[patch]: OpenLLM Client Fixes + Added Timeout Parameter (#17478) ... - OpenLLM was using outdated method to get the final text output from openllm client invocation which was raising the error. Therefore corrected that. - OpenLLM `_identifying_params` was getting the openllm's client configuration using outdated attributes which was raising error. - Updated the docstring for OpenLLM. - Added timeout parameter to be passed to underlying openllm client.	7 months ago
Mateusz Szewczyk	916332ef5b	ibm: added partners package `langchain_ibm`, added llm (#16512) ... - **Description:** Added `langchain_ibm` as an langchain partners package of IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (`WatsonxLLM`) - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - **Tag maintainer:** : --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
wulixuan	c776cfc599	community[minor]: integrate with model Yuan2.0 (#15411) ... 1. integrate with [`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md) 2. update `langchain.llms` 3. add a new doc for [Yuan2.0 integration](docs/docs/integrations/llms/yuan2.ipynb) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Alex Peplowski	70c296ae96	community[patch]: Expose Anthropic Retry Logic (#17069) ... **Description:** Expose Anthropic's retry logic, so that `max_retries` can be configured via langchain. Anthropic's retry logic is implemented in their Python SDK here: https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#retries --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Kate Silverstein	0bc4a9b3fc	community[minor]: Adds Llamafile as an LLM (#17431) ... * **Description:** Adds a simple LLM implementation for interacting with [llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models. * **Dependencies:** N/A * **Issue:** N/A **Detail** [llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you run LLMs locally from a single file on most computers without installing any dependencies. To use the llamafile LLM implementation, the user needs to: 1. Download a llamafile e.g. https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true 2. Make the file executable. 3. Run the llamafile in 'server mode'. (All llamafiles come packaged with a lightweight server; by default, the server listens at `http://localhost:8080`.) ```bash wget https://url/of/model.llamafile chmod +x model.llamafile ./model.llamafile --server --nobrowser ``` Now, the user can invoke the LLM via the LangChain client: ```python from langchain_community.llms.llamafile import Llamafile llm = Llamafile() llm.invoke("Tell me a joke.") ```	7 months ago
Nat Noordanus	8a3b74fe1f	community[patch]: Fix pydantic ForwardRef error in BedrockBase (#17416) ... - **Description:** Fixes a type annotation issue in the definition of BedrockBase. This issue was that the annotation for the `config` attribute includes a ForwardRef to `botocore.client.Config` which is only imported when `TYPE_CHECKING`. This can cause pydantic to raise an error like `pydantic.errors.ConfigError: field "config" not yet prepared so type is still a ForwardRef, ...`. - **Issue:** N/A - **Dependencies:** N/A - **Twitter handle:** `@__nat_n__`	7 months ago
Theo / Taeyoon Kang	1987f905ed	core[patch]: Support .yml extension for YAML (#16783) ... - **Description:** [AS-IS] When dealing with a yaml file, the extension must be .yaml. [TO-BE] In the absence of extension length constraints in the OS, the extension of the YAML file is yaml, but control over the yml extension must still be made. It's as if it's an error because it's a .jpg extension in jpeg support. - **Issue:** - - **Dependencies:** no dependencies required for this change,	7 months ago
Robby	0653aa469a	community[patch]: Invoke callback prior to yielding token (#17346) ... **Description:** Invoke callback prior to yielding token in stream method for watsonx. **Issue:** [Callback for on_llm_new_token should be invoked before the token is yielded by the model #16913](https://github.com/langchain-ai/langchain/issues/16913) Co-authored-by: Robby <h0rv@users.noreply.github.com>	7 months ago
Erick Friis	3a2eb6e12b	infra: add print rule to ruff (#16221) ... Added noqa for existing prints. Can slowly remove / will prevent more being intro'd	7 months ago
kYLe	c9999557bf	community[patch]: Modify LLMs/Anyscale work with OpenAI API v1 (#14206) ... <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> - **Description:** 1. Modify LLMs/Anyscale to work with OAI v1 2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale 3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name convention (reverted) --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
Leonid Ganeline	932c52c333	community[patch]: docstrings (#16810) ... - added missed docstrings - formated docstrings to the consistent form	7 months ago
Armin Stepanyan	641efcf41c	community: add runtime kwargs to HuggingFacePipeline (#17005) ... This PR enables changing the behaviour of huggingface pipeline between different calls. For example, before this PR there's no way of changing maximum generation length between different invocations of the chain. This is desirable in cases, such as when we want to scale the maximum output size depending on a dynamic prompt size. Usage example: ```python from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "gpt2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) hf = HuggingFacePipeline(pipeline=pipe) hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42}) ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Liang Zhang	7306600e2f	community[patch]: Support SerDe transform functions in Databricks LLM (#16752) ... **Description:** Databricks LLM does not support SerDe the transform_input_fn and transform_output_fn. After saving and loading, the LLM will be broken. This PR serialize these functions into a hex string using pickle, and saving the hex string in the yaml file. Using pickle to serialize a function can be flaky, but this is a simple workaround that unblocks many use cases. If more sophisticated SerDe is needed, we can improve it later. Test: Added a simple unit test. I did manual test on Databricks and it works well. The saved yaml looks like: ``` llm: _type: databricks cluster_driver_port: null cluster_id: null databricks_uri: databricks endpoint_name: databricks-mixtral-8x7b-instruct extra_params: {} host: e2-dogfood.staging.cloud.databricks.com max_tokens: null model_kwargs: null n: 1 stop: null task: null temperature: 0.0 transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e transform_output_fn: null ``` @baskaryan ```python from langchain_community.embeddings import DatabricksEmbeddings from langchain_community.llms import Databricks from langchain.chains import RetrievalQA from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import FAISS import mlflow embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") def transform_input(**request): request["messages"] = [ { "role": "user", "content": request["prompt"] } ] del request["prompt"] return request llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input) persist_dir = "faiss_databricks_embedding" # Create the vector db, persist the db to a local fs folder loader = TextLoader("state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) db = FAISS.from_documents(docs, embeddings) db.save_local(persist_dir) def load_retriever(persist_directory): embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") vectorstore = FAISS.load_local(persist_directory, embeddings) return vectorstore.as_retriever() retriever = load_retriever(persist_dir) retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever) with mlflow.start_run() as run: logged_model = mlflow.langchain.log_model( retrievalQA, artifact_path="retrieval_qa", loader_fn=load_retriever, persist_dir=persist_dir, ) # Load the retrievalQA chain loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri) print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}])) ```	7 months ago
François Paupier	929f071513	community[patch]: Fix error in `LlamaCpp` community LLM with Configurable Fields, 'grammar' custom type not available (#16995) ... - **Description:** Ensure the `LlamaGrammar` custom type is always available when instantiating a `LlamaCpp` LLM - **Issue:** #16994 - **Dependencies:** None - **Twitter handle:** @fpaupier --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Mohammad Mohtashim	3c4b24b69a	community[patch]: Fix the _call of HuggingFaceHub (#16891) ... Fixed the following identified issue: #16849 @baskaryan	7 months ago
Supreet Takkar	ae33979813	community[patch]: Allow adding ARNs as model_id to support Amazon Bedrock custom models (#16800) ... - **Description:** Adds an additional class variable to `BedrockBase` called `provider` that allows sending a model provider such as amazon, cohere, ai21, etc. Up until now, the model provider is extracted from the `model_id` using the first part before the `.`, such as `amazon` for `amazon.titan-text-express-v1` (see [supported list of Bedrock model IDs here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html)). But for custom Bedrock models where the ARN of the provisioned throughput must be supplied, the `model_id` is like `arn:aws:bedrock:...` so the `model_id` cannot be extracted from this. A model `provider` is required by the LangChain Bedrock class to perform model-based processing. To allow the same processing to be performed for custom-models of a specific base model type, passing this `provider` argument can help solve the issues. The alternative considered here was the use of `provider.arn:aws:bedrock:...` which then requires ARN to be extracted and passed separately when invoking the model. The proposed solution here is simpler and also does not cause issues for current models already using the Bedrock class. - **Issue:** N/A - **Dependencies:** N/A --------- Co-authored-by: Piyush Jain <piyushjain@duck.com>	7 months ago
Bagatur	66e45e8ab7	community[patch]: chat model mypy fixes (#17061) ... Related to #17048	7 months ago
Harrison Chase	4eda647fdd	infra: add -p to mkdir in lint steps (#17013) ... Previously, if this did not find a mypy cache then it wouldnt run this makes it always run adding mypy ignore comments with existing uncaught issues to unblock other prs --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	7 months ago
Bob Lin	546b757303	community: Add ChatGLM3 (#15265) ... Add [ChatGLM3](https://github.com/THUDM/ChatGLM3) and updated [chatglm.ipynb](https://python.langchain.com/docs/integrations/llms/chatglm) --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
baichuan-assistant	f8f2649f12	community: Add Baichuan LLM to community (#16724) ... Replace this entire comment with: - **Description:** Add Baichuan LLM to integration/llm, also updated related docs. Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>	8 months ago
hulitaitai	32cad38ec6	<langchain_community\llms\chatglm.py>: <Correcting "history"> (#16729) ... Use the real "history" provided by the original program instead of putting "None" in the history. - **Description:** I change one line in the code to make it return the "history" of the chat model. - **Issue:** At the moment it returns only the answers of the chat model. However the chat model himself provides a history more complet with the questions of the user. - **Dependencies:** no dependencies required for this change,	8 months ago
Bassem Yacoube	85e93e05ed	community[minor]: Update OctoAI LLM, Embedding and documentation (#16710) ... This PR includes updates for OctoAI integrations: - The LLM class was updated to fix a bug that occurs with multiple sequential calls - The Embedding class was updated to support the new GTE-Large endpoint released on OctoAI lately - The documentation jupyter notebook was updated to reflect using the new LLM sdk Thank you!	8 months ago
Zhuoyun(John) Xu	508bde7f40	community[patch]: Ollama - Pass headers to post request in async method (#16660) ... # Description A previous PR (https://github.com/langchain-ai/langchain/pull/15881) added option to pass headers to ollama endpoint, but headers are not pass to the async method.	8 months ago
Micah Parker	6543e585a5	community[patch]: Added support for Ollama's num_predict option in ChatOllama (#16633) ... Just a simple default addition to the options payload for a ollama generate call to support a max_new_tokens parameter. Should fix issue: https://github.com/langchain-ai/langchain/issues/14715	8 months ago
Bagatur	61e876aad8	openai[patch]: Explicitly support embedding dimensions (#16596)	8 months ago
Dmitry Tyumentsev	e86e66bad7	community[patch]: YandexGPT models - add sleep_interval (#16566) ... Added sleep between requests to prevent errors associated with simultaneous requests.	8 months ago
Rave Harpaz	c4e9c9ca29	community[minor]: Add OCI Generative AI integration (#16548) ... <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** Adding Oracle Cloud Infrastructure Generative AI integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API. Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm - **Issue:** None, - **Dependencies:** OCI Python SDK, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. Passed See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. we provide unit tests. However, we cannot provide integration tests due to Oracle policies that prohibit public sharing of api keys.   If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	8 months ago
Harel Gal	a91181fe6d	community[minor]: add support for Guardrails for Amazon Bedrock (#15099) ... Added support for optionally supplying 'Guardrails for Amazon Bedrock' on both types of model invocations (batch/regular and streaming) and for all models supported by the Amazon Bedrock service. @baskaryan @hwchase17 ```python llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, guardrails={"id": " <guardrail_id>", "version": "<guardrail_version>", "trace": True}, callbacks=[BedrockAsyncCallbackHandler()]) class BedrockAsyncCallbackHandler(AsyncCallbackHandler): """Async callback handler that can be used to handle callbacks from langchain.""" async def on_llm_error( self, error: BaseException, **kwargs: Any, ) -> Any: reason = kwargs.get("reason") if reason == "GUARDRAIL_INTERVENED": # kwargs contains additional trace information sent by 'Guardrails for Bedrock' service. print(f"""Guardrails: {kwargs}""") # streaming llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, streaming=True, guardrails={"id": "<guardrail_id>", "version": "<guardrail_version>"}) ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	8 months ago
chyroc	61da2ff24c	community[patch]: use SecretStr for yandex model secrets (#15463)	8 months ago
Alessio Serra	d628a80a5d	community[patch]: added 'conversational' as a valid task for hugginface endopoint models (#15761) ... - **Description:** added the conversational task to hugginFace endpoint in order to use models designed for chatbot programming. - **Dependencies:** None --------- Co-authored-by: Alessio Serra (ext.) <alessio.serra@partner.bmw.de> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	8 months ago
Shivani Modi	4e160540ff	community[minor]: Adding Konko Completion endpoint (#15570) ... This PR introduces update to Konko Integration with LangChain. 1. **New Endpoint Addition**: Integration of a new endpoint to utilize completion models hosted on Konko. 2. **Chat Model Updates for Backward Compatibility**: We have updated the chat models to ensure backward compatibility with previous OpenAI versions. 4. **Updated Documentation**: Comprehensive documentation has been updated to reflect these new changes, providing clear guidance on utilizing the new features and ensuring seamless integration. Thank you to the LangChain team for their exceptional work and for considering this PR. Please let me know if any additional information is needed. --------- Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MacBook-Pro.local> Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MBP.lan>	8 months ago
Facundo Santiago	92e6a641fd	feat: adding paygo api support for Azure ML / Azure AI Studio (#14560) ... - **Description:** Introducing support for LLMs and Chat models running in Azure AI studio and Azure ML using the new deployment mode pay-as-you-go (model as a service). - **Issue:** NA - **Dependencies:** None. - **Tag maintainer:** @prakharg-msft @gdyre - **Twitter handle:** @santiagofacundo Examples added: * [docs/docs/integrations/llms/azure_ml.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_endpoint.ipynb) * [docs/docs/integrations/chat/azureml_chat_endpoint.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
Massimiliano Pronesti	e529939c54	feat(llms): support more tasks in HuggingFaceHub LLM and remove deprecated dep (#14406) ... - **Description:** this PR upgrades the `HuggingFaceHub` LLM: * support more tasks (`translation` and `conversational`) * replaced the deprecated `InferenceApi` with `InferenceClient` * adjusted the overall logic to use the "recommended" model for each task when no model is provided, and vice-versa. - **Tag mainter(s)**: @baskaryan @hwchase17	8 months ago
DL	b9e7f6f38a	community[minor]: Bedrock async methods (#12477) ... Description: Added support for asynchronous streaming in the Bedrock class and corresponding tests. Primarily: async def aprepare_output_stream async def _aprepare_input_and_invoke_stream async def _astream async def _acall I've ensured that the code adheres to the project's linting and formatting standards by running make format, make lint, and make test. Issue: #12054, #11589 Dependencies: None Tag maintainer: @baskaryan Twitter handle: @dominic_lovric --------- Co-authored-by: Piyush Jain <piyushjain@duck.com>	8 months ago
Guillem Orellana Trullols	aad2aa7188	community[patch]: BedrockChat -> Support Titan express as chat model (#15408) ... Titan Express model was not supported as a chat model because LangChain messages were not "translated" to a text prompt. Co-authored-by: Guillem Orellana Trullols <guillem.orellana_trullols@siemens.com>	8 months ago
Tom Jorquera	1445ac95e8	community[patch]: Enable streaming for GPT4all (#16392) ... `streaming` param was never passed to model	8 months ago
mikeFore4	9d32af72ce	community[patch]: huggingface hub character removal bug fix (#16233) ... - **Description:** Some text-generation models on huggingface repeat the prompt in their generated response, but not all do! The tests use "gpt2" which DOES repeat the prompt and as such, the HuggingFaceHub class is hardcoded to remove the first few characters of the response (to match the len(prompt)). However, if you are using a model (such as the very popular "meta-llama/Llama-2-7b-chat-hf") that DOES NOT repeat the prompt in it's generated text, then the beginning of the generated text will be cut off. This code change fixes that bug by first checking whether the prompt is repeated in the generated response and removing it conditionally. - **Issue:** #16232 - **Dependencies:** N/A - **Twitter handle:** N/A	8 months ago
Fei Wang	d0e101e4e0	community[patch]: fix ollama astream (#16070) ... Update ollama.py	8 months ago
chyroc	d334efc848	community[patch]: fix top_p type hint (#15452) ... fix: https://github.com/langchain-ai/langchain/issues/15341 @efriis	8 months ago
Mateusz Szewczyk	251afda549	community[patch]: fix stop (stop_sequences) param on WatsonxLLM (#15541) ... - **Description:** Fix to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (stop (`stop_sequences`) param on watsonxLLM) - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),	8 months ago
shahrin014	86321a949f	community: Ollama - Parameter structure to follow official documentation (#16035) ... ## Feature - Follow parameter structure as per official documentation - top level parameters (e.g. model, system, template) will be passed as top level parameters - other parameters will be sent in options unless options is provided  ## Tests - Test if top level parameters handled properly - Test if parameters that are not top level parameters are handled as options - Test if options is provided, it will be passed as is	8 months ago
shahrin014	bdd90ae2ee	community: Ollama - Pass headers to post request (#15881) ... ## Feature - Set additional headers in constructor - Headers will be sent in post request This feature is useful if deploying Ollama on a cloud service such as hugging face, which requires authentication tokens to be passed in the request header. ## Tests - Test if header is passed - Test if header is not passed	8 months ago
Erick Friis	38523d7c57	together[minor]: add llm (#15853)	8 months ago
Erick Friis	85a4594ed7	community[patch]: more deprecations (#15782)	8 months ago
Bagatur	ee5bd986de	community[patch]: update oai deprecation message (#15681) ... addresses #15674	8 months ago
Nuno Campos	7ce4cd0709	Do not issue beta or deprecation warnings on internal calls (#15641)	8 months ago
Erick Friis	d136925c49	community[patch]: fix deprecation warnings on openai subclasses (#15621)	8 months ago
Erick Friis	ebc75c5ca7	openai[minor]: implement langchain-openai package (#15503) ... Todo - [x] copy over integration tests - [x] update docs with new instructions in #15513 - [x] add linear ticket to bump core -> community, community->langchain, and core->openai deps - [ ] (optional): add `pip install langchain-openai` command to each notebook using it - [x] Update docstrings to not need `openai` install - [x] Add serialization - [x] deprecate old models Contributor steps: - [x] Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml - [x] Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml Maintainer steps (Contributors should not do these): - [x] set up pypi and test pypi projects - [x] add credential secrets to Github Actions - [ ] add package to conda-forge Functional changes to existing classes: - now relies on openai client v1 (1.6.1) via concrete dep in langchain-openai package Codebase organization - some function calling stuff moved to `langchain_core.utils.function_calling` in order to be used in both community and langchain-openai	8 months ago
Harutaka Kawamura	73da8f863c	Remove unused `Params` (#14385) ... <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`. Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
Harutaka Kawamura	8ebf55ebbf	Fix `llms.Mlflow` example (#14386) ... <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> The example code for `llms.Mlflow` is outdated. Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
JuR-0	4dab37741a	Fix Bedrock broad error catching (#14398) ... Fixes #14347 <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** Added the traceback of the previous error to keep the initial error type, - **Issue:** #14347 , - **Dependencies:** None, - **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. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Julien Raffy <julien.raffy@emeria.eu> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
Bagatur	480626dc99	docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412) ... …tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```	8 months ago
Mateusz Szewczyk	cbfaccc424	WatsonxLLM updates/enhancements (#14598) ... - **Description:** updates/enhancements to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (prompt tuned models and prompt templates deployments support) - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan - **Twitter handle:** details in comment below. Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅ --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	8 months ago
David Křístek	a010f29013	fix: call correct stream method in ollama (#15104) ... Co-authored-by: David Kristek <david@David--MacBook-Pro.local>	8 months ago
NuODaniel	7773943a51	community:qianfan endpoint support init params & remove useless params definietion (#15381) ... - **Description:** - support custom kwargs in object initialization. For instantance, QPS differs from multiple object(chat/completion/embedding with diverse models), for which global env is not a good choice for configuration. - **Issue:** no - **Dependencies:** no - **Twitter handle:** no @baskaryan PTAL	8 months ago
Shuai Liu	4b53440e70	Upgrades the Tongyi LLM and ChatTongyi Model (#14793) ... - **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi Model - Fixed typos; it should be `Tongyi`, not `OpenAI`. - Fixed a bug in `stream_generate_with_retry`; it's a real stream generator now. - Fixed a bug in `validate_environment`; the `dashscope_api_key` should be properly handled when set by environment variables or initialization parameters. - Changed the `dashscope` response to incremental output by setting the parameter `incremental_output`, which eliminates the need for the prefix-removal trick. - Removed some unused parameters, like `n`, `prefix_messages`. - Added `_stream` method. - Added async methods support, such as `_astream`, `_agenerate`, `_abatch`. - **Dependencies:** No new dependencies. - **Tag maintainer:** @hwchase17 > PS: Some may be confused about the terms `dashscope`, `tongyi`, and `Qwen`: > - `dashscope`: A platform to deploy LLMs and provide APIs to invoke the LLM. > - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI. > - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`. > > We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	9 months ago
chyroc	1abcf441ae	Refactor: use SecretStr for Predibase llms (#15119)	9 months ago
chyroc	0a9a73a9c9	Refactor: use SecretStr for PipelineAI llms (#15120)	9 months ago
chyroc	d63ceb65b3	Refactor: use SecretStr for StochasticAI llms (#15118)	9 months ago
chyroc	674fde87d2	Refactor: use SecretStr for VolcEngineMaas llms (#15117)	9 months ago
chyroc	3cc1da2b38	Refactor: use SecretStr for Petals llms (#15121)	9 months ago
shroominic	e6f0cee896	community: Async Ollama + ChatOllama (#15169) ... **Description:** Adding async methods to booth OllamaLLM and ChatOllama to enable async streaming and async .on_llm_new_token callbacks. **Issue:** ChatOllama is not working in combination with an AsyncCallbackManager because the .on_llm_new_token method is not awaited.	9 months ago
chyroc	3a3f880e5a	Patch: improve ollama 404 api error message, fix #15147 (#15156) ... Make this issue more clearly exposed to developers	9 months ago
Philip Kiely - Baseten	6342da333a	community: refactor Baseten integration with new API endpoints & docs (#15017) ... - **Description:** In response to user feedback, this PR refactors the Baseten integration with updated model endpoints, as well as updates relevant documentation. This PR has been tested by end users in production and works as expected. - **Issue:** N/A - **Dependencies:** This PR actually removes the dependency on the `baseten` package! - **Twitter handle:** https://twitter.com/basetenco	9 months ago
Blane Honeycutt	3fc1b3553b	Community: Adds ability to pass a Config to the boto3 client used by Bedrock (#15029) ... # Description This PR adds the ability to pass a `botocore.config.Config` instance to the boto3 client instantiated by the Bedrock LLM. Currently, the Bedrock LLM doesn't support a way to pass a Config, which means that some settings (e.g., timeouts and retry configuration) require instantiating a new boto3 client with a Config and then replacing the LLM's client: ```python llm = Bedrock( region_name='us-west-2', model_id="anthropic.claude-v2", model_kwargs={'max_tokens_to_sample': 4096, 'temperature': 0}, ) llm.client = boto_client('bedrock-runtime', region_name='us-west-2', config=Config({'read_timeout': 300})) ``` # Issue N/A # Dependencies N/A	9 months ago
Michael Goin	501cc8311d	community[patch]: Fix generation_config not setting properly for DeepSparse (#15036) ... - **Description:** Tiny but important bugfix to use a more stable interface for specifying generation_config parameters for DeepSparse LLM	9 months ago
MING KANG	ed5e0cfe57	community: add OCI Endpoint (#14250) ... - **Description:** - [OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. This PR add integration for using LangChain with an LLM hosted on a [OCI Data Science Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). To authenticate, [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) has been used to automatically load credentials for invoking endpoint. - **Issue:** None - **Dependencies:** `oracle-ads` - **Tag maintainer:** @baskaryan - **Twitter handle:** None --------- Co-authored-by: Erick Friis <erick@langchain.dev>	9 months ago
Liang Zhang	6479aab74f	community[patch]: Add param "task" to Databricks LLM to work around serialization of transform_output_fn (#14933) ... **What is the reproduce code?** ```python from langchain.chains import LLMChain, load_chain from langchain.llms import Databricks from langchain.prompts import PromptTemplate def transform_output(response): # Extract the answer from the responses. return str(response["candidates"][0]["text"]) def transform_input(**request): full_prompt = f"""{request["prompt"]} Be Concise. """ request["prompt"] = full_prompt return request chat_model = Databricks( endpoint_name="llama2-13B-chat-Brambles", transform_input_fn=transform_input, transform_output_fn=transform_output, verbose=True, ) print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}")) llm_chain("colorful socks") # this works llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None. loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output ``` Error: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__ raise validation_error pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation text str type expected (type=type_error.str) request payload: {'query': 'What is a databricks notebook?'}'} ``` **What does the error mean?** When the LLM generates an answer, represented by a Generation data object. The Generation data object takes a str field called text, e.g. Generation(text=”blah”). However, the Databricks LLM tried to put a non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]}) Thus, pydantic errors. **Why the output format becomes incorrect after saving and loading the Databricks LLM?** Databrick LLM does not support serializing transform_input_fn and transform_output_fn, so they are not serialized into the model yaml file. When the Databricks LLM is loaded, it is recreated with transform_input_fn=None, transform_output_fn=None. Without transform_output_fn, the output text is not unwrapped, thus errors. Missing transform_output_fn causes this error. Missing transform_input_fn causes the additional prompt “Be Concise.” to be lost after saving and loading. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	9 months ago
AlpinDale	b0588774f1	community[minor]: Add Aphrodite Engine support (#14759) ... This PR adds support for PygmalionAI's [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine), based on vLLM's attention mechanism. At the moment, this PR does not include support for the API servers, but they will be added in a later PR. The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the version to prevent breakage due to changes in the aphrodite-engine library. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	9 months ago
Liu Jun	b0c48dc983	community[patch]: make ak and sk optional in qianfan endpoint (#14835) ... - **Description:** The Qianfan SDK offers multiple authentication methods, but in the `QianfanEndpoint` of Langchain, it currently only supports authentication through AK and SK. In order to accommodate users who wish to use alternative authentication methods, this pull request makes AK and SK optional. This change should not impact existing users, while allowing users to configure other authentication methods as per the Qianfan SDK documentation. - **Issue:** / - **Dependencies:** No - **Tag maintainer:** No - **Twitter handle:**	9 months ago
Dmitry Tyumentsev	50381abc42	community[patch]: Add retry logic to Yandex GPT API Calls (#14907) ... **Description:** Added logic for re-calling the YandexGPT API in case of an error --------- Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	9 months ago
Leonid Ganeline	b2fd41331e	docs: docstrings `langchain_community` update (#14889) ... Addded missed docstrings. Fixed inconsistency in docstrings. **Note** CC @efriis There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py` But, I didn't touch this file in this PR! Can it be some cache problems? I fixed this error.	9 months ago
Erick Friis	5f839beab9	community: replace deprecated davinci models (#14860) ... This is technically a breaking change because it'll switch out default models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI is shutting off those endpoints on 1/4 anyways. Feels less disruptive to switch out the default instead.	9 months ago
Bob Lin	5de1dc72b9	community[patch]: Update Tongyi default model_name (#14844) ... <img width="1305" alt="Screenshot 2023-12-18 at 9 54 01 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/c943fd81-cd48-46eb-8dff-4680424d9ba9"> The current model is no longer available.	9 months ago
Dmitry Tyumentsev	78ae276df7	community[patch]: fix agenerate return value (#14815) ... Fixed: - `_agenerate` return value in the YandexGPT Chat Model - duplicate line in the documentation Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	9 months ago
Dmitry Tyumentsev	dcead816df	community[patch]: Update YandexGPT API (#14773) ... Update LLMand Chat model to use new api version --------- Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	9 months ago
Lance Martin	42421860bc	Add image support for Ollama (#14713) ... Support [LLaVA](https://ollama.ai/library/llava): * Upgrade Ollama * `ollama pull llava` Ensure compatibility with [image prompt template](https://github.com/langchain-ai/langchain/pull/14263) --------- Co-authored-by: jacoblee93 <jacoblee93@gmail.com>	9 months ago
Leonid Kuligin	7f42811e14	google-genai[patch], community[patch]: Added support for new Google GenerativeAI models (#14530) ... Replace this entire comment with: - **Description:** added support for new Google GenerativeAI models - **Twitter handle:** lkuligin --------- Co-authored-by: Erick Friis <erick@langchain.dev>	9 months ago
William FH	75b8891399	Update Vertex AI to include Gemini (#14670) ... h/t to @lkuligin - **Description:** added new models on VertexAI - **Twitter handle:** @lkuligin --------- Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	9 months ago
Bagatur	ed58eeb9c5	community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) ... Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes	9 months ago