docs: update ZhipuAI ChatModel docstring #22934
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- Description: Update ZhipuAI ChatModel rich docstring
- Issue: the issue Standardize ChatModel docstrings and integration docs #22296
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- **Description:** Update ZhipuAI ChatModel rich docstring - **Issue:** the issue #22296
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* partners: fix numpy dep (#22858) Following https://github.com/langchain-ai/langchain/pull/22813, which added python 3.12 to CI, here we update numpy accordingly in partner packages. * [docs]: added info for TavilySearchResults (#22765) * experimental[major]: Force users to opt-in into code that relies on the python repl (#22860) This should make it obvious that a few of the agents in langchain experimental rely on the python REPL as a tool under the hood, and will force users to opt-in. * community[patch]: FAISS VectorStore deserializer should be opt-in (#22861) FAISS deserializer uses pickle module. Users have to opt-in to de-serialize. * ci: Add script to check for pickle usage in community (#22863) Add script to check for pickle usage in community. * experimental[patch]/docs[patch]: Update links to security docs (#22864) Minor update to newest version of security docs (content should be identical). * core: In astream_events v2 propagate cancel/break to the inner astream call (#22865) - previous behavior was for the inner astream to continue running with no interruption - also propagate break in core runnable methods * core[patch]: Treat type as a special field when merging lists (#22750) Should we even log a warning? At least for Anthropic, it's expected to get e.g. `text_block` followed by `text_delta`. @ccurme @baskaryan @efriis * core: release 0.2.6 (#22868) * langchain: release 0.2.4 (#22872) * Fix: lint errors and update Field alias in models.py and AutoSelectionScorer initialization (#22846) This PR addresses several lint errors in the core package of LangChain. Specifically, the following issues were fixed: 1.Unexpected keyword argument "required" for "Field" [call-arg] 2.tests/integration_tests/chains/test_cpal.py:263: error: Unexpected keyword argument "narrative_input" for "QueryModel" [call-arg] * Fix typo in vearch.md (#22840) Fix typo * docs: s/path_images/images/ for ImageCaptionLoader keyword arguments (#22857) Quick update to `ImageCaptionLoader` documentation to reflect what's in code. * docs: update NVIDIA Riva tool to use NVIDIA NIM for LLM (#22873) **Description:** Update the NVIDIA Riva tool documentation to use NVIDIA NIM for the LLM. Show how to use NVIDIA NIMs and link to documentation for LangChain with NIM. --------- Co-authored-by: Hayden Wolff <[email protected]> Co-authored-by: Isaac Francisco <[email protected]> * docs, cli[patch]: document loaders doc template (#22862) From: https://github.com/langchain-ai/langchain/pull/22290 --------- Co-authored-by: Eugene Yurtsev <[email protected]> * cli[patch]: Release 0.0.25 (#22876) * infra: lint new docs to match doc loader template (#22867) * docs: fixes for Elasticsearch integrations, cache doc and providers list (#22817) Some minor fixes in the documentation: - ElasticsearchCache initilization is now correct - List of integrations for ES updated * docs: `ReAct` reference (#22830) The `ReAct` is used all across LangChain but it is not referenced properly. Added references to the original paper. * community[minor]: Implement ZhipuAIEmbeddings interface (#22821) - **Description:** Implement ZhipuAIEmbeddings interface, include: - The `embed_query` method - The `embed_documents` method refer to [ZhipuAI Embedding-2](https://open.bigmodel.cn/dev/api#text_embedding) --------- Co-authored-by: Eugene Yurtsev <[email protected]> * docs: Astra DB vectorstore, adjust syntax for automatic-embedding example (#22833) Description: Adjusting the syntax for creating the vectorstore collection (in the case of automatic embedding computation) for the most idiomatic way to submit the stored secret name. Co-authored-by: Bagatur <[email protected]> * community[minor]: Prem Templates (#22783) This PR adds the feature add Prem Template feature in ChatPremAI. Additionally it fixes a minor bug for API auth error when API passed through arguments. * qdrant[patch]: Use collection_exists API instead of exceptions (#22764) ## Description Currently, the Qdrant integration relies on exceptions raised by [`get_collection` ](https://qdrant.tech/documentation/concepts/collections/#collection-info) to check if a collection exists. Using [`collection_exists`](https://qdrant.tech/documentation/concepts/collections/#check-collection-existence) is recommended to avoid missing any unhandled exceptions. This PR addresses this. ## Testing All integration and unit tests pass. No user-facing changes. * docs: Standardize ChatGroq (#22751) Updated ChatGroq doc string as per issue https://github.com/langchain-ai/langchain/issues/22296:"langchain_groq: updated docstring for ChatGroq in langchain_groq to match that of the description (in the appendix) provided in issue https://github.com/langchain-ai/langchain/issues/22296. " Issue: This PR is in response to issue https://github.com/langchain-ai/langchain/issues/22296, and more specifically the ChatGroq model. In particular, this PR updates the docstring for langchain/libs/partners/groq/langchain_groq/chat_model.py by adding the following sections: Instantiate, Invoke, Stream, Async, Tool calling, Structured Output, and Response metadata. I used the template from the Anthropic implementation and referenced the Appendix of the original issue post. I also noted that: `usage_metadata `returns none for all ChatGroq models I tested; there is no mention of image input in the ChatGroq documentation; unlike that of ChatHuggingFace, `.stream(messages)` for ChatGroq returned blocks of output. --------- Co-authored-by: lucast2021 <[email protected]> Co-authored-by: Bagatur <[email protected]> * anthropic[patch]: always add tool_result type to ToolMessage content (#22721) Anthropic tool results can contain image data, which are typically represented with content blocks having `"type": "image"`. Currently, these content blocks are passed as-is as human/user messages to Anthropic, which raises BadRequestError as it expects a tool_result block to follow a tool_use. Here we update ChatAnthropic to nest the content blocks inside a tool_result content block. Example: ```python import base64 import httpx from langchain_anthropic import ChatAnthropic from langchain_core.messages import AIMessage, HumanMessage, ToolMessage from langchain_core.pydantic_v1 import BaseModel, Field # Fetch image image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") class FetchImage(BaseModel): should_fetch: bool = Field(..., description="Whether an image is requested.") llm = ChatAnthropic(model="claude-3-sonnet-20240229").bind_tools([FetchImage]) messages = [ HumanMessage(content="Could you summon a beautiful image please?"), AIMessage( content=[ { "type": "tool_use", "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx", "name": "FetchImage", "input": {"should_fetch": True}, }, ], tool_calls=[ { "name": "FetchImage", "args": {"should_fetch": True}, "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx", }, ], ), ToolMessage( name="FetchImage", content=[ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": image_data, }, }, ], tool_call_id="toolu_01Rn6Qvj5m7955x9m9Pfxbcx", ), ] llm.invoke(messages) ``` Trace: https://smith.langchain.com/public/d27e4fc1-a96d-41e1-9f52-54f5004122db/r * docs[patch]: Expand embeddings docs (#22881) * docs: generate table for document loaders (#22871) Co-authored-by: Bagatur <[email protected]> * docs: doc loader feat table alignment (#22900) * community[minor]: add chat model llamacpp (#22589) - **PR title**: [community] add chat model llamacpp - **PR message**: - **Description:** This PR introduces a new chat model integration with llamacpp_python, designed to work similarly to the existing ChatOpenAI model. + Work well with instructed chat, chain and function/tool calling. + Work with LangGraph (persistent memory, tool calling), will update soon - **Dependencies:** This change requires the llamacpp_python library to be installed. @baskaryan --------- Co-authored-by: Bagatur <[email protected]> Co-authored-by: Bagatur <[email protected]> * docs: Fix typo in tutorial about structured data extraction (#22888) [Fixed typo](docs: Fix typo in tutorial about structured data extraction) * [Community]: HuggingFaceCrossEncoder `score` accounting for <not-relevant score,relevant score> pairs. (#22578) - **Description:** Some of the Cross-Encoder models provide scores in pairs, i.e., <not-relevant score (higher means the document is less relevant to the query), relevant score (higher means the document is more relevant to the query)>. However, the `HuggingFaceCrossEncoder` `score` method does not currently take into account the pair situation. This PR addresses this issue by modifying the method to consider only the relevant score if score is being provided in pair. The reason for focusing on the relevant score is that the compressors select the top-n documents based on relevance. - **Issue:** #22556 - Please also refer to this [comment](https://github.com/UKPLab/sentence-transformers/issues/568#issuecomment-729153075) * fireworks[patch]: add usage_metadata to (a)invoke and (a)stream (#22906) * anthropic[minor]: Adds streaming tool call support for Anthropic (#22687) Preserves string content chunks for non tool call requests for convenience. One thing - Anthropic events look like this: ``` RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start') RawContentBlockDeltaEvent(delta=TextDelta(text='<thinking>\nThe', type='text_delta'), index=0, type='content_block_delta') RawContentBlockDeltaEvent(delta=TextDelta(text=' provide', type='text_delta'), index=0, type='content_block_delta') ... RawContentBlockStartEvent(content_block=ToolUseBlock(id='toolu_01GJ6x2ddcMG3psDNNe4eDqb', input={}, name='get_weather', type='tool_use'), index=1, type='content_block_start') RawContentBlockDeltaEvent(delta=InputJsonDelta(partial_json='', type='input_json_delta'), index=1, type='content_block_delta') ``` Note that `delta` has a `type` field. With this implementation, I'm dropping it because `merge_list` behavior will concatenate strings. We currently have `index` as a special field when merging lists, would it be worth adding `type` too? If so, what do we set as a context block chunk? `text` vs. `text_delta`/`tool_use` vs `input_json_delta`? CC @ccurme @efriis @baskaryan * core[patch]: fix validation of @deprecated decorator (#22513) This PR moves the validation of the decorator to a better place to avoid creating bugs while deprecating code. Prevent issues like this from arising: https://github.com/langchain-ai/langchain/issues/22510 we should replace with a linter at some point that just does static analysis * community[patch]: SitemapLoader restrict depth of parsing sitemap (CVE-2024-2965) (#22903) This PR restricts the depth to which the sitemap can be parsed. Fix for: CVE-2024-2965 * community[major], experimental[patch]: Remove Python REPL from community (#22904) Remove the REPL from community, and suggest an alternative import from langchain_experimental. Fix for this issue: https://github.com/langchain-ai/langchain/issues/14345 This is not a bug in the code or an actual security risk. The python REPL itself is behaving as expected. The PR is done to appease blanket security policies that are just looking for the presence of exec in the code. --------- Co-authored-by: Erick Friis <[email protected]> * community[minor]: Fix long_context_reorder.py async (#22839) Implement `async def atransform_documents( self, documents: Sequence[Document], **kwargs: Any ) -> Sequence[Document]` for `LongContextReorder` * core[patch]: Fix FunctionCallbackHandler._on_tool_end (#22908) If the global `debug` flag is enabled, the agent will get the following error in `FunctionCallbackHandler._on_tool_end` at runtime. ``` Error in ConsoleCallbackHandler.on_tool_end callback: AttributeError("'list' object has no attribute 'strip'") ``` By calling str() before strip(), the error was avoided. This error can be seen at [debugging.ipynb](https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/debugging.ipynb). - Issue: NA - Dependencies: NA - Twitter handle: https://x.com/kiarina37 * dcos: Add admonition to PythonREPL tool (#22909) Add admonition to the documentation to make sure users are aware that the tool allows execution of code on the host machine using a python interpreter (by design). * docs: add groq to chatmodeltabs (#22913) * experimental: LLMGraphTransformer - added relationship properties. (#21856) - **Description:** The generated relationships in the graph had no properties, but the Relationship class was properly defined with properties. This made it very difficult to transform conditional sentences into a graph. Adding properties to relationships can solve this issue elegantly. The changes expand on the existing LLMGraphTransformer implementation but add the possibility to define allowed relationship properties like this: LLMGraphTransformer(llm=llm, relationship_properties=["Condition", "Time"],) - **Issue:** no issue found - **Dependencies:** n/a - **Twitter handle:** @IstvanSpace -Quick Test ================================================================= from dotenv import load_dotenv import os from langchain_community.graphs import Neo4jGraph from langchain_experimental.graph_transformers import LLMGraphTransformer from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.documents import Document load_dotenv() os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI") os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME") os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD") graph = Neo4jGraph() llm = ChatOpenAI(temperature=0, model_name="gpt-4o") llm_transformer = LLMGraphTransformer(llm=llm) #text = "Harry potter likes pies, but only if it rains outside" text = "Jack has a dog named Max. Jack only walks Max if it is sunny outside." documents = [Document(page_content=text)] llm_transformer_props = LLMGraphTransformer( llm=llm, relationship_properties=["Condition"], ) graph_documents_props = llm_transformer_props.convert_to_graph_documents(documents) print(f"Nodes:{graph_documents_props[0].nodes}") print(f"Relationships:{graph_documents_props[0].relationships}") graph.add_graph_documents(graph_documents_props) --------- Co-authored-by: Istvan Lorincz <[email protected]> Co-authored-by: Bagatur <[email protected]> * core: in astream_events v2 always await task even if already finished (#22916) - this ensures exceptions propagate to the caller * core: release 0.2.7 (#22917) * infra: remove nvidia from monorepo scheduled tests (#22915) Scheduled tests run in https://github.com/langchain-ai/langchain-nvidia/tree/main * docs: Fix wrongly referenced class name in confluence.py (#22879) Fixes #22542 Changed ConfluenceReader to ConfluenceLoader * templates: remove lockfiles (#22920) poetry will default to latest versions without * langchain: release 0.2.5 (#22922) * text-splitters[patch]: Fix HTMLSectionSplitter (#22812) Update former pull request: https://github.com/langchain-ai/langchain/pull/22654. Modified `langchain_text_splitters.HTMLSectionSplitter`, where in the latest version `dict` data structure is used to store sections from a html document, in function `split_html_by_headers`. The header/section element names serve as dict keys. This can be a problem when duplicate header/section element names are present in a single html document. Latter ones can replace former ones with the same name. Therefore some contents can be miss after html text splitting is conducted. Using a list to store sections can hopefully solve the problem. A Unit test considering duplicate header names has been added. --------- Co-authored-by: Bagatur <[email protected]> * community: release 0.2.5 (#22923) * cli[minor]: remove redefined DEFAULT_GIT_REF (#21471) remove redefined DEFAULT_GIT_REF Co-authored-by: Isaac Francisco <[email protected]> * experimental: release 0.0.61 (#22924) * docs: add ollama json mode (#22926) fixes #22910 * community: 'Solve the issue where the _search function in ElasticsearchStore supports passing a query_vector parameter, but the parameter does not take effect. (#21532) **Issue:** When using the similarity_search_with_score function in ElasticsearchStore, I expected to pass in the query_vector that I have already obtained. I noticed that the _search function does support the query_vector parameter, but it seems to be ineffective. I am attempting to resolve this issue. Co-authored-by: Isaac Francisco <[email protected]> * Update ollama.py with optional raw setting. (#21486) Ollama has a raw option now. https://github.com/ollama/ollama/blob/main/docs/api.md Thank you for contributing to LangChain! - [ ] **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" - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **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! - [ ] **Add tests and docs**: 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. - [ ] **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: Isaac Francisco <[email protected]> Co-authored-by: isaac hershenson <[email protected]> * docs:Fix mispelling in streaming doc (#22936) Description: Fix mispelling Issue: None Dependencies: None Twitter handle: None Co-authored-by: qcloud <[email protected]> * docs: update ZhipuAI ChatModel docstring (#22934) - **Description:** Update ZhipuAI ChatModel rich docstring - **Issue:** the issue #22296 * Improve llm graph transformer docstring (#22939) * infra: update integration test workflow (#22945) * community(you): Better support for You.com News API (#22622) ## Description While `YouRetriever` supports both You.com's Search and News APIs, news is supported as an afterthought. More specifically, not all of the News API parameters are exposed for the user, only those that happen to overlap with the Search API. This PR: - improves support for both APIs, exposing the remaining News API parameters while retaining backward compatibility - refactor some REST parameter generation logic - updates the docstring of `YouSearchAPIWrapper` - add input validation and warnings to ensure parameters are properly set by user - 🚨 Breaking: Limit the news results to `k` items If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. * docs: nim model name update (#22943) NIM Model name change in a notebook and mdx file. Thanks! * standard-tests[patch]: don't require str chunk contents (#22965) * Update sql_qa.ipynb (#22966) fixes #22798 fixes #22963 * docs: update databricks.ipynb (#22949) arbitary -> arbitrary * docs: Standardise formatting (#22948) Standardised formatting  * [Partner]: Add metadata to stream response (#22716) Adds `response_metadata` to stream responses from OpenAI. This is returned with `invoke` normally, but wasn't implemented for `stream`. --------- Co-authored-by: Chester Curme <[email protected]> * standard-tests[patch]: Release 0.1.1 (#22984) * docs: Update llamacpp ntbk (#22907) Co-authored-by: Bagatur <[email protected]> * community[minor]: add `ChatSnowflakeCortex` chat model (#21490) **Description:** This PR adds a chat model integration for [Snowflake Cortex](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions), which gives an instant access to industry-leading large language models (LLMs) trained by researchers at companies like Mistral, Reka, Meta, and Google, including [Snowflake Arctic](https://www.snowflake.com/en/data-cloud/arctic/), an open enterprise-grade model developed by Snowflake. **Dependencies:** Snowflake's [snowpark](https://pypi.org/project/snowflake-snowpark-python/) library is required for using this integration. **Twitter handle:** [@gethouseware](https://twitter.com/gethouseware) - [x] **Add tests and docs**: 1. integration tests: `libs/community/tests/integration_tests/chat_models/test_snowflake.py` 2. unit tests: `libs/community/tests/unit_tests/chat_models/test_snowflake.py` 3. example notebook: `docs/docs/integrations/chat/snowflake.ipynb` - [x] **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/ * openai[patch]: add stream_usage parameter (#22854) Here we add `stream_usage` to ChatOpenAI as: 1. a boolean attribute 2. a kwarg to _stream and _astream. Question: should the `stream_usage` attribute be `bool`, or `bool | None`? Currently I've kept it `bool` and defaulted to False. It was implemented on [ChatAnthropic](https://github.com/langchain-ai/langchain/blob/e832bbb48627aa9f00614e82e7ace60b7d8957c6/libs/partners/anthropic/langchain_anthropic/chat_models.py#L535) as a bool. However, to maintain support for users who access the behavior via OpenAI's `stream_options` param, this ends up being possible: ```python llm = ChatOpenAI(model_kwargs={"stream_options": {"include_usage": True}}) assert not llm.stream_usage ``` (and this model will stream token usage). Some options for this: - it's ok - make the `stream_usage` attribute bool or None - make an \_\_init\_\_ for ChatOpenAI, set a `._stream_usage` attribute and read `.stream_usage` from a property Open to other ideas as well. * community: Add Baichuan Embeddings batch size (#22942) - **Support batch size** Baichuan updates the document, indicating that up to 16 documents can be imported at a time - **Standardized model init arg names** - baichuan_api_key -> api_key - model_name -> model * Add RAG to conceptual guide (#22790) Co-authored-by: jacoblee93 <[email protected]> * docs: update universal init title (#22990) * community[minor]: add tool calling for DeepInfraChat (#22745) DeepInfra now supports tool calling for supported models. --------- Co-authored-by: Bagatur <[email protected]> * docs[patch]: Reorder streaming guide, add tags (#22993) CC @hinthornw * docs: Add some 3rd party tutorials (#22931) Langchain is very popular among developers in China, but there are still no good Chinese books or documents, so I want to add my own Chinese resources on langchain topics, hoping to give Chinese readers a better experience using langchain. This is not a translation of the official langchain documentation, but my understanding. --------- Co-authored-by: ccurme <[email protected]> * standard-tests[patch]: Update chat model standard tests (#22378) - Refactor standard test classes to make them easier to configure - Update openai to support stop_sequences init param - Update groq to support stop_sequences init param - Update fireworks to support max_retries init param - Update ChatModel.bind_tools to type tool_choice - Update groq to handle tool_choice="any". **this may be controversial** --------- Co-authored-by: Chester Curme <[email protected]> * core: run_in_executor: Wrap StopIteration in RuntimeError (#22997) - StopIteration can't be set on an asyncio.Future it raises a TypeError and leaves the Future pending forever so we need to convert it to a RuntimeError * infra: test all dependents on any change (#22994) * core[patch]: Release 0.2.8 (#23012) * community: OCI GenAI embedding batch size (#22986) Thank you for contributing to LangChain! - [x] **PR title**: "community: OCI GenAI embedding batch size" - [x] **PR message**: - **Issue:** #22985 - [ ] **Add tests and docs**: N/A - [x] **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, ccurme, vbarda, hwchase17. --------- Signed-off-by: Anders Swanson <[email protected]> Co-authored-by: Chester Curme <[email protected]> * docs: add bing search integration to agent (#22929) - [x] **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/ * core[minor]: message transformer utils (#22752) * docs[patch]: Update docs links (#23013) * docs[patch]: Adds evaluation sections (#23050) Also want to add an index/rollup page to LangSmith docs to enable linking to a how-to category as a group (e.g. https://docs.smith.langchain.com/how_to_guides/evaluation/) CC @agola11 @hinthornw * docs: Update how to docs for pydantic compatibility (#22983) Add missing imports in docs from langchain_core.tools BaseTool --------- Co-authored-by: Eugene Yurtsev <[email protected]> * Include "no escape" and "inverted section" mustache vars in Prompt.input_variables and Prompt.input_schema (#22981) * [Community]: FIxed the DocumentDBVectorSearch `_similarity_search_without_score` (#22970) - **Description:** The PR #22777 introduced a bug in `_similarity_search_without_score` which was raising the `OperationFailure` error. The mistake was syntax error for MongoDB pipeline which has been corrected now. - **Issue:** #22770 * community: Fix #22975 (Add SSL Verification Option to Requests Class in langchain_community) (#22977) - **PR title**: "community: Fix #22975 (Add SSL Verification Option to Requests Class in langchain_community)" - **PR message**: - **Description:** - Added an optional verify parameter to the Requests class with a default value of True. - Modified the get, post, patch, put, and delete methods to include the verify parameter. - Updated the _arequest async context manager to include the verify parameter. - Added the verify parameter to the GenericRequestsWrapper class and passed it to the Requests class. - **Issue:** This PR fixes issue #22975. - **Dependencies:** No additional dependencies are required for this change. - **Twitter handle:** @lunara_x You can check this change with below code. ```python from langchain_openai.chat_models import ChatOpenAI from langchain.requests import RequestsWrapper from langchain_community.agent_toolkits.openapi import planner from langchain_community.agent_toolkits.openapi.spec import reduce_openapi_spec with open("swagger.yaml") as f: data = yaml.load(f, Loader=yaml.FullLoader) swagger_api_spec = reduce_openapi_spec(data) llm = ChatOpenAI(model='gpt-4o') swagger_requests_wrapper = RequestsWrapper(verify=False) # modified point superset_agent = planner.create_openapi_agent(swagger_api_spec, swagger_requests_wrapper, llm, allow_dangerous_requests=True, handle_parsing_errors=True) superset_agent.run( "Tell me the number and types of charts and dashboards available." ) ``` --------- Co-authored-by: Harrison Chase <[email protected]> * [Community]: Fixed DDG DuckDuckGoSearchResults Docstring (#22968) - **Description:** A very small fix in the Docstring of `DuckDuckGoSearchResults` identified in the following issue. - **Issue:** #22961 --------- Co-authored-by: Harrison Chase <[email protected]> * docs: embeddings classes (#22927) Added a table with all Embedding classes. * docs: Standardize DocumentLoader docstrings (#22932) **Standardizing DocumentLoader docstrings (of which there are many)** This PR addresses issue #22866 and adds docstrings according to the issue's specified format (in the appendix) for files csv_loader.py and json_loader.py in langchain_community.document_loaders. In particular, the following sections have been added to both CSVLoader and JSONLoader: Setup, Instantiate, Load, Async load, and Lazy load. It may be worth adding a 'Metadata' section to the JSONLoader docstring to clarify how we want to extract the JSON metadata (using the `metadata_func` argument). The files I used to walkthrough the various sections were `example_2.json` from [HERE](https://support.oneskyapp.com/hc/en-us/articles/208047697-JSON-sample-files) and `hw_200.csv` from [HERE](https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html). --------- Co-authored-by: lucast2021 <[email protected]> Co-authored-by: isaac hershenson <[email protected]> * langchain[patch]: add tool messages formatter for tool calling agent (#22849) - **Description:** add tool_messages_formatter for tool calling agent, make tool messages can be formatted in different ways for your LLM. - **Issue:** N/A - **Dependencies:** N/A * langchain: add id_key option to EnsembleRetriever for metadata-based document merging (#22950) **Description:** - What I changed - By specifying the `id_key` during the initialization of `EnsembleRetriever`, it is now possible to determine which documents to merge scores for based on the value corresponding to the `id_key` element in the metadata, instead of `page_content`. Below is an example of how to use the modified `EnsembleRetriever`: ```python retriever = EnsembleRetriever(retrievers=[ret1, ret2], id_key="id") # The Document returned by each retriever must keep the "id" key in its metadata. ``` - Additionally, I added a script to easily test the behavior of the `invoke` method of the modified `EnsembleRetriever`. - Why I changed - There are cases where you may want to calculate scores by treating Documents with different `page_content` as the same when using `EnsembleRetriever`. For example, when you want to ensemble the search results of the same document described in two different languages. - The previous `EnsembleRetriever` used `page_content` as the basis for score aggregation, making the above usage difficult. Therefore, the score is now calculated based on the specified key value in the Document's metadata. **Twitter handle:** @shimajiroxyz * community: add KafkaChatMessageHistory (#22216) Add chat history store based on Kafka. Files added: `libs/community/langchain_community/chat_message_histories/kafka.py` `docs/docs/integrations/memory/kafka_chat_message_history.ipynb` New issue to be created for future improvement: 1. Async method implementation. 2. Message retrieval based on timestamp. 3. Support for other configs when connecting to cloud hosted Kafka (e.g. add `api_key` field) 4. Improve unit testing & integration testing. * LanceDB integration update (#22869) Added : - [x] relevance search (w/wo scores) - [x] maximal marginal search - [x] image ingestion - [x] filtering support - [x] hybrid search w reranking make test, lint_diff and format checked. * SemanticChunker : Feature Addition ("Semantic Splitting with gradient") (#22895) ```SemanticChunker``` currently provide three methods to split the texts semantically: - percentile - standard_deviation - interquartile I propose new method ```gradient```. In this method, the gradient of distance is used to split chunks along with the percentile method (technically) . This method is useful when chunks are highly correlated with each other or specific to a domain e.g. legal or medical. The idea is to apply anomaly detection on gradient array so that the distribution become wider and easy to identify boundaries in highly semantic data. I have tested this merge on a set of 10 domain specific documents (mostly legal). Details : - **Issue:** Improvement - **Dependencies:** NA - **Twitter handle:** [x.com/prajapat_ravi](https://x.com/prajapat_ravi) @hwchase17 --------- Co-authored-by: Raviraj Prajapat <[email protected]> Co-authored-by: isaac hershenson <[email protected]> * docs: `AWS` platform page update (#23063) Added a reference to the `GlueCatalogLoader` new document loader. * Update Fireworks link (#23058) * docs: add trim_messages to chatbot (#23061) * LanceDB example minor change (#23069) Removed package version `0.6.13` in the example. * core[patch],community[patch],langchain[patch]: `tenacity` dependency to version `>=8.1.0,<8.4.0` (#22973) Fix https://github.com/langchain-ai/langchain/issues/22972. - [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" - [x] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **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! - [x] **Add tests and docs**: 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. - [x] **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, ccurme, vbarda, hwchase17. * core[patch]: Document BaseStore (#23082) Add doc-string to BaseStore * community: glob multiple patterns when using DirectoryLoader (#22852) - **Description:** Updated *community.langchain_community.document_loaders.directory.py* to enable the use of multiple glob patterns in the `DirectoryLoader` class. Now, the glob parameter is of type `list[str] | str` and still defaults to the same value as before. I updated the docstring of the class to reflect this, and added a unit test to *community.tests.unit_tests.document_loaders.test_directory.py* named `test_directory_loader_glob_multiple`. This test also shows an example of how to use the new functionality. - ~~Issue:~~**Discussion Thread:** https://github.com/langchain-ai/langchain/discussions/18559 - **Dependencies:** None - **Twitter handle:** N/a - [x] **Add tests and docs** - Added test (described above) - Updated class docstring - [x] **Lint and test** --------- Co-authored-by: isaac hershenson <[email protected]> Co-authored-by: Harrison Chase <[email protected]> Co-authored-by: Isaac Francisco <[email protected]> * core[patch]: Release 0.2.9 (#23091) * community: add args_schema to SearxSearch (#22954) This change adds args_schema (pydantic BaseModel) to SearxSearchRun for correct schema formatting on LLM function calls Issue: currently using SearxSearchRun with OpenAI function calling returns the following error "TypeError: SearxSearchRun._run() got an unexpected keyword argument '__arg1' ". This happens because the schema sent to the LLM is "input: '{"__arg1":"foobar"}'" while the method should be called with the "query" parameter. --------- Co-authored-by: Harrison Chase <[email protected]> * core[minor]: Support multiple keys in get_from_dict_or_env (#23086) Support passing multiple keys for ge_from_dict_or_env * community[minor]: Implement Doctran async execution (#22372) **Description** The DoctranTextTranslator has an async transform function that was not implemented because [the doctran library](https://github.com/psychic-api/doctran) uses a sync version of the `execute` method. - I implemented the `DoctranTextTranslator.atransform_documents()` method using `asyncio.to_thread` to run the function in a separate thread. - I updated the example in the Notebook with the new async version. - The performance improvements can be appreciated when a big document is divided into multiple chunks. Relates to: - Issue #14645: https://github.com/langchain-ai/langchain/issues/14645 - Issue #14437: https://github.com/langchain-ai/langchain/issues/14437 - https://github.com/langchain-ai/langchain/pull/15264 --------- Co-authored-by: Eugene Yurtsev <[email protected]> * docs: Fix URL formatting in deprecation warnings (#23075) **Description** Updated the URLs in deprecation warning messages. The URLs were previously written as raw strings and are now formatted to be clickable HTML links. Example of a broken link in the current API Reference: https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.extraction.create_extraction_chain_pydantic.html <img width="942" alt="Screenshot 2024-06-18 at 13 21 07" src="https://github.com/langchain-ai/langchain/assets/4854600/a1b1863c-cd03-4af2-a9bc-70375407fb00"> * langchain[patch]: fix `OutputType` of OutputParsers and fix legacy API in OutputParsers (#19792) # Description This pull request aims to address specific issues related to the ambiguity and error-proneness of the output types of certain output parsers, as well as the absence of unit tests for some parsers. These issues could potentially lead to runtime errors or unexpected behaviors due to type mismatches when used, causing confusion for developers and users. Through clarifying output types, this PR seeks to improve the stability and reliability. Therefore, this pull request - fixes the `OutputType` of OutputParsers to be the expected type; - e.g. `OutputType` property of `EnumOutputParser` raises `TypeError`. This PR introduce a logic to extract `OutputType` from its attribute. - and fixes the legacy API in OutputParsers like `LLMChain.run` to the modern API like `LLMChain.invoke`; - Note: For `OutputFixingParser`, `RetryOutputParser` and `RetryWithErrorOutputParser`, this PR introduces `legacy` attribute with False as default value in order to keep the backward compatibility - and adds the tests for the `OutputFixingParser` and `RetryOutputParser`. The following table shows my expected output and the actual output of the `OutputType` of OutputParsers. I have used this table to fix `OutputType` of OutputParsers. | Class Name of OutputParser | My Expected `OutputType` (after this PR)| Actual `OutputType` [evidence](#evidence) (before this PR)| Fix Required | |---------|--------------|---------|--------| | BooleanOutputParser | `<class 'bool'>` | `<class 'bool'>` | NO | | CombiningOutputParser | `typing.Dict[str, Any]` | `TypeError` is raised | YES | | DatetimeOutputParser | `<class 'datetime.datetime'>` | `<class 'datetime.datetime'>` | NO | | EnumOutputParser(enum=MyEnum) | `MyEnum` | `TypeError` is raised | YES | | OutputFixingParser | The same type as `self.parser.OutputType` | `~T` | YES | | CommaSeparatedListOutputParser | `typing.List[str]` | `typing.List[str]` | NO | | MarkdownListOutputParser | `typing.List[str]` | `typing.List[str]` | NO | | NumberedListOutputParser | `typing.List[str]` | `typing.List[str]` | NO | | JsonOutputKeyToolsParser | `typing.Any` | `typing.Any` | NO | | JsonOutputToolsParser | `typing.Any` | `typing.Any` | NO | | PydanticToolsParser | `typing.Any` | `typing.Any` | NO | | PandasDataFrameOutputParser | `typing.Dict[str, Any]` | `TypeError` is raised | YES | | PydanticOutputParser(pydantic_object=MyModel) | `<class '__main__.MyModel'>` | `<class '__main__.MyModel'>` | NO | | RegexParser | `typing.Dict[str, str]` | `TypeError` is raised | YES | | RegexDictParser | `typing.Dict[str, str]` | `TypeError` is raised | YES | | RetryOutputParser | The same type as `self.parser.OutputType` | `~T` | YES | | RetryWithErrorOutputParser | The same type as `self.parser.OutputType` | `~T` | YES | | StructuredOutputParser | `typing.Dict[str, Any]` | `TypeError` is raised | YES | | YamlOutputParser(pydantic_object=MyModel) | `MyModel` | `~T` | YES | NOTE: In "Fix Required", "YES" means that it is required to fix in this PR while "NO" means that it is not required. # Issue No issues for this PR. # Twitter handle - [hmdev3](https://twitter.com/hmdev3) # Questions: 1. Is it required to create tests for legacy APIs `LLMChain.run` in the following scripts? - libs/langchain/tests/unit_tests/output_parsers/test_fix.py; - libs/langchain/tests/unit_tests/output_parsers/test_retry.py. 2. Is there a more appropriate expected output type than I expect in the above table? - e.g. the `OutputType` of `CombiningOutputParser` should be SOMETHING... # Actual outputs (before this PR) <div id='evidence'></div> <details><summary>Actual outputs</summary> ## Requirements - Python==3.9.13 - langchain==0.1.13 ```python Python 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import langchain >>> langchain.__version__ '0.1.13' >>> from langchain import output_parsers ``` ### `BooleanOutputParser` ```python >>> output_parsers.BooleanOutputParser().OutputType <class 'bool'> ``` ### `CombiningOutputParser` ```python >>> output_parsers.CombiningOutputParser(parsers=[output_parsers.DatetimeOutputParser(), output_parsers.CommaSeparatedListOutputParser()]).OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable CombiningOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `DatetimeOutputParser` ```python >>> output_parsers.DatetimeOutputParser().OutputType <class 'datetime.datetime'> ``` ### `EnumOutputParser` ```python >>> from enum import Enum >>> class MyEnum(Enum): ... a = 'a' ... b = 'b' ... >>> output_parsers.EnumOutputParser(enum=MyEnum).OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable EnumOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `OutputFixingParser` ```python >>> output_parsers.OutputFixingParser(parser=output_parsers.DatetimeOutputParser()).OutputType ~T ``` ### `CommaSeparatedListOutputParser` ```python >>> output_parsers.CommaSeparatedListOutputParser().OutputType typing.List[str] ``` ### `MarkdownListOutputParser` ```python >>> output_parsers.MarkdownListOutputParser().OutputType typing.List[str] ``` ### `NumberedListOutputParser` ```python >>> output_parsers.NumberedListOutputParser().OutputType typing.List[str] ``` ### `JsonOutputKeyToolsParser` ```python >>> output_parsers.JsonOutputKeyToolsParser(key_name='tool').OutputType typing.Any ``` ### `JsonOutputToolsParser` ```python >>> output_parsers.JsonOutputToolsParser().OutputType typing.Any ``` ### `PydanticToolsParser` ```python >>> from langchain.pydantic_v1 import BaseModel >>> class MyModel(BaseModel): ... a: int ... >>> output_parsers.PydanticToolsParser(tools=[MyModel, MyModel]).OutputType typing.Any ``` ### `PandasDataFrameOutputParser` ```python >>> output_parsers.PandasDataFrameOutputParser().OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable PandasDataFrameOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `PydanticOutputParser` ```python >>> output_parsers.PydanticOutputParser(pydantic_object=MyModel).OutputType <class '__main__.MyModel'> ``` ### `RegexParser` ```python >>> output_parsers.RegexParser(regex='$', output_keys=['a']).OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable RegexParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `RegexDictParser` ```python >>> output_parsers.RegexDictParser(output_key_to_format={'a':'a'}).OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable RegexDictParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `RetryOutputParser` ```python >>> output_parsers.RetryOutputParser(parser=output_parsers.DatetimeOutputParser()).OutputType ~T ``` ### `RetryWithErrorOutputParser` ```python >>> output_parsers.RetryWithErrorOutputParser(parser=output_parsers.DatetimeOutputParser()).OutputType ~T ``` ### `StructuredOutputParser` ```python >>> from langchain.output_parsers.structured import ResponseSchema >>> response_schemas = [ResponseSchema(name="foo",description="a list of strings",type="List[string]"),ResponseSchema(name="bar",description="a string",type="string"), ] >>> output_parsers.StructuredOutputParser.from_response_schemas(response_schemas).OutputType Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType raise TypeError( TypeError: Runnable StructuredOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type. ``` ### `YamlOutputParser` ```python >>> output_parsers.YamlOutputParser(pydantic_object=MyModel).OutputType ~T ``` <div> --------- Co-authored-by: Eugene Yurtsev <[email protected]> * core[patch]: Pin pydantic in py3.12.4 (#23130) * core[minor]: handle boolean data in draw_mermaid (#23135) This change should address graph rendering issues for edges with boolean data Example from langgraph: ```python from typing import Annotated, TypedDict from langchain_core.messages import AnyMessage from langgraph.graph import END, START, StateGraph from langgraph.graph.message import add_messages class State(TypedDict): messages: Annotated[list[AnyMessage], add_messages] def branch(state: State) -> bool: return 1 + 1 == 3 graph_builder = StateGraph(State) graph_builder.add_node("foo", lambda state: {"messages": [("ai", "foo")]}) graph_builder.add_node("bar", lambda state: {"messages": [("ai", "bar")]}) graph_builder.add_conditional_edges( START, branch, path_map={True: "foo", False: "bar"}, then=END, ) app = graph_builder.compile() print(app.get_graph().draw_mermaid()) ``` Previous behavior: ```python AttributeError: 'bool' object has no attribute 'split' ``` Current behavior: ```python %%{init: {'flowchart': {'curve': 'linear'}}}%% graph TD; __start__[__start__]:::startclass; __end__[__end__]:::endclass; foo([foo]):::otherclass; bar([bar]):::otherclass; __start__ -. ('a',) .-> foo; foo --> __end__; __start__ -. ('b',) .-> bar; bar --> __end__; classDef startclass fill:#ffdfba; classDef endclass fill:#baffc9; classDef otherclass fill:#fad7de; ``` * docs: use trim_messages in chatbot how to (#23139) * docs[patch]: Adds feedback input after thumbs up/down (#23141) CC @baskaryan * docs[patch]: Fix typo in feedback (#23146) * core[patch]: runnablewithchathistory from core.runnables (#23136) * openai[patch], standard-tests[patch]: don't pass in falsey stop vals (#23153) adds an image input test to standard-tests as well * anthropic: docstrings (#23145) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) * anthropic[patch]: test image input (#23155) * text-splitters: Introduce Experimental Markdown Syntax Splitter (#22257) #### Description This MR defines a `ExperimentalMarkdownSyntaxTextSplitter` class. The main goal is to replicate the functionality of the original `MarkdownHeaderTextSplitter` which extracts the header stack as metadata but with one critical difference: it keeps the whitespace of the original text intact. This draft reimplements the `MarkdownHeaderTextSplitter` with a very different algorithmic approach. Instead of marking up each line of the text individually and aggregating them back together into chunks, this method builds each chunk sequentially and applies the metadata to each chunk. This makes the implementation simpler. However, since it's designed to keep white space intact its not a full drop in replacement for the original. Since it is a radical implementation change to the original code and I would like to get feedback to see if this is a worthwhile replacement, should be it's own class, or is not a good idea at all. Note: I implemented the `return_each_line` parameter but I don't think it's a necessary feature. I'd prefer to remove it. This implementation also adds the following additional features: - Splits out code blocks and includes the language in the `"Code"` metadata key - Splits text on the horizontal rule `---` as well - The `headers_to_split_on` parameter is now optional - with sensible defaults that can be overridden. #### Issue Keeping the whitespace keeps the paragraphs structure and the formatting of the code blocks intact which allows the caller much more flexibility in how they want to further split the individuals sections of the resulting documents. This addresses the issues brought up by the community in the following issues: - https://github.com/langchain-ai/langchain/issues/20823 - https://github.com/langchain-ai/langchain/issues/19436 - https://github.com/langchain-ai/langchain/issues/22256 #### Dependencies N/A #### Twitter handle @RyanElston --------- Co-authored-by: isaac hershenson <[email protected]> * ibm: docstrings (#23149) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) * community: add **request_kwargs and expect TimeError AsyncHtmlLoader (#23068) - **Description:** add `**request_kwargs` and expect `TimeError` in `_fetch` function for AsyncHtmlLoader. This allows you to fill in the kwargs parameter when using the `load()` method of the `AsyncHtmlLoader` class. Co-authored-by: Yucolu <[email protected]> * docs: Overhaul Databricks components documentation (#22884) **Description:** Documentation at [integrations/llms/databricks](https://python.langchain.com/v0.2/docs/integrations/llms/databricks/) is not up-to-date and includes examples about chat model and embeddings, which should be located in the different corresponding subdirectories. This PR split the page into correct scope and overhaul the contents. **Note**: This PR might be hard to review on the diffs view, please use the following preview links for the changed pages. - `ChatDatabricks`: https://langchain-git-fork-b-step62-chat-databricks-doc-langchain.vercel.app/v0.2/docs/integrations/chat/databricks/ - `Databricks`: https://langchain-git-fork-b-step62-chat-databricks-doc-langchain.vercel.app/v0.2/docs/integrations/llms/databricks/ - `DatabricksEmbeddings`: https://langchain-git-fork-b-step62-chat-databricks-doc-langchain.vercel.app/v0.2/docs/integrations/text_embedding/databricks/ - [x] **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/ --------- Signed-off-by: B-Step62 <[email protected]> * text-splitters: Fix/recursive json splitter data persistence issue (#21529) Thank you for contributing to LangChain! **Description:** Noticed an issue with when I was calling `RecursiveJsonSplitter().split_json()` multiple times that I was getting weird results. I found an issue where `chunks` list in the `_json_split` method. If chunks is not provided when _json_split (which is the case when split_json calls _json_split) then the same list is used for subsequent calls to `_json_split`. You can see this in the test case i also added to this commit. Output should be: ``` [{'a': 1, 'b': 2}] [{'c': 3, 'd': 4}] ``` Instead you get: ``` [{'a': 1, 'b': 2}] [{'a': 1, 'b': 2, 'c': 3, 'd': 4}] ``` --------- Co-authored-by: Nuno Campos <[email protected]> Co-authored-by: isaac hershenson <[email protected]> Co-authored-by: Isaac Francisco <[email protected]> * docs[patch]: Standardize prerequisites in tutorial docs (#23150) CC @baskaryan * ai21: docstrings (#23142) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) * core[patch]: Update documentation in LLM namespace (#23138) Update documentation in lllm namespace. * core[patch]: Document embeddings namespace (#23132) Document embeddings namespace * core[patch]: Expand documentation in the indexing namespace (#23134) * community: move test to integration tests (#23178) Tests failing on master with > FAILED tests/unit_tests/embeddings/test_ovhcloud.py::test_ovhcloud_embed_documents - ValueError: Request failed with status code: 401, {"message":"Bad token; invalid JSON"} * community[patch]: Prevent unit tests from making network requests (#23180) * Prevent unit tests from making network requests * core[patch]: Add doc-string to document compressor (#23085) * core[patch]: Add documentation to load namespace (#23143) Document some of the modules within the load namespace * core[patch]: update test to catch circular imports (#23172) This raises ImportError due to a circular import: ```python from langchain_core import chat_history ``` This does not: ```python from langchain_core import runnables from langchain_core import chat_history ``` Here we update `test_imports` to run each import in a separate subprocess. Open to other ways of doing this! * core[patch]: Add an example to the Document schema doc-string (#23131) Add an example to the document schema * core[patch]: fix chat history circular import (#23182) * fix: MoonshotChat fails when setting the moonshot_api_key through the OS environment. (#23176) Close #23174 Co-authored-by: tianming <[email protected]> * docs: add bing search tool to ms platform (#23183) - [x] **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/ * prompty: docstring (#23152) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) --------- Co-authored-by: ccurme <[email protected]> * core[patch[: add exceptions propagation test for astream_events v2 (#23159) **Description:** `astream_events(version="v2")` didn't propagate exceptions in `langchain-core<=0.2.6`, fixed in the #22916. This PR adds a unit test to check that exceptions are propagated upwards. Co-authored-by: Sergey Kozlov <[email protected]> * langchain[small]: Change type to BasePromptTemplate (#23083) ```python Change from_llm( prompt: PromptTemplate ... ) ``` to ```python Change from_llm( prompt: BasePromptTemplate ... ) ``` * community[patch]: update sambastudio embeddings (#23133) Description: update sambastudio embeddings integration, now compatible with generic endpoints and CoE endpoints * community[patch]: sambanova llm integration improvement (#23137) - **Description:** sambanova sambaverse integration improvement: removed input parsing that was changing raw user input, and was making to use process prompt parameter as true mandatory * openai[patch]: image token counting (#23147) Resolves #23000 --------- Co-authored-by: isaac hershenson <[email protected]> Co-authored-by: ccurme <[email protected]> * upstage: move to external repo (#22506) * community[patch]: restore compatibility with SQLAlchemy 1.x (#22546) - **Description:** Restores compatibility with SQLAlchemy 1.4.x that was broken since #18992 and adds a test run for this version on CI (only for Python 3.11) - **Issue:** fixes #19681 - **Dependencies:** None - **Twitter handle:** `@krassowski_m` --------- Co-authored-by: Erick Friis <[email protected]> * infra: add more formatter rules to openai (#23189) Turns on https://docs.astral.sh/ruff/settings/#format_docstring-code-format and https://docs.astral.sh/ruff/settings/#format_skip-magic-trailing-comma ```toml [tool.ruff.format] docstring-code-format = true skip-magic-trailing-comma = true ``` * core[patch]: Add doc-strings to outputs, fix @root_validator (#23190) - Document outputs namespace - Update a vanilla @root_validator that was missed * core[patch]: Document messages namespace (#23154) - Moved doc-strings below attribtues in TypedDicts -- seems to render better on APIReference pages. * Provided more description and some simple code examples * infra: run CI on large diffs (#23192) currently we skip CI on diffs >= 300 files. think we should just run it on all packages instead --------- Co-authored-by: Erick Friis <[email protected]> * core[patch]: Document agent schema (#23194) * Document agent schema * Refer folks to langgraph for more information on how to create agents. * fireworks[patch]: fix api_key alias in Fireworks LLM (#23118) Thank you for contributing to LangChain! **Description** The current code snippet for `Fireworks` had incorrect parameters. This PR fixes those parameters. --------- Co-authored-by: Chester Curme <[email protected]> Co-authored-by: Bagatur <[email protected]> * core:Add optional max_messages to MessagePlaceholder (#16098) - **Description:** Add optional max_messages to MessagePlaceholder - **Issue:** [16096](https://github.com/langchain-ai/langchain/issues/16096) - **Dependencies:** None - **Twitter handle:** @davedecaprio Sometimes it's better to limit the history in the prompt itself rather than the memory. This is needed if you want different prompts in the chain to have different history lengths. --------- Co-authored-by: Harrison Chase <[email protected]> * docs: standard params (#23199) * standard-tests[patch]: test stop not stop_sequences (#23200) * fix https://github.com/langchain-ai/langchain/issues/23215 (#23216) fix bug The ZhipuAIEmbeddings class is not working. Co-authored-by: xu yandong <[email protected]> * huggingface[patch]: fix CI for python 3.12 (#23197) * huggingface: docstrings (#23148) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) Co-authored-by: ccurme <[email protected]> * Docs: Update Rag tutorial so it includes an additional notebook cell with pip installs of required langchain_chroma and langchain_community. (#23204) Description: Update Rag tutorial notebook so it includes an additional notebook cell with pip installs of required langchain_chroma and langchain_community. This fixes the issue with the rag tutorial gives you a 'missing modules' error if you run code in the notebook as is. --------- Co-authored-by: Chester Curme <[email protected]> * doc: replace function all with tool call (#23184) - [x] **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/ * partners[minor]: Fix value error message for with_structured_output (#22877) Currently, calling `with_structured_output()` with an invalid method argument raises `Unrecognized method argument. Expected one of 'function_calling' or 'json_format'`, but the JSON mode option [is now referred to](https://python.langchain.com/v0.2/docs/how_to/structured_output/#the-with_structured_output-method) by `'json_mode'`. This fixes that. Co-authored-by: Harrison Chase <[email protected]> * community: docstrings (#23202) Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) * docs: Update clean up API reference (#23221) - Fix bug with TypedDicts rendering inherited methods if inherting from typing_extensions.TypedDict rather than typing.TypedDict - Do not surface inherited pydantic methods for subclasses of BaseModel - Subclasses of RunnableSerializable will not how methods inherited from Runnable or from BaseModel - Subclasses of Runnable that not pydantic models will include a link to RunnableInterface (they still show inherited methods, we can fix this later) * docs: API reference remove Prev/Up/Next buttons (#23225) These do not work anyway. Let's remove them for now for simplicity. * core[minor]: Adds an in-memory implementation of RecordM…
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