194 Commits (30fb345342f2ae10d1d8fe10f93c548761a0395e)

Author	SHA1	Message	Date
Kirushikesh DB	12861273e1	experimental[patch]: Removed 'SQLResults:' from the LLMResponse in SQLDatabaseChain (#17104) ... **Description:** When using the SQLDatabaseChain with Llama2-70b LLM and, SQLite database. I was getting `Warning: You can only execute one statement at a time.`. ``` from langchain.sql_database import SQLDatabase from langchain_experimental.sql import SQLDatabaseChain sql_database_path = '/dccstor/mmdataretrieval/mm_dataset/swimming_record/rag_data/swimmingdataset.db' sql_db = get_database(sql_database_path) db_chain = SQLDatabaseChain.from_llm(mistral, sql_db, verbose=True, callbacks = [callback_obj]) db_chain.invoke({ "query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?" }) ``` Error: ``` Warning Traceback (most recent call last) Cell In[31], line 3 1 import langchain 2 langchain.debug=False ----> 3 db_chain.invoke({ 4 "query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?" 5 }) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs) 160 except BaseException as e: 161 run_manager.on_chain_error(e) --> 162 raise e 163 run_manager.on_chain_end(outputs) 164 final_outputs: Dict[str, Any] = self.prep_outputs( 165 inputs, outputs, return_only_outputs 166 ) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs) 149 run_manager = callback_manager.on_chain_start( 150 dumpd(self), 151 inputs, 152 name=run_name, 153 ) 154 try: 155 outputs = ( --> 156 self._call(inputs, run_manager=run_manager) 157 if new_arg_supported 158 else self._call(inputs) 159 ) 160 except BaseException as e: 161 run_manager.on_chain_error(e) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:198, in SQLDatabaseChain._call(self, inputs, run_manager) 194 except Exception as exc: 195 # Append intermediate steps to exception, to aid in logging and later 196 # improvement of few shot prompt seeds 197 exc.intermediate_steps = intermediate_steps # type: ignore --> 198 raise exc File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:143, in SQLDatabaseChain._call(self, inputs, run_manager) 139 intermediate_steps.append( 140 sql_cmd 141 ) # output: sql generation (no checker) 142 intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec --> 143 result = self.database.run(sql_cmd) 144 intermediate_steps.append(str(result)) # output: sql exec 145 else: File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:436, in SQLDatabase.run(self, command, fetch, include_columns) 425 def run( 426 self, 427 command: str, 428 fetch: Literal["all", "one"] = "all", 429 include_columns: bool = False, 430 ) -> str: 431 """Execute a SQL command and return a string representing the results. 432 433 If the statement returns rows, a string of the results is returned. 434 If the statement returns no rows, an empty string is returned. 435 """ --> 436 result = self._execute(command, fetch) 438 res = [ 439 { 440 column: truncate_word(value, length=self._max_string_length) (...) 443 for r in result 444 ] 446 if not include_columns: File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:413, in SQLDatabase._execute(self, command, fetch) 410 elif self.dialect == "postgresql": # postgresql 411 connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) --> 413 cursor = connection.execute(text(command)) 414 if cursor.returns_rows: 415 if fetch == "all": File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1416, in Connection.execute(self, statement, parameters, execution_options) 1414 raise exc.ObjectNotExecutableError(statement) from err 1415 else: -> 1416 return meth( 1417 self, 1418 distilled_parameters, 1419 execution_options or NO_OPTIONS, 1420 ) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/sql/elements.py:516, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options) 514 if TYPE_CHECKING: 515 assert isinstance(self, Executable) --> 516 return connection._execute_clauseelement( 517 self, distilled_params, execution_options 518 ) 519 else: 520 raise exc.ObjectNotExecutableError(self) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1639, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options) 1627 compiled_cache: Optional[CompiledCacheType] = execution_options.get( 1628 "compiled_cache", self.engine._compiled_cache 1629 ) 1631 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache( 1632 dialect=dialect, 1633 compiled_cache=compiled_cache, (...) 1637 linting=self.dialect.compiler_linting | compiler.WARN_LINTING, 1638 ) -> 1639 ret = self._execute_context( 1640 dialect, 1641 dialect.execution_ctx_cls._init_compiled, 1642 compiled_sql, 1643 distilled_parameters, 1644 execution_options, 1645 compiled_sql, 1646 distilled_parameters, 1647 elem, 1648 extracted_params, 1649 cache_hit=cache_hit, 1650 ) 1651 if has_events: 1652 self.dispatch.after_execute( 1653 self, 1654 elem, (...) 1658 ret, 1659 ) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1848, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw) 1843 return self._exec_insertmany_context( 1844 dialect, 1845 context, 1846 ) 1847 else: -> 1848 return self._exec_single_context( 1849 dialect, context, statement, parameters 1850 ) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1988, in Connection._exec_single_context(self, dialect, context, statement, parameters) 1985 result = context._setup_result_proxy() 1987 except BaseException as e: -> 1988 self._handle_dbapi_exception( 1989 e, str_statement, effective_parameters, cursor, context 1990 ) 1992 return result File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:2346, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec) 2344 else: 2345 assert exc_info[1] is not None -> 2346 raise exc_info[1].with_traceback(exc_info[2]) 2347 finally: 2348 del self._reentrant_error File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1969, in Connection._exec_single_context(self, dialect, context, statement, parameters) 1967 break 1968 if not evt_handled: -> 1969 self.dialect.do_execute( 1970 cursor, str_statement, effective_parameters, context 1971 ) 1973 if self._has_events or self.engine._has_events: 1974 self.dispatch.after_cursor_execute( 1975 self, 1976 cursor, (...) 1980 context.executemany, 1981 ) File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/default.py:922, in DefaultDialect.do_execute(self, cursor, statement, parameters, context) 921 def do_execute(self, cursor, statement, parameters, context=None): --> 922 cursor.execute(statement, parameters) Warning: You can only execute one statement at a time. ``` **Issue:** The Error occurs because when generating the SQLQuery, the llm_input includes the stop character of "\nSQLResult:", so for this user query the LLM generated response is **SELECT Time FROM men_butterfly_100m WHERE Swimmer = 'Lance Larson';\nSQLResult:** it is required to remove the SQLResult suffix on the llm response before executing it on the database. ``` llm_inputs = { "input": input_text, "top_k": str(self.top_k), "dialect": self.database.dialect, "table_info": table_info, "stop": ["\nSQLResult:"], } sql_cmd = self.llm_chain.predict( callbacks=_run_manager.get_child(), **llm_inputs, ).strip() if SQL_RESULT in sql_cmd: sql_cmd = sql_cmd.split(SQL_RESULT)[0].strip() result = self.database.run(sql_cmd) ``` <!-- 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:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **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. 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. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
T Cramer	540ebf35a9	community[patch]: Add explicit error message to Bedrock error output. (#17328) ... - **Description:** Propagate Bedrock errors into Langchain explicitly. Use-case: unset region error is hidden behind 'Could not load credentials...' message - **Issue:** [17654](https://github.com/langchain-ai/langchain/issues/17654) - **Dependencies:** None --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Luca Dorigo	f19229c564	core[patch]: fix beta, deprecated typing (#18877) ... **Description:** While not technically incorrect, the TypeVar used for the `@beta` decorator prevented pyright (and thus most vscode users) from correctly seeing the types of functions/classes decorated with `@beta`. This is in part due to a small bug in pyright (https://github.com/microsoft/pyright/issues/7448 ) - however, the `Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not doing anything - classes are `Callables` by default, so by my understanding binding to `Type` does not actually provide any more safety - the modified annotation still works correctly for both functions, properties, and classes. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
Tomaz Bratanic	b04e663426	experimental[patch]: Flatten relationships in LLM graph transformer (#19642)	6 months ago
Juan Jose Miguel Ovalle Villamil	1fe10a3e3d	experimental[patch]: Enhance LLMGraphTransformer with async processing and improved readability (#19205) ... - [x] **PR title**: "experimental: Enhance LLMGraphTransformer with async processing and improved readability" - [x] **PR message**: - **Description:** This pull request refactors the `process_response` and `convert_to_graph_documents` methods in the LLMGraphTransformer class to improve code readability and adds async versions of these methods for concurrent processing. The main changes include: - Simplifying list comprehensions and conditional logic in the process_response method for better readability. - Adding async versions aprocess_response and aconvert_to_graph_documents to enable concurrent processing of documents. These enhancements aim to improve the overall efficiency and maintainability of the `LLMGraphTransformer` class. - **Issue:** N/A - **Dependencies:** No additional dependencies required. - **Twitter handle:** @jjovalle99 - [x] **Add tests and docs**: N/A (This PR does not introduce a new integration) - [x] **Lint and test**: Ran make format, make lint, and make test from the root of the modified package(s). All tests pass successfully. Additional notes: - The changes made in this PR are backwards compatible and do not introduce any breaking changes. - The PR touches only the `LLMGraphTransformer` class within the experimental package. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Leonid Ganeline	3dc0f3c371	experimental[patch]: `PromptTemplate` import fix (#19617) ... Changed import of `PromptTemplate` from `langchain` to `langchain_core` in `langchain_experimental`	6 months ago
Leonid Ganeline	4159a4723c	experimental[patch]: update module doc strings (#19539) ... Added missed module descriptions. Fixed format.	6 months ago
Zihong	ff31cc1648	experimental: update the notebook link of semantic chunk. (#19253) ... update the notebook link of semantic chunk.	6 months ago
Cycle	77868b1974	experimental: add buffer_size hyperparameter to SemanticChunker as in source video (#19208) ... add buffer_size hyperparameter which used in combine_sentences function	6 months ago
Tomaz Bratanic	cda43c5a11	experimental[patch]: Fix LLM graph transformer default prompt (#18856) ... Some LLMs do not allow multiple user messages in sequence.	7 months ago
Tomaz Bratanic	246724faab	LLM graph transformer prompt engineering (#18843) ... A bit of prompt engineering to improve results	7 months ago
Alexander Dicke	66576948e0	experimental[minor]: adds mixtral wrapper (#17423) ... **Description:** Adds a chat wrapper for Mixtral models using the [prompt template](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format). --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Tomaz Bratanic	c8c592d3f1	experimental[minor]: Add LLM graph transformer (#18733) ... Add a class that constructs knowledge graphs based on text using an LLM.	7 months ago
Tomaz Bratanic	010a234f1e	docs: Fix diffbot graph transformer description (#18736) ... The previous docstring was invalid	7 months ago
Massimiliano Pronesti	3b975c6ebe	experimental[minor]: add support for modin in pandas agent (#18749) ... Added support for Intel's [modin](https://github.com/modin-project/modin) in `create_pandas_dataframe_agent`.	7 months ago
Erick Friis	4ac2cb4adc	anthropic[minor]: add tool calling (#18554)	7 months ago
matt haigh	a4896da2a0	Experimental: Add other threshold types to SemanticChunker (#16807) ... **Description** Adding different threshold types to the semantic chunker. I’ve had much better and predictable performance when using standard deviations instead of percentiles.  For all the documents I’ve tried, the distribution of distances look similar to the above: positively skewed normal distribution. All skews I’ve seen are less than 1 so that explains why standard deviations perform well, but I’ve included IQR if anyone wants something more robust. Also, using the percentile method backwards, you can declare the number of clusters and use semantic chunking to get an ‘optimal’ splitting. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	7 months ago
Leonid Ganeline	3f6bf852ea	experimental: docstrings update (#18048) ... Added missed docstrings. Formatted docsctrings to the consistent format.	7 months ago
Erick Friis	ed789be8f4	docs, templates: update schema imports to core (#17885) ... - chat models, messages - documents - agentaction/finish - baseretriever,document - stroutputparser - more messages - basemessage - format_document - baseoutputparser --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Pranav Agarwal	86ae48b781	experimental[minor]: Amazon Personalize support (#17436) ... ## Amazon Personalize support on Langchain This PR is a successor to this PR - https://github.com/langchain-ai/langchain/pull/13216 This PR introduces an integration with [Amazon Personalize](https://aws.amazon.com/personalize/) to help you to retrieve recommendations and use them in your natural language applications. This integration provides two new components: 1. An `AmazonPersonalize` client, that provides a wrapper around the Amazon Personalize API. 2. An `AmazonPersonalizeChain`, that provides a chain to pull in recommendations using the client, and then generating the response in natural language. We have added this to langchain_experimental since there was feedback from the previous PR about having this support in experimental rather than the core or community extensions. Here is some sample code to explain the usage. ```python from langchain_experimental.recommenders import AmazonPersonalize from langchain_experimental.recommenders import AmazonPersonalizeChain from langchain.llms.bedrock import Bedrock recommender_arn = "<insert_arn>" client=AmazonPersonalize( credentials_profile_name="default", region_name="us-west-2", recommender_arn=recommender_arn ) bedrock_llm = Bedrock( model_id="anthropic.claude-v2", region_name="us-west-2" ) chain = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client ) response = chain({'user_id': '1'}) ``` Reviewer: @3coins	7 months ago
Mattt394	7c6009b76f	experimental[patch]: Fixed typos in SmartLLMChain ideation and critique prompts (#11507) ... Noticed and fixed a few typos in the SmartLLMChain default ideation and critique prompts --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	7 months ago
DanisJiang	de9a6cdf16	experimental[patch]: Enhance protection against arbitrary code execution in PALChain (#17091) ... - **Description:** Block some ways to trigger arbitrary code execution bug in PALChain. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	7 months ago
Bagatur	c0ce93236a	experimental[patch]: fix zero-shot pandas agent (#17442)	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
Erick Friis	3a2eb6e12b	infra: add print rule to ruff (#16221) ... Added noqa for existing prints. Can slowly remove / will prevent more being intro'd	8 months ago
Eugene Yurtsev	780e84ae79	community[minor]: SQLDatabase Add fetch mode `cursor`, query parameters, query by selectable, expose execution options, and documentation (#17191) ... - **Description:** Improve `SQLDatabase` adapter component to promote code re-use, see [suggestion](https://github.com/langchain-ai/langchain/pull/16246#pullrequestreview-1846590962). - **Needed by:** GH-16246 - **Addressed to:** @baskaryan, @cbornet ## Details - Add `cursor` fetch mode - Accept SQL query parameters - Accept both `str` and SQLAlchemy selectables as query expression - Expose `execution_options` - Documentation page (notebook) about `SQLDatabase` [^1] See [About SQLDatabase](https://github.com/langchain-ai/langchain/blob/c1c7b763/docs/docs/integrations/tools/sql_database.ipynb). [^1]: Apparently there hasn't been any yet? --------- Co-authored-by: Andreas Motl <andreas.motl@crate.io>	8 months ago
Leonid Ganeline	563f325034	experimental[patch]: fixed import in `experimental` (#17078)	8 months ago
Giulio Zani	9f0b63dba0	experimental[patch]: Fixes issue #17060 (#17062) ... As described in issue #17060, in the case in which text has only one sentence the following function fails. Checking for that and adding a return case fixed the issue. ```python def split_text(self, text: str) -> List[str]: """Split text into multiple components.""" # Splitting the essay on '.', '?', and '!' single_sentences_list = re.split(r"(?<=[.?!])\s+", text) sentences = [ {"sentence": x, "index": i} for i, x in enumerate(single_sentences_list) ] sentences = combine_sentences(sentences) embeddings = self.embeddings.embed_documents( [x["combined_sentence"] for x in sentences] ) for i, sentence in enumerate(sentences): sentence["combined_sentence_embedding"] = embeddings[i] distances, sentences = calculate_cosine_distances(sentences) start_index = 0 # Create a list to hold the grouped sentences chunks = [] breakpoint_percentile_threshold = 95 breakpoint_distance_threshold = np.percentile( distances, breakpoint_percentile_threshold ) # If you want more chunks, lower the percentile cutoff indices_above_thresh = [ i for i, x in enumerate(distances) if x > breakpoint_distance_threshold ] # The indices of those breakpoints on your list # Iterate through the breakpoints to slice the sentences for index in indices_above_thresh: # The end index is the current breakpoint end_index = index # Slice the sentence_dicts from the current start index to the end index group = sentences[start_index : end_index + 1] combined_text = " ".join([d["sentence"] for d in group]) chunks.append(combined_text) # Update the start index for the next group start_index = index + 1 # The last group, if any sentences remain if start_index < len(sentences): combined_text = " ".join([d["sentence"] for d in sentences[start_index:]]) chunks.append(combined_text) return chunks ``` Co-authored-by: Giulio Zani <salamanderxing@Giulios-MBP.homenet.telecomitalia.it>	8 months ago
Bagatur	7d03d8f586	docs: fix docstring examples (#16889)	8 months ago
Bagatur	b0347f3e2b	docs: add csv use case (#16756)	8 months ago
Massimiliano Pronesti	1bc8d9a943	experimental[patch]: missing resolution strategy in anonymization (#16653) ... - **Description:** Presidio-based anonymizers are not working because `_remove_conflicts_and_get_text_manipulation_data` was being called without a conflict resolution strategy. This PR fixes this issue. In addition, it removes some mutable default arguments (antipattern). To reproduce the issue, just run the very first cell of this [notebook](https://python.langchain.com/docs/guides/privacy/2/) from langchain's documentation. <!-- 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:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **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. 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. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->	8 months ago
Bagatur	bccb07f93e	core[patch]: simple prompt pretty printing (#15968)	8 months ago
Harrison Chase	20abe24819	experimental[minor]: Add semantic chunker (#15799)	9 months ago
Bagatur	baeac236b6	langchain[patch], experimental[patch]: update utilities imports (#15438)	9 months ago
Bagatur	1678d6ca17	langchain[patch], experimental[patch], docs: update tools imports (#15433)	9 months ago
Bagatur	fa5d49f2c1	docs, experimental[patch], langchain[patch], community[patch]: update storage imports (#15429) ... ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```	9 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 ```	9 months ago
Bagatur	8e0d5813c2	langchain[patch], experimental[patch]: replace langchain.schema imports (#15410) ... Import from core instead. Ran: ```bash git grep -l 'from langchain.schema\.output_parser' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.output_parser/from\ langchain_core.output_parsers/g" git grep -l 'from langchain.schema\.messages' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.messages/from\ langchain_core.messages/g" git grep -l 'from langchain.schema\.document' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.document/from\ langchain_core.documents/g" git grep -l 'from langchain.schema\.runnable' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.runnable/from\ langchain_core.runnables/g" git grep -l 'from langchain.schema\.vectorstore' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.vectorstore/from\ langchain_core.vectorstores/g" git grep -l 'from langchain.schema\.language_model' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.language_model/from\ langchain_core.language_models/g" git grep -l 'from langchain.schema\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.embeddings/from\ langchain_core.embeddings/g" git grep -l 'from langchain.schema\.storage' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.storage/from\ langchain_core.stores/g" git checkout master libs/langchain/tests/unit_tests/schema/ make format cd libs/experimental make format cd ../langchain make format ```	9 months ago
Nuno Campos	eb5e250188	Propagate context vars in all classes/methods ... - Any direct usage of ThreadPoolExecutor or asyncio.run_in_executor needs manual handling of context vars	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
Oleksandr Yaremchuk	d82a3828f2	Improve prompt injection detection (#14842) ... - **Description:** This is addition to [my previous PR](https://github.com/langchain-ai/langchain/pull/13930) with improvements to flexibility allowing different models and notebook to use ONNX runtime for faster speed. Since the last PR, [our model](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection) got more than 660k downloads, and with the [public benchmark](https://huggingface.co/spaces/laiyer/prompt-injection-benchmark) showed much fewer false-positives than the previous one from deepset. Additionally, on the ONNX runtime, it can be running 3x faster on the CPU, which might be handy for builders using Langchain. **Issue:** N/A - **Dependencies:** N/A - **Tag maintainer:** N/A - **Twitter handle:** `@laiyer_ai`	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	10 months ago
Anish Nag	6da0cfea0e	experimental[patch]: SmartLLMChain Output Key Customization (#14466) ... **Description** The `SmartLLMChain` was was fixed to output key "resolution". Unfortunately, this prevents the ability to use multiple `SmartLLMChain` in a `SequentialChain` because of colliding output keys. This change simply gives the option the customize the output key to allow for sequential chaining. The default behavior is the same as the current behavior. Now, it's possible to do the following: ``` from langchain.chat_models import ChatOpenAI from langchain.prompts import PromptTemplate from langchain_experimental.smart_llm import SmartLLMChain from langchain.chains import SequentialChain joke_prompt = PromptTemplate( input_variables=["content"], template="Tell me a joke about {content}.", ) review_prompt = PromptTemplate( input_variables=["scale", "joke"], template="Rate the following joke from 1 to {scale}: {joke}" ) llm = ChatOpenAI(temperature=0.9, model_name="gpt-4-32k") joke_chain = SmartLLMChain(llm=llm, prompt=joke_prompt, output_key="joke") review_chain = SmartLLMChain(llm=llm, prompt=review_prompt, output_key="review") chain = SequentialChain( chains=[joke_chain, review_chain], input_variables=["content", "scale"], output_variables=["review"], verbose=True ) response = chain.run({"content": "chickens", "scale": "10"}) print(response) ``` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	10 months ago
Erick Friis	b3f226e8f8	core[patch], langchain[patch], experimental[patch]: import CI (#14414)	10 months ago
Bagatur	b2280fd874	core[patch], langchain[patch]: fix required deps (#14373)	10 months ago
kavinraj A S	ab6b41937a	Fixed a typo in smart_llm prompt (#13052) ... <!-- 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. -->	10 months ago
Lance Martin	66848871fc	Multi-modal RAG template (#14186) ... * OpenCLIP embeddings * GPT-4V --------- Co-authored-by: Erick Friis <erick@langchain.dev>	10 months ago
Eun Hye Kim	f758c8adc4	Fix #11737 issue (extra_tools option of create_pandas_dataframe_agent is not working) (#13203) ... - **Description:** Fix #11737 issue (extra_tools option of create_pandas_dataframe_agent is not working), - **Issue:** #11737 , - **Dependencies:** no, - **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17 I needed this method at work, so I modified it myself and used it. There is a similar issue(#11737) and PR(#13018) of @PyroGenesis, so I combined my code at the original PR. You may be busy, but it would be great help for me if you checked. Thank you. - **Twitter handle:** @lunara_x If you need an .ipynb example about this, please tag me. I will share what I am working on after removing any work-related content. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	10 months ago
Abdul	82102c99b3	langchain[patch]: Running SQLDatabaseChain adds prefix "SQLQuery:\n" (#14058) ... - **Issue:** https://github.com/langchain-ai/langchain/issues/12077 --------- Co-authored-by: Abdul Kader Maliyakkal <maliyakk@amazon.com>	10 months ago
James Braza	24385a00de	core[minor], langchain[patch], experimental[patch]: Added missing `py.typed` to `langchain_core` (#14143) ... See PR title. From what I can see, `poetry` will auto-include this. Please let me know if I am missing something here. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	10 months ago
Lance Martin	cbe4753e1a	Update Open CLIP embd (#14155) ... Prior default model required a large amt of RAM and often crashed Jupyter ntbk kernel.	10 months ago
Jacob Lee	3328507f11	langchain[patch], experimental[minor]: Adds OllamaFunctions wrapper (#13330) ... CC @baskaryan @hwchase17 @jmorganca Having a bit of trouble importing `langchain_experimental` from a notebook, will figure it out tomorrow ~Ah and also is blocked by #13226~ --------- Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>	10 months ago
Leonid Ganeline	bf5787f58b	experimental[patch]: fixed namespace bug (#13585) ... It was : `from langchain.schema.prompts import BasePromptTemplate` but because of the breaking change in the ns, it is now `from langchain.schema.prompt_template import BasePromptTemplate` This bug prevents building the API Reference for the langchain_experimental	10 months ago
Johannes Foulds	fc40bd4cdb	AnthropicFunctions function_call compatibility (#13901) ... - **Description:** Updates to `AnthropicFunctions` to be compatible with the OpenAI `function_call` functionality. - **Issue:** The functionality to indicate `auto`, `none` and a forced function_call was not completely implemented in the existing code. - **Dependencies:** None - **Tag maintainer:** @baskaryan , and any of the other maintainers if needed. - **Twitter handle:** None I have specifically tested this functionality via AWS Bedrock with the Claude-2 and Claude-Instant models.	10 months ago
Oleksandr Yaremchuk	c0277d06e8	experimental[patch] Update prompt injection model (#13930) ... - **Description:** Existing model used for Prompt Injection is quite outdated but we fine-tuned and open-source a new model based on the same model deberta-v3-base from Microsoft - [laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection). It supports more up-to-date injections and less prone to false-positives. - **Dependencies:** No - **Tag maintainer:** - - **Twitter handle:** @alex_yaremchuk --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	10 months ago
Bob Lin	e6ebde9688	experimental[patch]: Add experimental.agent imports (#13839) ... - **Description:** The experimental package needs to be compatible with the usage of importing agents For example, if i use `from langchain.agents import create_pandas_dataframe_agent`, running the program will prompt the following information: ``` Traceback (most recent call last): File "/Users/dongwm/test/main.py", line 1, in <module> from langchain.agents import create_pandas_dataframe_agent File "/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 87, in __getattr__ raise ImportError( ImportError: create_pandas_dataframe_agent has been moved to langchain experimental. See https://github.com/langchain-ai/langchain/discussions/11680 for more information. Please update your import statement from: `langchain.agents.create_pandas_dataframe_agent` to `langchain_experimental.agents.create_pandas_dataframe_agent`. ``` But when I changed to `from langchain_experimental.agents import create_pandas_dataframe_agent`, it was actually wrong: ```python Traceback (most recent call last): File "/Users/dongwm/test/main.py", line 2, in <module> from langchain_experimental.agents import create_pandas_dataframe_agent ImportError: cannot import name 'create_pandas_dataframe_agent' from 'langchain_experimental.agents' (/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain_experimental/agents/__init__.py) ``` I should use `from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent`. In order to solve the problem and make it compatible, I added additional import code to the langchain_experimental package. Now it can be like this Used `from langchain_experimental.agents import create_pandas_dataframe_agent` - **Twitter handle:** [lin_bob57617](https://twitter.com/lin_bob57617)	10 months ago
Bagatur	c61e30632e	BUG: more core fixes (#13665) ... Fix some circular deps: - move PromptValue into top level module bc both PromptTemplates and OutputParsers import - move tracer context vars to `tracers.context` and import them in functions in `callbacks.manager` - add core import tests	10 months ago
Martin Krasser	79ed66f870	EXPERIMENTAL Generic LLM wrapper to support chat model interface with configurable chat prompt format (#8295) ... ## Update 2023-09-08 This PR now supports further models in addition to Lllama-2 chat models. See [this comment](#issuecomment-1668988543) for further details. The title of this PR has been updated accordingly. ## Original PR description This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to serve Llama-2 chat models (like `LlamaCPP`, `HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`, converts a list of chat messages into the [required Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`. Usage example: ```python # uses a locally hosted Llama2 chat model llm = HuggingFaceTextGenInference( inference_server_url="http://127.0.0.1:8080/", max_new_tokens=512, top_k=50, temperature=0.1, repetition_penalty=1.03, ) # Wrap llm to support Llama2 chat prompt format. # Resulting model is a chat model model = Llama2Chat(llm=llm) messages = [ SystemMessage(content="You are a helpful assistant."), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{text}"), ] prompt = ChatPromptTemplate.from_messages(messages) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = LLMChain(llm=model, prompt=prompt, memory=memory) # use chat model in a conversation # ... ``` Also part of this PR are tests and a demo notebook. - Tag maintainer: @hwchase17 - Twitter handle: `@mrt1nz` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	10 months ago
Bagatur	1c67db4c18	Move OAI assistants to langchain and add callbacks (#13236)	10 months ago
Lance Martin	d2e50b3108	Add Chroma multimodal cookbook (#12952) ... Pending: * https://github.com/chroma-core/chroma/pull/1294 * https://github.com/chroma-core/chroma/pull/1293 --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>	11 months ago
Bagatur	8b2a82b5ce	Bagatur/docs smith context (#13139)	11 months ago
Bagatur	55aeff6777	oai assistant multiple actions (#13068)	11 months ago
Bagatur	57e19989f6	Bagatur/oai assistant (#13010)	11 months ago
Noam Gat	14e8c74736	LM Format Enforcer Integration + Sample Notebook (#12625) ... ## Description This PR adds support for [lm-format-enforcer](https://github.com/noamgat/lm-format-enforcer) to LangChain.  The library is similar to jsonformer / RELLM which are supported in Langchain, but has several advantages such as - Batching and Beam search support - More complete JSON Schema support - LLM has control over whitespace, improving quality - Better runtime performance due to only calling the LLM's generate() function once per generate() call. The integration is loosely based on the jsonformer integration in terms of project structure. ## Dependencies No compile-time dependency was added, but if `lm-format-enforcer` is not installed, a runtime error will occur if it is trying to be used. ## Tests Due to the integration modifying the internal parameters of the underlying huggingface transformer LLM, it is not possible to test without building a real LM, which requires internet access. So, similar to the jsonformer and RELLM integrations, the testing is via the notebook. ## Twitter Handle [@noamgat](https://twitter.com/noamgat) Looking forward to hearing feedback! --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	11 months ago
Predrag Gruevski	f94e24dfd7	Install and use `ruff format` instead of black for code formatting. (#12585) ... Best to review one commit at a time, since two of the commits are 100% autogenerated changes from running `ruff format`: - Install and use `ruff format` instead of black for code formatting. - Output of `ruff format .` in the `langchain` package. - Use `ruff format` in experimental package. - Format changes in experimental package by `ruff format`. - Manual formatting fixes to make `ruff .` pass.	11 months ago
Harrison Chase	0ca539eb85	Clean up deprecated agents and update __init__ in experimental (#12231) ... Update init paths in experimental	11 months ago
Shorthills AI	25c98dbba9	Fixed some grammatical and Exception types issues (#12015) ... Fixed some grammatical issues and Exception types. @baskaryan , @eyurtsev --------- Co-authored-by: Sanskar Tanwar <142409040+SanskarTanwarShorthillsAI@users.noreply.github.com> Co-authored-by: UpneetShorthillsAI <144228282+UpneetShorthillsAI@users.noreply.github.com> Co-authored-by: HarshGuptaShorthillsAI <144897987+HarshGuptaShorthillsAI@users.noreply.github.com> Co-authored-by: AdityaKalraShorthillsAI <143726711+AdityaKalraShorthillsAI@users.noreply.github.com> Co-authored-by: SakshiShorthillsAI <144228183+SakshiShorthillsAI@users.noreply.github.com>	11 months ago
Nikhil Jha	dff24285ea	Comprehend Moderation 0.2 (#11730) ... This PR replaces the previous `Intent` check with the new `Prompt Safety` check. The logic and steps to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Prompt Safety information in the LLM prompt or answer remains unchanged. This implementation updates the code and configuration types with respect to `Prompt Safety`. ### Usage sample ```python from langchain_experimental.comprehend_moderation import (BaseModerationConfig, ModerationPromptSafetyConfig, ModerationPiiConfig, ModerationToxicityConfig ) pii_config = ModerationPiiConfig( labels=["SSN"], redact=True, mask_character="X" ) toxicity_config = ModerationToxicityConfig( threshold=0.5 ) prompt_safety_config = ModerationPromptSafetyConfig( threshold=0.5 ) moderation_config = BaseModerationConfig( filters=[pii_config, toxicity_config, prompt_safety_config] ) comp_moderation_with_config = AmazonComprehendModerationChain( moderation_config=moderation_config, #specify the configuration client=comprehend_client, #optionally pass the Boto3 Client verbose=True ) template = """Question: {question} Answer:""" prompt = PromptTemplate(template=template, input_variables=["question"]) responses = [ "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", "Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here." ] llm = FakeListLLM(responses=responses) llm_chain = LLMChain(prompt=prompt, llm=llm) chain = ( prompt | comp_moderation_with_config | {llm_chain.input_keys[0]: lambda x: x['output'] } | llm_chain | { "input": lambda x: x['text'] } | comp_moderation_with_config ) try: response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"}) except Exception as e: print(str(e)) else: print(response['output']) ``` ### Output ```python > Entering new AmazonComprehendModerationChain chain... Running AmazonComprehendModerationChain... Running pii Validation... Running toxicity Validation... Running prompt safety Validation... > Finished chain. > Entering new AmazonComprehendModerationChain chain... Running AmazonComprehendModerationChain... Running pii Validation... Running toxicity Validation... Running prompt safety Validation... > Finished chain. Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876. ``` --------- Co-authored-by: Jha <nikjha@amazon.com> Co-authored-by: Anjan Biswas <anjanavb@amazon.com> Co-authored-by: Anjan Biswas <84933469+anjanvb@users.noreply.github.com>	11 months ago
Erick Friis	95ae40ff90	Fix Anthropic Functions ainvoke (#12215) ... Removes custom `NotImplementedError` in experimental anthropic functions, allowing it to fallback on default `ainvoke` implementation.	11 months ago
Harrison Chase	ee69116761	move csv agent to langchain experimental (#12113)	11 months ago
Harrison Chase	03bf6ef473	add missing init files (#12114)	11 months ago
Bagatur	85302a9ec1	Add CI check that integration tests compile (#12090)	11 months ago
Predrag Gruevski	392df7b2e3	Type hints on varargs and kwargs that take anything should be `Any`. (#11950) ... Type hinting `*args` as `List[Any]` means that each positional argument should be a list. Type hinting `**kwargs` as `Dict[str, Any]` means that each keyword argument should be a dict of strings. This is almost never what we actually wanted, and doesn't seem to be what we want in any of the cases I'm replacing here.	11 months ago
Predrag Gruevski	dcd0392423	Upgrade to newer black (23.10) and ruff (first 0.1.x!) versions. (#11944) ... Minor lint dependency version upgrade to pick up latest functionality. Ruff's new v0.1 version comes with lots of nice features, like fix-safety guarantees and a preview mode for not-yet-stable features: https://astral.sh/blog/ruff-v0.1.0	11 months ago
maks-operlejn-ds	42dcc502c7	Anonymizer small fixes (#11915)	11 months ago
Predrag Gruevski	7c0f1bf23f	Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) ... Part of upgrading our CI to use Poetry 1.6.1.	11 months ago
Eugene Yurtsev	0d37b4c27d	Add python,pandas,xorbits,spark agents to experimental (#11774) ... See for contex https://github.com/langchain-ai/langchain/discussions/11680	12 months ago
Erick Friis	1861cc7100	General anthropic functions, steps towards experimental integration tests (#11727) ... To match change in js here https://github.com/langchain-ai/langchainjs/pull/2892 Some integration tests need a bit more work in experimental:  Pretty sure the sqldatabase ones are an actual regression or change in interface because it's returning a placeholder. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	12 months ago
Suresh Kumar Ponnusamy	70f7558db2	langchain-experimental: Add allow_list support in experimental/data_anonymizer (#11597) ... - **Description:** Add allow_list support in langchain experimental data-anonymizer package - **Issue:** no - **Dependencies:** no - **Tag maintainer:** @hwchase17 - **Twitter handle:**	12 months ago
Kwanghoon Choi	fbb82608cd	Fixed a bug in reporting Python code validation (#11522) ... - **Description:** fixed a bug in pal-chain when it reports Python code validation errors. When node.func does not have any ids, the original code tried to print node.func.id in raising ValueError. - **Issue:** n/a, - **Dependencies:** no dependencies, - **Tag maintainer:** @hazzel-cn, @eyurtsev - **Twitter handle:** @lazyswamp --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	12 months ago
Eugene Yurtsev	c9bce5bbfb	Add version to langchain_experimental (#11613) ... Add version to langchain experimental	12 months ago
maks-operlejn-ds	f64522fbaf	Reset deanonymizer mapping (#11559) ... @hwchase17 @baskaryan	12 months ago
maks-operlejn-ds	b14b65d62a	Support all presidio entities (#11558) ... https://microsoft.github.io/presidio/supported_entities/ @baskaryan @hwchase17	12 months ago
maks-operlejn-ds	4d62def9ff	Better deanonymizer matching strategy (#11557) ... @baskaryan, @hwchase17	12 months ago
Qihui Xie	57ade13b2b	fix llm_inputs duplication problem in intermediate_steps in SQLDatabaseChain (#10279) ... Use `.copy()` to fix the bug that the first `llm_inputs` element is overwritten by the second `llm_inputs` element in `intermediate_steps`. ***Problem description:*** In [line 127]( c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L127C17-L127C17)), the `llm_inputs` of the sql generation step is appended as the first element of `intermediate_steps`: ``` intermediate_steps.append(llm_inputs) # input: sql generation ``` However, `llm_inputs` is a mutable dict, it is updated in [line 179](https://github.com/langchain-ai/langchain/blob/master/libs/experimental/langchain_experimental/sql/base.py#L179) for the final answer step: ``` llm_inputs["input"] = input_text ``` Then, the updated `llm_inputs` is appended as another element of `intermediate_steps` in [line 180](c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L180)): ``` intermediate_steps.append(llm_inputs) # input: final answer ``` As a result, the final `intermediate_steps` returned in [line 189](c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L189C43-L189C43)) actually contains two same `llm_inputs` elements, i.e., the `llm_inputs` for the sql generation step overwritten by the one for final answer step by mistake. Users are not able to get the actual `llm_inputs` for the sql generation step from `intermediate_steps` Simply calling `.copy()` when appending `llm_inputs` to `intermediate_steps` can solve this problem.	12 months ago
Bagatur	a3a2ce623e	Revise vowpal_wabbit notebook	12 months ago
Bagatur	8fafa1af91	merge	12 months ago
maks-operlejn-ds	2aae1102b0	Instance anonymization (#10501) ... ### Description Add instance anonymization - if `John Doe` will appear twice in the text, it will be treated as the same entity. The difference between `PresidioAnonymizer` and `PresidioReversibleAnonymizer` is that only the second one has a built-in memory, so it will remember anonymization mapping for multiple texts: ``` >>> anonymizer = PresidioAnonymizer() >>> anonymizer.anonymize("My name is John Doe. Hi John Doe!") 'My name is Noah Rhodes. Hi Noah Rhodes!' >>> anonymizer.anonymize("My name is John Doe. Hi John Doe!") 'My name is Brett Russell. Hi Brett Russell!' ``` ``` >>> anonymizer = PresidioReversibleAnonymizer() >>> anonymizer.anonymize("My name is John Doe. Hi John Doe!") 'My name is Noah Rhodes. Hi Noah Rhodes!' >>> anonymizer.anonymize("My name is John Doe. Hi John Doe!") 'My name is Noah Rhodes. Hi Noah Rhodes!' ``` ### Twitter handle @deepsense_ai / @MaksOpp ### Tag maintainer @baskaryan @hwchase17 @hinthornw --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	12 months ago
Eugene Yurtsev	fcccde406d	Add SymbolicMathChain to experiment in preparation for deprecation (#11129) ... Move symbolic math chain to experimental	12 months ago
Predrag Gruevski	c9986bc3a9	Tweak type hints to match dependency's behavior. (#11355) ... Needs #11353 to merge first, and a new `langchain` to be published with those changes.	12 months ago
Predrag Gruevski	5d6b83d9cf	Make a copy of external data instead of mutating another object's attributes. (#11349) ... Fix for a bug surfaced as part of #11339. `mypy` caught this since the types didn't match up.	12 months ago
Mohammad Mohtashim	3bddd708f7	Add memory to sql chain (#8597) ... continuation of PR #8550 @hwchase17 please see and merge. And also close the PR #8550. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>	12 months ago
Eugene Yurtsev	5e2d5047af	add LLMBashChain to experimental (#11305) ... Add LLMBashChain to experimental	12 months ago
Kazuki Maeda	a363ab5292	rename repo namespace to langchain-ai (#11259) ... ### Description renamed several repository links from `hwchase17` to `langchain-ai`. ### Why I discovered that the README file in the devcontainer contains an old repository name, so I took the opportunity to rename the old repository name in all files within the repository, excluding those that do not require changes. ### Dependencies none ### Tag maintainer @baskaryan ### Twitter handle [kzk_maeda](https://twitter.com/kzk_maeda)	12 months ago
Haozhe	4c97a10bd0	fix code injection vuln (#11233) ... - **Description:** Fix a code injection vuln by adding one more keyword into the filtering list - **Issue:** N/A - **Dependencies:** N/A - **Tag maintainer:** - **Twitter handle:** Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	1 year ago
PaperMoose	5d7c6d1bca	Synthetic Data generation (#9472) ... --------- Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Harrison Chase	5f13668fa0	Harrison/move vectorstore base (#11030)	1 year ago
Nuno Campos	7b13292e35	Remove python eval from vector sql db chain (#10937) ... <!-- 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/hwchase17/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. -->	1 year ago
Harrison Chase	777b33b873	fix experimental imports (#10875)	1 year ago
Mateusz Wosinski	a29cd89923	Synthetic data generation (#9759) ... ### Description Implements synthetic data generation with the fields and preferences given by the user. Adds showcase notebook. Corresponding prompt was proposed for langchain-hub. ### Example ``` output = chain({"fields": {"colors": ["blue", "yellow"]}, "preferences": {"style": "Make it in a style of a weather forecast."}}) print(output) # {'fields': {'colors': ['blue', 'yellow']}, 'preferences': {'style': 'Make it in a style of a weather forecast.'}, 'text': "Good morning! Today's weather forecast brings a beautiful combination of colors to the sky, with hues of blue and yellow gently blending together like a mesmerizing painting."} ``` ### Twitter handle @deepsense_ai @matt_wosinski --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago