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https://api.github.com/repos/langchain-ai/langchain/issues/7840
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https://github.com/langchain-ai/langchain/pull/7840
| 1,807,975,581 |
PR_kwDOIPDwls5VrpjG
| 7,840 |
Add filter for metadata, text filter for page_content in AwaDB
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[] | null | 2 | 2023-07-17T14:58:10 | 2023-07-17T23:11:51 | 2023-07-17T23:11:43 |
CONTRIBUTOR
| null |
1.add the filter for metadata
2.add text filter in page_content
3.fix the bug of similarity_search_by_vector with scores
Improvement and fix bug for the AwaDB
Please review @rlancemartin, @eyurtsev, Thanks!
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I_kwDOIPDwls5rw2Uf
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Self Query Retriever with Google Flan T5 models issue
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[] | null | 3 | 2023-07-17T14:54:36 | 2023-11-03T18:01:40 | 2023-10-23T16:06:16 |
NONE
| null |
### System Info
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Traceback (most recent call last) โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/output_parsers/json.py:32 โ
โ in parse_and_check_json_markdown โ
โ โ
โ 29 โ
โ 30 def parse_and_check_json_markdown(text: str, expected_keys: List[str]) -> dict: โ
โ 31 โ try: โ
โ โฑ 32 โ โ json_obj = parse_json_markdown(text) โ
โ 33 โ except json.JSONDecodeError as e: โ
โ 34 โ โ raise OutputParserException(f"Got invalid JSON object. Error: {e}") โ
โ 35 โ for key in expected_keys: โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/output_parsers/json.py:25 โ
โ in parse_json_markdown โ
โ โ
โ 22 โ json_str = json_str.strip() โ
โ 23 โ โ
โ 24 โ # Parse the JSON string into a Python dictionary โ
โ โฑ 25 โ parsed = json.loads(json_str) โ
โ 26 โ โ
โ 27 โ return parsed โ
โ 28 โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/json/__init__.py:346 in loads โ
โ โ
โ 343 โ if (cls is None and object_hook is None and โ
โ 344 โ โ โ parse_int is None and parse_float is None and โ
โ 345 โ โ โ parse_constant is None and object_pairs_hook is None and not kw): โ
โ โฑ 346 โ โ return _default_decoder.decode(s) โ
โ 347 โ if cls is None: โ
โ 348 โ โ cls = JSONDecoder โ
โ 349 โ if object_hook is not None: โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/json/decoder.py:337 in decode โ
โ โ
โ 334 โ โ containing a JSON document). โ
โ 335 โ โ โ
โ 336 โ โ """ โ
โ โฑ 337 โ โ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) โ
โ 338 โ โ end = _w(s, end).end() โ
โ 339 โ โ if end != len(s): โ
โ 340 โ โ โ raise JSONDecodeError("Extra data", s, end) โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/json/decoder.py:355 in raw_decode โ
โ โ
โ 352 โ โ try: โ
โ 353 โ โ โ obj, end = self.scan_once(s, idx) โ
โ 354 โ โ except StopIteration as err: โ
โ โฑ 355 โ โ โ raise JSONDecodeError("Expecting value", s, err.value) from None โ
โ 356 โ โ return obj, end โ
โ 357 โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Traceback (most recent call last) โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/query_constructor/b โ
โ ase.py:37 in parse โ
โ โ
โ 34 โ โ try: โ
โ 35 โ โ โ expected_keys = ["query", "filter"] โ
โ 36 โ โ โ allowed_keys = ["query", "filter", "limit"] โ
โ โฑ 37 โ โ โ parsed = parse_and_check_json_markdown(text, expected_keys) โ
โ 38 โ โ โ if len(parsed["query"]) == 0: โ
โ 39 โ โ โ โ parsed["query"] = " " โ
โ 40 โ โ โ if parsed["filter"] == "NO_FILTER" or not parsed["filter"]: โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/output_parsers/json.py:34 โ
โ in parse_and_check_json_markdown โ
โ โ
โ 31 โ try: โ
โ 32 โ โ json_obj = parse_json_markdown(text) โ
โ 33 โ except json.JSONDecodeError as e: โ
โ โฑ 34 โ โ raise OutputParserException(f"Got invalid JSON object. Error: {e}") โ
โ 35 โ for key in expected_keys: โ
โ 36 โ โ if key not in json_obj: โ
โ 37 โ โ โ raise OutputParserException( โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
OutputParserException: Got invalid JSON object. Error: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Traceback (most recent call last) โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/IPython/core/magics/execution.py:132 โ
โ 5 in time โ
โ โ
โ 1322 โ โ else: โ
โ 1323 โ โ โ st = clock2() โ
โ 1324 โ โ โ try: โ
โ โฑ 1325 โ โ โ โ exec(code, glob, local_ns) โ
โ 1326 โ โ โ โ out=None โ
โ 1327 โ โ โ โ # multi-line %%time case โ
โ 1328 โ โ โ โ if expr_val is not None: โ
โ in <module>:1 โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/base.py:149 in โ
โ __call__ โ
โ โ
โ 146 โ โ โ ) โ
โ 147 โ โ except (KeyboardInterrupt, Exception) as e: โ
โ 148 โ โ โ run_manager.on_chain_error(e) โ
โ โฑ 149 โ โ โ raise e โ
โ 150 โ โ run_manager.on_chain_end(outputs) โ
โ 151 โ โ final_outputs: Dict[str, Any] = self.prep_outputs( โ
โ 152 โ โ โ inputs, outputs, return_only_outputs โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/base.py:143 in โ
โ __call__ โ
โ โ
โ 140 โ โ ) โ
โ 141 โ โ try: โ
โ 142 โ โ โ outputs = ( โ
โ โฑ 143 โ โ โ โ self._call(inputs, run_manager=run_manager) โ
โ 144 โ โ โ โ if new_arg_supported โ
โ 145 โ โ โ โ else self._call(inputs) โ
โ 146 โ โ โ ) โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/conversational_retr โ
โ ieval/base.py:110 in _call โ
โ โ
โ 107 โ โ โ ) โ
โ 108 โ โ else: โ
โ 109 โ โ โ new_question = question โ
โ โฑ 110 โ โ docs = self._get_docs(new_question, inputs) โ
โ 111 โ โ new_inputs = inputs.copy() โ
โ 112 โ โ new_inputs["question"] = new_question โ
โ 113 โ โ new_inputs["chat_history"] = chat_history_str โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/conversational_retr โ
โ ieval/base.py:191 in _get_docs โ
โ โ
โ 188 โ โ return docs[:num_docs] โ
โ 189 โ โ
โ 190 โ def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]: โ
โ โฑ 191 โ โ docs = self.retriever.get_relevant_documents(question) โ
โ 192 โ โ return self._reduce_tokens_below_limit(docs) โ
โ 193 โ โ
โ 194 โ async def _aget_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]: โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/retrievers/self_query/base โ
โ .py:96 in get_relevant_documents โ
โ โ
โ 93 โ โ inputs = self.llm_chain.prep_inputs({"query": query}) โ
โ 94 โ โ structured_query = cast( โ
โ 95 โ โ โ StructuredQuery, โ
โ โฑ 96 โ โ โ self.llm_chain.predict_and_parse(callbacks=callbacks, **inputs), โ
โ 97 โ โ ) โ
โ 98 โ โ if self.verbose: โ
โ 99 โ โ โ print(structured_query) โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/llm.py:281 in โ
โ predict_and_parse โ
โ โ
โ 278 โ โ ) โ
โ 279 โ โ result = self.predict(callbacks=callbacks, **kwargs) โ
โ 280 โ โ if self.prompt.output_parser is not None: โ
โ โฑ 281 โ โ โ return self.prompt.output_parser.parse(result) โ
โ 282 โ โ else: โ
โ 283 โ โ โ return result โ
โ 284 โ
โ โ
โ /home/cloud/anaconda3/envs/mir/lib/python3.10/site-packages/langchain/chains/query_constructor/b โ
โ ase.py:50 in parse โ
โ โ
โ 47 โ โ โ โ **{k: v for k, v in parsed.items() if k in allowed_keys} โ
โ 48 โ โ โ ) โ
โ 49 โ โ except Exception as e: โ
โ โฑ 50 โ โ โ raise OutputParserException( โ
โ 51 โ โ โ โ f"Parsing text\n{text}\n raised following error:\n{e}" โ
โ 52 โ โ โ ) โ
โ 53 โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
OutputParserException: Parsing text
json "query": "patient medical notes", "ID": "11542052" "old": ""
raised following error:
Got invalid JSON object. Error: Expecting value: line 1 column 1 (char 0)
### Who can help?
@hwchase17
@ag
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
model_id = 'google/flan-t5-xl'
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
model=model_id,
tokenizer=tokenizer,
max_length=2048,
temperature=0.1,
top_p=0.95,
repetition_penalty=1.
)
llm = HuggingFacePipeline(pipeline=pipe)
document_content_description = "Patient medical notes"
metadata_field_info = [
AttributeInfo(
name="ID",
description="The unique identifier 'ID' of the patient",
type="string",
),
AttributeInfo(
name="source",
description="source of the document",
type="string",
),
]
retriever = SelfQueryRetriever.from_llm(
llm,
db,
document_content_description,
metadata_field_info,
verbose = True
)
memory=ConversationBufferMemory(memory_key="chat_history",output_key='answer')
chain = ConversationalRetrievalChain.from_llm(
llm = llm,
retriever=retriever,
memory=memory,
get_chat_history=lambda h :h)
### Expected behavior
Expected behavior should be something as attached
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https://github.com/langchain-ai/langchain/pull/7838
| 1,807,964,185 |
PR_kwDOIPDwls5Vrm_o
| 7,838 |
fix
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[] | null | 1 | 2023-07-17T14:52:50 | 2023-07-17T14:53:13 | 2023-07-17T14:53:12 |
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PR_kwDOIPDwls5VrmWU
| 7,837 |
Add CLI command to create a new project
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[] | null | 5 | 2023-07-17T14:51:12 | 2023-10-04T19:43:43 | 2023-10-04T19:43:42 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5Vrdyh
| 7,836 |
bump 235
|
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[] | null | 1 | 2023-07-17T14:31:23 | 2023-07-17T16:37:22 | 2023-07-17T16:37:21 |
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I_kwDOIPDwls5rwpy2
| 7,835 |
BaseRetriever: Latest langchain update is breaking the implementation of extended classes
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[] | null | 7 | 2023-07-17T14:28:52 | 2023-11-24T16:07:44 | 2023-11-24T16:07:44 |
NONE
| null |
### System Info
MacOS: Ventura 13.4
langchain: 0.0.234
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Extend the class BaseRetriever like the following:
ยดยดยด
class DocumentRetrieverExtended(BaseRetriever):
def __init__(self, retriever, vector_field, text_field, k=3, return_source_documents=False, score_threshold=None, **kwargs):
self.k = k
self.vector_field = vector_field
self.text_field = text_field
self.return_source_documents = return_source_documents
self.retriever = retriever
self.filter = filter
self.score_threshold = score_threshold
self.kwargs = kwargs
ยดยดยด
2. Provide DocumentRetrieverExtended as retriever object to ConversationalRetrievalChainExtended.from_llm()
3. You will receive the following exception:
```
File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "DocumentRetrieverExtended" object has no field "k"
```
### Expected behavior
Until version 0.0.189 it was working properly. Please consider to fix the bug or update the langchain documentation for providing the new way of implementing it (Honestly I was expecting that new releases should not break what was working previously)
|
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| 1,807,906,358 |
I_kwDOIPDwls5rwnY2
| 7,834 |
ConversationalRetrievalChain: Add parameter for not invoking self.question_generator.run()
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[] | null | 1 | 2023-07-17T14:23:37 | 2023-10-23T16:06:22 | 2023-10-23T16:06:22 |
NONE
| null |
### Feature request
ConversationalRetrievalChain is implementing in the **_call()** method the following behavior:
```
if chat_history_str:
callbacks = _run_manager.get_child()
new_question = self.question_generator.run(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
```
Please add the possibility to avoid this step, like the following:
```
qa = ConversationalRetrievalChainExtended.from_llm(
llm=self.llm,
retriever=self.retriever,
combine_docs_chain_kwargs={"prompt": PROMPT},
return_source_documents=True,
verbose=True,
memory=self.memory,
generate_question=False
)
```
In order to avoid the generation of a new question
### Motivation
The current behavior is not generic and applicable to all the LLMs. Some Foundation Models require a specific format for the template (e.g.) and this is causing exceptions on generating the question (e.g. multiple times Anthropic is generating empty results). Moreover, most of the people is already formatting the template for having the desired behavior. Please include this feature ASAP
### Your contribution
I've already extended the class. Please let me know if you need the code (is a small change you should apply, strange it is not the standard behavior)
|
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I_kwDOIPDwls5rv_ie
| 7,833 |
raise AttributeError(name) from None AttributeError: OPENAI_FUNCTIONS
|
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[] | null | 5 | 2023-07-17T13:04:07 | 2023-07-24T22:40:56 | 2023-07-18T05:34:13 |
NONE
| null |
### System Info
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The above code snippet is not working when i'm trying to give LLM an input via streamlit or flask. Error occurs when we are trying to use:``` agent_type=AgentType.OPENAI_FUNCTIONS,```. It's Raising the error like as Below:
```
raise AttributeError(name) from None
AttributeError: OPENAI_FUNCTIONS
```
### Expected behavior
I expect that when we give input from either post request via FLASK or via streamlit input box, it should accept the input and should pass on this input to agent to run it and give the result accordingly,.
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| 1,807,733,119 |
PR_kwDOIPDwls5Vq0cs
| 7,832 |
Add client params to aleph alpha (llm and embedding)
|
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| false | null |
[] | null | 2 | 2023-07-17T12:58:50 | 2023-08-04T03:17:39 | 2023-08-04T03:17:38 |
CONTRIBUTOR
| null |
To make it easier to use aleph alpha models via Langchain, we added the client params to the constructor. This is especially useful for our on-premise users who need to specify the host address.
Also, we updated the default versions of the params so they are consistent with our [API](https://docs.aleph-alpha.com/api/semantic-embed/)
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| 1,807,692,296 |
PR_kwDOIPDwls5VqrYQ
| 7,830 |
WIP: Weaviate MMR search to support fetch_k greater than amount of docs
|
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[] | null | 5 | 2023-07-17T12:39:31 | 2023-11-15T23:36:58 | 2023-11-15T23:36:57 |
NONE
| null |
- Description: Currently, when searching the weaviate vector db with a fetch_k parameter greater than the amount of docs, an error is thrown,
- Issue: #7829
- Dependencies: -,
- Tag maintainer: -,
- Twitter handle: -
Maintainer(s):
- VectorStores rlancemartin, eyurtsev
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I_kwDOIPDwls5rvtOb
| 7,829 |
Weaviate MMR Search fails with too high fetch_k parameter
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[] | null | 3 | 2023-07-17T12:26:21 | 2023-10-23T16:06:27 | 2023-10-23T16:06:26 |
NONE
| null |
### System Info
Using requirements:
* langchain==0.0.234
* weaviate-client==3.22.1
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error occurs, when there are less documents stored in weaviate than trying to use with the fetch_k parameter. Otherwise, this will lead to the following error:
> File "...\Lib\site-packages\langchain\vectorstores\weaviate.py", line 273, in max_marginal_relevance_search
return self.max_marginal_relevance_search_by_vector(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...\Lib\site-packages\langchain\vectorstores\weaviate.py", line 324, in max_marginal_relevance_search_by_vector
docs.append(Document(page_content=text, metadata=meta))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...\Lib\site-packages\langchain\load\serializable.py", line 74, in __init__
super().__init__(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__pydantic.error_wrappers.ValidationError: 1 validation error for Document page_content
none is not an allowed value (type=type_error.none.not_allowed)
Example code for reproduction:
See integration test in fork: https://github.com/yannickulmrich/langchain-issue-7829/commit/a3fea1d08a7e55894ee18099bd5f5751aecf9159
### Expected behavior
I expect the vectorstore to not fail, when the fetch_k parameter is higher, than the amount of Documents in the vector db.
Especially since the default value of the fetch_k parameter is set to 20, this can lead to a lot of unwanted errors, when trying to use the vector db for the first time with few documents to test the behaviour.
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| 1,807,476,168 |
I_kwDOIPDwls5ru-XI
| 7,828 |
Rephrased question causing issues
|
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[] | null | 2 | 2023-07-17T10:37:53 | 2023-10-23T16:06:31 | 2023-10-23T16:06:31 |
NONE
| null |
### System Info
I have a use case where I ask an AI agent to check if an article adheres to certain guidelines in documents. However, the recent changes in the BaseConversationalRetrievalChain _call function are causing issues. The process of requesting information from OpenAI can be divided into two steps.
In Step 1, the request, chat history, and system prompt are sent, and the question is rephrased. This step aims to prevent the retrieval of irrelevant documents and ask a more accurate question.
In Step 2, the rephrased question, along with the rephrased chat history and documents, is sent to OpenAI to receive an answer. The problem arises after the upgrade, as the library now sends the rephrased question instead of the original question.
In the process of rephrasing the question, the article is removed from the body of the question. As a result, the AI bot is no longer able to respond effectively.
A possible fix, is sending original question in Step2, it would be nice to be able to decide on it using function parameter.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Document
Doc: Golden guidelines for article
Golden guidelines for articles are as following:
1. Keep the article content as short possible
2. Donโt add new content. It should be a refinement and improvement of what is already there to maintain accuracy
3. Use a clear and concise writing style: Keep sentences short and to the point.
4. Organise content using headings and subheadings: Divide the article into sections with relevant headings, making it easy for readers to find the information they need.
5. Make sure page titles and section headings are action-focused and not in question format
6. Use bullet points and numbered lists to show information in a clear and organised structure
7. Highlight important information using bold or italic text: Emphasise key points or specific instructions by using bold or italic formatting.
------
ask this question:
Review the following document and check if it is following golden guidelines, here is my doc
---------
What is an eTicket?
Instead of a paper ticket, your ticket will be emailed to you as a PDF attachment. If you miss the email, it will also be available for download from the trip itinerary page of your TravelPerk account.
This saves you time at the train station as you no longer have to print a ticket from a ticket machine.
How do I get an eTicket?
When searching for a train in the UK:
Select your preferred time and click on See tickets.
Scroll down to Ticket delivery method.
Select Get an eTicket.
Click on Add to trip.
You will receive the PDF ticket with your TravelPerk confirmation email. You will also be able to download it from your account under Trips -> select a specific trip.
How do I use my eTicket?
Itโs really easy! Simply open the PDF ticket on your phone and scan the barcode when you travel.
If there is no scanner at the station, just show the eTicket to a staff member at the barrier.
rephrased question:
Review provided article
final response: As an AI, I can't review the article titled "What is an eTicket?" because you haven't provided the content of the article. Please provide the article content
### Expected behavior
expected answer would be sth like this:
The article is well-structured and follows the Golden guidelines for articles. Here are some points to note:
1. Relevance: The article is very relevant to its intended audience, which seems to be those using eTickets for train travel in the UK.
2. Clarity: The article is clear and easy to understand. The language is simple and the sentences are concise.
3. Depth: The article provides a good level of detail on its subject. It answers many potential questions a reader might have about eTickets and provides step-by-step instructions on how to get and use them.
4. Accuracy: The information in the article appears to be accurate, although without further context or sources, it's hard to verify.
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| 1,807,441,167 |
I_kwDOIPDwls5ru10P
| 7,827 |
Chroma and Milvus be larger than 10
|
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[] | null | 1 | 2023-07-17T10:15:14 | 2023-10-23T16:06:37 | 2023-10-23T16:06:36 |
NONE
| null |
### System Info
vector_store = Milvus.from_documents(
text,
embedding=das_embedding,
connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT}
)
ValueError: status_code: 400
code: InvalidParameter
message: batch size is invalid, it should not be larger than 10.: payload.input.contents
text[:10]ๆปก่ถณ๏ผๅ
ถไปไธ่ก
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.vectorstores import Milvus
vector_store = Milvus.from_documents(
text,
embedding=das_embedding,
connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT}
)
### Expected behavior
ValueError: status_code: 400
code: InvalidParameter
message: batch size is invalid, it should not be larger than 10.: payload.input.contents
่ฟไธช้ๅถๆงๆกไปถ10ๆกๆไนๆน
|
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PR_kwDOIPDwls5VprkD
| 7,826 |
docs: Improve ApifyDatasetLoader docstring
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[] | null | 3 | 2023-07-17T09:51:13 | 2023-07-20T15:36:31 | 2023-07-20T15:36:31 |
CONTRIBUTOR
| null |
I noticed the `ApifyDatasetLoader` is lacking a code example in the docstring. I'm adding it along with a link to Apify's docs about langchain integration to improve developer experience.
PS: I'd be happy to update the search in https://integrations.langchain.com/ to also consider the description, not just the title. But AFAIK the source code for the page is not public, right?
|
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I_kwDOIPDwls5ruBa_
| 7,825 |
Cannot set up dev container because of Poetry solving dependencies forever
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[] | null | 13 | 2023-07-17T08:08:53 | 2023-11-29T02:52:29 | null |
CONTRIBUTOR
| null |
### System Info
OS: Ubuntu 22.04
Docker version: Docker version 20.10.21, build 20.10.21-0ubuntu1~22.04.3
VSCode: 1.69.0
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
1. Go to this documentation page: https://github.com/hwchase17/langchain/tree/master/.devcontainer
2. Click the "Dev Containers Open" button
3. The dev image build is stuck at the last step (Poetry dependencies installation).
When making the Poetry more verbose, it appears that it loops forever on dependencies, like this:
```
8: derived: pytz-deprecation-shim
8: fact: pytz-deprecation-shim (0.1.0.post0) depends on tzdata (*)
8: fact: pytz-deprecation-shim (0.1.0.post0) depends on backports.zoneinfo (*)
8: selecting pytz-deprecation-shim (0.1.0.post0)
8: fact: tensorflow-hub (0.14.0) depends on numpy (>=1.12.0)
8: fact: tensorflow-hub (0.14.0) depends on protobuf (>=3.19.6)
8: selecting tensorflow-hub (0.14.0)
8: selecting termcolor (2.3.0)
8: fact: watchfiles (0.19.0) depends on anyio (>=3.0.0)
8: selecting watchfiles (0.19.0)
8: selecting pathspec (0.11.1)
8: fact: grpcio-reflection (1.47.5) depends on protobuf (>=3.12.0)
8: fact: grpcio-reflection (1.47.5) depends on grpcio (>=1.47.5)
8: selecting grpcio-reflection (1.47.5)
8: selecting iniconfig (2.0.0)
8: fact: pycares (4.3.0) depends on cffi (>=1.5.0)
8: selecting pycares (4.3.0)
8: derived: cffi (>=1.5.0)
8: selecting colored (1.4.4)
8: fact: azure-core (1.28.0) depends on requests (>=2.18.4)
8: fact: azure-core (1.28.0) depends on six (>=1.11.0)
8: fact: azure-core (1.28.0) depends on typing-extensions (>=4.3.0)
```
I even tried to wait for 5 hours, but it was not enough (my internet connection works correctly).
### Expected behavior
The dev container environment should be up and ready in a couple of minutes.
|
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| 1,807,216,493 |
I_kwDOIPDwls5rt-9t
| 7,824 |
How to use multiple tags in metadata filtering
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[] | null | 6 | 2023-07-17T08:03:05 | 2023-10-10T12:33:48 | null |
NONE
| null |
### Issue you'd like to raise.
I am using below chain. I want to use multiple categories in the filters. My logic is to bring the results from category=c1 OR category=c2 OR category=c3. How can we modify below code the achieve the objective
`chain = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), retriever= vectorstore.as_retriever(search_kwargs={'filter': {'category':category}}), memory=memory, return_source_documents = True) `
### Suggestion:
_No response_
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| 1,807,105,415 |
I_kwDOIPDwls5rtj2H
| 7,823 |
DOC: SupabaseVectorStore.from_documents read operation timed out.
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[] | null | 2 | 2023-07-17T06:49:38 | 2023-07-24T22:51:06 | 2023-07-18T05:27:54 |
NONE
| null |
### Issue with current documentation:
```
# We're using the default `documents` table here. You can modify this by passing in a `table_name` argument to the `from_documents` method.
vector_store = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase)
```
### Idea or request for content:
throw error: httpx.ReadTimeout: The read operation timed out
Is it because the documents are too large? Is there a way to change the timeout?
|
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I_kwDOIPDwls5rs_Vp
| 7,816 |
DOC: Azure Cognitive Search Vector Store
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[] | null | 1 | 2023-07-17T04:51:13 | 2023-10-23T16:06:41 | 2023-10-23T16:06:41 |
NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/azuresearch
### Idea or request for content:
Can anyone explain more about how to create the index?
If your run the example you get:
`ResourceNotFoundError: () The index 'langchain-vector-demo' for service 'cognitivesearchitest' was not found.
`
I split docs and add title to metadata, so now, my documents are **_page_content_** and **_metadata_**(source, title, start_index)
Do I need to manually create the 'cognitivesearchitest' index with what fields? Only with id and then the index is updated when inserting the docs?
Regards.
|
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PR_kwDOIPDwls5VoFt7
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Add option to embed documents without tokenizing using OpenAI interface
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NONE
| null |
This PR updates `embedding_ctx_length` to be optional. If this value is nil, we will perform a batched call to `embed_with_retry` without tokenizing the text. If it is not nil, we will call `_get_len_safe_embeddings` as per usual. This allows us to use the OpenAI interface for embedding documents without having to rely on the OpenAI model tokenizers.
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PR_kwDOIPDwls5VoBfK
| 7,814 |
minor langsmith notebook fixes
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[] | null | 1 | 2023-07-17T04:17:56 | 2023-07-17T04:27:04 | 2023-07-17T04:27:03 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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!
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,
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Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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I_kwDOIPDwls5rswuh
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Azure Cognitive Search
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[] | null | 8 | 2023-07-17T03:50:56 | 2023-11-28T20:28:26 | 2023-07-27T14:33:52 |
NONE
| null |
### System Info
...
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to use vector search as shown below:
`os.environ["AZURESEARCH_FIELDS_CONTENT_VECTOR"] = "section_summary_vector"
os.environ["AZURESEARCH_FIELDS_CONTENT"] = "section_of_summary"
docs = vector_store.similarity_search(
query="What did the president say about Ketanji Brown Jackson",
k=3,
engine = "gpt35turbo",
search_type="similarity"
)
print(docs[0].page_content)`
I am getting an error saying:
HttpResponseError: () Unknown field 'content_vector' in vector field list.
Parameter name: vectorFields
Code:
Message: Unknown field 'content_vector' in vector field list.
Parameter name: vectorFields
It seems that the custom vector field and is not being used by the function.
### Expected behavior
Be able to customize the vector field to use in doing vector similarity search.
|
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| 1,806,893,153 |
PR_kwDOIPDwls5Vn7Q7
| 7,812 |
Export Tracer from callbacks
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[] | null | 1 | 2023-07-17T03:47:29 | 2023-07-17T03:58:15 | 2023-07-17T03:58:14 |
COLLABORATOR
| null |
Improve discoverability
|
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| 1,806,876,086 |
PR_kwDOIPDwls5Vn3jK
| 7,811 |
add filter to sklearn vector store functions
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[] | null | 1 | 2023-07-17T03:22:52 | 2023-07-22T02:22:44 | 2023-07-22T02:22:44 |
CONTRIBUTOR
| null |
# What
- This is to add filter option to sklearn vectore store functions
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add filter to sklearn vectore store functions.
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5Vn01q
| 7,810 |
docs(zilliz): connection_args add token description for serverless clโฆ
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[] | null | 2 | 2023-07-17T03:02:48 | 2023-07-18T16:31:40 | 2023-07-18T16:31:39 |
CONTRIBUTOR
| null |
Description:
Currently, Zilliz only support dedicated clusters using a pair of username and password for connection. Regarding serverless clusters, they can connect to them by using API keys( [ see official note detail](https://docs.zilliz.com/docs/manage-cluster-credentials)), so I add API key(token) description in Zilliz docs to make it more obvious and convenient for this group of users to better utilize Zilliz. No changes done to code.
Maintainer:
@baskaryan
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Add/test faiss
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[] | null | 2 | 2023-07-17T02:51:10 | 2023-07-18T15:30:37 | 2023-07-18T15:30:37 |
CONTRIBUTOR
| null |
# What
- Add missing test cases to faiss vectore stores
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add missing tests to faiss vectore stores
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5Vnir9
| 7,808 |
Fix chat example output mapper
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[] | null | 1 | 2023-07-17T00:40:05 | 2023-07-17T00:47:06 | 2023-07-17T00:47:05 |
COLLABORATOR
| null |
Was only serializing when no key was provided
|
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PR_kwDOIPDwls5Vnfhb
| 7,807 |
Share client in evaluation callback
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[] | null | 1 | 2023-07-17T00:04:47 | 2023-07-17T00:47:39 | 2023-07-17T00:47:38 |
COLLABORATOR
| null |
Guarantee the evaluator traces go to same endpoint
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| 7,806 |
add serializer methods
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[] | null | 1 | 2023-07-16T23:43:05 | 2023-07-18T22:43:12 | 2023-07-18T22:42:45 |
CONTRIBUTOR
| null |
Added serialize and deserialize methods in faiss vectorstore. there was a method called save_local method but instead of saving the file to local disk, I wanted it in the format so that i can save in the SQL database in Blob field. I used this method while i was working on something.
i wanted to add tests and add to documentation but make is not working ..... see #6182
@rlancemartin @eyurtsev
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PR_kwDOIPDwls5Vndmd
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specify comparators
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I_kwDOIPDwls5rsOGP
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vectorstores Chroma client_settings anonymized_telemetry=False dont work
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[] | null | 4 | 2023-07-16T23:24:48 | 2023-11-03T07:31:03 | null |
NONE
| null |
### System Info
0.0.234 MacOS Big Sur
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a vectorstore with some documents
Try this code to retrieve the documents
`
from chromadb.config import Settings
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(
collection_name="langchain_store",
embedding_function=embeddings,
client_settings=Settings(anonymized_telemetry=False),
persist_directory="./dist/vectordb",
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
docs = retriever.get_relevant_documents("quantos litros de oleo vai no motor?")
print(docs)`
The result is [] no documents
Now if I change the client_settings parameter to
client_settings=Settings(anonymized_telemetry=False, chroma_db_impl="duckdb+parquet", persist_directory="./dist/vectordb"),
I get the results correct
### Expected behavior
Should be able to set
client_settings=Settings(anonymized_telemetry=False) # to disable telemetry
and receive the results back from the vectorstore
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I_kwDOIPDwls5rsNy-
| 7,803 |
Confluence loader usage of id causes a conflict with Weaviate
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[] | null | 5 | 2023-07-16T23:19:43 | 2023-10-21T16:40:00 | 2023-10-21T16:39:51 |
NONE
| null |
### System Info
Langchain Version 0.0.233
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Embed from the confluence loader documents direct to weaviate.from_documents and you will get errors related to the id. (e.g.: {'error': [{'message': "'id' is a reserved property name, no such prop with name 'id' found in class 'LangChain_96f9046045fd4623acec34b0ee6acebb' in the schema. Check your schema files for which properties in this class are available"}]}_
**** Code to reproduce ****
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(url="https://yoursite.atlassian.com/wiki", token="12345")
documents = loader.load(
space_key="SPACE", include_attachments=True, limit=50, max_pages=50
)
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)
I can fix it by creating a new list with documents that have pageid instead of id in metadata:
new_documents = []
for doc in documents:
metadata = doc.metadata.copy()
metadata['pageid'] = metadata.pop('id')
new_doc = Document(page_content=doc.page_content, metadata=metadata)
new_documents.append(new_doc)
and then continue with :
db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)
### Expected behavior
Documents to get embedded without the error of: {'error': [{'message': "'id' is a reserved property name, no such prop with name 'id' found in class 'LangChain_96f9046045fd4623acec34b0ee6acebb' in the schema. Check your schema files for which properties in this class are available"}]}
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PR_kwDOIPDwls5VnVrS
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Bm25 retriever without specific vector store dependency
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Description
This PR aims to enhance the flexibility of retrievers by creating a new BM25 retriever that doesn't depend on a vector store backend. The current [ElasticSearchBM25Retriever](https://github.com/hwchase17/langchain/blob/c58d35765daa063b87733e006a4d65e2e1f388d3/langchain/retrievers/elastic_search_bm25.py) is limited to ElasticSearch, but this new retriever can be used with more flexibility. The BM25 implementation used in this retriever is based on [Gensim](https://github.com/RaRe-Technologies/gensim/blob/3ae286ee7970ab5ffebfcb2ea383e1bf4b5f0b8b/gensim/models/bm25model.py), which is widely used in many applications. With this new retriever, users can now utilize BM25 retrieval without being tied to a specific backend.
### Dependencies
[Gensim](https://github.com/RaRe-Technologies/gensim)
@rlancemartin, @eyurtsev
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Nc/protocol
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[] | null | 1 | 2023-07-16T20:23:26 | 2023-07-26T19:16:48 | 2023-07-26T19:16:47 |
COLLABORATOR
| null |
Objects implementing Runnable: BasePromptTemplate, LLM, ChatModel, Chain, Retriever, OutputParser
- [x] Implement Runnable in base Retriever
- [x] Raise TypeError in operator methods for unsupported things
- [x] Implement dict which calls values in parallel and outputs dict with results
- [x] Merge in `+` for prompts
- [x] Confirm precedence order for operators, ideal would be `+` `|`, https://docs.python.org/3/reference/expressions.html#operator-precedence
- [x] Add support for openai functions, ie. Chat Models must return messages
- [x] Implement BaseMessageChunk return type for BaseChatModel, a subclass of BaseMessage which implements __add__ to return BaseMessageChunk, concatenating all str args
- [x] Update implementation of stream/astream for llm and chat models to use new `_stream`, `_astream` optional methods, with default implementation in base class `raise NotImplementedError` use https://stackoverflow.com/a/59762827 to see if it is implemented in base class
- [x] Delete the IteratorCallbackHandler (leave the async one because people using)
- [x] Make BaseLLMOutputParser implement Runnable, accepting either str or BaseMessage
For a future PR:
- [ ] Implement Runnable in BaseTool
- [ ] Export some openai function utils?
- [ ] Add `functions` and `function_call` args to chatopenai
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PR_kwDOIPDwls5VnKZ2
| 7,799 |
prompt ergonomics
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pip install langchain[all] takes forever to resove dependencies
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NONE
| null |
### System Info
This is on `Python 3.10.6`, on a clean virtual env, on `Ubuntu 22.04` server w/o any GPU installed.
On the other hand, `pip install langchain[llms]` installs without problem.
Here is the output of `pip install langchain[all]`, for langchain==0.0.234
[1.txt](https://github.com/hwchase17/langchain/files/12064621/1.txt)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce
1. Create venv
2. Source activation script to enter venv
3. pip install langchain[all]
Langchain version tested is 0.0.234.
### Expected behavior
Install w/o having to try multiple versions of dependencies
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PR_kwDOIPDwls5VnIw0
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add HuggingGPT
|
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[] | null | 1 | 2023-07-16T16:53:08 | 2023-07-18T03:17:35 | 2023-07-18T03:17:35 |
CONTRIBUTOR
| null |
Add HuggingGPT Agent, see https://arxiv.org/abs/2303.17580.
|
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| 1,806,644,448 |
PR_kwDOIPDwls5VnH9K
| 7,796 |
Add examples to docstrings
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[] | null | 1 | 2023-07-16T16:39:51 | 2023-07-16T19:05:57 | 2023-07-16T19:05:56 |
COLLABORATOR
| null |
and:
- remove dataset name from autogenerated project name
- print out project name to view
|
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PR_kwDOIPDwls5VnG_6
| 7,795 |
Add HuggingGPT Agent
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[] | null | 1 | 2023-07-16T16:22:50 | 2023-07-16T16:34:07 | 2023-07-16T16:34:07 |
CONTRIBUTOR
| null |
Add HuggingGPT Agent, see https://arxiv.org/abs/2303.17580.
|
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PR_kwDOIPDwls5VnEQT
| 7,794 |
Accept LLM via constructor
|
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[] | null | 1 | 2023-07-16T15:41:42 | 2023-07-16T15:46:38 | 2023-07-16T15:46:37 |
COLLABORATOR
| null | null |
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| 1,806,612,057 |
PR_kwDOIPDwls5VnBpm
| 7,793 |
Update to Anthropic's claude-v2
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[] | null | 9 | 2023-07-16T14:57:35 | 2023-07-17T15:55:49 | 2023-07-17T15:55:49 |
CONTRIBUTOR
| null | null |
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| 1,806,596,774 |
I_kwDOIPDwls5rrnqm
| 7,791 |
MultiQueryRetriever.from_llm has abandon param type BaseLLM
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[] | null | 2 | 2023-07-16T14:11:08 | 2023-10-22T16:06:06 | 2023-10-22T16:06:05 |
NONE
| null |
### System Info
MAC OS M2
langchain: 0.0.234
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
MultiQueryRetriever.from_llm has abandon param type BaseLLM
```
@classmethod
def from_llm(
cls,
retriever: BaseRetriever,
llm: BaseLLM,
prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
parser_key: str = "lines",
) -> "MultiQueryRetriever":
```
when I use OpenAI, it prints warning:
```
UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
```
llm parameter type looks like it needs to be changed to BaseLanguageModel
### Expected behavior
llm parameter type looks like it needs to be changed to BaseLanguageModel
|
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| 1,806,579,479 |
I_kwDOIPDwls5rrjcX
| 7,790 |
HuggingFaceEndpoint returns truncated answer (could this be just a first chunk of a larger reply?)
|
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[] | null | 1 | 2023-07-16T13:18:48 | 2023-10-22T16:06:11 | 2023-10-22T16:06:10 |
NONE
| null |
### System Info
Python 3.9.6
langchain==0.0.229
MacOS on Apple M2 hardware
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
This may be related to https://github.com/hwchase17/langchain/issues/7785 , I am not sure. When I use HuggingFaceEndpoint in regular, non-streaming mode, I see that the reply comes truncated.
1. Deploy Falcon-7b https://huggingface.co/tiiuae/falcon-7b
2. Use RetrievalQA and HuggingFaceEndpoint as described in https://github.com/hwchase17/langchain/issues/7785
3. Use the prompt described in https://github.com/hwchase17/langchain/issues/7786 (not sure if you really need this to reproduce)
4. Call it using structured_result = my_qna({query_key: query})
5. The result will come back truncated. No streaming is performed, no token callbacks run, so you will get the json from the reply: structured_result["result"]
In my case, the question and answer in Portuguese:
```
O que รฉ a sรฉrie G?
A sรฉrie G da Scania traz uma cabina com uma estรฉtica moderna e um confort
```
The last word is truncated, and the whole rest of the explanation is missing.
### Expected behavior
A complete answer, full text. Or chunk-based callbacks via streaming, as I point out at https://github.com/hwchase17/langchain/issues/7785
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| 1,806,578,036 |
PR_kwDOIPDwls5Vm7KX
| 7,789 |
Add google place ID to google places tool response
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[] | null | 1 | 2023-07-16T13:14:07 | 2023-07-20T17:15:25 | 2023-07-20T16:04:32 |
CONTRIBUTOR
| null |
- Description: this change will add the google place ID of the found location to the response of the GooglePlacesTool
- Issue: Not applicable
- Dependencies: no dependencies
- Tag maintainer: @hinthornw
- Twitter handle: Not applicable
|
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| 1,806,570,994 |
I_kwDOIPDwls5rrhXy
| 7,788 |
Unable to Apply frequencyPenalty Parameter to ChatOpenAI Class
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[] | null | 2 | 2023-07-16T12:52:03 | 2023-10-30T16:05:43 | 2023-10-30T16:05:42 |
NONE
| null |
### System Info
Issue:
I'm experiencing an issue while trying to apply the `frequencyPenalty` parameter to the `ChatOpenAI` class in my Flask server setup. I'm running a Flask server that handles a POST query and returns a streaming response from GPT using LlamaIndex.
When I attempt to apply the `frequencyPenalty` parameter to `ChatOpenAI` instance, the server throws a warning:
```python
WARNING! frequencyPenalty is not default parameter.
frequencyPenalty was transferred to model_kwargs.
Please confirm that frequencyPenalty is what you intended.
```
Upon reviewing the documentation, I didn't find a clear way to apply the `frequencyPenalty` to the `ChatOpenAI` class.
Here's a snippet of my current code where I'm experiencing this issue:
```python
# LLM that supports streaming
llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k", streaming=True, frequencyPenalty=6)
llm_predictor = LLMPredictor(llm=llm)
```
I'm using `ChatOpenAI` to create an instance of the LLM that supports streaming. I intended to apply `frequencyPenalty` to this instance but encountered the above warning.
For further reference, the complete code of my Flask server setup is shared in the original post.
Any guidance on how to correctly apply the `frequencyPenalty` to `ChatOpenAI` would be greatly appreciated. Thanks in advance!
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import openai
from flask import Flask, request, Response
from flask_cors import CORS
from dotenv import load_dotenv
import os
import pandas as pd
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext, LLMPredictor, Document
from langchain.chat_models import ChatOpenAI
import logging
app = Flask(__name__)
CORS(app)
load_dotenv()
# Get the API key from the environment variable
api_key = os.getenv('OPENAI_API_KEY')
# Set the OpenAI API key directly
openai.api_key = api_key
# Loading documents from an Excel file
df = pd.read_excel('data/SupplierComplete.xlsx')
# Convert DataFrame rows into documents
documents = [
Document(text=' '.join(f'{name}: {value}' for name, value in zip(df.columns, map(str, row.values))))
for _, row in df.iterrows()
]
# LLM that supports streaming
llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k", streaming=True, frequencyPenalty=6)
llm_predictor = LLMPredictor(llm=llm)
# Construct a simple vector index
index = GPTVectorStoreIndex.from_documents(documents, service_context=ServiceContext.from_defaults(llm_predictor=llm_predictor))
# Configure query engine to use streaming
query_engine = index.as_query_engine(streaming=True, similarity_top_k=2)
@app.route('/api/query', methods=['POST'])
def query():
try:
# Get the payload from the request
payload = request.json
# Log the received payload
logging.info(f"Received payload: {payload}")
# Update the LLMPredictor parameters based on the payload
llm_predictor.max_tokens = payload.get('max_tokens', 256)
llm_predictor.llm.temperature = payload.get('temperature', 0.9)
# Get the system message from the payload
system_message = [m['content'] for m in payload['messages'] if m['role'] == 'system'][0]
# Get the question from the messages in the payload
user_message = [m['content'] for m in payload['messages'] if m['role'] == 'user'][-1]
# Combine system message and user message
question = system_message + ' ' + user_message
# Now, query returns a StreamingResponse object
streaming_response = query_engine.query(question)
def response_stream():
for text in streaming_response.response_gen:
yield text + "\n"
return Response(response_stream(), mimetype="text/event-stream")
except Exception as e:
logging.error(f"Exception occurred: {e}")
return Response(f"Server error: {e}", status=500)
if __name__ == '__main__':
# Start the server, to run this script use "python llama_index_server.py" in terminal
# Configure logging level
logging.basicConfig(level=logging.DEBUG)
app.run(host='0.0.0.0', port=5000, debug=True)
```
### Expected behavior
I expect to be able to apply the frequencyPenalty parameter to the ChatOpenAI class without encountering any warning messages or errors. Ideally, the frequencyPenalty should influence the LLM's generation by tuning the model's likelihood to avoid frequently occurring responses. I'm expecting this to work seamlessly with other parameters and configurations I'm setting up for my ChatOpenAI instance.
If there is a specific way or an alternate parameter to achieve this functionality, I would expect the documentation to clearly illustrate this. Also, any warnings or error messages should provide actionable insights or steps for resolution.
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| 1,806,568,070 |
I_kwDOIPDwls5rrgqG
| 7,787 |
In AmazonKendraRetriever, user should have access to a page_content formatter in order to format the Kendra ResultItem as desired
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[] | null | 3 | 2023-07-16T12:42:17 | 2023-10-26T16:06:14 | 2023-10-26T16:06:13 |
CONTRIBUTOR
| null |
### Feature request
In AmazonKendraRetriever, user should have access to a page_content formatter in order to format a Kendra ResultItem as desired, e.g. by combining all sorts of possible document attributes with the title and excerpt of the item.
Currently, the [AmazonKendraRetriever](https://github.com/hwchase17/langchain/blob/master/langchain/retrievers/kendra.py#L30) does not expose a template or allow the user to overwrite how the value of the Document page_content is generated.
Let me know what you think. I am open to suggestions.
@3coins @baskaryan
### Motivation
The Amazon Kendra result item provides not only the title and excerpt but all sorts of customizable document attributes that could be combined to improve the result of the LLM completion.
For instance, according to the official [Amazon Kendra documentation](https://docs.aws.amazon.com/kendra/latest/APIReference/API_RetrieveResultItem.html):
> DocumentAttributes
> An array of document fields/attributes assigned to a document in the search results. For example, the document author (_author) or the source URI (_source_uri) of the document.
>
> Type: Array of [DocumentAttribute](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DocumentAttribute.html) objects
>
> Required: No
Different use cases could leverage on the ability to change the Document page_content final value as close to the Kendra Retriever as possible.
### Your contribution
I would be glad to work on this feature. I have already started with a simple prototype and I could issue a PR proposing that change.
|
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I_kwDOIPDwls5rremP
| 7,786 |
HuggingFaceEndpoint returns empty string in "text-generation" mode with prompt template
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[] | null | 1 | 2023-07-16T12:15:34 | 2023-10-22T16:06:21 | 2023-10-22T16:06:20 |
NONE
| null |
### System Info
Python 3.9.6
langchain==0.0.229
MacOS on Apple M2 hardware
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I provisioned [cerebras/Cerebras-GPT-2.7B](https://huggingface.co/cerebras/Cerebras-GPT-2.7B) and noticed I was getting an empty string when asking a question with my prompt, in "text-generation" mode. Upon debugging I found the issue:
```
if self.task == "text-generation":
# Text generation return includes the starter text.
text = generated_text[0]["generated_text"][len(prompt) :]. # <==== HERE
elif self.task == "text2text-generation":
text = generated_text[0]["generated_text"]
elif self.task == "summarization":
text = generated_text[0]["summary_text"]
```
I had to patch it like this:
```
text = generated_text[0]["generated_text"]
```
That is so because generated_text[0]["generated_text"] is NOT returning with the prompt text. Therefore, [len(prompt) :] effectively discards the actual answer. Basically, from debugging I see that the logic I want is the same as is coded under "text2text-generation". But I am using "text-generation" and the answer *does not* come with the prompt text back. Any chance the logic for these 2 are swapped?
My prompt:
'''
You are a useful and cordial assistant. Your objective is to provide precise and
relevant information about Thinksurance. You must answer the questions formulated by the
human user with attention and only based on the context provided. You should never
invent facts. If you can't find the answer using the supplied context, just say that you
are unable to answer.
Context:
{context}
Question:
{question}
'''
### Expected behavior
As described above, the expected behaviour is to get the answer back, not an empty string, when a prompt is used and in "text-generation" mode.
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I_kwDOIPDwls5rrdzI
| 7,785 |
streaming support for LLM, from HuggingFaceEndpoint
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[] | null | 6 | 2023-07-16T12:04:36 | 2024-01-08T06:49:06 | 2023-12-01T16:09:02 |
NONE
| null |
### System Info
Python 3.9.6
langchain==0.0.229
MacOS on Apple M2 hardware
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
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- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I use RetrievalQA.from_chain_type(llm=llm, ...) and with these LLMs streaming works:
- ChatOpenAI. (I can pass streaming=True)
- OpenAI. (I can pass streaming=True)
However, with HuggingFaceEndpoint the streaming just does not work. It does not even take streaming=bool.
### Expected behavior
I am able to switch LLM engines via config, and would like to have the streaming feature as the output is incrementally shown to the user, which is great for slow responses (much better usability - no need to wait for the entire time to compute the entire answer). Unfortunately this is not possible with HuggingFaceEndpoint.
https://github.com/hwchase17/langchain/issues/2918 talks about HuggingFaceHub , which is not really my case (HuggingFaceEndpoint)
Is there a simple way to make it work with RetrievalQA.from_chain_type(llm=llm,...) where the llm is created like
```
llm = HuggingFaceEndpoint(endpoint_url=my_endpoint,
task="text-generation",
streaming=True
callbacks = callbacks,
model_kwargs={"temperature": temperature, "max_length": 1024})
```
Thanks!
|
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| 1,806,548,419 |
I_kwDOIPDwls5rrb3D
| 7,784 |
ReplitLM Model_addition_in_langchain
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[] | null | 3 | 2023-07-16T11:36:07 | 2023-10-22T16:06:26 | 2023-10-22T16:06:25 |
NONE
| null |
### Feature request
I want to integrate the MPT style code LLM, ReplitLM from Replit into the langchain.
### Motivation
I want a code LLM in langchain, as ReplitLM has been continuously providing good results either fine tuned or not.
I was working on a project for analyzing complex code patterns and structures but generalized LLMs fails on this task, so I want to add code LLMs in langchain.
### Your contribution
I want just a little guidance on how to integrate any new LLM into the framework, and then i'll start working on the PR and submit it in ASAP.
|
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PR_kwDOIPDwls5Vm1cg
| 7,783 |
fix #7782: check title and excerpt separately for page_content
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[] | null | 2 | 2023-07-16T11:35:30 | 2023-07-19T01:46:23 | 2023-07-19T01:46:23 |
CONTRIBUTOR
| null |
- Description: check title and excerpt separately for page_content so that if title is empty but excerpt is present, the page_content will only contain the excerpt
- Issue: #7782
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
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I_kwDOIPDwls5rraKv
| 7,782 |
In the AmazonKendraRetriever, the Document page_content is empty for ResultItem with Type ANSWER when using the Query API
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[] | null | 1 | 2023-07-16T11:11:02 | 2023-07-19T01:46:28 | 2023-07-19T01:46:28 |
CONTRIBUTOR
| null |
### System Info
- LangChain version: 0.0.234
- Platform: Local and AWS ECS
- Python version: 3.9
### Who can help?
@3coins
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install latest version of Langchain
2. Follow instructions here: https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/amazon_kendra_retriever
3. Ask for a specific quesiton that will fall back to using the Query API that returns a ResultItem with type ANSWER and inspect the content as following
I replaced the original values with "----------------".
```json
{
"Id": "----------------",
"Type": "ANSWER",
"Format": "TABLE",
"AdditionalAttributes": [
{
"Key": "AnswerText",
"ValueType": "TEXT_WITH_HIGHLIGHTS_VALUE",
"Value": {
"TextWithHighlightsValue": {
"Text": "----------------",
"Highlights": [
{
"BeginOffset": 70,
"EndOffset": 81,
"TopAnswer": false,
"Type": "STANDARD"
}
]
}
}
}
],
"DocumentId": "----------------",
"DocumentTitle": {
"Text": "",
"Highlights": []
},
"DocumentExcerpt": {
"Text": "----------------",
"Highlights": [
{
"BeginOffset": 0,
"EndOffset": 81,
"TopAnswer": false,
"Type": "STANDARD"
}
]
},
...
}
```
will result in:
```
Document(page_content='', metadata={'type': 'ANSWER', 'source': '----------------', 'title': '', 'excerpt': '----------------'})
```
### Expected behavior
The document page_content should contain at least the Amazon Kendra ResultItem excerpt. According to the [Amazon Kendra documentation](https://docs.aws.amazon.com/kendra/latest/APIReference/API_QueryResultItem.html), the DocumentTitle is not required, therefore we should not expect it in order to return the page_content as seen on the [AmazonKendraRetriever code](https://github.com/hwchase17/langchain/blob/master/langchain/retrievers/kendra.py#L41).
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PR_kwDOIPDwls5Vmyqg
| 7,781 |
Exposes Kendra result item DocumentAttributes in the document metadata
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[] | null | 3 | 2023-07-16T10:44:25 | 2023-07-19T01:46:38 | 2023-07-19T01:46:38 |
CONTRIBUTOR
| null |
- Description: exposes the ResultItem DocumentAttributes as document metadata with key 'document_attributes' and refactors AmazonKendraRetriever by providing a ResultItem base class in order to avoid duplicate code;
- Tag maintainer: @3coins @hupe1980 @dev2049 @baskaryan
- Twitter handle: wilsonleao
### Why?
Some use cases depend on specific document attributes returned by the retriever in order to improve the quality of the overall completion and adjust what will be displayed to the user. For the sake of consistency, we need to expose the DocumentAttributes as document metadata so we are sure that we are using the values returned by the kendra request issued by langchain.
I would appreciate your review @3coins @hupe1980 @dev2049. Thank you in advance!
### References
- [Amazon Kendra DocumentAttribute](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DocumentAttribute.html)
- [Amazon Kendra DocumentAttributeValue](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DocumentAttributeValue.html)
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| 1,806,498,956 |
I_kwDOIPDwls5rrPyM
| 7,780 |
Issue: Why is the KNNRetriever existed?
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[] | null | 1 | 2023-07-16T08:38:23 | 2023-10-22T16:06:31 | 2023-10-22T16:06:30 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
The KNNRetriever calculates the cosine similarity of documents and retrieves the top 'n' documents. Its behavior is identical to `FAISS.similarity_search(query)`. What is the rationale behind creating a separate KNNRetriever?
### Suggestion:
Remove the KNNRetriever module.
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PR_kwDOIPDwls5VmqNL
| 7,779 |
add bm25 module
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[] | null | 1 | 2023-07-16T08:06:54 | 2023-07-17T23:15:01 | 2023-07-17T14:30:18 |
CONTRIBUTOR
| null |
- Description: Add a BM25 Retriever that do not need Elastic search
- Dependencies: rank_bm25(if it is not installed it will be install by using pip, just like TFIDFRetriever do)
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: DayuanJian21687
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[] | null | 6 | 2023-07-16T07:53:03 | 2023-09-13T09:48:21 | 2023-07-18T04:50:38 |
NONE
| null |
### System Info
Python 3.8.10
gpt4all==1.0.5
langchain==0.0.234
pydantic==1.10.11
pydantic-core==2.3.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Code snippet:**
from langchain import PromptTemplate, LLMChain
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import GPT4All
embedding_model_name = "hkunlp/instructor-large"
persist_directory = 'db'
callbacks = [StreamingStdOutCallbackHandler()]
model_path = "/home/imrohankar/gpttest/DocGPT/models/ggml-gpt4all-j-v1.3-groovy.bin"
llm = GPT4All(
model = model_path,
callbacks = callbacks,
verbose = False
)
**Error:**
Traceback (most recent call last):
File "/home/imrohankar/gpttest/DocGPT/conversation.py", line 15, in <module>
llm = GPT4All(
File "/home/imrohankar/gpttest/DocGPT/venv/lib/python3.8/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for GPT4All
__root__
'type' object is not subscriptable (type=type_error)
**Note:** Tried downgrading pydantic, langchain versions but still the error. I am unable to understand why it gives type error while initializing GPT4All
### Expected behavior
I would expect it to find the model file at the model path
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PR_kwDOIPDwls5Vml9o
| 7,777 |
Add more HTTP methods supports for 'APIChain'
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NONE
| null |
- Description: added more HTTP methods supports for APIChain, such as POST method
- Issue: Closes #2184
- Dependencies: noops
- Tag maintainer: @baskaryan, @hinthornw
- Twitter handle: noops
# Introduction
There are many (#2184) requirements for APIChain to support more HTTP requests, especially POST requests.
However, current APIChain only supports GET requests, because it simply appends the request parameters to the URL path.
In this pull request, I propose defining a standard RequestParams type for all http requests, to standardize the request parameters for api docs.
# Test
1. POST
I used the [Moderations](https://platform.openai.com/docs/api-reference/moderations/create) interface from OPEN_AI to test sending POST requests.
2. GET
I re-executed the currently existing GET request method (in TMDB Example), and the results remained consistent.
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PR_kwDOIPDwls5VmcNg
| 7,774 |
Add LLM for ChatGLM(2)-6B API
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[] | null | 6 | 2023-07-16T04:12:58 | 2023-07-25T07:16:50 | 2023-07-17T14:27:17 |
CONTRIBUTOR
| null |
Description:
Add LLM for ChatGLM-6B & ChatGLM2-6B API
Related Issue:
Will the langchain support ChatGLM? #4766
Add support for selfhost models like ChatGLM or transformer models #1780
Dependencies:
No extra library install required.
It wraps api call to a ChatGLM(2)-6B server(start with api.py), so api endpoint is required to run.
Tag maintainer: @mlot
Any comments on this PR would be appreciated.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add LLM for ChatGLM-6B & ChatGLM2-6B API
- Issue: Will the langchain support ChatGLM? #4766
- Dependencies: No extra library dependencies required. User need to provide endpoint url of a ChatGLM(2)-6B api server.
- Tag maintainer: @limpo2000 in twitter
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5VmbyO
| 7,773 |
Try er/redis index retrieval retry00
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[] | null | 4 | 2023-07-16T04:01:02 | 2023-07-19T01:33:51 | 2023-07-18T17:49:50 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Modified the code to return the document id from the redis document search as metadata.
- Issue: the issue # it fixes retrieval of id as metadata as string
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5VmbZV
| 7,772 |
implement GitHub toolkit and notebook example
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[] | null | 2 | 2023-07-16T03:48:57 | 2023-07-21T02:33:09 | 2023-07-21T02:33:09 |
CONTRIBUTOR
| null |
Hey @hinthornw,
It seems you're the guy for new toolkits.
### Description
This PR is my implementation of a CRUD toolkit for GitHub. It implements functions to create, read, update, and delete files on a GitHub repository. Also, it has 2 functions to get a list of issues and get more information on a specific issue (including comments etc.) allowing agents to interact with other collaborators.
### Dependencies
[PyGitHub](https://github.com/PyGithub/PyGithub)
### Testing
I have included a notebook which showcases an example of an agent completing an issue on a test repository which can be found in the toolkits docs.
My Twitter handle is @trevordobbertin if this is something that the team feels is cool enough for a shoutout.
Thanks!
|
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| 1,806,410,744 |
PR_kwDOIPDwls5VmaPk
| 7,771 |
fix: pgvector search_type of similarity_score_threshold not working
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[] | null | 3 | 2023-07-16T03:14:33 | 2023-07-22T03:08:37 | 2023-07-19T14:20:52 |
CONTRIBUTOR
| null |
- Description: VectorStoreRetriever->similarity_score_threshold with search_type of "similarity_score_threshold" not working with the following two minor issues,
- Issue: 1. In line 237 of `vectorstores/base.py`, "score_threshold" is passed to `_similarity_search_with_relevance_scores` as in the kwargs, while score_threshold is not a valid argument of this method. As a fix, before calling `_similarity_search_with_relevance_scores`, score_threshold is popped from kwargs. 2. In line 596 to 607 of `vectorstores/pgvector.py`, it's checking the distance_strategy against the string in Enum. However, self.distance_strategy will get the property of distance_strategy from line 316, where the callable function is passed. To solve this issue, self.distance_strategy is changed to self._distance_strategy to avoid calling the property method.,
- Dependencies: No,
- Tag maintainer: @rlancemartin, @eyurtsev,
- Twitter handle: No
|
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I_kwDOIPDwls5rq4U_
| 7,770 |
Capturing all content from Output Parser / OpenAI Function Calling
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[] | null | 2 | 2023-07-16T02:27:58 | 2023-10-22T16:06:37 | 2023-10-22T16:06:36 |
NONE
| null |
### System Info
Langchain Version: 0.0.207
### Who can help?
Is there a way to get the whole output with Output Parser or OpenAI function calling?
I have a simple prompt where I get the LLM to output responses to a set of questions, and I would like to get a structured response that separates the question number and the response to the question generated by the LLM.
I have been testing it out with Structured Output Parsers but when I use it, the answers I get are often a shortened version of the original answer. This depends on the description I put in the answer_schema below. I've tried a variety of descriptions to capture as much of the original response as possible, but it is always lacking a lot of detail.
```
question_number_schema = ResponseSchema(name = "question_number",
description = "Question Number, e.g. 1, 2, 3", type = "number")
answer_schema = ResponseSchema(name = "answer",
description = "Detailed Response")
response_schemas = [question_number_schema, answer_schema]
```
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
For example, if my original output without the Structured Output Parser was:
```
'''
Question 3: Unsupervised fine-tuning involves updating a pre-existing language model using unstructured datasets such as research papers, articles, forums, or websites. This approach allows the language model to learn patterns and vocabularies within the data without the need for explicit human labeling.
Question 4: Supervised fine-tuning leverages labeled samples of data to train the language model. These labeled samples consist of input prompts paired with corresponding desired outputs, providing explicit guidance on the desired structure or behavior. This approach is used when specific outputs or classifications are required, such as text classification.
'''
```
I'm currently getting something like:
```
{"Question 3": "Unsupervised fine-tuning updates pre-existing language models using unstructured datasets.",
"Question 4": "Supervised fine-tuning trains language models with labeled samples."}
```
### Expected behavior
I'd like to get something like this:
```
{"Question 3": "Unsupervised fine-tuning involves updating a pre-existing language model using unstructured datasets such as research papers, articles, forums, or websites. This approach allows the language model to learn patterns and vocabularies within the data without the need for explicit human labeling.",
"Question 4": "Supervised fine-tuning leverages labeled samples of data to train the language model. These labeled samples consist of input prompts paired with corresponding desired outputs, providing explicit guidance on the desired structure or behavior. This approach is used when specific outputs or classifications are required, such as text classification."}
```
This isn't the exact example I'm using, but the idea is similar.
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PR_kwDOIPDwls5VmV1H
| 7,769 |
add test for knn retriever
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[] | null | 1 | 2023-07-16T00:59:24 | 2023-07-18T16:52:12 | 2023-07-18T16:52:12 |
CONTRIBUTOR
| null |
# What
- This is to add test for knn retriever.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add test to knn retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,806,384,529 |
PR_kwDOIPDwls5VmVbj
| 7,768 |
Add test for svm retriever
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[] | null | 1 | 2023-07-16T00:47:28 | 2023-07-18T16:57:24 | 2023-07-18T16:57:24 |
CONTRIBUTOR
| null |
# What
- This is to add unit test for svm retriever.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add test for svm retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,806,364,565 |
PR_kwDOIPDwls5VmR6i
| 7,767 |
update LangSmith notebook
|
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[] | null | 2 | 2023-07-15T23:09:07 | 2023-07-16T04:05:10 | 2023-07-16T04:05:09 |
COLLABORATOR
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<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5VmKjB
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feat: moving azure OpenAI API version to the latest 2023-05-15
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[] | null | 2 | 2023-07-15T20:50:38 | 2023-07-18T16:50:16 | 2023-07-18T16:50:16 |
CONTRIBUTOR
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Moving to the latest non-preview Azure OpenAI API version=2023-05-15. The previous 2023-03-15-preview doesn't have support, SLA etc. For instance, OpenAI SDK has moved to this version https://github.com/openai/openai-python/releases/tag/v0.27.7
@baskaryan
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I_kwDOIPDwls5rqh_E
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Google Palm 2 as underlying LLM for Agent
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[] | null | 3 | 2023-07-15T20:08:33 | 2023-12-25T16:01:37 | null |
NONE
| null |
### Feature request
I want to be able to use Google Palm (bison) as the underlying LLM for agents. Currently I'm using chatGPT, but I also want to experiment how Google Palm performs with tool-picking. I have API access to I can experiment with it.
### Motivation
I propose Google's Palm because I've anecdotally seen examples where it performed well in summarization and decision-making tasks. That makes me think it would be a very compelling candidate for
### Your contribution
Will I need to over-ride some class within langchain? If you can give me step-by-step instructions, I might be able to help and submit a PR. Thanks!
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PR_kwDOIPDwls5VmA_p
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[nit] Apply default value for ChatOpenAI client
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[] | null | 2 | 2023-07-15T19:06:01 | 2023-07-19T14:45:16 | 2023-07-19T14:37:12 |
CONTRIBUTOR
| null |
This is a small convenience PR to give a default to the `client` attribute of ChatOpenAI, avoiding a warning from type checkers about it being missing when initializing the class. The attribute appears to be set during validation already using a default value.
@baskaryan
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PR_kwDOIPDwls5Vl4k5
| 7,759 |
Update langsmith version
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[] | null | 1 | 2023-07-15T17:28:09 | 2023-07-15T19:01:42 | 2023-07-15T19:01:41 |
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PR_kwDOIPDwls5VlzWU
| 7,758 |
Fixed sparql SELECT and UPDATE query function
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[] | null | 10 | 2023-07-15T16:33:26 | 2023-09-05T18:46:49 | 2023-07-25T01:29:30 |
CONTRIBUTOR
| null |
- Description: Changed "SELECT" and "UPDTAE" intent check from "=" to "in",
- Issue: Based on my own testing, most of the LLM (StarCoder, NeoGPT3, etc..) doesn't return a single word response ("SELECT" / "UPDATE") through this modification, we can accomplish the same output without curated prompt engineering.
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @aditya_0290
Thank you for maintaining this library, Keep up the good efforts.
|
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PR_kwDOIPDwls5VlzCp
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remove unused import in voice assistant doc
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[] | null | 2 | 2023-07-15T16:31:26 | 2023-07-18T17:00:14 | 2023-07-18T16:51:29 |
CONTRIBUTOR
| null |
Description: Removed unused import in voice_assistant doc.
Tag maintainer: @baskaryan
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5Vlwx4
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Async recursive url loader
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CONTRIBUTOR
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Description: This PR implements a new document loader, async_recursive_url_loader, based on the recursive_loader. It not only made the original loader async but also added a few features, such as limiting the depth of the access, and customizable extractors(from the raw webpage to the text of the Document object), so that users can use other tools to extract the webpage. This PR also includes the document and test for the new loader.
Issue: N/A
Dependencies: asyncio, aiohttp
Tag maintainer: @rlancemartin, @eyurtsev
Twitter handle: @ Zend_Nihility
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PR_kwDOIPDwls5Vlwk9
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Fix ntbk link in docs
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COLLABORATOR
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Minor fix to running to [docs](https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa).
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bump 234
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PR_kwDOIPDwls5Vliuj
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Notebook update: sales agent with tools
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CONTRIBUTOR
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- Description: This is an update to a previously published notebook.
Sales Agent now has access to tools, and this notebook shows how to use a Product Knowledge base
to reduce hallucinations and act as a better sales person!
- Issue: N/A
- Dependencies: `chromadb openai tiktoken`
- Tag maintainer: @baskaryan @hinthornw
- Twitter handle: @FilipMichalsky
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[] | null | 1 | 2023-07-15T14:32:29 | 2023-07-20T14:17:03 | 2023-07-18T19:14:51 |
CONTRIBUTOR
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(#7654)
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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@hwchase17, @baskaryan
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PR_kwDOIPDwls5VlIhw
| 7,751 |
added support for Google Images search
|
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[] | null | 2 | 2023-07-15T10:31:54 | 2023-07-15T14:04:18 | 2023-07-15T14:04:18 |
CONTRIBUTOR
| null |
- Description: Added Google Image Search support for SerpAPIWrapper
- Issue: NA
- Dependencies: None
- Tag maintainer: @hinthornw
- Twitter handle: @sausheong
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Document loader/debug
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[] | null | 2 | 2023-07-15T09:39:53 | 2023-07-15T14:18:28 | 2023-07-15T14:18:28 |
CONTRIBUTOR
| null |
Description: Added debugging output in DirectoryLoader to identify the file being processed.
Issue: [Need a trace or debug feature in Lanchain DirectoryLoader #7725](https://github.com/hwchase17/langchain/issues/7725)
Dependencies: No additional dependencies are required.
Tag maintainer: @rlancemartin, @eyurtsev
This PR enhances the DirectoryLoader with debugging output to help diagnose issues when loading documents. This new feature does not add any dependencies and has been tested on a local machine.
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PR_kwDOIPDwls5Vk21R
| 7,749 |
upgrade chroma to 0.4.0
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[] | null | 2 | 2023-07-15T06:00:25 | 2023-07-19T00:20:55 | 2023-07-19T00:20:55 |
CONTRIBUTOR
| null |
** This should land Monday the 17th **
Chroma is upgrading from `0.3.29` to `0.4.0`. `0.4.0` is easier to build, more durable, faster, smaller, and more extensible. This comes with a few changes:
1. A simplified and improved client setup. Instead of having to remember weird settings, users can just do `EphemeralClient`, `PersistentClient` or `HttpClient` (the underlying direct `Client` implementation is also still accessible)
2. We migrated data stores away from `duckdb` and `clickhouse`. This changes the api for the `PersistentClient` that used to reference `chroma_db_impl="duckdb+parquet"`. Now we simply set `is_persistent=true`. `is_persistent` is set for you to `true` if you use `PersistentClient`.
3. Because we migrated away from `duckdb` and `clickhouse` - this also means that users need to migrate their data into the new layout and schema. Chroma is committed to providing extension notification and tooling around any schema and data migrations (for example - this PR!).
After upgrading to `0.4.0` - if users try to access their data that was stored in the previous regime, the system will throw an `Exception` and instruct them how to use the migration assistant to migrate their data. The migration assitant is a pip installable CLI: `pip install chroma_migrate`. And is runnable by calling `chroma_migrate`
-- TODO ADD here is a short video demonstrating how it works.
Please reference the readme at [chroma-core/chroma-migrate](https://github.com/chroma-core/chroma-migrate) to see a full write-up of our philosophy on migrations as well as more details about this particular migration.
Please direct any users facing issues upgrading to our Discord channel called [#get-help](https://discord.com/channels/1073293645303795742/1129200523111841883). We have also created a [email listserv](https://airtable.com/shrHaErIs1j9F97BE) to notify developers directly in the future about breaking changes.
TODO
- [x] Migrated any `duckdb+parquet` strings to the new format
- [ ] Notified users about the breaking change (this PR, other suggestions?)
- [x] Move pypi target away from feature-branch and to `0.4.0` after `0.4.0` is released.
- [x] We need to merge in a more flexible range for `fastapi` to the chroma branch - as is in-progress here https://github.com/chroma-core/chroma/pull/807
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PR_kwDOIPDwls5Vk2xN
| 7,748 |
Improved GPT4All model streaming tokens without adding callbacks
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[] | null | 4 | 2023-07-15T05:59:38 | 2023-08-29T01:20:23 | 2023-08-29T01:20:23 |
NONE
| null |
- **Description:** Right now token streaming is enabled by adding callbacks. But there was no streaming of tokens occuring after adding callbacks.Latest version of gpt4all: version `1.0.3` addresses this issue and hence I tried to integrate that in the existing function.I have kept the code for supporting callbacks for streaming, however I think that can be removed in coming versions. Also added docs for the same.
- **Issue:** This commit fixes issue #7747.,
- Dependencies: No dependency change,
- Tag maintainer: @hwchase17 @agola11,
- Twitter handle: @AnindyadeepS
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I_kwDOIPDwls5rpBHi
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Streaming does not work using streaming callbacks for gpt4all model
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[] | null | 6 | 2023-07-15T05:47:15 | 2023-11-08T09:59:43 | null |
NONE
| null |
### System Info
**System Information**
System: `Linux`
OS: `Pop OS`
Langchain version: `0.0.232`
Python version: `3.9.17`
gpt4all version: used for both version `1.0.1` and version `1.0.3`.
### Who can help?
Models:
@hwchase17
Streaming Callbacks:
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```python3
import gpt4all as gpt
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
template = """
Let's think step by step of the question: {question}
Based on all the thought the final answer becomes:
"""
prompt = PromptTemplate(template=template, input_variables=["question"])
local_path = (
"./model/ggml-gpt4all-j-v1.3-groovy.bin"
)
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(
model=local_path,
backend="llama",
verbose=True, callbacks=callbacks
)
llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
```
### Expected behavior
I should tokens streaming and printing in the terminal one at a time, but everything I get all at once.
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PR_kwDOIPDwls5VkqGi
| 7,746 |
feat: temp fixed Could not parse LLM output on agents folder
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[] | null | 4 | 2023-07-15T03:59:02 | 2023-07-25T03:45:19 | 2023-07-25T02:20:37 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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),
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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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5Vkp2R
| 7,745 |
Added search parameters to qdrant max_marginal_relevance_search
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[] | null | 12 | 2023-07-15T03:54:22 | 2023-08-24T21:11:31 | 2023-08-24T21:11:30 |
CONTRIBUTOR
| null |
Adds the qdrant search filter/params to the `max_marginal_relevance_search` method, which is present on others. I did not add `offset` for pagination, because it's behavior would be ambiguous in this setting (since we fetch extra and down-select).
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5Vklgm
| 7,744 |
docs: Mendable Search Improvements
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[] | null | 2 | 2023-07-15T02:48:21 | 2023-07-15T14:19:21 | 2023-07-15T14:19:21 |
CONTRIBUTOR
| null |
- New pin-to-side (button). This functionality allows you to search the docs while asking the AI for questions
- Fixed the search bar in Firefox that won't detect a mouse click
- Fixes and improvements overall in the model's performance
Thank you!
@dev2049 @nfcampos
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PR_kwDOIPDwls5Vkldi
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Add GPT4All embeddings
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COLLABORATOR
| null |
Support for [GPT4All embeddings](https://docs.gpt4all.io/gpt4all_python_embedding.html)
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I_kwDOIPDwls5roc8O
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DOC: Arxiv API Tool code snippet very instable and produces very often an OutputParserException
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[] | null | 2 | 2023-07-15T00:46:11 | 2023-10-21T16:06:35 | 2023-10-21T16:06:34 |
NONE
| null |
### Issue with current documentation:
```
pip install arxiv
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
```
```
llm = ChatOpenAI(temperature=0.0)
tools = load_tools(
["arxiv"],
)
```
```
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent_chain.run(
"What's the paper 1605.08386 about?",
)
```
very often causes an OutputParserError due to the failure of parsing the final thought.
`OutputParserException: Could not parse LLM output`
### Idea or request for content:
It seems that just changing the llm to :
`llm = OpenAI(temperature=0.0)`
helps a lot with the output success completion.
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PR_kwDOIPDwls5VkUW8
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fix: make error message clear when input_key arg is required
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[] | null | 2 | 2023-07-15T00:28:03 | 2023-10-14T00:21:21 | 2023-10-14T00:21:21 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Makes it more clear that input_key argument is required when there are multiple input variables in a prompt,
- Issue: #2013,
- Tag maintainer: @hwchase17,
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Description: Makes it more clear that input_key argument is required with multiple input variables in a prompt,
- Issue: #2013,
- Tag maintainer: @hwchase17,
|
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PR_kwDOIPDwls5VkTRl
| 7,740 |
Simple Import fix in Tools Exception Docs
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[] | null | 3 | 2023-07-15T00:18:54 | 2023-07-16T11:14:11 | 2023-07-15T14:25:34 |
CONTRIBUTOR
| null |
Issue: #7720
@hinthornw
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PR_kwDOIPDwls5VkR0c
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Fireworks Client Library
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[] | null | 1 | 2023-07-15T00:08:32 | 2023-07-15T00:09:22 | 2023-07-15T00:09:05 |
CONTRIBUTOR
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WIP
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I_kwDOIPDwls5roPmy
| 7,738 |
Issue: Chain call methods are confusing (LLMChain vs SimpleSequentialChain)
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[] | null | 2 | 2023-07-14T23:21:30 | 2023-12-13T16:08:18 | 2023-12-13T16:08:17 |
NONE
| null |
### Issue you'd like to raise.
According to [here](https://python.langchain.com/docs/modules/chains/how_to/call_methods), all subclass inherited from the Chain class will have the `__call__()` and the `run()` methods to launch the chain. And according to the [LLMChain API](https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html?highlight=llmchain#langchain.chains.llm.LLMChain) and [SimpleSequentialChain API](https://api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SimpleSequentialChain.html#langchain.chains.sequential.SimpleSequentialChain), both of them are inherited from the Chain class. However I found LLMChain and SimpleSequentialChain accept different kinds of input patterns when calling `__call__()` and the `run()`, which are highly confusing.
To demonstrate, consider the following setup, where we create an LLMChain that runs a prompt template that simply asks the LLM to repeat what is told, and attach the LLMChain to a SimpleSequentialChain:
```
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain.prompts import StringPromptTemplate
from pydantic import BaseModel, validator
class CustomTemplate(StringPromptTemplate, BaseModel):
"""A custom prompt template that takes in the path to a json file as input, and formats the prompt template."""
@validator("input_variables")
def validate_input_variables(cls, v):
"""Validate that the input variables are correct."""
if len(v) == 0 or "CustomKwarg" not in v:
raise ValueError("CustomKwarg keyword argument must be provided .")
return v
def format(self, **kwargs) -> str:
return "Repeat the following: \"" + kwargs["CustomKwarg"] + "\""
llmchain = LLMChain(llm=llm,
prompt=CustomTemplate(input_variables=["CustomKwarg"]),
verbose=True)
simplesequentialchain = SimpleSequentialChain(chains=[llmchain])
```
where ``llm`` is a custom llm.
In the prompt template, we expect a keyword argument (kwarg) called `CustomKwarg` to be provided when we launch the chains. There are, however, several ways to provide `CustomKwarg`, e.g. directly providing the value for `CustomKwarg`, or providing a dictionary that maps a key "CustomKwarg" to the value of `CustomKwarg`. Also, sometimes we need to specify `input` or `inputs` as the keyword argument when we make the function call. These lead to many possible combination of syntaxes to launch the chains, and I found that the syntaxes accepted by the LLMChain and SimpleSequentialChain very different and inconsistent. Assuming we want to provide "XYZ!" as the value for CustomKwarg to the chain, see the summary of the launching results below.
|#|command | `c = llmchain` | `c = simplesequentialchain` |
|-|--------------------------------------------- | -----------| --------------------------|
|1|`c("XYZ!")`| O | O|
|2|`c({"CustomKwarg": "XYZ!"})`| O | `Missing some input keys: {'input'}` |
|3|`c({"input": "XYZ!"})`| `Missing some input keys: {'CustomKwarg'}` | O |
|4| `c({"inputs": "XYZ!"})`| `Missing some input keys: {'CustomKwarg'}`| `Missing some input keys: {'input'}` |
|5|`c(input={"CustomKwarg": "XYZ!"})`| `Chain.__call__() got an unexpected keyword argument 'input'` | `Chain.__call__() got an unexpected keyword argument 'input'` |
|6|`c(inputs={"CustomKwarg": "XYZ!"})`| O | `Missing some input keys: {'input'}` |
|7|`c.run("Hello there!")`| O | O |
|8|`c.run({"CustomKwarg": "XYZ!"})`| O | `Missing some input keys: {'input'}` |
|9|`c.run({"input": "XYZ!"})`| `Missing some input keys: {'CustomKwarg'}` | O |
|10|`c.run({"inputs": "Hello there!"})`| `Missing some input keys: {'CustomKwarg'}` | `Missing some input keys: {'input'}`|
|11|`c.run(input={"CustomKwarg": "XYZ!"})`| `Missing some input keys: {'CustomKwarg'}` | O |
|12|`c.run(inputs={"CustomKwarg": "XYZ!"})`| `Missing some input keys: {'CustomKwarg'}` | `Missing some input keys: {'input'}`|
The allowed syntax patterns are highly inconsistent among the two types of chains. For the same syntax, the two types of chains can even fail on different error messages, which is very confusing. Since both of the chains are subclasses of Chain, one would expect them to behave similarly, especially for those methods inherited from the Chain class (`__call__()` and `run()`).
Note: I am using **LangChain ver. 0.0.225**.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5VkD9K
| 7,737 |
fix: absolute URLs from links in `RecursiveUrlLoader`
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NONE
| null |
## Description
This PR solves an absolute link problem in `RecursiveUrlLoader`. When the "href" of a link on the page contains an absolute URL, the link is ignored.
Example: `<a href="http://test.com/example"></a>` will be ignored when base_url is `http://test.com/`.
@rlancemartin @eyurtsev
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I_kwDOIPDwls5roEle
| 7,736 |
Vulnerabilities: CVE-2023-36258, CVE-2023-3454, CVE-2023-34541, CVE-2023-36188, CVE-2023-36189
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[] | null | 2 | 2023-07-14T22:32:45 | 2023-10-21T16:06:40 | 2023-10-21T16:06:39 |
NONE
| null |
### System Info
Hi.
I wanted to deploy application with Langchain but I am unable to pass security scans because of the following vulnerabilities:
[CVE-2023-36258](https://nvd.nist.gov/vuln/detail/CVE-2023-36258)
[CVE-2023-34540](https://nvd.nist.gov/vuln/detail/CVE-2023-34540)
[CVE-2023-34541](https://nvd.nist.gov/vuln/detail/CVE-2023-34541)
[CVE-2023-36188](https://nvd.nist.gov/vuln/detail/CVE-2023-36188)
[CVE-2023-36189](https://nvd.nist.gov/vuln/detail/CVE-2023-36189)
I am unable to disable security scans. Are there any temporal fixes?
@hwchase17
@JamalRahman
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
pip install langchain
### Expected behavior
be able to pass Aqua Scanner
|
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| 7,735 |
Document loader/debug
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[] | null | 1 | 2023-07-14T21:36:35 | 2023-07-14T22:32:30 | 2023-07-14T22:32:30 |
CONTRIBUTOR
| null |
- Description: Added debugging output in DirectoryLoader to identify the file being processed.
- Issue: https://github.com/hwchase17/langchain/issues/7725
- Dependencies: No additional dependencies are required.
- Tag maintainer: @rlancemartin, @eyurtsev
This PR enhances the DirectoryLoader with debugging output to help diagnose issues when loading documents. This new feature does not add any dependencies and has been tested on a local machine.
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PR_kwDOIPDwls5VjzYS
| 7,734 |
Fix eval loader when overriding arguments
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[] | null | 1 | 2023-07-14T21:34:19 | 2023-07-15T15:30:33 | 2023-07-15T15:30:32 |
COLLABORATOR
| null |
- Update the negative criterion descriptions to prevent bad predictions
- Add support for normalizing the string distance
- Fix potential json deserializing into float issues in the example mapper
|
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I_kwDOIPDwls5rnyNc
| 7,733 |
WIKIPEDIA Language setting not applied in run method
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[] | null | 4 | 2023-07-14T21:14:07 | 2023-07-21T23:43:33 | 2023-07-21T23:43:33 |
NONE
| null |
### System Info
```
langchain==0.0.228
langchain/utilities/wikipedia.py
```
The run method does not call the language change before execution, so it always runs with lang="en"
```
def run(self, query: str) -> str:
"""Run Wikipedia search and get page summaries."""
#define languaje
self.wiki_client.set_lang(self.lang) #<-- this line fixes the issue
page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
summaries = []
for page_title in page_titles[: self.top_k_results]:
if wiki_page := self._fetch_page(page_title):
if summary := self._formatted_page_summary(page_title, wiki_page):
summaries.append(summary)
if not summaries:
return "No good Wikipedia Search Result was found"
return "\n\n".join(summaries)[: self.doc_content_chars_max]
```
```
### Who can help?
@nfcampos @leo-gan @hwc
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
llm = ChatOpenAI(openai_api_key=openai_api_key,temperature=0)
tools = load_tools(["wikipedia"])
tools[0].api_wrapper.top_k_results=1
tools[0].api_wrapper.lang="es"
agent= initialize_agent(
tools,
llm,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose = True)
agent("Mi primer Issue en Github")
### Expected behavior
http://**es**.wikipedia.com....
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| 1,805,574,631 |
PR_kwDOIPDwls5VjsFa
| 7,732 |
Update markdown links for tracing
|
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CONTRIBUTOR
| null |
Updated all old `It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See [here](...)` link to -> https://python.langchain.com/docs/modules/callbacks/how_to/tracing
|
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I_kwDOIPDwls5rnsmV
| 7,731 |
SagemakerEndpoint model doesn't return full output..only when prompted with langchain
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[] | null | 8 | 2023-07-14T20:56:40 | 2023-10-31T16:06:15 | 2023-10-31T16:06:14 |
NONE
| null |
### System Info
infra - sagemaker
model - model deployed with `HuggingFaceModel(...).deploy()`
langchain version - v0.0.233
chain types used - RetrievalQA, load_qa_chain
I have a huggingface model deployed behind a sagemaker endpoint which produces outputs as expected when run prediction against it directly. However, when I initialize it with SagemakerEndpoint class from langchain, it only return two characters and sometimes an empty string. I scoured through the internet and langchain docs for the last couple days and my initialization and chain prompting aspects of the code seem to be in line with the docs guidelines and anecdotal recommendations laid out.
I think this either a lack integration support for huggingface models deployed with sagemaker or I'm missing something here that's not been written in the docs and examples. Please review and let me know either way.
### Below code will reproduce the behavior I'm experiencing.
```
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import ContentHandlerBase, LLMContentHandler
endpoint = "xxxxxx-2023-07-14-05-34-901"
parameters = {
"do_sample": True,
"top_p": 0.95,
"temperature": 0.1,
"max_new_tokens": 256,
"num_return_sequences": 4,
}
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({"inputs": prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]['generated_text']
content_handler = ContentHandler()
sm_llm=SagemakerEndpoint(
endpoint_name=endpoint,
region_name="us-west-2",
model_kwargs= parameters,
content_handler=content_handler,
)
vectordb = Chroma(persist_directory="db", embedding_function=embedding, collection_name="docs")
retriever = vectordb.as_retriever(search_kwargs={'k':3})
print("retriever: ", retriever)
qa_chain = RetrievalQA.from_chain_type(llm=sm_llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
```
#### Here's the output (or lack thereof) from prompting with langchain, in above code.
```
system_prompt = """<|SYSTEM|># Your are a helpful and harmless assistant for providing clear and succinct answers to questions."""
question = "What is your purpose?"
query = (system_prompt + "<|USER|>" + question + "<|ASSISTANT|>")
llm_response = qa_chain(query)
print(llm_response['result'])
---------------------------------------------------------------------------
Output:
Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
<some doc context from vectordb goes here - removed due to info sensitivity>
Question: # <|SYSTEM|># Your are a helpful and harmless assistant for providing clear and succint answers to questions.
What is your purpose?
Helpful Answer:
```
As shown above, there's no output for 'Helpful Answer: '.
#### However when I prompt the model directly with a predictor like below, it returns the full output as expected.
### Model returns the full output with the below code which runs direct prediction against the endpoint.
```
import boto3
from sagemaker.huggingface import HuggingFacePredictor
from sagemaker.session import Session
sm_session = Session(boto_session=boto3.session.Session())
payload = {
"inputs": "What is your purpose?",
"parameters": {"max_new_tokens": 256, "do_sample": True}
}
local_llm = HuggingFacePredictor(endpoint, sm_session)
chat = local_llm.predict(data=payload)
result = chat[0]["generated_text"]
print(result)
```
#### Output for direct prediction from the code executed above.
```
What is your purpose?
You seem important. What is your value? What can you do that makes you unique? What is your unique value?
* You are important to the people who know you best.
* When you accomplish the things you want to do, you will become valuable to the people who matter to you.
* We value you because you are special
```
As it can be seen above, the model returns an output (not a great one) but the fact is it does when I'm prompted and not when wrapped with SagemakerEndpoint class and prompted with prompted with langchain.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Expected behavior
SagemakerEndpoint Model returning the full output, similar to how it does when prompted directly with a predictor.
|
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PR_kwDOIPDwls5VjiEb
| 7,730 |
add missing import
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[] | null | 2 | 2023-07-14T20:29:56 | 2023-07-15T00:03:24 | 2023-07-15T00:03:24 |
CONTRIBUTOR
| null |
Just a nit documentation fix
@baskaryan
|
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PR_kwDOIPDwls5VjdEC
| 7,728 |
add support for truncate arg for HuggingFaceTextGenInference class
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[] | null | 3 | 2023-07-14T20:13:31 | 2023-07-14T20:26:40 | 2023-07-14T20:23:57 |
CONTRIBUTOR
| null |
Fixes https://github.com/hwchase17/langchain/issues/7650
* add support for `truncate` argument of `HugginFaceTextGenInference`
@baskaryan
|
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PR_kwDOIPDwls5VjV7z
| 7,727 |
rfc
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[] | null | 1 | 2023-07-14T19:57:42 | 2023-07-25T02:13:39 | 2023-07-25T02:13:39 |
COLLABORATOR
| null |
exploring pkg per chain. seeing what moving PALChain would look like. imagine most top level stuff moved down into another langchain dir. so repo struct something like. not langchain package would still import PalChain
langchain
  | - langchain (could rename src)
   | - ...
  | - pyproject.toml
  | - poetry.lock
  | tests
   | - ...
  | ...
langchain-pal
  | ...
langchain-core
  | ...
Makefile
...
|
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| 1,805,380,651 |
PR_kwDOIPDwls5VjAaR
| 7,726 |
Add ability to load (deserialize) objects from other namespaces
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[] | null | 7 | 2023-07-14T18:55:24 | 2023-08-08T16:04:44 | 2023-07-26T15:59:29 |
CONTRIBUTOR
| null |
I have some Prompt subclasses in my project that I'd like to be able to deserialize in callbacks. Right now `loads()`/`load()` will bail when it encounters my object, but I know I can trust the objects because they're in my own projects.
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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- Agents / Tools / Toolkits: @hinthornw
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5rm6eZ
| 7,725 |
Issue: Need a trace or debug feature in Lanchain DirectoryLoader
|
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[] | null | 2 | 2023-07-14T18:45:13 | 2023-10-21T16:06:45 | 2023-10-21T16:06:44 |
NONE
| null |
### Issue you'd like to raise.
I've set "langchain.debug=True"; however, it does not work for the DirectoryLoader. I have a notebook that tried to load a dozen or more PDFs, and typically, at least one of the files fails (see attached). I looked at the code, and as far as I can tell, there is no trace or debug feature in (https://github.com/hwchase17/langchain/tree/master/langchain/document_loaders).
My issue is that the loader code is a black box. I can't tell which file is failing; therefore, I have to process each one individually to find out which one is failing. It would be beneficial if a trace/debugger could help me identify which file it's failing on.
TIA
<img width="912" alt="Screen Shot 2023-07-14 at 9 04 56 AM" src="https://github.com/hwchase17/langchain/assets/457288/fd5b7732-1040-4c73-91dc-abc41fb9cadd">
### Suggestion:
Please make a debug option for "https://github.com/hwchase17/langchain/tree/master/langchain/document_loaders" code.
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