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| 1,812,439,499 |
PR_kwDOIPDwls5V6zWN
| 7,950 |
Reduce generation of temporary objects
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[] | null | 1 | 2023-07-19T17:59:15 | 2023-08-04T04:24:09 | 2023-08-04T04:24:09 |
CONTRIBUTOR
| null |
Just a tiny change to use `list.append(...)` and `list.extend(...)` instead of `list += [...]` so that no unnecessary temporary lists are created.
Since its a tiny miscellaneous thing I guess @baskaryan is the maintainer to tag?
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I_kwDOIPDwls5sBzMg
| 7,949 |
Ability to translate/interpret LLM tool requests
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[] | null | 1 | 2023-07-19T17:39:47 | 2023-10-25T16:05:56 | 2023-10-25T16:05:56 |
NONE
| null |
### Feature request
I'm getting this back from my LLM:
Observation: Use Search is not a valid tool, try another one.
Obviously the LLM would like to use the search tool, but didn't request it just right. It seems LangChain has been thoroughly tested with OpenAI, but not so much other models. Which is totally understandable considering there are now thousands of models and variations.
Although I'm digging through the code to find a way to do this myself, it would be awesome if LangChain had a well documented module we could enable to interpret or translate response from the LLM.
Obviously in this case I could use the tool list, and string find, and if any of the tool names are found to go with that tool.
### Motivation
Increased compatibility with open source LLM
### Your contribution
Generic example:
tool_names = # python list
for tool_name in tool_names:
if ai_reply.find(tool_name) >= 0:
# use tool
|
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PR_kwDOIPDwls5V6msO
| 7,948 |
bump
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[] | null | 1 | 2023-07-19T17:22:43 | 2023-07-19T17:23:22 | 2023-07-19T17:23:21 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5V6mF0
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fix
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[] | null | 1 | 2023-07-19T17:20:48 | 2023-07-19T17:23:11 | 2023-07-19T17:23:10 |
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!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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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| 1,812,350,601 |
I_kwDOIPDwls5sBkaJ
| 7,946 |
OpenAI functions Agent is not working with SQLDatabaseChain.
|
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[] | null | 2 | 2023-07-19T17:01:47 | 2023-10-25T16:06:01 | 2023-10-25T16:06:00 |
NONE
| null |
### Issue with current documentation:
Hello every one!
I am following the tutorial of [OpenAI function Agent ](https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent).
I downloaded the chinook database in MySQL , I made the connection successfully and got good answers from this database using the [SQLDataBase agent tutorial.](https://python.langchain.com/docs/modules/agents/toolkits/sql_database). However, when I try to do sql query using the OpenAI function agent it fails.
I got the following error:
```InvalidRequestError: 'MySQL DB' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.1.name```
When I change to other kind of agents such as CHAT_ZERO_SHOT_REACT_DESCRIPTION is works good.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5V6T3r
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Refactored `sql_database`
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[] | null | 2 | 2023-07-19T16:28:41 | 2023-07-21T15:20:33 | 2023-07-21T05:17:55 |
COLLABORATOR
| null |
The `sql_database.py` is unnecessarily placed in the root code folder.
A similar code is usually placed in the `utilities/`.
As a byproduct of this placement, the sql_database is [placed on the top level of classes in the API Reference](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.sql_database) which is confusing and not correct.
- moved the `sql_database.py` from the root code folder to the `utilities/`
@baskaryan
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Error when loading JSON file using S3FileLoader
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[] | null | 3 | 2023-07-19T16:06:55 | 2023-11-12T10:43:32 | 2023-10-25T16:06:06 |
NONE
| null |
### System Info
langchain: 0.0.236
### Who can help?
@eyurtsev
When attempting to load a JSON file from S3, I encounter the following error:
`An error occurred: Json schema does not match the Unstructured schema`
Don't know if it is related to https://github.com/hwchase17/langchain/issues/2222
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use this code:
```
from langchain.document_loaders import S3FileLoader
loader = S3FileLoader(bucket, file_key)
documents = loader.load()
```
Getting this error:
`An error occurred: Json schema does not match the Unstructured schema`
### Expected behavior
I anticipate that it will load the file into the documents, just as it does for other file types I use.
|
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PR_kwDOIPDwls5V6F_S
| 7,943 |
Add etherscan document loader
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CONTRIBUTOR
| null |
@rlancemartin
The modification includes:
* etherscanLoader
* test_etherscan
* document ipynb
I have run the test, lint, format, and spell check. I do encounter a linting error on ipynb, I am not sure how to address that.
```
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:55: error: Name "null" is not defined [name-defined]
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:76: error: Name "null" is not defined [name-defined]
Found 2 errors in 1 file (checked 1 source file)
```
- Description: The Etherscan loader uses etherscan api to load transaction histories under specific accounts on Ethereum Mainnet.
- No dependency is introduced by this PR.
- Twitter handle: glazecl
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PR_kwDOIPDwls5V58Bw
| 7,941 |
VectorStoreRetrieverMemory exclude additional input keys feature
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[] | null | 1 | 2023-07-19T15:23:52 | 2023-07-20T15:09:24 | 2023-07-20T14:23:27 |
CONTRIBUTOR
| null |
- Description: Added a parameter in VectorStoreRetrieverMemory which filters the input given by the key when constructing the buffering the document for Vector. This feature is helpful if you have certain inputs apart from the VectorMemory's own memory_key that needs to be ignored e.g when using combined memory, we might need to filter the memory_key of the other memory, Please see the issue.
- Issue: #7695
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5V50zP
| 7,940 |
Getting SQL cmd directly from SQLDatabase Chain.
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[] | null | 2 | 2023-07-19T15:05:44 | 2023-08-01T20:52:13 | 2023-07-21T05:36:55 |
CONTRIBUTOR
| null |
- Description: Get SQL Cmd directly generated by SQL-Database Chain without executing it in the DB engine.
- Issue: #4853
- Tag maintainer: @hinthornw,@baskaryan
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[nit] Add default value for ChatOpenAI client
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[] | null | 2 | 2023-07-19T14:44:26 | 2023-07-28T05:38:39 | 2023-07-28T05:38:32 |
CONTRIBUTOR
| null |
Micro convenience PR to avoid warning regarding missing `client` parameter. It is always set during initialization.
@baskaryan
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bump 236
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[] | null | 1 | 2023-07-19T14:28:13 | 2023-07-19T14:51:42 | 2023-07-19T14:51:41 |
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PR_kwDOIPDwls5V5Wwp
| 7,937 |
Update openai.py compatibility with azure 2023-07-01-preview
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[] | null | 1 | 2023-07-19T13:59:14 | 2023-07-19T14:31:18 | 2023-07-19T14:31:18 |
CONTRIBUTOR
| null |
Fixed missing "content" field in azure.
Added a check for "content" in _dict (missing for azure api=2023-07-01-preview)
@baskaryan
|
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I_kwDOIPDwls5sABXc
| 7,936 |
DOC: Passing Context to Conversational Chain
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[] | null | 3 | 2023-07-19T13:25:19 | 2023-11-13T16:07:51 | 2023-11-13T16:07:51 |
NONE
| null |
### Issue with current documentation:
We don't have enough documentation Conversational chain and found only documentation relates conversational retrieval chain. We are looking for separating the retriever functionality. Please provide some examples on passing context (lang chain documents ) to conversational chain
### Idea or request for content:
_No response_
|
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| 7,935 |
"create_structured_output_chain" creates awful schema, deteriorates performance and should be fixed or removed
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[] | null | 3 | 2023-07-19T10:50:29 | 2023-11-08T16:17:23 | 2023-11-08T16:17:15 |
CONTRIBUTOR
| null |
### System Info
Langchain v0.0.235
### Who can help?
@hwchase17 @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
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Trying out the new [create_structured_output_chain](https://github.com/hwchase17/langchain/blame/9d7e57f5c01f9ac5c8caa439ff083de98f96fdde/langchain/chains/openai_functions/base.py#L267) introduced in #7270 with the setup like described in the [docs](https://python.langchain.com/docs/modules/chains/popular/openai_functions) I wondered about the bad performance. Then I looked at the function call....
Using a very simple example:
```python
class LanguageCode(BaseModel):
""""A single language code in ISO 639-1 format"""
language_code: str = Field(..., description="Language code (e.g. 'en', 'de', 'fr')")
class LanguageClassification(BaseModel):
"""Classify the languages of a user prompt."""
language_codes: list[LanguageCode] = Field(default_factory=list, description="A list of all languages present in the whole text. Exclude code sections, loanwords and technical terms in the text when deciding on the language codes. You have to output at least one language code, even if you are not certain or the text is very short!")
main_language_code: LanguageCode = Field(..., description="Main Language of the text.")
chat_prompt_language = ChatPromptTemplate.from_messages(
[
SystemMessagePromptTemplate.from_template("You are a world-class linguist and fluent in all major languages. Your job is to determine which languages are present in the user text and which one is the main language."),
HumanMessagePromptTemplate.from_template("{text}"),
]
)
chain = create_structured_output_chain(LanguageClassification, llm, chat_prompt_language, verbose=True)
```
Looking at `chain.llm_kwargs['functions']`:
```python
[{'name': '_OutputFormatter',
'description': 'Output formatter. Should always be used to format your response to the user.',
'parameters': {'title': '_OutputFormatter',
'description': 'Output formatter. Should always be used to format your response to the user.',
'type': 'object',
'properties': {'output': {'$ref': '#/definitions/LanguageClassification'}},
'required': ['output'],
'definitions': {'LanguageCode': {'title': 'LanguageCode',
'description': '"A single language code in ISO 639-1 format',
'type': 'object',
'properties': {'language_code': {'title': 'Language Code',
'description': "Language code (e.g. 'en', 'de', 'fr')",
'type': 'string'}},
'required': ['language_code']},
'LanguageClassification': {'title': 'LanguageClassification',
'description': 'Classify the languages of a user prompt.',
'type': 'object',
'properties': {'language_codes': {'title': 'Language Codes',
'description': 'A list of all languages present in the whole text. Exclude code sections, loanwords and technical terms in the text when deciding on the language codes. You have to output at least one language code, even if you are not certain or the text is very short!',
'type': 'array',
'items': {'$ref': '#/definitions/LanguageCode'}},
'main_language_code': {'title': 'Main Language Code',
'description': 'Main Language of the text.',
'allOf': [{'$ref': '#/definitions/LanguageCode'}]}},
'required': ['main_language_code']}}}}]
```
What? I can barely parse this monstrosity, how should GPT3.5 do this?
It is very well known by now, that the function- and variable names as well as the structure and simplicity/unambiguousness (and even the order of arguments) is very important for the performance of function calls.
This implementation wastes a lot of tokens and is detrimental for performance (especially for users who put their trust in langchain and don't benchmark results); and that for a key feature.
Maybe there are features that some additional complexity but this should never introduce a significant performance/token tax for completely unrelated and important use cases?
### Expected behavior
Just as an example, using the exact same model/code with @jxnl s [OpenAISchema](https://github.com/jxnl/openai_function_call) and nothing else:
```python
llm_kwargs={"functions": [LanguageClassification.openai_schema], "function_call": {'name':"LanguageClassification"}}
chain = LLMChain(llm=llm, prompt=chat_prompt_language, verbose=True, llm_kwargs=llm_kwargs)
```
we get the following schema:
```python
{'name': 'LanguageClassification',
'description': 'Classify the languages of a user prompt.',
'parameters': {'type': 'object',
'properties': {'language_codes': {'description': 'A list of all languages present in the whole text. Exclude code sections, loanwords and technical terms in the text when deciding on the language codes. You have to output at least one language code, even if you are not certain or the text is very short!',
'type': 'array',
'items': {'$ref': '#/definitions/LanguageCode'}},
'main_language_code': {'description': 'Main Language of the text.',
'allOf': [{'$ref': '#/definitions/LanguageCode'}]}},
'required': ['language_codes', 'main_language_code'],
'definitions': {'LanguageCode': {'description': '"A single language code in ISO 639-1 format',
'type': 'object',
'properties': {'language_code': {'description': "Language code (e.g. 'en', 'de', 'fr')",
'type': 'string'}},
'required': ['language_code']}}}}
```
Half the tokens and way easier to read/understand which leads to way better performance and robustness.
This can be parsed in a single line:
```python
res=chain.generate([{'text':"..."}])
LanguageClassification(**json.loads(res.generations[0][0].message.additional_kwargs['function_call']["arguments"])
```
And thus there isn't even more code necessary compared to the native langchain implementation.
I didn't do any extensive benchmarks, but for the usecase above, the Langchain implementation was slower and returned wrong results way more often; which doesn't surprise me, given well-known function_call best practices.
Sorry if I might sound slightly offensive here, but I love to use langchain due to the many integrations and my familarity with it, but problems/implementations like this will really, really hurt adoption in the longterm imho. Hidden complexity can have itsreasons, but hidden complexity that deteriorates results significantly for important use cases without obvious reasons is really the worst.
EDIT
The `create_extraction_chain_pydantic` is better, but still unnecessary bloated (and as often, it´s not clear from the documentation, what the differences are and which one is preferred for which use case... - `create_structured_output_chain` seems to be advertised as "Popular"):
```python
[{'name': 'information_extraction',
'description': 'Extracts the relevant information from the passage.',
'parameters': {'type': 'object',
'properties': {'info': {'type': 'array',
'items': {'type': 'object',
'properties': {'language_codes': {'title': 'language_codes',
'description': 'A list of all languages present in the whole text. Exclude code sections, loanwords and technical terms in the text when deciding on the language codes. You have to output at least one language code, even if you are not certain or the text is very short!',
'type': 'array',
'items': {'description': '"A single language code in ISO 639-1 format',
'type': 'object',
'properties': {'language_code': {'description': "Language code (e.g. 'en', 'de', 'fr')",
'type': 'string'}},
'required': ['language_code']}},
'main_language_code': {'title': 'main_language_code',
'description': 'Main Language of the text.',
'allOf': [{'description': '"A single language code in ISO 639-1 format',
'type': 'object',
'properties': {'language_code': {'description': "Language code (e.g. 'en', 'de', 'fr')",
'type': 'string'}},
'required': ['language_code']}]}},
'required': ['main_language_code']}}},
'required': ['info']}}]
``
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PR_kwDOIPDwls5V4EJm
| 7,933 |
Add regex control over separators in character text splitter
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[] | null | 1 | 2023-07-19T10:24:01 | 2023-08-04T03:25:24 | 2023-08-04T03:25:24 |
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!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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
-->
#7854
Added the ability to use the `separator` ase a regex or a simple character.
Fixed a bug where `start_index` was incorrectly counting from -1.
Who can review?
@eyurtsev
@hwchase17
@mmz-001
|
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I_kwDOIPDwls5r-1uR
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ImportError: cannot import name 'ChatMessage' from 'vertexai.language_models' (/usr/local/lib/python3.10/dist-packages/vertexai/language_models/__init__.py)
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[] | null | 3 | 2023-07-19T10:17:30 | 2023-11-08T21:53:02 | 2023-11-08T21:52:53 |
NONE
| null |
### System Info
I run my code in google colab. [this is link to code.](https://colab.research.google.com/drive/1vqTz68WVT7qCGpDSahROztCUphmXy9xI?usp=sharing)
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 79
model name : Intel(R) Xeon(R) CPU @ 2.20GHz
stepping : 0
microcode : 0xffffffff
cpu MHz : 2199.998
cache size : 56320 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 0
initial apicid : 0
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed
bogomips : 4399.99
clflush size : 64
cache_alignment : 64
address sizes : 46 bits physical, 48 bits virtual
power management:
processor : 1
vendor_id : GenuineIntel
cpu family : 6
model : 79
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stepping : 0
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cache size : 56320 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 1
initial apicid : 1
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed
bogomips : 4399.99
clflush size : 64
cache_alignment : 64
address sizes : 46 bits physical, 48 bits virtual
power management:
### Who can help?
@hwchase17
@agola11
### 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
```
! pip install google-cloud-aiplatform==1.26 langchain==0.0.232
! pip uninstall shapely
! pip install shapely<2.0.0
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = ''
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
AIMessagePromptTemplate
)
from langchain.schema import HumanMessage, SystemMessage
from langchain.chat_models import ChatVertexAI
prompt = f"""Give me a jok!"""
system_template = (
"Act as an conversationl chat bot"
)
human_template = "{prompt}"
system_message_prompt = SystemMessagePromptTemplate.from_template(system_template)
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages(
[system_message_prompt, human_message_prompt]
)
messages = chat_prompt.format_prompt(prompt=prompt).to_messages()
model = ChatVertexAI(model_name="chat-bison@001")
response = model(messages)
```
### Expected behavior
It should get response, but I face this err.
```
/usr/local/lib/python3.10/dist-packages/langchain/chat_models/vertexai.py in _parse_chat_history(history)
46 first place.
47 """
---> 48 from vertexai.language_models import ChatMessage
49
50 vertex_messages, context = [], None
ImportError: cannot import name 'ChatMessage' from 'vertexai.language_models' (/usr/local/lib/python3.10/dist-packages/vertexai/language_models/__init__.py)
```
I think it should read from _vertexai._language_models_ instead.
|
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PR_kwDOIPDwls5V4ArF
| 7,931 |
Integrate NLP Cloud embeddings endpoint
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CONTRIBUTOR
| null |
Add embeddings for [NLPCloud](https://docs.nlpcloud.com/#embeddings).
|
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PR_kwDOIPDwls5V38IY
| 7,930 |
Add Golden Query Tool
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[] | null | 1 | 2023-07-19T10:00:59 | 2023-07-20T14:03:21 | 2023-07-20T14:03:20 |
CONTRIBUTOR
| null |
**Description:** Golden Query is a wrapper on top of the [Golden Query API](https://docs.golden.com/reference/query-api) which enables programmatic access to query results on entities across Golden's Knowledge Base. For more information about Golden API, please see the [Golden API Getting Started](https://docs.golden.com/reference/getting-started) page.
**Issue:** None
**Dependencies:** requests(already present in project)
**Tag maintainer:** @hinthornw
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PR_kwDOIPDwls5V3zlk
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chore(hf-text-gen): extract default params for reusing
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[] | null | 1 | 2023-07-19T09:37:15 | 2023-07-20T13:49:12 | 2023-07-20T13:49:12 |
CONTRIBUTOR
| null |
This PR extract common code (default generation params) for `HuggingFaceTextGenInference`.
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I_kwDOIPDwls5r-LyC
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SET search_path TO {self._schema} is executing by SQLDatabase for all databases except 'snowflake' and 'bigquery'.
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| false | null |
[] | null | 4 | 2023-07-19T08:42:23 | 2023-12-13T16:08:13 | 2023-12-13T16:08:12 |
NONE
| null |
### System Info
LangChain Version- 0.0.235, Windows, Python Version-3.9.16
As per below source code of SQLDatabase, before executing any sql query, connection.exec_driver_sql(f"SET search_path TO {self._schema}") is executed for all database except 'snowflake' and 'bigquery'.
if self._schema is not None:
if self.dialect == "snowflake":
connection.exec_driver_sql(
f"ALTER SESSION SET search_path='{self._schema}'"
)
elif self.dialect == "bigquery":
connection.exec_driver_sql(f"SET @@dataset_id='{self._schema}'")
else:
connection.exec_driver_sql(f"SET search_path TO {self._schema}")
cursor = connection.execute(text(command))
As per my knowledge, The SET search_path command is specific to PostgreSQL, not Oracle. This is why I am getting the following error.
sqlalchemy.exc.DatabaseError: (oracledb.exceptions.DatabaseError) ORA-00922: missing or invalid option
[SQL: SET search_path TO evr1]
(Background on this error at: https://sqlalche.me/e/20/4xp6)
Oracle does not recognize this command.
I think it is better to use SCHEMA.TABLE in all sql queries.
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
oracle_connection_str = f"oracle+oracledb://{username}:{password}@{hostname}:{port}/?service_name={service_name}"
db = SQLDatabase.from_uri(
oracle_connection_str,
schema="evr1",
include_tables=[ ],
sample_rows_in_table_info=3,
)
llm = ChatOpenAI(model_name=GPT_MODEL, temperature=0, openai_api_key=OpenAI_API_KEY)
toolkit = SQLDatabaseToolkit(
db=db,
llm=llm,
)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
return_intermediate_steps=True,
handle_parsing_errors=_handle_error,
verbose=True,
)
response = agent_executor(user_input)
### Expected behavior
Should not execute SET search_path TO {self._schema} for Oracle
|
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| 1,811,380,064 |
PR_kwDOIPDwls5V3KBY
| 7,927 |
Adding endpoint_url to embeddings/bedrock.py and updated docs
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[] | null | 1 | 2023-07-19T07:56:15 | 2023-07-24T10:04:28 | 2023-07-20T14:26:00 |
CONTRIBUTOR
| null |
BedrockEmbeddings does not have endpoint_url so that switching to custom endpoint is not possible. I have access to Bedrock custom endpoint and cannot use BedrockEmbeddings
|
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| 1,811,368,550 |
I_kwDOIPDwls5r90pm
| 7,926 |
llm can only use OpenAI?
|
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[] | null | 4 | 2023-07-19T07:49:20 | 2023-10-26T16:05:48 | 2023-10-26T16:05:48 |
NONE
| null |
### Issue you'd like to raise.
when I use langchain.agents,the parameter llm can only use OpenAI or other large language model ?
### Suggestion:
_No response_
|
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| 1,811,334,507 |
I_kwDOIPDwls5r9sVr
| 7,925 |
Bedrock Embeddings: Add support for endpoint_url
|
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[] | null | 2 | 2023-07-19T07:27:08 | 2023-10-25T16:06:16 | 2023-10-25T16:06:15 |
NONE
| null |
### System Info
Bedrock Embeddings doesn't have support for modifying the endpoint_url, the LLMs one have.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] 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
Not able to provide a custom endpoint_url
### Expected behavior
Able to provide a custom endpoint_url
|
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| 1,811,300,473 |
I_kwDOIPDwls5r9kB5
| 7,924 |
Issue: Cannot replicate search function on Langchain Milvus
|
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[] | null | 2 | 2023-07-19T07:06:55 | 2023-10-25T16:06:22 | 2023-10-25T16:06:22 |
NONE
| null |
### Issue you'd like to raise.
Hi I've tried doing the search functionalities using pymilvus and everything works perfectly fine doing similarity search, i am able to get the most relevant documents. However, when i try using the same credentials and parameters on Langchain.vectorstore.Milvus i am unable to replicate the results. Not sure if the connection is correct but my similarity search is returning a blank list. This is the exact same credentials i'm using currently for my on-prem Milvus
`vdb = Milvus(
collection_name = 'bbc_news',
embedding_function = embedding_model,
connection_args = {'host': 'server_host','port':'19530','user':'admin','password':'password','database':'db_dsg'},
consistency_level = "Session",
index_params = index_params,
search_params = search_params
)`
`vdb.similarity_search("What is the price of crude oil")`
Returns a blank list. Am i doing anything wrong here?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5r9cnT
| 7,923 |
InvalidRequestError: Resource not found. when running pandas_dataframe_agent over AzureOpenAI
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[] | null | 3 | 2023-07-19T06:44:17 | 2023-10-30T12:05:13 | 2023-10-25T16:06:25 |
NONE
| null |
### System Info
langchain version: 0.0.216
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents import create_csv_agent
from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.chat_models import AzureChatOpenAI
from langchain.llms import AzureOpenAI
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
os.environ["OPENAI_API_BASE"] = "https://####.openai.azure.com"
os.environ["OPENAI_API_KEY"] = "#####"
from langchain.llms import OpenAI
import pandas as pd
df = pd.read_csv("maccabi.csv")
agent = create_pandas_dataframe_agent(AzureOpenAI(temperature=0), df, verbose=True)
agent.run("how many rows are there?")
getting following error:
InvalidRequestError Traceback (most recent call last)
Cell In[4], line 2
1 #agent.run("how many players's job is scorer and what is their name")
----> 2 agent.run("how many rows are there?")
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:290](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:290), in Chain.run(self, callbacks, tags, *args, **kwargs)
288 if len(args) != 1:
289 raise ValueError("`run` supports only one positional argument.")
--> 290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:166](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:166), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:160](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:160), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File [~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:987](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:987), in AgentExecutor._call(self, inputs, run_manager)
985 # We now enter the agent loop (until it returns something).
986 while self._should_continue(iterations, time_elapsed):
--> 987 next_step_output = self._take_next_step(
988 name_to_tool_map,
989 color_mapping,
990 inputs,
991 intermediate_steps,
992 run_manager=run_manager,
993 )
994 if isinstance(next_step_output, AgentFinish):
995 return self._return(
996 next_step_output, intermediate_steps, run_manager=run_manager
997 )
File [~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:792](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:792), in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
786 """Take a single step in the thought-action-observation loop.
787
788 Override this to take control of how the agent makes and acts on choices.
789 """
790 try:
791 # Call the LLM to see what to do.
--> 792 output = self.agent.plan(
793 intermediate_steps,
794 callbacks=run_manager.get_child() if run_manager else None,
795 **inputs,
796 )
797 except OutputParserException as e:
798 if isinstance(self.handle_parsing_errors, bool):
File [~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:443](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/agents/agent.py:443), in Agent.plan(self, intermediate_steps, callbacks, **kwargs)
431 """Given input, decided what to do.
432
433 Args:
(...)
440 Action specifying what tool to use.
441 """
442 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
--> 443 full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
444 return self.output_parser.parse(full_output)
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:252](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:252), in LLMChain.predict(self, callbacks, **kwargs)
237 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
238 """Format prompt with kwargs and pass to LLM.
239
240 Args:
(...)
250 completion = llm.predict(adjective="funny")
251 """
--> 252 return self(kwargs, callbacks=callbacks)[self.output_key]
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:166](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:166), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:160](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/base.py:160), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:92](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:92), in LLMChain._call(self, inputs, run_manager)
87 def _call(
88 self,
89 inputs: Dict[str, Any],
90 run_manager: Optional[CallbackManagerForChainRun] = None,
91 ) -> Dict[str, str]:
---> 92 response = self.generate([inputs], run_manager=run_manager)
93 return self.create_outputs(response)[0]
File [~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:102](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/chains/llm.py:102), in LLMChain.generate(self, input_list, run_manager)
100 """Generate LLM result from inputs."""
101 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
--> 102 return self.llm.generate_prompt(
103 prompts,
104 stop,
105 callbacks=run_manager.get_child() if run_manager else None,
106 **self.llm_kwargs,
107 )
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:141](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:141), in BaseLLM.generate_prompt(self, prompts, stop, callbacks, **kwargs)
133 def generate_prompt(
134 self,
135 prompts: List[PromptValue],
(...)
138 **kwargs: Any,
139 ) -> LLMResult:
140 prompt_strings = [p.to_string() for p in prompts]
--> 141 return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:227](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:227), in BaseLLM.generate(self, prompts, stop, callbacks, tags, **kwargs)
221 raise ValueError(
222 "Asked to cache, but no cache found at `langchain.cache`."
223 )
224 run_managers = callback_manager.on_llm_start(
225 dumpd(self), prompts, invocation_params=params, options=options
226 )
--> 227 output = self._generate_helper(
228 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
229 )
230 return output
231 if len(missing_prompts) > 0:
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:178](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:178), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
176 for run_manager in run_managers:
177 run_manager.on_llm_error(e)
--> 178 raise e
179 flattened_outputs = output.flatten()
180 for manager, flattened_output in zip(run_managers, flattened_outputs):
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:165](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/base.py:165), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
155 def _generate_helper(
156 self,
157 prompts: List[str],
(...)
161 **kwargs: Any,
162 ) -> LLMResult:
163 try:
164 output = (
--> 165 self._generate(
166 prompts,
167 stop=stop,
168 # TODO: support multiple run managers
169 run_manager=run_managers[0] if run_managers else None,
170 **kwargs,
171 )
172 if new_arg_supported
173 else self._generate(prompts, stop=stop)
174 )
175 except (KeyboardInterrupt, Exception) as e:
176 for run_manager in run_managers:
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:336](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:336), in BaseOpenAI._generate(self, prompts, stop, run_manager, **kwargs)
334 choices.extend(response["choices"])
335 else:
--> 336 response = completion_with_retry(self, prompt=_prompts, **params)
337 choices.extend(response["choices"])
338 if not self.streaming:
339 # Can't update token usage if streaming
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:106](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:106), in completion_with_retry(llm, **kwargs)
102 @retry_decorator
103 def _completion_with_retry(**kwargs: Any) -> Any:
104 return llm.client.create(**kwargs)
--> 106 return _completion_with_retry(**kwargs)
File [~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:289](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:289), in BaseRetrying.wraps..wrapped_f(*args, **kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> 289 return self(f, *args, **kw)
File [~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:379](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:379), in Retrying.__call__(self, fn, *args, **kwargs)
377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
378 while True:
--> 379 do = self.iter(retry_state=retry_state)
380 if isinstance(do, DoAttempt):
381 try:
File [~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:314](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:314), in BaseRetrying.iter(self, retry_state)
312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain)
313 if not (is_explicit_retry or self.retry(retry_state)):
--> 314 return fut.result()
316 if self.after is not None:
317 self.after(retry_state)
File [/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:438](https://file+.vscode-resource.vscode-cdn.net/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:438), in Future.result(self, timeout)
436 raise CancelledError()
437 elif self._state == FINISHED:
--> 438 return self.__get_result()
440 self._condition.wait(timeout)
442 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File [/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:390](https://file+.vscode-resource.vscode-cdn.net/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:390), in Future.__get_result(self)
388 if self._exception:
389 try:
--> 390 raise self._exception
391 finally:
392 # Break a reference cycle with the exception in self._exception
393 self = None
File [~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:382](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:382), in Retrying.__call__(self, fn, *args, **kwargs)
380 if isinstance(do, DoAttempt):
381 try:
--> 382 result = fn(*args, **kwargs)
383 except BaseException: # noqa: B902
384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File [~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:104](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/langchain/llms/openai.py:104), in completion_with_retry.._completion_with_retry(**kwargs)
102 @retry_decorator
103 def _completion_with_retry(**kwargs: Any) -> Any:
--> 104 return llm.client.create(**kwargs)
File [~/Library/Python/3.9/lib/python/site-packages/openai/api_resources/completion.py:25](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/openai/api_resources/completion.py:25), in Completion.create(cls, *args, **kwargs)
23 while True:
24 try:
---> 25 return super().create(*args, **kwargs)
26 except TryAgain as e:
27 if timeout is not None and time.time() > start + timeout:
File [~/Library/Python/3.9/lib/python/site-packages/openai/api_resources/abstract/engine_api_resource.py:153](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/openai/api_resources/abstract/engine_api_resource.py:153), in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
127 @classmethod
128 def create(
129 cls,
(...)
136 **params,
137 ):
138 (
139 deployment_id,
140 engine,
(...)
150 api_key, api_base, api_type, api_version, organization, **params
151 )
--> 153 response, _, api_key = requestor.request(
154 "post",
155 url,
156 params=params,
157 headers=headers,
158 stream=stream,
159 request_id=request_id,
160 request_timeout=request_timeout,
161 )
163 if stream:
164 # must be an iterator
165 assert not isinstance(response, OpenAIResponse)
File [~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:230](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:230), in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout)
209 def request(
210 self,
211 method,
(...)
218 request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
219 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
220 result = self.request_raw(
221 method.lower(),
222 url,
(...)
228 request_timeout=request_timeout,
229 )
--> 230 resp, got_stream = self._interpret_response(result, stream)
231 return resp, got_stream, self.api_key
File [~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:624](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:624), in APIRequestor._interpret_response(self, result, stream)
616 return (
617 self._interpret_response_line(
618 line, result.status_code, result.headers, stream=True
619 )
620 for line in parse_stream(result.iter_lines())
621 ), True
622 else:
623 return (
--> 624 self._interpret_response_line(
625 result.content.decode("utf-8"),
626 result.status_code,
627 result.headers,
628 stream=False,
629 ),
630 False,
631 )
File [~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:687](https://file+.vscode-resource.vscode-cdn.net/Users/talzigm/Library/CloudStorage/OneDrive-AMDOCS/Documents/openai/notebooks/autogpt/~/Library/Python/3.9/lib/python/site-packages/openai/api_requestor.py:687), in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream)
685 stream_error = stream and "error" in resp.data
686 if stream_error or not 200 <= rcode < 300:
--> 687 raise self.handle_error_response(
688 rbody, rcode, resp.data, rheaders, stream_error=stream_error
689 )
690 return resp
InvalidRequestError: Resource not found
### Expected behavior
getting correct response
|
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PR_kwDOIPDwls5V2vWV
| 7,922 |
Add k param for return top documents
|
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[] | null | 2 | 2023-07-19T06:34:35 | 2023-07-22T07:47:04 | 2023-07-22T07:46:56 |
NONE
| null |
- Description: Add k parameter to return top document,
- Issue: the issue # it fixes (if applicable),
- Dependencies: no,
- Tag maintainer: @rlancemartin, @eyurtsev
|
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I_kwDOIPDwls5r9Lia
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Issue: Not getting good chat results on enabling Coversation Memory in langchain
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[] | null | 10 | 2023-07-19T05:47:22 | 2023-10-27T16:06:29 | 2023-10-27T16:06:28 |
NONE
| null |
### Issue you'd like to raise.
Hi,
Here is how i have initilized conversation memory
memory = ConversationSummaryBufferMemory(llm=llm_model,memory_key="chat_history",return_messages=True,max_token_limit=500)
Here is how I have used ConversationalRetrievalChain
chain=ConversationalRetrievalChain.from_llm(llm_model,retriever=vector.as_retriever(search_kwargs={"k": 10}), memory=memory,verbose=True)
I could see that answer for first question will be good, on asking further questions with
result = chain({"question": <question>}) answer from bot is not good for few questions. With verbose enabled I have observed that apart from the actual history langchain is adding few more conversations by itself. Is there a way to supress this(adding additional conversation)
I also tried with ConversationBufferWindowMemory aswell. There also Iam seeing the performance drop. I tried with new langchain version as well "0.0.235", there also Iam seeing the same issue. Could you help me to undersatnd if this is an existing issue or any mistake am I doing while configuring
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5V2ZRc
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Embeddings Callbacks
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[] | null | 5 | 2023-07-19T05:08:49 | 2023-12-14T13:45:06 | 2023-10-03T01:50:51 |
COLLABORATOR
| null |
Similar to https://github.com/hwchase17/langchainjs/pull/1859 from the JS lib (though haven't made serializable yet).
Example run: https://dev.smith.langchain.com/public/674633d8-9c38-4cdc-bd11-53202879fad1/r
Would like alignment on
- New base class vs update existing
- Whether OK to stop supporting 'chunk_size' at call time for a handful
And this still doesn't address our retriever -> vector store -> embeddings flows. Will need to update a lot of those similarity score methods to accept callbacks since they're all called under the hood.
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PR_kwDOIPDwls5V2XEO
| 7,919 |
improve insert performance for hologres
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[] | null | 3 | 2023-07-19T05:00:18 | 2023-08-01T07:05:12 | 2023-08-01T07:05:12 |
CONTRIBUTOR
| null |
Hi,
I made some code changes on Hologres vector store to improve the data insertion performance.
The code has passed the format/lint/spell check. And I have run the unit test for Hologres connecting to my own database.
Please check this PR, thanks!
@rlancemartin, @eyurtsev
Best,
Changgeng
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Docs Nit
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[] | null | 1 | 2023-07-19T04:42:44 | 2023-07-19T04:47:29 | 2023-07-19T04:47:28 |
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reverting openai embeddings changes
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PR_kwDOIPDwls5V1p2Z
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Equal Sized Chunks | Adds BalancedRecursiveCharacterTextSplitter to text_splitter.py
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CONTRIBUTOR
| null |
- Description: the RecursiveCharacterTextSplitter often leaves final chunks that are too small too be useful. I added this class to ensure that all chunk sizes conform to the desired chunk size. Included docs and a Juypter notebook.
- Issue: None,
- Dependencies: None,
- Tag maintainer: @rlancemartin, @eyurtsev @baskaryan,
- Twitter handle: @J_Shelby_J
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PR_kwDOIPDwls5V1cBH
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Fix load map reduce documents chain
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CONTRIBUTOR
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This PR updates _load_reduce_documents_chain to handle `reduce_documents_chain` and `combine_documents_chain` config
Please review @hwchase17, @baskaryan
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PR_kwDOIPDwls5V1UAR
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add serializer methods
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[] | null | 1 | 2023-07-18T22:53:36 | 2023-08-04T14:21:27 | 2023-08-04T03:10:36 |
CONTRIBUTOR
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Description: I have added two methods serializer and deserializer methods. There was method called save local but it saves the to the local disk. I wanted the vectorstore in the format using which i can push it to the sql database's blob field. I have used this while i was working on something
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5V1J2y
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Add Llama-v2 to Llama.cpp notebook
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PR_kwDOIPDwls5V1AAF
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fix docs
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PR_kwDOIPDwls5V0-Oa
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Qdrant Vectorstore
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CONTRIBUTOR
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<!-- 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!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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
-->
@baskaryan @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5V03ht
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pass callbacks along baby ai
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[] | null | 1 | 2023-07-18T20:56:57 | 2023-07-20T05:40:35 | 2023-07-20T05:40:34 |
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PR_kwDOIPDwls5V0yPc
| 7,907 |
Allow chat models that do not return token usage
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[] | null | 3 | 2023-07-18T20:39:11 | 2023-07-19T06:58:55 | 2023-07-19T01:12:09 |
CONTRIBUTOR
| null |
- Description: It allows to use chat models that do not return token usage
- Issue: [#7900](https://github.com/hwchase17/langchain/issues/7900)
- Dependencies: None
- Tag maintainer: @agola11 @hwchase17
- Twitter handle: @alonsosilva
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PR_kwDOIPDwls5V0xJ6
| 7,906 |
Added Databricks support to MLflow Callback
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[] | null | 4 | 2023-07-18T20:35:35 | 2023-07-26T01:23:55 | 2023-07-26T01:23:55 |
CONTRIBUTOR
| null |
Added a quick check to make integration easier with Databricks; another option would be to make a new class, but this seemed more straightfoward.
cc: @liangz1 Can this be done in a more straightfoward way?
|
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I_kwDOIPDwls5r7JHt
| 7,905 |
Implementing search threshold on PGVector
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[] | null | 1 | 2023-07-18T20:27:50 | 2023-07-18T20:29:07 | 2023-07-18T20:29:07 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Implementing _similarity_search_with_relevance_scores on PGVector so users can set "search_type" to "similarity_score_threshold" without raising NotImplementedError.
```
retriever = pgvector.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.7})
results = retriever.get_relevant_documents(query)
```
Using the search threshold on PGVector to avoid unrelated documents in the results.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5V0qtc
| 7,904 |
add pr template instructions
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[] | null | 1 | 2023-07-18T20:15:19 | 2023-07-18T20:22:29 | 2023-07-18T20:22:28 |
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I_kwDOIPDwls5r6_T8
| 7,902 |
Async calls for HuggingFaceHub
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[] | null | 6 | 2023-07-18T19:59:17 | 2023-11-15T16:08:10 | 2023-11-15T16:08:09 |
NONE
| null |
### Feature request
It would be nice if HuggingFaceHub models could be called in async mode (concurrently), as currently supported by Anthropic and OpenAI models.
### Motivation
I wanted to compare performance of some models in a bunch of tasks. I was comparing Anthropic and OpenAI models, and when I tried using a HuggingFaceHub model, I noticed this functionality is not implemented.
### Your contribution
I would like to know if someone is already taking on this. I could try to replicate code from other APIs to do it, but I am not sure I would be able to get to a level that is high enough to submit to LangChain.
|
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PR_kwDOIPDwls5V0lgS
| 7,901 |
Adding code to allow pandas to show all columns instead of truncating…
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[] | null | 3 | 2023-07-18T19:57:58 | 2023-07-20T14:02:02 | 2023-07-20T14:02:02 |
CONTRIBUTOR
| null |
- Description: Adding code to set pandas dataframe to display all the columns. Otherwise, some data get truncated (it puts a "..." in the middle and just shows the first 4 and last 4 columns) and the LLM doesn't realize it isn't getting the full data. Default value is 8, so this helps Dataframes larger than that.
- Issue: none
- Dependencies: none
- Tag maintainer: @hinthornw
- Twitter handle: none
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ChatOpenAI needs usage field that Google PaLM 2 Bison doesn't provide
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[] | null | 2 | 2023-07-18T19:25:22 | 2023-09-22T07:58:21 | 2023-09-22T07:58:21 |
CONTRIBUTOR
| null |
### System Info
Langhain v0.0.235
Python v3.11
### Who can help?
@agola11 @hw
### 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
I'm using OpenRouter which uses OpenAI SDK to provide different models. I encounter the problem when I use the model 'google/palm-2-chat-bison'
Here is the [gist](https://gist.github.com/alonsosilvaallende/6788eaa388bfa7e60ce84e7e155a86b5) reproducing the error.
Otherwise, here is the code:
```python
import os
import openai
from langchain.chat_models import ChatOpenAI
openai.api_base = "https://openrouter.ai/api/v1"
openai.api_key = os.getenv("OPENROUTER_API_KEY")
OPENROUTER_REFERRER = "https://github.com/alexanderatallah/openrouter-streamlit"
chat = ChatOpenAI(model_name="google/palm-2-chat-bison",
temperature=2,
headers={"HTTP-Referer": OPENROUTER_REFERRER})
chat.predict("Tell me a joke")
```
Error message:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[3], line 13
7 OPENROUTER_REFERRER = "https://github.com/alexanderatallah/openrouter-streamlit"
8 chat = ChatOpenAI(
9 model_name="google/palm-2-chat-bison",
10 temperature=2,
11 headers={"HTTP-Referer": OPENROUTER_REFERRER}
12 )
---> 13 chat.predict("Tell me a joke")
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/base.py:385, in BaseChatModel.predict(self, text, stop, **kwargs)
383 else:
384 _stop = list(stop)
--> 385 result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
386 return result.content
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/base.py:349, in BaseChatModel.__call__(self, messages, stop, callbacks, **kwargs)
342 def __call__(
343 self,
344 messages: List[BaseMessage],
(...)
347 **kwargs: Any,
348 ) -> BaseMessage:
--> 349 generation = self.generate(
350 [messages], stop=stop, callbacks=callbacks, **kwargs
351 ).generations[0][0]
352 if isinstance(generation, ChatGeneration):
353 return generation.message
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/base.py:125, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, **kwargs)
123 if run_managers:
124 run_managers[i].on_llm_error(e)
--> 125 raise e
126 flattened_outputs = [
127 LLMResult(generations=[res.generations], llm_output=res.llm_output)
128 for res in results
129 ]
130 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/base.py:115, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, **kwargs)
112 for i, m in enumerate(messages):
113 try:
114 results.append(
--> 115 self._generate_with_cache(
116 m,
117 stop=stop,
118 run_manager=run_managers[i] if run_managers else None,
119 **kwargs,
120 )
121 )
122 except (KeyboardInterrupt, Exception) as e:
123 if run_managers:
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/base.py:262, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
258 raise ValueError(
259 "Asked to cache, but no cache found at `langchain.cache`."
260 )
261 if new_arg_supported:
--> 262 return self._generate(
263 messages, stop=stop, run_manager=run_manager, **kwargs
264 )
265 else:
266 return self._generate(messages, stop=stop, **kwargs)
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/openai.py:372, in ChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
370 return ChatResult(generations=[ChatGeneration(message=message)])
371 response = self.completion_with_retry(messages=message_dicts, **params)
--> 372 return self._create_chat_result(response)
File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chat_models/openai.py:394, in ChatOpenAI._create_chat_result(self, response)
389 gen = ChatGeneration(
390 message=message,
391 generation_info=dict(finish_reason=res.get("finish_reason")),
392 )
393 generations.append(gen)
--> 394 llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
395 return ChatResult(generations=generations, llm_output=llm_output)
KeyError: 'usage'
### Expected behavior
I expect that it doesn't give me an error since exactly the same code works when I use the model 'openai/gpt-3.5-turbo' instead of the model 'google/palm-2-chat-bison'.
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| 1,810,462,902 |
PR_kwDOIPDwls5V0H_V
| 7,898 |
docs(custom_chain) fix typo
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[] | null | 2 | 2023-07-18T18:19:03 | 2023-07-19T01:03:19 | 2023-07-19T01:03:19 |
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
-->
Fix typo in the document of custom_chain
|
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| 7,897 |
CSV Agent JSON Parsing Errors
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open
| false | null |
[] | null | 6 | 2023-07-18T18:13:05 | 2023-10-14T12:09:47 | null |
NONE
| null |
### System Info
Occasional error out of CSV agent with JSON parsing error. Typically occurs when prompting a multi step task, but some multi step tasks are handled fine. Even in the same multi step task, depending on the wording of the prompt it can be run successfully but with different wording will error out.
Heres an example of the message returned.
File "C:\Users\\env\Lib\site-packages\langchain\agents\openai_functions_agent\base.py", line 212, in plan
agent_decision = _parse_ai_message(predicted_message)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\\env\Lib\site-packages\langchain\agents\openai_functions_agent\base.py", line 114, in _parse_ai_message
raise OutputParserException(
langchain.schema.OutputParserException: Could not parse tool input: {'name': 'python', 'arguments': "df_filtered = df[df['Version1Text'].str.contains('using your budget')]\nlabel_counts = df_filtered['Label'].value_counts()\nlabel_counts"} because the `arguments` is not valid JSON.
### Who can help?
@hwchase17 @agol
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce behavior:
1. Start up CSV agent
2. One example prompt that errors: "of the rows where 'Version1Text' includes 'using your budget' what are the counts of each of the unique 'Label' values"
### Expected behavior
Expected behavior is to subset the csv based on the provided conditions and then return counts
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I_kwDOIPDwls5r6TzL
| 7,896 |
Issue: OpenAI Function Agent with custom prompt from memory
|
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[] | null | 2 | 2023-07-18T18:07:22 | 2023-07-18T21:27:03 | 2023-07-18T21:27:02 |
NONE
| null |
### Issue you'd like to raise.
Hi,
I'm updating my code to use the new OpenAI function calling structure.
Requirements:
- New messages saved in DynamoDB together with past messages for a user
- Custom prompt : 10 last messages from DynamoDB memory of the user
- Function calling
### Past code
The `create_prompt_from_messages(n)` function create a custom prompt based on n last messages.
```python
chain = LLMChain(llm=llm,
prompt=create_prompt_from_messages(10),
verbose=False,
memory=memory
)
```
### New code without custom prompt
This code below works but send **all** past messages to the LLM. I want to limit to **n** last messages. I didn't find a way to pass custom prompt to an agent using `AgentType.OPENAI_FUNCTIONS`
Note that I don't want to delete past messages from the database.
```python
message_history = DynamoDBChatMessageHistory(table_name=conversation_table_name, session_id='0')
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=message_history, return_messages=True)
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="chat_history")],
}
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS, # or OPENAI_MULTI_FUNCTIONS ?
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory
)
```
How can I only sent n last message from the agent memory? Or create the a custom prompt and pass it to the OpenAI function agent?
Thank you in advance!
### Suggestion:
Create a Notebook with this use case for OpenAI Function agent.
|
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I_kwDOIPDwls5r6SAW
| 7,895 |
Pydantic exception when creating GPT4All model
|
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| false | null |
[] | null | 2 | 2023-07-18T18:01:23 | 2023-10-24T16:05:43 | 2023-10-24T16:05:42 |
NONE
| null |
### System Info
Mac OS
Versions:
Python 3.8.15
Package | Version
-----------------------|--------
aiohttp | 3.8.4
aiosignal | 1.3.1
async-timeout | 4.0.2
attrs |23.1.0
certifi |2023.5.7
charset-normalizer | 3.2.0
dataclasses-json | 0.5.9
frozenlist | 1.4.0
**gpt4all** | **1.0.5**
greenlet | 2.0.2
idna | 3.4
**langchain** | **0.0.234**
langsmith | 0.0.5
marshmallow | 3.19.0
marshmallow-enum | 1.5.1
multidict | 6.0.4
mypy-extensions | 1.0.0
numexpr | 2.8.4
numpy | 1.24.4
openapi-schema-pydantic | 1.2.4
packaging | 23.1
pip | 23.2
**pydantic** | **1.10.11**
PyYAML | 6.0
requests | 2.31.0
setuptools | 56.0.0
SQLAlchemy | 2.0.19
tenacity | 8.2.2
tqdm | 4.65.0
typing_extensions | 4.7.1
typing-inspect | 0.9.0
urllib3 | 2.0.3
yarl | 1.9.2
### Who can help?
@agola11 @hwchase17
### Information
- [X] 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is a minimum code example to reproduce the error:
```python
from langchain.llms.gpt4all import GPT4All
llm = GPT4All(model="./models/gpt4all-lora-quantized-ggml.bin")
```
I get the following error:
```
Traceback (most recent call last):
File "gpt4all_me.py", line 3, in <module>
llm = GPT4All(model="./models/gpt4all-lora-quantized-ggml.bin")
File "/home/cserpell/git/activelooplangchain/a/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__ -> __root__
__init__() takes 1 positional argument but 2 were given (type=type_error)
```
I tried giving the directory without the `./`, without the `./model/`, putting the file in the current directory, and some other options, with no success.
### Expected behavior
The exception should not be raised, and GPT4All model should be available to use.
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| 7,894 |
simplified namespace
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[] | null | 2 | 2023-07-18T17:57:48 | 2023-07-23T20:40:22 | 2023-07-23T20:40:22 |
COLLABORATOR
| null |
label: documentation
Simplified namespace to reduce a column length in the API Reference table for the `Agents`.
Right now the `agents.agent_toolkits.azure_cognitive_services.toolkit.AzureCognitiveServicesToolkit` name makes the first column [unbalanced with the second column](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.agents). The first column is too wide and the second column is too narrow, which wraps the class descriptions in several lines.
This update reduces the ^ namespace to `agents.agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit` and balances the columns.
@baskaryan
@hwchase17
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Add llama-v2 to docs
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docstrings `chains`
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[] | null | 2 | 2023-07-18T17:33:54 | 2023-07-19T01:29:31 | 2023-07-19T01:25:43 |
COLLABORATOR
| null |
Added/updated docstrings.
@baskaryan
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CONTRIBUTOR
| null |
- Description: version check to make sure chromadb >=0.4.0 does not throw an error, and uses the default sqlite persistence engine when the directory is set,
- Issue: the issue #7887
For attention of
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5Vz1HN
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Amadeus Flight and Travel Search Tool
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[] | null | 3 | 2023-07-18T17:11:55 | 2023-07-20T13:59:30 | 2023-07-20T13:59:29 |
CONTRIBUTOR
| null |
## Background
With the addition on email and calendar tools, LangChain is continuing to complete its functionality to automate business processes.
## Challenge
One of the pieces of business functionality that LangChain currently doesn't have is the ability to search for flights and travel in order to book business travel.
## Changes
This PR implements an integration with the [Amadeus](https://developers.amadeus.com/) travel search API for LangChain, enabling seamless search for flights with a single authentication process.
## Who can review?
@hinthornw
## Appendix
@tsolakoua and @minjikarin, I utilized your [amadeus-python](https://github.com/amadeus4dev/amadeus-python) library extensively. Given the rising popularity of LangChain and similar AI frameworks, the convergence of libraries like amadeus-python and tools like this one is likely. So, I wanted to keep you updated on our progress.
<!-- 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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I_kwDOIPDwls5r5qai
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StructuredChatAgent uses SQLDatabaseToolkit tools in wrong order
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[] | null | 5 | 2023-07-18T16:20:09 | 2023-11-07T16:07:08 | 2023-11-07T16:07:07 |
NONE
| null |
### System Info
Langchain version - 0.0.235
Python version - 3.10
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hello,
I'm trying to use a Structured Chat agent with SQL tools as well as a VectorStoreQA tool in order to retrieve data from a Postgres database and our Pinecone store. For the Agent's chat model, I'm using Azure OpenAI's gpt-3.5-turbo version 0301.
I have tried providing the tools via the SQLDatabaseToolkit's get_tools() function as well as importing the individual tools and modifying their descriptions, but I get the same issue with both approaches. Despite the descriptions and formatting instructions provided below, the agent has a lot of trouble with querying the DB.
It almost always starts off with 'sql_db_query' to construct a query that errors out, which then prompts the agent to check the list of tables and then their schemas. From here, it can usually arrive at the correct query - but this is not the correct behavior. Other times, it will get stuck in a loop of constructing a query, getting an error, and then either checking the query or just constructing a new one. The errors are due to the schema that it hallucinates because it didn't use the 'sql_db_list_tables' and 'sql_db_schema' tools first.
In the code excerpt below, you can see the formatting instructions provided as well as the descriptions that I'm giving the SQL tools.
Has anybody else had issues with SQL database tools and the StructuredChat agent? Should I be using a different type of agent for this?
``````
FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an
action_input key (tool input).
For SQL queries, ALWAYS use the available tools in this order:
1. sql_db_list_tables
2. sql_db_schema
3. sql_db_query_checker
4. sql_db_query
Valid "action" values: "Final Answer" or {tool_names}
Provide only ONE action per $JSON_BLOB, as shown:
```
{{{{
"action": $TOOL_NAME,
"action_input": $INPUT
}}}}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{{{{
"action": "Final Answer",
"action_input": "Final response to human"
}}}}
```"""
vectorstore_info = VectorStoreInfo(
name="incident_resolution_instructions",
description="MOP Documents that help users resolve incidents for different devices and causes",
vectorstore=pineconeStore,
)
llm = AzureChatOpenAI(temperature=0, verbose=True,
deployment_name='chatgpt-35', model_name="gpt-35-turbo", max_tokens=4000)
query_sql_database_tool_description = (
"ONLY use this tool AFTER using 'sql_db_schema' and 'sql_db_query_checker'."
"Input to this tool is a detailed and correct SQL query, output is a "
"result from the database. If the query is not correct, an error message "
"will be returned. If an error is returned, rewrite the query, check the "
"query, and try again. If you encounter an issue with Unknown column "
"'xxxx' in 'field list', use 'sql_db_schema' to query the correct table "
"fields."
)
info_sql_database_tool_description = (
"""
ALWAYS use this tool second AFTER 'sql_db_list_tables'.
Input to this tool is a comma-separated list of tables, output is the
schema and sample rows for those tables.
Be sure that the tables actually exist by calling 'sql_db_list_tables'
first! Example Input: table1, table2, table3
"""
)
list_sql_database_tool_description = (
"ALWAYS use this tool first. Input to this tool is an empty string '', output is the list of PostgreSQL tables in the database."
)
query_checker_sql_database_tool_description = (
"""
ALWAYS Use this tool third AFTER 'sql_db_list_tables' and 'sql_db_schema'.
ALWAYS use this tool to double check if your query is correct before executing it.
ALWAYS use this tool BEFORE executing a query with 'sql_db_query'
"""
)
tools = [
ListSQLDatabaseTool(db=db, description=list_sql_database_tool_description),
InfoSQLDatabaseTool(db=db, description=info_sql_database_tool_description),
QuerySQLDataBaseTool(db=db, description=query_sql_database_tool_description),
QuerySQLCheckerTool(db=db, description=query_checker_sql_database_tool_description, llm=AzureOpenAI(temperature=0, verbose=True,
deployment_name='chatgpt-35', model_name="gpt-35-turbo", max_tokens=4000)),
VectorStoreQATool(vectorstore=vectorstore_info.vectorstore, llm=AzureOpenAI(temperature=0, verbose=True,
deployment_name='chatgpt-35', model_name="gpt-35-turbo", max_tokens=4000), name="incident_resolution_steps", description="Documentation detailing steps to resolve an incident for various device types such as Routers and Switches.")
]
agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, memory=memory, agent_kwargs={
'prefix': PREFIX,
'format_instructions': FORMAT_INSTRUCTIONS,
'suffix': SUFFIX,
'input_variables': ["input", "chat_history", "agent_scratchpad"]
})
response = agent_chain.run(input=event['question'])
``````
### Expected behavior
The agent should:
- Follow the order provided in the default descriptions of each SQLDatabase Tool
- Follow instructions for tools provided in the formatting instructions or prompt suffix
|
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| 1,810,240,198 |
PR_kwDOIPDwls5VzXmV
| 7,888 |
SelfQuery support for deeplake
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closed
| false | null |
[] | null | 5 | 2023-07-18T15:59:05 | 2023-07-24T21:22:34 | 2023-07-24T21:22:33 |
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
-->
Added support SelfQuery for Deeplake
|
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I_kwDOIPDwls5r5gRj
| 7,887 |
ChromaDB 0.4+ is no longer compatible with client config
|
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open
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[] | null | 50 | 2023-07-18T15:56:56 | 2023-11-05T14:07:23 | null |
CONTRIBUTOR
| null |
### System Info
- Python 3.9.13
- langchain-0.0.235-py3-none-any.whl
- chromadb-0.4.0-py3-none-any.whl
### 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
Steps to reproduce:
1. Create a Chroma store which is locally persisted
```
store = Chroma.from_texts(
texts=docs, embedding=embeddings, metadatas=metadatas, persist_directory=environ["DB_DIR"]
)
```
2. Get the error `You are using a deprecated configuration of Chroma. Please pip install chroma-migrate and run `chroma-migrate` to upgrade your configuration. See https://docs.trychroma.com/migration for more information or join our discord at https://discord.gg/8g5FESbj for help!`
3. Suffer
### Expected behavior
1. Create locally persisted Chroma store
2. Use Chroma store
The issue:
Starting chromadb 0.40 the chroma_db_impl is no longer a supported parameter, it uses sqlite instead.
Removing the line
`
chroma_db_impl="duckdb+parquet",
`
from langchain.vectorstores/chroma.py solves the issue, but the earlier DB cannot be used or migrated.
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| 1,810,223,070 |
I_kwDOIPDwls5r5c_e
| 7,886 |
Encryption Key support
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[] | null | 3 | 2023-07-18T15:49:30 | 2023-12-25T16:09:34 | 2023-12-25T16:09:34 |
NONE
| null |
### Feature request
When entering the embed text in the database like pgvector, I would like to encrypt the raw text with KMS Key or any such encryption and use raw text for generating embedding
### Motivation
Security
### Your contribution
N/A
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I_kwDOIPDwls5r5X-W
| 7,885 |
Difference between ConversationChain and ConversationalRetrievalChain
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[] | null | 3 | 2023-07-18T15:39:20 | 2023-11-16T13:39:15 | 2023-10-24T16:05:47 |
NONE
| null |
### Issue you'd like to raise.
what is the difference between a conversationChain and a conversationalRetrieval chain. I had originially assumed that the conversational retrieval chain would be able to take in documents, input, and memory (which I have gotten to successfully work) and was under the assumption that the conversationChain could not take in our own documents. However, I found a demo online that suggests otherwise. So what exactly is the difference? It was natural to me to use conversationalretrieval chain becuase I had personal documents I wanted to use and I knew that the retrieval chains were made for that.
### Suggestion:
_No response_
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PR_kwDOIPDwls5Vy0RA
| 7,884 |
docs: added comment from azure llm to azure chat about GPT-4
|
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[] | null | 2 | 2023-07-18T14:42:05 | 2023-07-18T15:13:58 | 2023-07-18T15:05:41 |
CONTRIBUTOR
| null |
Azure GPT-4 models can't be accessed via LLM model. It's easy to miss that and a lot of discussions about that are on the Internet. Therefore I added a comment in Azure LLM docs that mentions that and points to Azure Chat OpenAI docs.
@baskaryan
|
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I_kwDOIPDwls5r4sGQ
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Inconsistent behaviour with the 'python_repl' tool and the ZeroShotAgent
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[] | null | 2 | 2023-07-18T14:12:21 | 2023-10-24T16:05:53 | 2023-10-24T16:05:52 |
NONE
| null |
### Issue you'd like to raise.
When using the python_repl tool with the ZeroShotAgent, I keep getting the following error: `Observation: SyntaxError('invalid syntax', ('<string>', 2, 1, '%matplotlib inline\n'))
Thought:`
The agent keeps looping over and over since it does not understand the issue with the magic command.
Is this a known issue? Do we have a fix or ideally a way to enforce some behaviours within the python tool, via a custom prompt?
### Suggestion:
_No response_
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I_kwDOIPDwls5r4WEf
| 7,881 |
Integrating Langchain to FHIR API
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[] | null | 8 | 2023-07-18T13:24:11 | 2023-11-09T14:43:56 | null |
NONE
| null |
### Feature request
I think it'd be a good idea to have Langchain integrated into the fast healthcare interoperability resource (FHIR) API. Integrating chatting techniques with FHIR and having the ability to interact with ChatOpenAI would give FHIR more visibility, versatility, and adaptability in terms of use cases in healthcare.
### Motivation
Integrating a ChatOpenAI with FHIR (Fast Healthcare Interoperability Resources) can provide several benefits in the healthcare domain:
**Real-time Communication:** Integrating the chat techniques such as ChatOpenAI with FHIR, will have the conversations seamlessly linked to relevant patient health records and clinical data, enhancing the context and relevance of the discussions.
**Collaborative Decision-Making**: By integrating a chat with FHIR, healthcare teams can discuss patient cases, share insights, exchange knowledge, and make informed decisions together. The ability to refer to FHIR resources, such as clinical notes, lab results, or medication information, within the chat streamlines the decision-making process.
**Contextual Information:** FHIR provides a standardized format for representing and exchanging healthcare data. Integrating the chat stream with FHIR allows relevant patient data, such as demographics, diagnoses, allergies, medications, or procedures, to be readily accessible within the chat interface.
**Workflow Efficiency**: By integrating the Lnangchain's ChatOpenAI with FHIR, healthcare professionals can conveniently access patient data and perform necessary actions within the chat interface. For example, they can request lab results, schedule appointments, prescribe medications, or document clinical notes directly within the chat stream. This integration reduces the need for switching between multiple systems, streamlines workflow, and enhances productivity.
**Continuity of Care:** Integrating the chat stream with FHIR helps ensure continuity of care by maintaining a historical record of discussions, decisions, and interventions in the patient's health record. This allows healthcare professionals to refer to previous conversations, review treatment plans, and track the progression of care over time. It also supports care coordination and handoffs between healthcare providers.
**Patient Engagement:** Chat streams integrated with FHIR can empower patients to actively participate in their own healthcare journey. Patients can securely communicate with healthcare providers, ask questions, receive educational materials, or provide updates on their health status. Having access to their FHIR-based health data within the chat stream can enable patients to have informed discussions and take ownership of their care.
The motivation for integrating Langchain with ChatOpenAI is endless.
### Your contribution
I thought of this because I have been studying FHIR for a while now. I am yet to understand the nitty-gritty but I am sure the API has a schema https://fhir-ru.github.io/downloads.html that can be downloaded and integrated. In addition, Langchain developers have built API agents, they can do it.
I am happy to provide more information.
|
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| 1,809,854,935 |
PR_kwDOIPDwls5VyC8o
| 7,880 |
ArangoDB/AQL support for Graph QA Chain
|
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[] | null | 8 | 2023-07-18T12:44:22 | 2023-07-24T22:16:53 | 2023-07-24T22:16:53 |
CONTRIBUTOR
| null |
**Description**: Serves as an introduction to LangChain's support for [ArangoDB](https://github.com/arangodb/arangodb), similar to https://github.com/hwchase17/langchain/pull/7165 and https://github.com/hwchase17/langchain/pull/4881
**Issue**: No issue has been created for this feature
**Dependencies**: `python-arango` has been added as an optional dependency via the `CONTRIBUTING.md` guidelines
**Twitter handle**: [at]arangodb
- Integration test has been added
- Notebook has been added: [graph_arangodb_qa.ipynb](https://github.com/amahanna/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb)
[](https://colab.research.google.com/github/amahanna/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb)
```
docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb
```
```
pip install git+https://github.com/amahanna/langchain.git
```
```python
from arango import ArangoClient
from langchain.chat_models import ChatOpenAI
from langchain.graphs import ArangoGraph
from langchain.chains import ArangoGraphQAChain
db = ArangoClient(hosts="localhost:8529").db(name="_system", username="root", password="", verify=True)
graph = ArangoGraph(db)
chain = ArangoGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph)
chain.run("Is Ned Stark alive?")
```
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showing progress or message under process
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[] | null | 9 | 2023-07-18T10:58:52 | 2023-10-26T16:05:59 | 2023-10-26T16:05:58 |
NONE
| null |
### System Info
I am using the following codes to get api call:
user_query = prompt.format_prompt(user_prompt=input_text)
user_query_output = chat_model(user_query.to_messages())
I am using Django and since it takes some time to get response the entire page freezes. Is there anyway to show progress bar or at least a message it is under progress?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
include the below code under views.py:
user_query = prompt.format_prompt(user_prompt=input_text)
user_query_output = chat_model(user_query.to_messages())
upon calling api the page freezes which is normal
### Expected behavior
A message or progress bar
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openai.error.InvalidRequestError: The completion operation does not work with the specified model, gpt-4. Please choose different model and try again. You can learn more about which models can be used with each operation here: https://go.microsoft.com/fwlink/?linkid=2197993.
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| false | null |
[] | null | 2 | 2023-07-18T10:01:28 | 2023-07-19T04:44:19 | 2023-07-19T04:44:19 |
NONE
| null |
### System Info
langchain==0.0.219
python 3.9
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using the gpt-4 model with azureOpenAI using the below code.
```
from langchain.llms import AzureOpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
import os
import openai
openai.api_type = "azure"
openai.api_base = os.getenv("OPENAI_API_BASE")
openai.api_version = "2023-03-15-preview"
openai.api_key = os.getenv("OPENAI_API_KEY")
DEPLOYMENT_NAME = 'gpt-4 model'
llm = AzureOpenAI(
openai_api_base=os.getenv("OPENAI_API_BASE"),
openai_api_version="2023-03-15-preview",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=os.getenv("OPENAI_API_KEY"),
openai_api_type="azure",
)
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
query = "sample query"
my_loader = DirectoryLoader('/Data', glob='**/*.pdf')
docs = my_loader.load()
text_split = RecursiveCharacterTextSplitter(chunk_size = 3000, chunk_overlap = 2)
texts = text_split.split_documents(docs)
docsearch = Chroma.from_documents(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))],persist_directory="./official_db").as_retriever(search_type="similarity")
docs = docsearch.get_relevant_documents(query)
chain = load_qa_chain(llm, chain_type="stuff")
result = chain.run(input_documents=docs, question=query)
```
But it returns the errror
```
openai.error.InvalidRequestError: The completion operation does not work with the specified model, gpt-4. Please choose different model and try again. You can learn more about which models can be used with each operation here: https://go.microsoft.com/fwlink/?linkid=2197993.
```
**Note: The same code is working well with gpt-3.5 in azureOpenAI llm**
### Expected behavior
It should able to integrate gpt-4 without any issue.
|
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| 1,809,594,246 |
PR_kwDOIPDwls5VxJuS
| 7,877 |
Add Portkey LLMOps integration
|
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[] | null | 3 | 2023-07-18T09:57:05 | 2023-07-20T16:08:45 | 2023-07-20T16:08:44 |
CONTRIBUTOR
| null |
Integrating Portkey, which adds production features like caching, tracing, tagging, retries, etc. to langchain apps.
- Dependencies: None
- Twitter handle: https://twitter.com/portkeyai
- test_portkey.py added for tests
- example notebook added in new utilities folder in modules
Also fixed a bug with OpenAIEmbeddings where headers weren't passing.
cc @baskaryan
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I_kwDOIPDwls5r2rF2
| 7,876 |
Difference between chain() and chain.run()
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[] | null | 6 | 2023-07-18T09:02:23 | 2024-01-14T19:04:13 | 2023-10-25T16:06:46 |
NONE
| null |
### Issue you'd like to raise.
I came to know that we can do the document question answering in langchain in different ways. One way is - using **load_qa_chain**
Also it seems there are two ways to use **load_qa_chain**
1) with **run()**
```
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
result = chain.run(input_documents=docs, question=query)
```
2)without **run**
```
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
```
what is the exact difference between these two methods?
### Suggestion:
_No response_
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| 7,874 |
Docs Nits
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[] | null | 1 | 2023-07-18T08:26:36 | 2023-07-18T08:50:15 | 2023-07-18T08:50:15 |
COLLABORATOR
| null |
Add links to reference docs
|
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I_kwDOIPDwls5r2NPF
| 7,873 |
Refactoring telegram loader
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[] | null | 2 | 2023-07-18T07:55:37 | 2023-10-24T16:06:13 | 2023-10-24T16:06:12 |
NONE
| null |
### Feature request
The current Telegram loader is not very flexible. For example:
```
async for message in client.iter_messages(self.chat_entity):
```
Here are the arguments available in the api
```
def iter_messages(
self: 'TelegramClient',
entity: 'hints.EntityLike',
limit: float = None,
*,
offset_date: 'hints.DateLike' = None,
offset_id: int = 0,
max_id: int = 0,
min_id: int = 0,
add_offset: int = 0,
search: str = None,
filter: 'typing.Union[types.TypeMessagesFilter, typing.Type[types.TypeMessagesFilter]]' = None,
from_user: 'hints.EntityLike' = None,
wait_time: float = None,
ids: 'typing.Union[int, typing.Sequence[int]]' = None,
reverse: bool = False,
reply_to: int = None,
scheduled: bool = False
) -> 'typing.Union[_MessagesIter, _IDsIter]':
"""
Iterator over the messages for the given chat.
The default order is from newest to oldest, but this
behaviour can be changed with the `reverse` parameter.
If either `search`, `filter` or `from_user` are provided,
:tl:`messages.Search` will be used instead of :tl:`messages.getHistory`.
.. note::
Telegram's flood wait limit for :tl:`GetHistoryRequest` seems to
be around 30 seconds per 10 requests, therefore a sleep of 1
second is the default for this limit (or above).
Arguments
entity (`entity`):
The entity from whom to retrieve the message history.
It may be `None` to perform a global search, or
to get messages by their ID from no particular chat.
Note that some of the offsets will not work if this
is the case.
Note that if you want to perform a global search,
you **must** set a non-empty `search` string, a `filter`.
or `from_user`.
limit (`int` | `None`, optional):
Number of messages to be retrieved. Due to limitations with
the API retrieving more than 3000 messages will take longer
than half a minute (or even more based on previous calls).
The limit may also be `None`, which would eventually return
the whole history.
offset_date (`datetime`):
Offset date (messages *previous* to this date will be
retrieved). Exclusive.
offset_id (`int`):
Offset message ID (only messages *previous* to the given
ID will be retrieved). Exclusive.
max_id (`int`):
All the messages with a higher (newer) ID or equal to this will
be excluded.
min_id (`int`):
All the messages with a lower (older) ID or equal to this will
be excluded.
add_offset (`int`):
Additional message offset (all of the specified offsets +
this offset = older messages).
search (`str`):
The string to be used as a search query.
filter (:tl:`MessagesFilter` | `type`):
The filter to use when returning messages. For instance,
:tl:`InputMessagesFilterPhotos` would yield only messages
containing photos.
from_user (`entity`):
Only messages from this entity will be returned.
wait_time (`int`):
Wait time (in seconds) between different
:tl:`GetHistoryRequest`. Use this parameter to avoid hitting
the ``FloodWaitError`` as needed. If left to `None`, it will
default to 1 second only if the limit is higher than 3000.
If the ``ids`` parameter is used, this time will default
to 10 seconds only if the amount of IDs is higher than 300.
ids (`int`, `list`):
A single integer ID (or several IDs) for the message that
should be returned. This parameter takes precedence over
the rest (which will be ignored if this is set). This can
for instance be used to get the message with ID 123 from
a channel. Note that if the message doesn't exist, `None`
will appear in its place, so that zipping the list of IDs
with the messages can match one-to-one.
.. note::
At the time of writing, Telegram will **not** return
:tl:`MessageEmpty` for :tl:`InputMessageReplyTo` IDs that
failed (i.e. the message is not replying to any, or is
replying to a deleted message). This means that it is
**not** possible to match messages one-by-one, so be
careful if you use non-integers in this parameter.
reverse (`bool`, optional):
If set to `True`, the messages will be returned in reverse
order (from oldest to newest, instead of the default newest
to oldest). This also means that the meaning of `offset_id`
and `offset_date` parameters is reversed, although they will
still be exclusive. `min_id` becomes equivalent to `offset_id`
instead of being `max_id` as well since messages are returned
in ascending order.
You cannot use this if both `entity` and `ids` are `None`.
reply_to (`int`, optional):
If set to a message ID, the messages that reply to this ID
will be returned. This feature is also known as comments in
posts of broadcast channels, or viewing threads in groups.
This feature can only be used in broadcast channels and their
linked megagroups. Using it in a chat or private conversation
will result in ``telethon.errors.PeerIdInvalidError`` to occur.
When using this parameter, the ``filter`` and ``search``
parameters have no effect, since Telegram's API doesn't
support searching messages in replies.
.. note::
This feature is used to get replies to a message in the
*discussion* group. If the same broadcast channel sends
a message and replies to it itself, that reply will not
be included in the results.
scheduled (`bool`, optional):
If set to `True`, messages which are scheduled will be returned.
All other parameter will be ignored for this, except `entity`.
Yields
Instances of `Message <telethon.tl.custom.message.Message>`.
Example
.. code-block:: python
# From most-recent to oldest
async for message in client.iter_messages(chat):
print(message.id, message.text)
# From oldest to most-recent
async for message in client.iter_messages(chat, reverse=True):
print(message.id, message.text)
# Filter by sender
async for message in client.iter_messages(chat, from_user='me'):
print(message.text)
# Server-side search with fuzzy text
async for message in client.iter_messages(chat, search='hello'):
print(message.id)
# Filter by message type:
from telethon.tl.types import InputMessagesFilterPhotos
async for message in client.iter_messages(chat, filter=InputMessagesFilterPhotos):
print(message.photo)
# Getting comments from a post in a channel:
async for message in client.iter_messages(channel, reply_to=123):
print(message.chat.title, message.text)
"""
```
Of particular interest to me is the ability to specify `offset_date`, `limit`, and `reverse`. Other users may have other needs. I understand that the `BaseLoader` load method signature doesn't allow for any arguments. That leaves two options (perhaps there is a third way?):
1. Provide something like `**telegram_kwargs` in the TelegramApiChatLoader loader constructor, e.g.
```
class TelegramChatApiLoader(BaseLoader):
"""Loader that loads Telegram chat json directory dump."""
def __init__(
self,
chat_entity: Optional[EntityLike] = None,
api_id: Optional[int] = None,
api_hash: Optional[str] = None,
username: Optional[str] = None,
file_path: str = "telegram_data.json",
**telegram_kwargs # <---- add this new argument
):
```
2. Alternatively, refactor the `fetch_data_from_telegram` method and extract `async for message in client.iter_messages(self.chat_entity):` into a separate method that can be overriden by child classes, e.g.:
```
async def fetch_data_from_telegram(self) -> None:
"""Fetch data from Telegram API and save it as a JSON file."""
from telethon.sync import TelegramClient
data = []
async with TelegramClient(self.username, self.api_id, self.api_hash) as client:
# change this line to call a local method
async for message in self.iter_messages(client, self.chat_entity):
is_reply = message.reply_to is not None
reply_to_id = message.reply_to.reply_to_msg_id if is_reply else None
data.append(
{
"sender_id": message.sender_id,
"text": message.text,
"date": message.date.isoformat(),
"message.id": message.id,
"is_reply": is_reply,
"reply_to_id": reply_to_id,
}
)
with open(self.file_path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
**# Add this new method**
def iter_messages(client: TelegramClient, chat_entity: Optional[EntityLike]):
return client.iter_messages(self.chat_entity, self._offset, self._limit, ...):
```
A child class can then override the constructor and the `iter_message` method.
My current approach is to override the entire `fetch_data_from_telegram` method. This is problematic since I am duplicating code that may change in future versions of langchain
### Motivation
My use case is pretty simple, return all message starting from a certain date. As I understand the current implementation, the entire history is returned.
### Your contribution
I'm happy to submit a PR if one of the abovementioned approaches is approved.
|
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I_kwDOIPDwls5r2GA0
| 7,872 |
OperationalError with docsearch
|
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[] | null | 8 | 2023-07-18T07:40:27 | 2023-10-24T16:06:18 | 2023-10-24T16:06:17 |
NONE
| null |
### System Info
Running on Colab.
### 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
from langchain.document_loaders import GoogleDriveLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
os.environ["OPENAI_API_KEY"] = 'Your API Key'
loader = GoogleDriveLoader(
folder_id="Your folder ID",
recursive=True,
)
docs = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
split_docs = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(split_docs, embeddings)
### Expected behavior
Expect to load embedding into Chroma, then create a QA object upon that. The code was able to run without a problem yesterday but encountered an error today
|
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PR_kwDOIPDwls5VwKXJ
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LS0010
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[] | null | 1 | 2023-07-18T07:19:24 | 2023-07-18T07:28:38 | 2023-07-18T07:28:38 |
COLLABORATOR
| null |
Bump langsmith version. Has some additional UX improvements
|
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PR_kwDOIPDwls5VvrUD
| 7,870 |
Some mitigations for RCE in PAL chain
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[] | null | 1 | 2023-07-18T05:51:46 | 2023-07-18T05:58:48 | 2023-07-18T05:58:47 |
COLLABORATOR
| null |
Some docstring / small nits to #6003
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| 1,809,140,912 |
I_kwDOIPDwls5r1Uyw
| 7,869 |
Can't use SQLdatabasechain with Multipromptchain
|
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[] | null | 2 | 2023-07-18T05:25:09 | 2023-10-24T16:06:23 | 2023-10-24T16:06:22 |
NONE
| null |
**When i try to use Multiprompt chain getting the below error. Any Suggestions for Solving this Issue??**
ValidationError: 16 validation errors for MultiPromptChain destination_chains -> list -> database extra fields not permitted (type=value_error.extra) destination_chains -> list -> input_key extra fields not permitted (type=value_error.extra) destination_chains -> list -> llm_chain extra fields not permitted (type=value_error.extra) destination_chains -> list -> query_checker_prompt extra fields not permitted (type=value_error.extra) destination_chains -> list -> return_direct extra fields not permitted (type=value_error.extra) destination_chains -> list -> return_intermediate_steps extra fields not permitted (type=value_error.extra) destination_chains -> list -> top_k extra fields not permitted (type=value_error.extra) destination_chains -> list -> use_query_checker extra fields not permitted (type=value_error.extra) destination_chains -> query -> database extra fields not permitted (type=value_error.extra) destination_chains -> query -> input_key extra fields not permitted (type=value_error.extra) destination_chains -> query -> llm_chain extra fields not permitted (type=value_error.extra) destination_chains -> query -> query_checker_prompt extra fields not permitted (type=value_error.extra) destination_chains -> query -> return_direct extra fields not permitted (type=value_error.extra) destination_chains -> query -> return_intermediate_steps extra fields not permitted (type=value_error.extra) destination_chains -> query -> top_k extra fields not permitted (type=value_error.extra) destination_chains -> query -> use_query_checker extra fields not permitted (type=value_error.extra)
**Getting the Above error when i try to Use the Below code. Any Suggestions to Solve the Above error??**
physics_template = """You are a very smart Chatbot for helping users with physics-related questions. \
You excel at answering queries about the laws of nature and phenomena. \
When you don't have an answer, you admit that you don't know.
Here is a physics question:
{input}"""
math_template = """You are a highly skilled mathematician Chatbot. \
You specialize in answering math questions of all levels of difficulty. \
You break down complex problems into simpler components and provide comprehensive solutions.
Here is a math question:
{input}"""
prompt_infos = [
{
"name": "list",
"description": "Good for answering questions about query the data",
"prompt_template": physics_template,
},
{
"name": "query",
"description": "Good for answering math questions",
"prompt_template": math_template,
},
]
db = SQLDatabase.from_uri(
**"YOUR DATABASE URI")**
llm = OpenAI(temperature=0, model="text-davinci-003", max_tokens=1000,
openai_api_key="**YOUR OPENAI API KEY**")
destination_chains = {}
textcontainer = st.container()
with textcontainer:
input = st.text_input("Query: ", key="input")
if input:
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
db_chain = SQLDatabaseChain(
llm=llm, database=db, verbose=True,prompt=prompt)
destination_chains[name] = db_chain
default_chain = ConversationChain(llm=llm, output_key="text")
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
db_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True,
)
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Vvkjq
| 7,868 |
Feature: Add openai_api_model attribute to Doctran models
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[] | null | 2 | 2023-07-18T05:17:24 | 2023-08-21T02:08:08 | 2023-07-20T14:27:56 |
CONTRIBUTOR
| null |
- Description: Added the ability to define the open AI model.
- Issue: Currently the Doctran instance uses gpt-4 by default, this does not work if the user has no access to gpt -4.
- rlancemartin, @eyurtsev, @baskaryan
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PR_kwDOIPDwls5VvNU5
| 7,866 |
docstrings `agents`
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[] | null | 2 | 2023-07-18T03:51:52 | 2023-07-18T16:01:05 | 2023-07-18T09:23:25 |
COLLABORATOR
| null |
Added/Updated docstrings for `agents`
@baskaryan
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I_kwDOIPDwls5r03eN
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Chroma vectorstore add_texts() method does not return ids when there is a metadatas argument
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[] | null | 0 | 2023-07-18T03:42:15 | 2023-07-28T23:17:32 | 2023-07-28T23:17:32 |
NONE
| null |
### System Info
latest
### 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
collection.add_texts(["coucou"], metadatas = [{ 'source' : "here"}])
returns an empty list
### Expected behavior
original add_texts() method :
https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py#L144
I fixed it by adding the ids_copy at the beginning :
```
...
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
ids_copy = ids
...
```
And by returning it :
```
return ids_copy
```
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PR_kwDOIPDwls5VvJq0
| 7,864 |
add HuggingGPT Agent
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[] | null | 3 | 2023-07-18T03:26:14 | 2023-07-24T17:50:23 | 2023-07-24T17:50:23 |
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: add HuggingGPT Agent, see the [paper](https://arxiv.org/abs/2303.17580) and the [original project](https://github.com/microsoft/JARVIS).
- Notebook: add a [notebook](https://github.com/tricktreat/langchain/blob/bc840fe4307690505b3c0b051e0e2d9726777437/docs/extras/use_cases/autonomous_agents/hugginggpt.ipynb) showing its use.
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| 1,808,847,391 |
PR_kwDOIPDwls5Vumns
| 7,863 |
docs: Mendable: Fixes pretty sources not working
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[] | null | 1 | 2023-07-18T00:52:30 | 2023-07-18T01:23:46 | 2023-07-18T01:23:46 |
CONTRIBUTOR
| null |
This new version fixes the"Verified Sources" display that got broken. Instead of displaying the full URL, it shows the title of the page the source is from.
|
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PR_kwDOIPDwls5VudnL
| 7,862 |
Add metadata and page_content filters of documents in AwaDB
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[] | null | 2 | 2023-07-17T23:54:44 | 2023-07-18T14:50:18 | 2023-07-18T14:50:18 |
CONTRIBUTOR
| null |
1. Add the metadata filter of documents.
2. Add the text page_content filter of documents
3. fix the bug of similarity_search_with_score
Improvement and fix bug of AwaDB
Fix the conflict https://github.com/hwchase17/langchain/pull/7840
@rlancemartin @eyurtsev Thanks!
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PR_kwDOIPDwls5VuV40
| 7,861 |
WeaviateHybridSearchRetriever option to enable scores.
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[] | null | 2 | 2023-07-17T23:16:01 | 2023-07-18T14:57:18 | 2023-07-18T14:57:18 |
CONTRIBUTOR
| null |
Description: This PR adds the option to retrieve scores and explanations in the WeaviateHybridSearchRetriever. This feature improves the usability of the retriever by allowing users to understand the scoring logic behind the search results and further refine their search queries.
Issue: This PR is a solution to the issue #7855
Dependencies: This PR does not introduce any new dependencies.
Tag maintainer: @rlancemartin, @eyurtsev
I have included a unit test for the added feature, ensuring that it retrieves scores and explanations correctly. I have also included an example notebook demonstrating its use.
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PR_kwDOIPDwls5VuSTb
| 7,860 |
Adding Docs for `PromptLayerCallbackHandler`
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[] | null | 2 | 2023-07-17T23:01:43 | 2023-07-18T14:51:16 | 2023-07-18T14:51:16 |
CONTRIBUTOR
| null |
@baskaryan
Here I am adding documentation for the `PromptLayerCallbackHandler`.
When we created the initial PR for the callback handler the docs were causing issues, so we merged without the docs.
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[] | null | 3 | 2023-07-17T22:51:27 | 2023-07-18T16:00:49 | 2023-07-18T14:51:45 |
COLLABORATOR
| null |
Added/updated the docstrings from `output_parsers`
@baskaryan
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[] | null | 2 | 2023-07-17T21:48:03 | 2023-07-18T01:37:11 | 2023-07-18T00:47:17 |
COLLABORATOR
| null |
Added/updated docstrings `retrievers`
@baskaryan
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PR_kwDOIPDwls5Vt0dV
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Google Cloud Enterprise Search retriever
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[] | null | 4 | 2023-07-17T21:01:15 | 2023-07-27T16:26:44 | 2023-07-19T01:24:08 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Added a retriever that encapsulated Google Cloud Enterprise Search.
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5Vtvp1
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Add text_content kwarg to BrowserlessLoader
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CONTRIBUTOR
| null |
Added keyword argument to toggle between getting the text content of a site versus its HTML when using the `BrowserlessLoader`
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WeaviateHybridSearchRetriever has no option to retrieve scores and explanations
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[] | null | 2 | 2023-07-17T20:38:13 | 2023-07-18T19:50:18 | 2023-07-18T19:50:18 |
CONTRIBUTOR
| null |
### Feature request
The WeaviateHybridSearchRetriever does not currently have an option to retrieve scores and explanations. The lack of this feature limits the usability of the retriever, as users cannot gain insights into the scoring logic behind the search results. The feature to retrieve scores and explanations, as provided in the Weaviate library, needs to be integrated into the WeaviateHybridSearchRetriever.
Relevant Weaviate library documentation: [Hybrid search](https://weaviate.io/developers/weaviate/search/hybrid)
### Motivation
Having access to scores and explanations is crucial for users who need to understand how each result has been scored by the hybrid search algorithm. Such understanding can help in refining search queries and filtering results that do not meet certain score thresholds. In the absence of this feature, it becomes difficult to optimize search queries and the quality of the search results can be compromised.
### Your contribution
I am ready to contribute to implementing this feature. I propose to enhance the WeaviateHybridSearchRetriever to include the retrieval of score and explainScore properties from the Weaviate library. This will involve adding relevant parameters and methods in the WeaviateHybridSearchRetriever class. I will also ensure the addition of appropriate tests to validate the functionality and correctness of the implementation.
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[] | null | 6 | 2023-07-17T19:53:47 | 2023-10-26T16:06:03 | 2023-10-26T16:06:03 |
NONE
| null |
### System Info
Langchain version 0.0.226
M1 Mac
Python 3.11.3
### Who can help?
@hwchase17 @mmz-001
### 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
```
from langchain.text_splitter import CharacterTextSplitter
def main():
sample_text = "This is a series of short sentences. I want them to be separated at the periods. Three sentences should be enough."
text_splitter = CharacterTextSplitter(separator=". ", chunk_size=30, chunk_overlap=0)
chunks = text_splitter.split_text(sample_text)
for chunk in chunks:
print("CHUNK:", chunk)
if __name__ == "__main__":
main()
```
Output in version 0.0.225:
```
CHUNK: This is a series of short sentences
CHUNK: I want them to be separated at the periods
CHUNK: Three sentences should be enough.
```
Output in version 0.0.226:
```
CHUNK: Thi. i. serie. o. shor
CHUNK: sentences. wan. the. t. b
CHUNK: separate. a. th. periods
CHUNK: Thre. sentence. shoul. b
CHUNK: enough.
```
### Expected behavior
The output seen in version 0.0.225 should be the same in version 0.0.226.
I suspect that the bug is related to the fix for this issue https://github.com/hwchase17/langchain/pull/7263. We have also noticed that in recent versions, the metadata start_index is always -1 when using create_documents(). Please let me know if I should file a separate issue for this.
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PR_kwDOIPDwls5VtRlK
| 7,853 |
propagate RetrievalQA chain callbacks through its own LLMChain and StuffDocumentsChain
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[] | null | 2 | 2023-07-17T19:21:20 | 2023-08-08T16:04:44 | 2023-08-04T03:11:58 |
CONTRIBUTOR
| null |
This is another case, similar to #5572 and #7565 where the callbacks are getting dropped during construction of the chains.
tagging @hwchase17 and @agola11 for callbacks propagation
<!-- 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_kwDOIPDwls5VtIEJ
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update notebook
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[] | null | 1 | 2023-07-17T19:01:15 | 2023-07-17T21:46:43 | 2023-07-17T21:46:42 |
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Error while creating WeaviateHybridSearchRetriever instance
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[] | null | 2 | 2023-07-17T18:49:19 | 2023-10-02T22:24:09 | null |
NONE
| null |
### System Info
LangChain version: 0.0.232
Python version: 3.10.8
Platform: Windows 11, VS Code
For following usage of WeaviateHybridSearchRetriever -
`w_url = os.environ["WEAVIATE_URL"]`
`api_key_w= weaviate.AuthApiKey(api_key=os.environ["WEAVIATE_API_KEY"])`
`wclient = weaviate.Client(url=w_url,
auth_client_secret=api_key_w,
additional_headers={
"X-Openai-Api-Key": os.getenv("OPENAI_API_KEY"),
},)`
`retriever = WeaviateHybridSearchRetriever(wclient, index_name="testindex", text_key="text")`
Getting following error for the line of retriever instance creation -
`
TypeError: Serializable.__init__() takes 1 positional argument but 2 were given
`
### Who can help?
@hwchase17
@agola11
### 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
Follow the steps given in the official documentation - https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/weaviate-hybrid
### Expected behavior
Retriever should be able to initiate correctly and should able to return queried documents.
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PR_kwDOIPDwls5VtBPM
| 7,850 |
feat: optional post-processing for Unstructured loaders
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CONTRIBUTOR
| null |
### Summary
Adds a post-processing method for Unstructured loaders that allows users to optionally modify or clean extracted elements.
### Testing
```python
from langchain.document_loaders import UnstructuredFileLoader
from unstructured.cleaners.core import clean_extra_whitespace
loader = UnstructuredFileLoader(
"./example_data/layout-parser-paper.pdf",
mode="elements",
post_processors=[clean_extra_whitespace],
)
docs = loader.load()
docs[:5]
```
### Reviewrs
- @rlancemartin
- @eyurtsev
- @hwchase17
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PR_kwDOIPDwls5Vs5uM
| 7,849 |
Add post-processing to Web Base url loader
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[] | null | 2 | 2023-07-17T18:25:12 | 2023-07-27T15:00:10 | 2023-07-27T15:00:09 |
COLLABORATOR
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Text loaded from urls can have low quality formatting (e.g., repeated newlines) that we want to post-process.
We add regex substitution and newline compression to WebBaseLoader.
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PR_kwDOIPDwls5Vs5n8
| 7,848 |
docstrings `tools`
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[] | null | 2 | 2023-07-17T18:24:57 | 2023-07-18T01:41:26 | 2023-07-18T00:50:20 |
COLLABORATOR
| null |
Added docstrings in `tools`.
@baskaryan
|
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PR_kwDOIPDwls5VssPu
| 7,847 |
add ls link
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| 1,808,178,675 |
PR_kwDOIPDwls5VsWBg
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fix: SQLDatabaseChain not return sql query even if return_direct=True
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[] | null | 2 | 2023-07-17T16:55:07 | 2023-08-04T03:11:37 | 2023-08-04T03:11:37 |
NONE
| null |
**Description:** SQLDatabaseChain not return sql query even if return_direct=True
**Issue:**
for SQLDatabaseChain, although its parameter set to return_direct=True, it still returns results (e.g., USB wire 98, Laptop 90, Desktop 86, ....) instead of sql query (e.g., SELECT name AS "Product Category" FROM category ORDER BY category_id LIMIT 10;)
I found that it's because final_result variable is wrongly set to result variable instead of sql_cmd if self.return_direct = True, which is different from the documentation: python.langchain.com/docs/modules/chains/popular/sqlite
**How to replicate the issue**:
```import os
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
from langchain.chat_models import ChatOpenAI
os.environ["OPENAI_API_KEY"] = "<your openai api key>"
db = SQLDatabase.from_uri("<path to your database>")
llm = ChatOpenAI(temperature=0, verbose=True, model_name="gpt-3.5-turbo")
question = "What are the top 10 product categories"
raw_sql = db_chain(question)
raw_sql # you will see the output will not be sql query before this bug fix
````
**Tag mantainer:** @hinthornw
**Dependencies:** No change, as it's just a simple fix by replacing a variable
**Supporting Evidence:**
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I_kwDOIPDwls5rxmio
| 7,845 |
DOC: What is the difference between the various QA chains?
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[] | null | 2 | 2023-07-17T16:45:46 | 2023-11-03T16:06:52 | 2023-11-03T16:06:51 |
NONE
| null |
### Issue with current documentation:
I think there is lacking documentation on the multitude of chains regarding QA and retrieval.
There is:
- [retrieval_qa](https://github.com/hwchase17/langchain/tree/master/langchain/chains/retrieval_qa)
- [question_answering](https://github.com/hwchase17/langchain/tree/master/langchain/chains/question_answering)
- [qa_with_sources](https://github.com/hwchase17/langchain/tree/master/langchain/chains/qa_with_sources)
- [conversational_retrieval](https://github.com/hwchase17/langchain/tree/master/langchain/chains/conversational_retrieval)
- [chat_vector_db](https://github.com/hwchase17/langchain/tree/master/langchain/chains/chat_vector_db)
- [question answering](https://github.com/hwchase17/langchain/tree/master/langchain/chains/question_answering)
Which one should be used when? Which are the base chains used by the others etc?
### Idea or request for content:
A structured documentation of the different chains are needed. Maybe some refactoring of the directory structure to group chains.
|
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PR_kwDOIPDwls5VsS9D
| 7,844 |
docstrings `prompts`
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[] | null | 2 | 2023-07-17T16:45:20 | 2023-07-18T14:58:23 | 2023-07-18T14:58:23 |
COLLABORATOR
| null |
Added missed docstrings in `prompts`
@baskaryan
|
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fix nb
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| 1,808,066,576 |
I_kwDOIPDwls5rxOgQ
| 7,841 |
InvalidVectorQuery error when using AzureSearch with vector db
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[] | null | 7 | 2023-07-17T15:46:42 | 2023-11-15T16:07:13 | 2023-11-15T16:07:12 |
NONE
| null |
### System Info
langchain==0.0.234, windows 10, azure-identity==1.13.0, Python 3.11.4
### Who can help?
I manage to create an index in Azure Cognitive Search with _id_, _content_, _vector_content_ and _metadata_ fields.
I check that docs and chunks are not nulls.
I'm getting and error when querying the vector store.
docs:
[azuresearch-langchain-example](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/azuresearch)
Any fix for this?
@hwchase17
@agola11
Regards
### 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
Embedding is working as I test:
```
# Check that embbding is working
input_text = "This is for demonstration."
outcome = embeddings.embed_query(input_text)
```
When I'm trying to query with:
```
# Perform a similarity search
docs = vector_store.similarity_search(
query="What did the president say about Ketanji Brown Jackson",
k=3,
search_type="similarity",
)
print(docs[0].page_content)
```
Error:
```
HttpResponseError: (InvalidRequestParameter) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ ] } }'
Parameter name: vector
Code: InvalidRequestParameter
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ ] } }'
Parameter name: vector
Exception Details: (InvalidVectorQuery) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ ] } }'
Code: InvalidVectorQuery
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ ] } }'
```
### Expected behavior
I can't define is is the azure cognitive configuration index that i manually add or a bug in the code.
Splitting and adding chunks to the vector store (Azure Cognitive Search) all where dont without any warning.
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